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authorGard Spreemann <gspr@nonempty.org>2020-07-09 08:50:11 +0200
committerGard Spreemann <gspr@nonempty.org>2020-07-09 08:50:11 +0200
commit616c6039614e96b6cfd88eb7db25ce11c7302c30 (patch)
treea745ec24604f660b256dbaee214affa497c9c21e
parentd62f05daf348b4e554056f298c66cbd64f5e3c6e (diff)
parenta16b9471d7114ec08977479b7249efe747702b97 (diff)
Merge branch 'dfsg/latest' into debian/sid
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-rw-r--r--examples/domain-adaptation/plot_otda_laplacian.py (renamed from docs/source/auto_examples/plot_otda_classes.py)69
-rw-r--r--examples/domain-adaptation/plot_otda_linear_mapping.py (renamed from docs/source/auto_examples/plot_otda_linear_mapping.py)6
-rw-r--r--examples/domain-adaptation/plot_otda_mapping.py (renamed from docs/source/auto_examples/plot_otda_mapping.py)6
-rw-r--r--examples/domain-adaptation/plot_otda_mapping_colors_images.py (renamed from examples/plot_otda_mapping_colors_images.py)14
-rw-r--r--examples/domain-adaptation/plot_otda_semi_supervised.py (renamed from docs/source/auto_examples/plot_otda_semi_supervised.py)2
-rw-r--r--examples/gromov/README.txt4
-rw-r--r--examples/gromov/plot_barycenter_fgw.py (renamed from examples/plot_barycenter_fgw.py)11
-rw-r--r--examples/gromov/plot_fgw.py (renamed from examples/plot_fgw.py)16
-rw-r--r--examples/gromov/plot_gromov.py (renamed from docs/source/auto_examples/plot_gromov.py)0
-rwxr-xr-xexamples/gromov/plot_gromov_barycenter.py (renamed from examples/plot_gromov_barycenter.py)9
-rw-r--r--examples/others/README.txt5
-rw-r--r--examples/others/plot_WDA.py (renamed from examples/plot_WDA.py)10
-rw-r--r--examples/plot_OT_1D.py1
-rw-r--r--examples/plot_OT_1D_smooth.py2
-rw-r--r--examples/plot_OT_2D_samples.py2
-rw-r--r--examples/plot_OT_L1_vs_L2.py2
-rw-r--r--examples/plot_compute_emd.py6
-rw-r--r--examples/plot_gromov.py106
-rw-r--r--examples/plot_optim_OTreg.py7
-rw-r--r--examples/plot_otda_color_images.py165
-rw-r--r--examples/plot_otda_d2.py172
-rw-r--r--examples/plot_otda_linear_mapping.py144
-rw-r--r--examples/plot_otda_mapping.py125
-rw-r--r--examples/plot_otda_semi_supervised.py148
-rw-r--r--examples/plot_screenkhorn_1D.py (renamed from examples/plot_UOT_1D.py)37
-rw-r--r--examples/plot_stochastic.py101
-rw-r--r--examples/unbalanced-partial/README.txt3
-rw-r--r--examples/unbalanced-partial/plot_UOT_1D.py (renamed from docs/source/auto_examples/plot_UOT_1D.py)0
-rw-r--r--examples/unbalanced-partial/plot_UOT_barycenter_1D.py (renamed from examples/plot_UOT_barycenter_1D.py)6
-rwxr-xr-xexamples/unbalanced-partial/plot_partial_wass_and_gromov.py165
-rw-r--r--notebooks/plot_OT_1D.ipynb248
-rw-r--r--notebooks/plot_OT_1D_smooth.ipynb302
-rw-r--r--notebooks/plot_OT_2D_samples.ipynb366
-rw-r--r--notebooks/plot_OT_L1_vs_L2.ipynb374
-rw-r--r--notebooks/plot_UOT_1D.ipynb210
-rw-r--r--notebooks/plot_UOT_barycenter_1D.ipynb336
-rw-r--r--notebooks/plot_WDA.ipynb316
-rw-r--r--notebooks/plot_barycenter_1D.ipynb308
-rw-r--r--notebooks/plot_barycenter_fgw.ipynb312
-rw-r--r--notebooks/plot_barycenter_lp_vs_entropic.ipynb497
-rw-r--r--notebooks/plot_compute_emd.ipynb248
-rw-r--r--notebooks/plot_convolutional_barycenter.ipynb176
-rw-r--r--notebooks/plot_fgw.ipynb359
-rw-r--r--notebooks/plot_free_support_barycenter.ipynb169
-rw-r--r--notebooks/plot_gromov.ipynb265
-rw-r--r--notebooks/plot_gromov_barycenter.ipynb391
-rw-r--r--notebooks/plot_optim_OTreg.ipynb732
-rw-r--r--notebooks/plot_otda_classes.ipynb305
-rw-r--r--notebooks/plot_otda_color_images.ipynb337
-rw-r--r--notebooks/plot_otda_d2.ipynb321
-rw-r--r--notebooks/plot_otda_linear_mapping.ipynb339
-rw-r--r--notebooks/plot_otda_mapping.ipynb288
-rw-r--r--notebooks/plot_otda_mapping_colors_images.ipynb378
-rw-r--r--notebooks/plot_otda_semi_supervised.ipynb294
-rw-r--r--notebooks/plot_stochastic.ipynb563
-rw-r--r--ot/__init__.py32
-rw-r--r--ot/bregman.py570
-rw-r--r--ot/da.py674
-rw-r--r--ot/datasets.py23
-rw-r--r--ot/dr.py4
-rw-r--r--ot/gpu/__init__.py5
-rw-r--r--ot/gromov.py206
-rw-r--r--ot/lp/EMD.h2
-rw-r--r--ot/lp/EMD_wrapper.cpp9
-rw-r--r--ot/lp/__init__.py247
-rw-r--r--ot/lp/emd_wrap.pyx27
-rw-r--r--ot/lp/network_simplex_simple.h7
-rw-r--r--ot/optim.py8
-rwxr-xr-xot/partial.py1062
-rw-r--r--ot/plot.py3
-rw-r--r--ot/smooth.py4
-rw-r--r--ot/unbalanced.py107
-rw-r--r--ot/utils.py28
-rw-r--r--requirements.txt9
-rwxr-xr-xsetup.py104
-rw-r--r--test/test_bregman.py42
-rw-r--r--test/test_da.py210
-rw-r--r--test/test_gromov.py4
-rw-r--r--test/test_optim.py33
-rw-r--r--test/test_ot.py67
-rwxr-xr-xtest/test_partial.py208
-rw-r--r--test/test_stochastic.py8
-rw-r--r--test/test_unbalanced.py9
-rw-r--r--test/test_utils.py4
307 files changed, 5042 insertions, 23763 deletions
diff --git a/.circleci/artifact_path b/.circleci/artifact_path
new file mode 100644
index 0000000..aa9acb8
--- /dev/null
+++ b/.circleci/artifact_path
@@ -0,0 +1 @@
+0/docs/build/html/index.html
diff --git a/.circleci/config.yml b/.circleci/config.yml
new file mode 100644
index 0000000..9701ad1
--- /dev/null
+++ b/.circleci/config.yml
@@ -0,0 +1,137 @@
+# Tagging a commit with [circle front] will build the front page and perform test-doc.
+# Tagging a commit with [circle full] will build everything.
+version: 2
+jobs:
+ build_docs:
+ docker:
+ - image: circleci/python:3.7-stretch
+ steps:
+ - checkout
+ - run:
+ name: Set BASH_ENV
+ command: |
+ echo "set -e" >> $BASH_ENV
+ echo "export DISPLAY=:99" >> $BASH_ENV
+ echo "export OPENBLAS_NUM_THREADS=4" >> $BASH_ENV
+ echo "BASH_ENV:"
+ cat $BASH_ENV
+
+ - run:
+ name: Merge with upstream
+ command: |
+ echo $(git log -1 --pretty=%B) | tee gitlog.txt
+ echo ${CI_PULL_REQUEST//*pull\//} | tee merge.txt
+ if [[ $(cat merge.txt) != "" ]]; then
+ echo "Merging $(cat merge.txt)";
+ git remote add upstream git://github.com/PythonOT/POT.git;
+ git pull --ff-only upstream "refs/pull/$(cat merge.txt)/merge";
+ git fetch upstream master;
+ fi
+
+ # Load our data
+ - restore_cache:
+ keys:
+ - data-cache-0
+ - pip-cache
+
+ - run:
+ name: Spin up Xvfb
+ command: |
+ /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1400x900x24 -ac +extension GLX +render -noreset;
+
+ # https://github.com/ContinuumIO/anaconda-issues/issues/9190#issuecomment-386508136
+ # https://github.com/golemfactory/golem/issues/1019
+ - run:
+ name: Fix libgcc_s.so.1 pthread_cancel bug
+ command: |
+ sudo apt-get install qt5-default
+
+ - run:
+ name: Get Python running
+ command: |
+ python -m pip install --user --upgrade --progress-bar off pip
+ python -m pip install --user --upgrade --progress-bar off -r requirements.txt
+ python -m pip install --user --upgrade --progress-bar off -r docs/requirements.txt
+ python -m pip install --user --upgrade --progress-bar off ipython "https://api.github.com/repos/sphinx-gallery/sphinx-gallery/zipball/master" memory_profiler
+ python -m pip install --user -e .
+
+ - save_cache:
+ key: pip-cache
+ paths:
+ - ~/.cache/pip
+
+ # Look at what we have and fail early if there is some library conflict
+ - run:
+ name: Check installation
+ command: |
+ which python
+ python -c "import ot"
+
+ # Build docs
+ - run:
+ name: make html
+ command: |
+ cd docs;
+ make html;
+
+ # Save the outputs
+ - store_artifacts:
+ path: docs/build/html/
+ destination: dev
+ - persist_to_workspace:
+ root: docs/build
+ paths:
+ - html
+
+ deploy:
+ docker:
+ - image: circleci/python:3.6-jessie
+ steps:
+ - attach_workspace:
+ at: /tmp/build
+ - run:
+ name: Fetch docs
+ command: |
+ set -e
+ mkdir -p ~/.ssh
+ echo -e "Host *\nStrictHostKeyChecking no" > ~/.ssh/config
+ chmod og= ~/.ssh/config
+ if [ ! -d ~/PythonOT.github.io ]; then
+ git clone git@github.com:/PythonOT/PythonOT.github.io.git ~/PythonOT.github.io --depth=1
+ fi
+ - run:
+ name: Deploy docs
+ command: |
+ set -e;
+ if [ "${CIRCLE_BRANCH}" == "master" ]; then
+ git config --global user.email "circle@PythonOT.com";
+ git config --global user.name "Circle CI";
+ cd ~/PythonOT.github.io;
+ git checkout master
+ git remote -v
+ git fetch origin
+ git reset --hard origin/master
+ git clean -xdf
+ echo "Deploying dev docs for ${CIRCLE_BRANCH}.";
+ cp -a /tmp/build/html/* .;
+ touch .nojekyll;
+ git add -A;
+ git commit -m "CircleCI update of dev docs (${CIRCLE_BUILD_NUM}).";
+ git push origin master;
+ else
+ echo "No deployment (build: ${CIRCLE_BRANCH}).";
+ fi
+
+workflows:
+ version: 2
+
+ default:
+ jobs:
+ - build_docs
+ - deploy:
+ requires:
+ - build_docs
+ filters:
+ branches:
+ only:
+ - master
diff --git a/.github/requirements_strict.txt b/.github/requirements_strict.txt
new file mode 100644
index 0000000..d7539c5
--- /dev/null
+++ b/.github/requirements_strict.txt
@@ -0,0 +1,7 @@
+numpy==1.16.*
+scipy==1.0.*
+cython==0.23.*
+matplotlib
+cvxopt
+scikit-learn
+pytest
diff --git a/.github/workflows/build_tests.yml b/.github/workflows/build_tests.yml
new file mode 100644
index 0000000..41b08b3
--- /dev/null
+++ b/.github/workflows/build_tests.yml
@@ -0,0 +1,131 @@
+name: build
+
+on:
+ push:
+
+ pull_request:
+
+ create:
+ branches:
+ - 'master'
+ tags:
+ - '**'
+
+jobs:
+ linux:
+
+ runs-on: ubuntu-latest
+ strategy:
+ max-parallel: 4
+ matrix:
+ python-version: [3.5, 3.6, 3.7, 3.8]
+
+ steps:
+ - uses: actions/checkout@v1
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v1
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install -r requirements.txt
+ pip install flake8 pytest "pytest-cov<2.6" codecov
+ pip install -U "sklearn"
+ - name: Lint with flake8
+ run: |
+ # stop the build if there are Python syntax errors or undefined names
+ flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+ # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+ flake8 examples/ ot/ test/ --count --max-line-length=127 --statistics
+ - name: Install POT
+ run: |
+ pip install -e .
+ - name: Run tests
+ run: |
+ python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot
+ - name: Upload codecov
+ run: |
+ codecov
+
+
+ linux-minimal-deps:
+
+ runs-on: ubuntu-latest
+ strategy:
+ max-parallel: 4
+ matrix:
+ python-version: [3.6]
+
+ steps:
+ - uses: actions/checkout@v1
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v1
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install -r .github/requirements_strict.txt
+ pip install pytest
+ pip install -U "sklearn"
+ - name: Install POT
+ run: |
+ pip install -e .
+ - name: Run tests
+ run: |
+ python -m pytest -v test/ ot/ --ignore ot/gpu/
+
+
+ macos:
+ runs-on: macOS-latest
+ strategy:
+ max-parallel: 4
+ matrix:
+ python-version: [3.7]
+
+ steps:
+ - uses: actions/checkout@v1
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v1
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install -r requirements.txt
+ pip install pytest "pytest-cov<2.6"
+ pip install -U "sklearn"
+ - name: Install POT
+ run: |
+ pip install -e .
+ - name: Run tests
+ run: |
+ python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot
+
+
+ windows:
+ runs-on: windows-2019
+ strategy:
+ max-parallel: 4
+ matrix:
+ python-version: [3.7]
+
+ steps:
+ - uses: actions/checkout@v1
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v1
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install -r requirements.txt
+ pip install pytest "pytest-cov<2.6"
+ pip install -U "sklearn"
+ - name: Install POT
+ run: |
+ pip install -e .
+ - name: Run tests
+ run: |
+ python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot
diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
new file mode 100644
index 0000000..662a604
--- /dev/null
+++ b/.github/workflows/build_wheels.yml
@@ -0,0 +1,50 @@
+name: Build dist and wheels
+
+on:
+ release:
+ push:
+ branches:
+ - "master"
+
+jobs:
+ build_wheels:
+ name: ${{ matrix.os }}
+ runs-on: ${{ matrix.os }}
+ strategy:
+ matrix:
+ os: [ubuntu-latest, macos-latest, windows-latest]
+ # macos-latest, windows-latest
+
+ steps:
+ - uses: actions/checkout@v1
+ - name: Set up Python 3.8
+ uses: actions/setup-python@v1
+ with:
+ python-version: 3.8
+
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install -r requirements.txt
+ pip install -U "cython"
+
+ - name: Install cibuildwheel
+ run: |
+ python -m pip install cibuildwheel==1.3.0
+
+ - name: Install Visual C++ for Python 2.7
+ if: startsWith(matrix.os, 'windows')
+ run: |
+ choco install vcpython27 -f -y
+
+ - name: Build wheel
+ env:
+ CIBW_SKIP: "pp*-win* pp*-macosx* cp2* pp*" # remove pypy on mac and win (wrong version)
+ CIBW_BEFORE_BUILD: "pip install numpy cython"
+ run: |
+ python -m cibuildwheel --output-dir wheelhouse
+
+ - uses: actions/upload-artifact@v1
+ with:
+ name: wheels
+ path: ./wheelhouse
diff --git a/.github/workflows/circleci-redirector.yml b/.github/workflows/circleci-redirector.yml
new file mode 100644
index 0000000..ae7bfca
--- /dev/null
+++ b/.github/workflows/circleci-redirector.yml
@@ -0,0 +1,13 @@
+name: circleci-redirector
+on: [status]
+jobs:
+ circleci_artifacts_redirector_job:
+ runs-on: ubuntu-latest
+ name: Run CircleCI artifacts redirector
+ steps:
+ - name: GitHub Action step
+ uses: larsoner/circleci-artifacts-redirector-action@master
+ with:
+ repo-token: ${{ secrets.GITHUB_TOKEN }}
+ artifact-path: 0/dev/index.html
+ circleci-jobs: build_docs
diff --git a/.gitignore b/.gitignore
index dadf84c..a2ace7c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -106,3 +106,15 @@ ENV/
# coverage output folder
cov_html/
+
+docs/source/modules/generated/*
+docs/source/_build/*
+
+# local debug folder
+debug
+
+# vscode parameters
+.vscode
+
+# pytest cahche
+.pytest_cache \ No newline at end of file
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 5b3a26e..0000000
--- a/.travis.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-dist: xenial # required for Python >= 3.7
-language: python
-matrix:
- # allow_failures:
- # - os: osx
- # - os: windows
- include:
- - os: linux
- sudo: required
- python: 3.5
- - os: linux
- sudo: required
- python: 3.6
- - os: linux
- sudo: required
- python: 3.7
- - os: linux
- sudo: required
- python: 2.7
- # - os: osx
- # sudo: required
- # language: generic
- # - name: "Python 3.7.3 on Windows"
- # os: windows # Windows 10.0.17134 N/A Build 17134
- # language: shell # 'language: python' is an error on Travis CI Windows
- # before_install: choco install python
- # env: PATH=/c/Python37:/c/Python37/Scripts:$PATH
-# before_script: # configure a headless display to test plot generation
-# - "export DISPLAY=:99.0"
-# - sleep 3 # give xvfb some time to start
-before_install:
- - ./.travis/before_install.sh
-# command to install dependencies
-install:
- - pip install -r requirements.txt
- - pip install -U "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests + scipy version: otherwise, pymanopt fails, cf <https://github.com/pymanopt/pymanopt/issues/77>
- - pip install flake8 pytest "pytest-cov<2.6"
- - pip install .
-# command to run tests + check syntax style
-services:
- - xvfb
-script:
- - python setup.py develop
- - flake8 examples/ ot/ test/
- - python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot
- # - py.test ot test
diff --git a/.travis/before_install.sh b/.travis/before_install.sh
deleted file mode 100755
index 0ae6249..0000000
--- a/.travis/before_install.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-if [[ $TRAVIS_OS_NAME == 'osx' ]]; then
-
- # Install some custom requirements on OS X
- # e.g. brew install pyenv-virtualenv
- #brew update
- #brew install python
- sudo easy_install -U pip
-
-else
- # Install some custom requirements on Linux
- sudo apt-get update -q
- sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev
-fi
diff --git a/Makefile b/Makefile
index cafda8e..70cdbdd 100644
--- a/Makefile
+++ b/Makefile
@@ -58,6 +58,12 @@ release_test :
rdoc :
pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md
+ sed -i 's,https://pythonot.github.io/auto_examples/,auto_examples/,g' docs/source/readme.rst
+ pandoc --from=markdown --to=rst --output=docs/source/releases.rst RELEASES.md
+ sed -i 's,https://pot.readthedocs.io/en/latest/,,g' docs/source/releases.rst
+ sed -i 's,https://github.com/rflamary/POT/blob/master/notebooks/,auto_examples/,g' docs/source/releases.rst
+ sed -i 's,.ipynb,.html,g' docs/source/releases.rst
+ sed -i 's,https://pythonot.github.io/auto_examples/,auto_examples/,g' docs/source/releases.rst
notebook :
ipython notebook --matplotlib=inline --notebook-dir=notebooks/
diff --git a/README.md b/README.md
index d8bb051..5626742 100644
--- a/README.md
+++ b/README.md
@@ -2,43 +2,65 @@
[![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT)
[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot)
-[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT)
-[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest)
+[![Build Status](https://github.com/PythonOT/POT/workflows/build/badge.svg)](https://github.com/PythonOT/POT/actions)
+[![Codecov Status](https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg)](https://codecov.io/gh/PythonOT/POT)
[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot)
[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot)
-[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE)
+[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/PythonOT/POT/blob/master/LICENSE)
+This open source Python library provide several solvers for optimization
+problems related to Optimal Transport for signal, image processing and machine
+learning.
-This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.
+Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/)
-It provides the following solvers:
+Source Code (MIT): [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT)
-* OT Network Flow solver for the linear program/ Earth Movers Distance [1].
-* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy).
+POT provides the following generic OT solvers (links to examples):
+
+* [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] .
+* [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7].
+* Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html) with optional GPU implementation (requires cupy).
+* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_convolutional_barycenter.html) [21] and unmixing [4].
* Sinkhorn divergence [23] and entropic regularization OT from empirical data.
-* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17].
-* Non regularized Wasserstein barycenters [16] with LP solver (only small scale).
-* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4].
-* Optimal transport for domain adaptation with group lasso regularization [5]
-* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
-* Linear OT [14] and Joint OT matrix and mapping estimation [8].
-* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
-* Gromov-Wasserstein distances and barycenters ([13] and regularized [12])
-* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
-* Non regularized free support Wasserstein barycenters [20].
-* Unbalanced OT with KL relaxation distance and barycenter [10, 25].
-
-Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
+* [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17].
+* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale).
+* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html) (exact [13] and regularized [12])
+ * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) [24]
+* [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
+* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
+* [Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25].
+* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
+ formulations).
+
+POT provides the following Machine Learning related solvers:
+
+* [Optimal transport for domain
+ adaptation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html)
+ with [group lasso regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html) [5] [30] and [semi
+ supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html).
+* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_mapping.html) [8].
+* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/others/plot_WDA.html) [11] (requires autograd + pymanopt).
+* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27].
+
+Some other examples are available in the [documentation](https://pythonot.github.io/auto_examples/index.html).
#### Using and citing the toolbox
-If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference:
+If you use this toolbox in your research and find it useful, please cite POT
+using the following reference:
+```
+Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library,
+Website: https://pythonot.github.io/, 2017
+```
+
+In Bibtex format:
```
@misc{flamary2017pot,
title={POT Python Optimal Transport library},
author={Flamary, R{'e}mi and Courty, Nicolas},
-url={https://github.com/rflamary/POT},
+url={https://pythonot.github.io/},
year={2017}
}
```
@@ -47,7 +69,7 @@ year={2017}
The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules:
-- Numpy (>=1.11)
+- Numpy (>=1.16)
- Scipy (>=1.0)
- Cython (>=0.23)
- Matplotlib (>=1.5)
@@ -64,9 +86,9 @@ You can install the toolbox through PyPI with:
```
pip install POT
```
-or get the very latest version by downloading it and then running:
+or get the very latest version by running:
```
-python setup.py install --user # for user install (no root)
+pip install -U https://github.com/PythonOT/POT/archive/master.zip # with --user for user install (no root)
```
@@ -129,34 +151,10 @@ T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT
ba=ot.barycenter(A,M,reg) # reg is regularization parameter
```
-
-
-
### Examples and Notebooks
-The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/).
-
+The examples folder contain several examples and use case for the library. The full documentation with examples and output is available on [https://PythonOT.github.io/](https://PythonOT.github.io/).
-Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look:
-
-* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb)
-* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb)
-* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb)
-* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb)
-* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb)
-* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb)
-* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb)
-* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb)
-* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb)
-* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb)
-* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb)
-* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb)
-* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb)
-* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb)
-* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb)
-
-
-You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/).
## Acknowledgements
@@ -167,19 +165,21 @@ This toolbox has been created and is maintained by
The contributors to this library are
-* [Alexandre Gramfort](http://alexandre.gramfort.net/)
-* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/)
+* [Alexandre Gramfort](http://alexandre.gramfort.net/) (CI, documentation)
+* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) (Partial OT)
* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation)
* [Léo Gautheron](https://github.com/aje) (GPU implementation)
-* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1)
-* [Stanislas Chambon](https://slasnista.github.io/)
-* [Antoine Rolet](https://arolet.github.io/)
+* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) (DA classes)
+* [Stanislas Chambon](https://slasnista.github.io/) (DA classes)
+* [Antoine Rolet](https://arolet.github.io/) (EMD solver debug)
* Erwan Vautier (Gromov-Wasserstein)
-* [Kilian Fatras](https://kilianfatras.github.io/)
+* [Kilian Fatras](https://kilianfatras.github.io/) (Stochastic solvers)
* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home)
-* [Vayer Titouan](https://tvayer.github.io/)
+* [Vayer Titouan](https://tvayer.github.io/) (Gromov-Wasserstein -, Fused-Gromov-Wasserstein)
* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT)
* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein)
+* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn)
+* [Ievgen Redko](https://ievred.github.io/) (Laplacian DA, JCPOT)
This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):
@@ -252,4 +252,14 @@ You can also post bug reports and feature requests in Github issues. Make sure t
[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML).
-[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS).
+[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS).
+
+[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS).
+
+[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019.
+
+[28] Caffarelli, L. A., McCann, R. J. (2010). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730.
+
+[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276.
+
+[30] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). [Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching](https://remi.flamary.com/biblio/flamary2014optlaplace.pdf), NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014.
diff --git a/RELEASES.md b/RELEASES.md
index 66eee19..adb7fc1 100644
--- a/RELEASES.md
+++ b/RELEASES.md
@@ -1,7 +1,46 @@
-# POT Releases
+# Releases
+## 0.7.0
+*May 2020*
-## 0.6 Year 3
+This is the new stable release for POT. We made a lot of changes in the documentation and added several new features such as Partial OT, Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes. One important change is that we have created the GitHub organization [PythonOT](https://github.com/PythonOT) that now owns the main POT repository [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) and the repository for the new documentation is now hosted at [https://PythonOT.github.io/](https://PythonOT.github.io/).
+
+This is the first release where the Python 2.7 tests have been removed. Most of the toolbox should still work but we do not offer support for Python 2.7 and will close related Issues.
+
+A lot of changes have been done to the documentation that is now hosted on [https://PythonOT.github.io/](https://PythonOT.github.io/) instead of readthedocs. It was a hard choice but readthedocs did not allow us to run sphinx-gallery to update our beautiful examples and it was a huge amount of work to maintain. The documentation is now automatically compiled and updated on merge. We also removed the notebooks from the repository for space reason and also because they are all available in the [example gallery](https://pythonot.github.io/auto_examples/index.html). Note that now the output of the documentation build for each commit in the PR is available to check that the doc builds correctly before merging which was not possible with readthedocs.
+
+The CI framework has also been changed with a move from Travis to Github Action which allows to get faster tests on Windows, MacOS and Linux. We also now report our coverage on [Codecov.io](https://codecov.io/gh/PythonOT/POT) and we have a reasonable 92% coverage. We also now generate wheels for a number of OS and Python versions at each merge in the master branch. They are available as outputs of this [action](https://github.com/PythonOT/POT/actions?query=workflow%3A%22Build+dist+and+wheels%22). This will allow simpler multi-platform releases from now on.
+
+In terms of new features we now have [OTDA Classes for unbalanced OT](https://pythonot.github.io/gen_modules/ot.da.html#ot.da.UnbalancedSinkhornTransport), a new Domain adaptation class form [multi domain problems (JCPOT)](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html#sphx-glr-auto-examples-domain-adaptation-plot-otda-jcpot-py), and several solvers to solve the [Partial Optimal Transport](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html#sphx-glr-auto-examples-unbalanced-partial-plot-partial-wass-and-gromov-py) problems.
+
+This release is also the moment to thank all the POT contributors (old and new) for helping making POT such a nice toolbox. A lot of changes (also in the API) are comming for the next versions.
+
+
+#### Features
+
+- New documentation on [https://PythonOT.github.io/](https://PythonOT.github.io/) (PR #160, PR #143, PR #144)
+- Documentation build on CircleCI with sphinx-gallery (PR #145,PR #146, #155)
+- Run sphinx gallery in CI (PR #146)
+- Remove notebooks from repo because available in doc (PR #156)
+- Build wheels in CI (#157)
+- Move from travis to GitHub Action for Windows, MacOS and Linux (PR #148, PR #150)
+- Partial Optimal Transport (PR#141 and PR #142)
+- Laplace regularized OTDA (PR #140)
+- Multi source DA with target shift (PR #137)
+- Screenkhorn algorithm (PR #121)
+
+#### Closed issues
+
+- Bug in Unbalanced OT example (Issue #127)
+- Clean Cython output when calling setup.py clean (Issue #122)
+- Various Macosx compilation problems (Issue #113, Issue #118, PR#130)
+- EMD dimension mismatch (Issue #114, Fixed in PR #116)
+- 2D barycenter bug for non square images (Issue #124, fixed in PR #132)
+- Bad value in EMD 1D (Issue #138, fixed in PR #139)
+- Log bugs for Gromov-Wassertein solver (Issue #107, fixed in PR #108)
+- Weight issues in barycenter function (PR #106)
+
+## 0.6.0
*July 2019*
This is the first official stable release of POT and this means a jump to 0.6!
@@ -69,7 +108,7 @@ bring new features and solvers to the library.
- Issue #72 Macosx build problem
-## 0.5.0 Year 2
+## 0.5.0
*Sep 2018*
POT is 2 years old! This release brings numerous new features to the
@@ -127,7 +166,7 @@ Deprecated OTDA Classes were removed from ot.da and ot.gpu for version 0.5
* Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip
-## 0.4 Community edition
+## 0.4
*15 Sep 2017*
This release contains a lot of contribution from new contributors.
@@ -152,7 +191,7 @@ This release contains a lot of contribution from new contributors.
* Correct bug in emd on windows
-## 0.3 Summer release
+## 0.3
*7 Jul 2017*
* emd* and sinkhorn* are now performed in parallel for multiple target distributions
@@ -173,7 +212,7 @@ This release contains a lot of contribution from new contributors.
-## V0.1.11 New years resolution
+## 0.1.11
*5 Jan 2017*
* Add sphinx gallery for better documentation
@@ -181,24 +220,22 @@ This release contains a lot of contribution from new contributors.
* Add simple tic() toc() functions for timing
-## V0.1.10
+## 0.1.10
*7 Nov 2016*
* numerical stabilization for sinkhorn (log domain and epsilon scaling)
-## V0.1.9 DA classes and mapping
+## 0.1.9
*4 Nov 2016*
* Update classes and examples for domain adaptation
* Joint OT matrix and mapping estimation
-## V0.1.7
+## 0.1.7
*31 Oct 2016*
* Original Domain adaptation classes
-
-
-## PyPI version 0.1.3
+## 0.1.3
* pipy works
diff --git a/_config.yml b/_config.yml
new file mode 100644
index 0000000..c741881
--- /dev/null
+++ b/_config.yml
@@ -0,0 +1 @@
+theme: jekyll-theme-slate \ No newline at end of file
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 0000000..fbd1b07
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,16 @@
+coverage:
+ precision: 2
+ round: down
+ range: "70...100"
+ status:
+ project:
+ default:
+ target: auto
+ threshold: 0.01
+ patch: false
+ changes: false
+comment:
+ layout: "header, diff, sunburst, uncovered"
+ behavior: default
+codecov:
+ token: 057953e4-d263-41c0-913c-5d45c0371df9 \ No newline at end of file
diff --git a/docs/cache_nbrun b/docs/cache_nbrun
index 8a95023..ac49515 100644
--- a/docs/cache_nbrun
+++ b/docs/cache_nbrun
@@ -1 +1 @@
-{"plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_UOT_1D.ipynb": "fc7dd383e625597bd59fff03a8430c91", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_color_images.ipynb": "f804d5806c7ac1a0901e4542b1eaa77b", "plot_fgw.ipynb": "2ba3e100e92ecf4dfbeb605de20b40ab", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_barycenter_fgw.ipynb": "e14100dd276bff3ffdfdf176f1b6b070", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_UOT_barycenter_1D.ipynb": "c72f0bfb6e1a79710dad3fef9f5c557c", "plot_otda_mapping_colors_images.ipynb": "cc8bf9a857f52e4a159fe71dfda19018", "plot_stochastic.ipynb": "e18253354c8c1d72567a4259eb1094f7", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_OT_2D_samples.ipynb": "912a77c5dd0fc0fafa03fac3d86f1502"} \ No newline at end of file
+{"plot_otda_color_images.ipynb": "128d0435c08ebcf788913e4adcd7dd00", "plot_partial_wass_and_gromov.ipynb": "82242f8390df1d04806b333b745c72cf", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_screenkhorn_1D.ipynb": "af7b8a74a1be0f16f2c3908f5a178de0", "plot_otda_laplacian.ipynb": "d92cc0e528b9277f550daaa6f9d18415", "plot_OT_L1_vs_L2.ipynb": "288230c4e679d752a511353c96c134cb", "plot_otda_semi_supervised.ipynb": "568b39ffbdf6621dd6de162df42f4f21", "plot_fgw.ipynb": "f4de8e6939ce2b1339b3badc1fef0f37", "plot_otda_d2.ipynb": "07ef3212ff3123f16c32a5670e0167f8", "plot_compute_emd.ipynb": "299f6fffcdbf48b7c3268c0136e284f8", "plot_barycenter_fgw.ipynb": "9e813d3b07b7c0c0fcc35a778ca1243f", "plot_convolutional_barycenter.ipynb": "fdd259bfcd6d5fe8001efb4345795d2f", "plot_optim_OTreg.ipynb": "bddd8e49f092873d8980d41ae4974e19", "plot_UOT_1D.ipynb": "2658d5164165941b07539dae3cb80a0f", "plot_OT_1D_smooth.ipynb": "f3e1f0e362c9a78071a40c02b85d2305", "plot_barycenter_1D.ipynb": "f6fa5bc13d9811f09792f73b4de70aa0", "plot_otda_mapping.ipynb": "1bb321763f670fc945d77cfc91471e5e", "plot_OT_1D.ipynb": "0346a8c862606d11f36d0aa087ecab0d", "plot_gromov_barycenter.ipynb": "a7999fcc236d90a0adeb8da2c6370db3", "plot_UOT_barycenter_1D.ipynb": "dd9b857a8c66d71d0124d4a2c30a51dd", "plot_otda_mapping_colors_images.ipynb": "16faae80d6ea8b37d6b1f702149a10de", "plot_stochastic.ipynb": "64f23a8dcbab9823ae92f0fd6c3aceab", "plot_otda_linear_mapping.ipynb": "82417d9141e310bf1f2c2ecdb550094b", "plot_otda_classes.ipynb": "8836a924c9b562ef397af12034fa1abb", "plot_free_support_barycenter.ipynb": "be9d0823f9d7774a289311b9f14548eb", "plot_gromov.ipynb": "de06b1dbe8de99abae51c2e0b64b485d", "plot_otda_jcpot.ipynb": "65482cbfef5c6c1e5e73998aeb5f4b10", "plot_OT_2D_samples.ipynb": "9a9496792fa4216b1059fc70abca851a", "plot_barycenter_lp_vs_entropic.ipynb": "334840b69a86898813e50a6db0f3d0de"} \ No newline at end of file
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000..256706b
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,6 @@
+sphinx_gallery
+sphinx_rtd_theme
+numpydoc
+memory_profiler
+pillow
+networkx
diff --git a/docs/requirements_rtd.txt b/docs/requirements_rtd.txt
new file mode 100644
index 0000000..e3999d6
--- /dev/null
+++ b/docs/requirements_rtd.txt
@@ -0,0 +1,14 @@
+sphinx_gallery
+numpydoc
+memory_profiler
+pillow
+networkx
+numpy
+scipy>=1.0
+cython
+matplotlib
+autograd
+pymanopt==0.2.4; python_version <'3'
+pymanopt; python_version >= '3'
+cvxopt
+scikit-learn \ No newline at end of file
diff --git a/docs/rtd/conf.py b/docs/rtd/conf.py
new file mode 100644
index 0000000..814db75
--- /dev/null
+++ b/docs/rtd/conf.py
@@ -0,0 +1,6 @@
+from recommonmark.parser import CommonMarkParser
+
+source_parsers = {'.md': CommonMarkParser}
+
+source_suffix = ['.md']
+master_doc = 'index' \ No newline at end of file
diff --git a/docs/rtd/index.md b/docs/rtd/index.md
new file mode 100644
index 0000000..734574e
--- /dev/null
+++ b/docs/rtd/index.md
@@ -0,0 +1,5 @@
+<meta http-equiv="refresh" content="0; URL=https://PythonOT.github.io">
+
+# POT: Python Optimal Transport
+
+The documentation has been moved to : [https://PythonOT.github.io](https://PythonOT.github.io) \ No newline at end of file
diff --git a/docs/source/_templates/module.rst b/docs/source/_templates/module.rst
new file mode 100644
index 0000000..5ad89be
--- /dev/null
+++ b/docs/source/_templates/module.rst
@@ -0,0 +1,57 @@
+{{ fullname }}
+{{ underline }}
+
+.. automodule:: {{ fullname }}
+
+ {% block functions %}
+ {% if functions %}
+
+ Functions
+ ---------
+
+ {% for item in functions %}
+
+ .. autofunction:: {{ item }}
+
+ .. include:: backreferences/{{fullname}}.{{item}}.examples
+
+ .. raw:: html
+
+ <div class="sphx-glr-clear"></div>
+
+ {%- endfor %}
+ {% endif %}
+ {% endblock %}
+
+ {% block classes %}
+ {% if classes %}
+
+ Classes
+ -------
+
+ {% for item in classes %}
+ .. autoclass:: {{ item }}
+ :members:
+
+ .. include:: backreferences/{{fullname}}.{{item}}.examples
+
+ .. raw:: html
+
+ <div class="sphx-glr-clear"></div>
+
+ {%- endfor %}
+ {% endif %}
+ {% endblock %}
+
+ {% block exceptions %}
+ {% if exceptions %}
+
+ Exceptions
+ ----------
+
+ .. autosummary::
+ {% for item in exceptions %}
+ {{ item }}
+ {%- endfor %}
+ {% endif %}
+ {% endblock %} \ No newline at end of file
diff --git a/docs/source/all.rst b/docs/source/all.rst
index c968aa1..d7b878f 100644
--- a/docs/source/all.rst
+++ b/docs/source/all.rst
@@ -1,88 +1,36 @@
+.. _sphx_glr_api_reference:
-Python modules
-==============
+API and modules
+===============
-ot
---
+.. currentmodule:: ot
-.. automodule:: ot
- :members:
-
-ot.lp
------
-.. automodule:: ot.lp
- :members:
-
-ot.bregman
-----------
-
-.. automodule:: ot.bregman
- :members:
-
-ot.smooth
------
-.. automodule:: ot.smooth
- :members:
-
-ot.gromov
-----------
-
-.. automodule:: ot.gromov
- :members:
-
-
-ot.optim
---------
-
-.. automodule:: ot.optim
- :members:
-ot.da
---------
+:py:mod:`ot`:
-.. automodule:: ot.da
- :members:
+.. autosummary::
+ :toctree: gen_modules/
+ :template: module.rst
-ot.gpu
---------
+ lp
+ bregman
+ smooth
+ gromov
+ optim
+ da
+ gpu
+ dr
+ utils
+ datasets
+ plot
+ stochastic
+ unbalanced
+ partial
-.. automodule:: ot.gpu
- :members:
+.. autosummary::
+ :toctree: ../modules/generated/
+ :template: module.rst
-ot.dr
---------
-
-.. automodule:: ot.dr
- :members:
-
-
-ot.utils
---------
-
-.. automodule:: ot.utils
- :members:
-
-ot.datasets
------------
-
-.. automodule:: ot.datasets
- :members:
-
-ot.plot
--------
-
-.. automodule:: ot.plot
- :members:
-
-ot.stochastic
--------------
-
-.. automodule:: ot.stochastic
+.. automodule:: ot
:members:
-
-ot.unbalanced
--------------
-
-.. automodule:: ot.unbalanced
- :members:
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@@ -1,535 +0,0 @@
-:orphan:
-
-POT Examples
-============
-
-This is a gallery of all the POT example files.
-
-
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of EMD and Sinkhorn transport plans and their visualiz...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_OT_1D.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_OT_1D
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of Unbalanced Optimal transport using a Kullback-Leibl...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_UOT_1D.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_UOT_1D
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="Illustrates the use of the generic solver for regularized OT with user-designed regularization ...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_optim_OTreg
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="Illustration of 2D Wasserstein barycenters if discributions that are weighted sum of diracs.">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_free_support_barycenter_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_free_support_barycenter.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_free_support_barycenter
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of EMD, Sinkhorn and smooth OT plans and their visuali...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_smooth_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_OT_1D_smooth.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_OT_1D_smooth
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to show how to use the Gromov-Wassertsein distance computation in POT....">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_gromov.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_gromov
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="Shows how to compute multiple EMD and Sinkhorn with two differnt ground metrics and plot their ...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_compute_emd.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_compute_emd
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to illustrate how the Convolutional Wasserstein Barycenter function of...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_convolutional_barycenter.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_convolutional_barycenter
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip=" ">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_otda_linear_mapping.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_otda_linear_mapping
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example illustrate the use of WDA as proposed in [11].">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_WDA.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_WDA
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="Illustration of 2D optimal transport between discributions that are weighted sum of diracs. The...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_OT_2D_samples
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to show how to use the stochatic optimization algorithms for descrete ...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_stochastic.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_stochastic
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example presents a way of transferring colors between two images with Optimal Transport as...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_color_images_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_otda_color_images.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_otda_color_images
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of regularized Wassersyein Barycenter as proposed in [...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_barycenter_1D
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="OT for domain adaptation with image color adaptation [6] with mapping estimation [8].">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_otda_mapping_colors_images
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of regularized Wassersyein Barycenter as proposed in [...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_UOT_barycenter_1D.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_UOT_barycenter_1D
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example presents how to use MappingTransport to estimate at the same time both the couplin...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_otda_mapping.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_otda_mapping
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example introduces a semi supervised domain adaptation in a 2D setting. It explicits the p...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_otda_semi_supervised.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_otda_semi_supervised
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of FGW for 1D measures[18].">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_fgw_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_fgw.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_fgw
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example introduces a domain adaptation in a 2D setting and the 4 OTDA approaches currently...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_classes_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_otda_classes.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_otda_classes
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example introduces a domain adaptation in a 2D setting. It explicits the problem of domain...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_otda_d2.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_otda_d2
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="2D OT on empirical distributio with different gound metric.">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_OT_L1_vs_L2
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of regularized Wasserstein Barycenter as proposed in [...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_barycenter_lp_vs_entropic
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation barycenter of labeled graphs using FGW">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_fgw_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_barycenter_fgw.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_barycenter_fgw
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to show how to use the Gromov-Wasserstein distance computation in POT....">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_barycenter_thumb.png
-
- :ref:`sphx_glr_auto_examples_plot_gromov_barycenter.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /auto_examples/plot_gromov_barycenter
-.. raw:: html
-
- <div style='clear:both'></div>
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download all examples in Python source code: auto_examples_python.zip <//home/rflamary/PYTHON/POT/docs/source/auto_examples/auto_examples_python.zip>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//home/rflamary/PYTHON/POT/docs/source/auto_examples/auto_examples_jupyter.zip>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb
deleted file mode 100644
index bd0439e..0000000
--- a/docs/source/auto_examples/plot_OT_1D.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans\nand their visualization.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot distributions and loss matrix\n----------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve Sinkhorn\n--------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py
deleted file mode 100644
index f33e2a4..0000000
--- a/docs/source/auto_examples/plot_OT_1D.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-====================
-1D optimal transport
-====================
-
-This example illustrates the computation of EMD and Sinkhorn transport plans
-and their visualization.
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-import ot.plot
-from ot.datasets import make_1D_gauss as gauss
-
-##############################################################################
-# Generate data
-# -------------
-
-
-#%% parameters
-
-n = 100 # nb bins
-
-# bin positions
-x = np.arange(n, dtype=np.float64)
-
-# Gaussian distributions
-a = gauss(n, m=20, s=5) # m= mean, s= std
-b = gauss(n, m=60, s=10)
-
-# loss matrix
-M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
-M /= M.max()
-
-
-##############################################################################
-# Plot distributions and loss matrix
-# ----------------------------------
-
-#%% plot the distributions
-
-pl.figure(1, figsize=(6.4, 3))
-pl.plot(x, a, 'b', label='Source distribution')
-pl.plot(x, b, 'r', label='Target distribution')
-pl.legend()
-
-#%% plot distributions and loss matrix
-
-pl.figure(2, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
-
-##############################################################################
-# Solve EMD
-# ---------
-
-
-#%% EMD
-
-G0 = ot.emd(a, b, M)
-
-pl.figure(3, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')
-
-##############################################################################
-# Solve Sinkhorn
-# --------------
-
-
-#%% Sinkhorn
-
-lambd = 1e-3
-Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)
-
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')
-
-pl.show()
diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst
deleted file mode 100644
index b97d67c..0000000
--- a/docs/source/auto_examples/plot_OT_1D.rst
+++ /dev/null
@@ -1,199 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_OT_1D.py:
-
-
-====================
-1D optimal transport
-====================
-
-This example illustrates the computation of EMD and Sinkhorn transport plans
-and their visualization.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Remi Flamary <remi.flamary@unice.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- import ot.plot
- from ot.datasets import make_1D_gauss as gauss
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
-
- #%% parameters
-
- n = 100 # nb bins
-
- # bin positions
- x = np.arange(n, dtype=np.float64)
-
- # Gaussian distributions
- a = gauss(n, m=20, s=5) # m= mean, s= std
- b = gauss(n, m=60, s=10)
-
- # loss matrix
- M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
- M /= M.max()
-
-
-
-
-
-
-
-
-Plot distributions and loss matrix
-----------------------------------
-
-
-
-.. code-block:: python
-
-
- #%% plot the distributions
-
- pl.figure(1, figsize=(6.4, 3))
- pl.plot(x, a, 'b', label='Source distribution')
- pl.plot(x, b, 'r', label='Target distribution')
- pl.legend()
-
- #%% plot distributions and loss matrix
-
- pl.figure(2, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_001.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_002.png
- :scale: 47
-
-
-
-
-Solve EMD
----------
-
-
-
-.. code-block:: python
-
-
-
- #%% EMD
-
- G0 = ot.emd(a, b, M)
-
- pl.figure(3, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_005.png
- :align: center
-
-
-
-
-Solve Sinkhorn
---------------
-
-
-
-.. code-block:: python
-
-
-
- #%% Sinkhorn
-
- lambd = 1e-3
- Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)
-
- pl.figure(4, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_007.png
- :align: center
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Err
- -------------------
- 0|8.187970e-02|
- 10|3.460174e-02|
- 20|6.633335e-03|
- 30|9.797798e-04|
- 40|1.389606e-04|
- 50|1.959016e-05|
- 60|2.759079e-06|
- 70|3.885166e-07|
- 80|5.470605e-08|
- 90|7.702918e-09|
- 100|1.084609e-09|
- 110|1.527180e-10|
-
-
-**Total running time of the script:** ( 0 minutes 0.561 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_OT_1D.py <plot_OT_1D.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_OT_1D.ipynb <plot_OT_1D.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.ipynb b/docs/source/auto_examples/plot_OT_1D_smooth.ipynb
deleted file mode 100644
index d523f6a..0000000
--- a/docs/source/auto_examples/plot_OT_1D_smooth.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# 1D smooth optimal transport\n\n\nThis example illustrates the computation of EMD, Sinkhorn and smooth OT plans\nand their visualization.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot distributions and loss matrix\n----------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve Sinkhorn\n--------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Sinkhorn\n\nlambd = 2e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve Smooth OT\n--------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Smooth OT with KL regularization\n\nlambd = 2e-3\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n\npl.show()\n\n\n#%% Smooth OT with KL regularization\n\nlambd = 1e-1\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n\npl.figure(6, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.py b/docs/source/auto_examples/plot_OT_1D_smooth.py
deleted file mode 100644
index b690751..0000000
--- a/docs/source/auto_examples/plot_OT_1D_smooth.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-===========================
-1D smooth optimal transport
-===========================
-
-This example illustrates the computation of EMD, Sinkhorn and smooth OT plans
-and their visualization.
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-import ot.plot
-from ot.datasets import make_1D_gauss as gauss
-
-##############################################################################
-# Generate data
-# -------------
-
-
-#%% parameters
-
-n = 100 # nb bins
-
-# bin positions
-x = np.arange(n, dtype=np.float64)
-
-# Gaussian distributions
-a = gauss(n, m=20, s=5) # m= mean, s= std
-b = gauss(n, m=60, s=10)
-
-# loss matrix
-M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
-M /= M.max()
-
-
-##############################################################################
-# Plot distributions and loss matrix
-# ----------------------------------
-
-#%% plot the distributions
-
-pl.figure(1, figsize=(6.4, 3))
-pl.plot(x, a, 'b', label='Source distribution')
-pl.plot(x, b, 'r', label='Target distribution')
-pl.legend()
-
-#%% plot distributions and loss matrix
-
-pl.figure(2, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
-
-##############################################################################
-# Solve EMD
-# ---------
-
-
-#%% EMD
-
-G0 = ot.emd(a, b, M)
-
-pl.figure(3, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')
-
-##############################################################################
-# Solve Sinkhorn
-# --------------
-
-
-#%% Sinkhorn
-
-lambd = 2e-3
-Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)
-
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')
-
-pl.show()
-
-##############################################################################
-# Solve Smooth OT
-# --------------
-
-
-#%% Smooth OT with KL regularization
-
-lambd = 2e-3
-Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')
-
-pl.figure(5, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')
-
-pl.show()
-
-
-#%% Smooth OT with KL regularization
-
-lambd = 1e-1
-Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')
-
-pl.figure(6, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')
-
-pl.show()
diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.rst b/docs/source/auto_examples/plot_OT_1D_smooth.rst
deleted file mode 100644
index 5a0ebd3..0000000
--- a/docs/source/auto_examples/plot_OT_1D_smooth.rst
+++ /dev/null
@@ -1,242 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_OT_1D_smooth.py:
-
-
-===========================
-1D smooth optimal transport
-===========================
-
-This example illustrates the computation of EMD, Sinkhorn and smooth OT plans
-and their visualization.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Remi Flamary <remi.flamary@unice.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- import ot.plot
- from ot.datasets import make_1D_gauss as gauss
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
-
- #%% parameters
-
- n = 100 # nb bins
-
- # bin positions
- x = np.arange(n, dtype=np.float64)
-
- # Gaussian distributions
- a = gauss(n, m=20, s=5) # m= mean, s= std
- b = gauss(n, m=60, s=10)
-
- # loss matrix
- M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
- M /= M.max()
-
-
-
-
-
-
-
-
-Plot distributions and loss matrix
-----------------------------------
-
-
-
-.. code-block:: python
-
-
- #%% plot the distributions
-
- pl.figure(1, figsize=(6.4, 3))
- pl.plot(x, a, 'b', label='Source distribution')
- pl.plot(x, b, 'r', label='Target distribution')
- pl.legend()
-
- #%% plot distributions and loss matrix
-
- pl.figure(2, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_001.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_002.png
- :scale: 47
-
-
-
-
-Solve EMD
----------
-
-
-
-.. code-block:: python
-
-
-
- #%% EMD
-
- G0 = ot.emd(a, b, M)
-
- pl.figure(3, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_005.png
- :align: center
-
-
-
-
-Solve Sinkhorn
---------------
-
-
-
-.. code-block:: python
-
-
-
- #%% Sinkhorn
-
- lambd = 2e-3
- Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)
-
- pl.figure(4, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')
-
- pl.show()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_007.png
- :align: center
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Err
- -------------------
- 0|7.958844e-02|
- 10|5.921715e-03|
- 20|1.238266e-04|
- 30|2.469780e-06|
- 40|4.919966e-08|
- 50|9.800197e-10|
-
-
-Solve Smooth OT
---------------
-
-
-
-.. code-block:: python
-
-
-
- #%% Smooth OT with KL regularization
-
- lambd = 2e-3
- Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')
-
- pl.figure(5, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')
-
- pl.show()
-
-
- #%% Smooth OT with KL regularization
-
- lambd = 1e-1
- Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')
-
- pl.figure(6, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')
-
- pl.show()
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_009.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_010.png
- :scale: 47
-
-
-
-
-**Total running time of the script:** ( 0 minutes 1.053 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_OT_1D_smooth.py <plot_OT_1D_smooth.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_OT_1D_smooth.ipynb <plot_OT_1D_smooth.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb
deleted file mode 100644
index dad138b..0000000
--- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# 2D Optimal transport between empirical distributions\n\n\nIllustration of 2D optimal transport between discributions that are weighted\nsum of diracs. The OT matrix is plotted with the samples.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n# Kilian Fatras <kilian.fatras@irisa.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters and data generation\n\nn = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% plot samples\n\npl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute EMD\n-----------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute Sinkhorn\n----------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Emprirical Sinkhorn\n----------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGes = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n\npl.figure(7)\npl.imshow(Ges, interpolation='nearest')\npl.title('OT matrix empirical sinkhorn')\n\npl.figure(8)\not.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn from samples')\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py
deleted file mode 100644
index 63126ba..0000000
--- a/docs/source/auto_examples/plot_OT_2D_samples.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-====================================================
-2D Optimal transport between empirical distributions
-====================================================
-
-Illustration of 2D optimal transport between discributions that are weighted
-sum of diracs. The OT matrix is plotted with the samples.
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-# Kilian Fatras <kilian.fatras@irisa.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-import ot.plot
-
-##############################################################################
-# Generate data
-# -------------
-
-#%% parameters and data generation
-
-n = 50 # nb samples
-
-mu_s = np.array([0, 0])
-cov_s = np.array([[1, 0], [0, 1]])
-
-mu_t = np.array([4, 4])
-cov_t = np.array([[1, -.8], [-.8, 1]])
-
-xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)
-xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)
-
-a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples
-
-# loss matrix
-M = ot.dist(xs, xt)
-M /= M.max()
-
-##############################################################################
-# Plot data
-# ---------
-
-#%% plot samples
-
-pl.figure(1)
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.legend(loc=0)
-pl.title('Source and target distributions')
-
-pl.figure(2)
-pl.imshow(M, interpolation='nearest')
-pl.title('Cost matrix M')
-
-##############################################################################
-# Compute EMD
-# -----------
-
-#%% EMD
-
-G0 = ot.emd(a, b, M)
-
-pl.figure(3)
-pl.imshow(G0, interpolation='nearest')
-pl.title('OT matrix G0')
-
-pl.figure(4)
-ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.legend(loc=0)
-pl.title('OT matrix with samples')
-
-
-##############################################################################
-# Compute Sinkhorn
-# ----------------
-
-#%% sinkhorn
-
-# reg term
-lambd = 1e-3
-
-Gs = ot.sinkhorn(a, b, M, lambd)
-
-pl.figure(5)
-pl.imshow(Gs, interpolation='nearest')
-pl.title('OT matrix sinkhorn')
-
-pl.figure(6)
-ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.legend(loc=0)
-pl.title('OT matrix Sinkhorn with samples')
-
-pl.show()
-
-
-##############################################################################
-# Emprirical Sinkhorn
-# ----------------
-
-#%% sinkhorn
-
-# reg term
-lambd = 1e-3
-
-Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd)
-
-pl.figure(7)
-pl.imshow(Ges, interpolation='nearest')
-pl.title('OT matrix empirical sinkhorn')
-
-pl.figure(8)
-ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.legend(loc=0)
-pl.title('OT matrix Sinkhorn from samples')
-
-pl.show()
diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst
deleted file mode 100644
index 1f1d713..0000000
--- a/docs/source/auto_examples/plot_OT_2D_samples.rst
+++ /dev/null
@@ -1,273 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_OT_2D_samples.py:
-
-
-====================================================
-2D Optimal transport between empirical distributions
-====================================================
-
-Illustration of 2D optimal transport between discributions that are weighted
-sum of diracs. The OT matrix is plotted with the samples.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Remi Flamary <remi.flamary@unice.fr>
- # Kilian Fatras <kilian.fatras@irisa.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- import ot.plot
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- #%% parameters and data generation
-
- n = 50 # nb samples
-
- mu_s = np.array([0, 0])
- cov_s = np.array([[1, 0], [0, 1]])
-
- mu_t = np.array([4, 4])
- cov_t = np.array([[1, -.8], [-.8, 1]])
-
- xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)
- xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)
-
- a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples
-
- # loss matrix
- M = ot.dist(xs, xt)
- M /= M.max()
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- #%% plot samples
-
- pl.figure(1)
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.legend(loc=0)
- pl.title('Source and target distributions')
-
- pl.figure(2)
- pl.imshow(M, interpolation='nearest')
- pl.title('Cost matrix M')
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png
- :scale: 47
-
-
-
-
-Compute EMD
------------
-
-
-
-.. code-block:: python
-
-
- #%% EMD
-
- G0 = ot.emd(a, b, M)
-
- pl.figure(3)
- pl.imshow(G0, interpolation='nearest')
- pl.title('OT matrix G0')
-
- pl.figure(4)
- ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.legend(loc=0)
- pl.title('OT matrix with samples')
-
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png
- :scale: 47
-
-
-
-
-Compute Sinkhorn
-----------------
-
-
-
-.. code-block:: python
-
-
- #%% sinkhorn
-
- # reg term
- lambd = 1e-3
-
- Gs = ot.sinkhorn(a, b, M, lambd)
-
- pl.figure(5)
- pl.imshow(Gs, interpolation='nearest')
- pl.title('OT matrix sinkhorn')
-
- pl.figure(6)
- ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.legend(loc=0)
- pl.title('OT matrix Sinkhorn with samples')
-
- pl.show()
-
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_009.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_010.png
- :scale: 47
-
-
-
-
-Emprirical Sinkhorn
-----------------
-
-
-
-.. code-block:: python
-
-
- #%% sinkhorn
-
- # reg term
- lambd = 1e-3
-
- Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd)
-
- pl.figure(7)
- pl.imshow(Ges, interpolation='nearest')
- pl.title('OT matrix empirical sinkhorn')
-
- pl.figure(8)
- ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.legend(loc=0)
- pl.title('OT matrix Sinkhorn from samples')
-
- pl.show()
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_013.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_014.png
- :scale: 47
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- Warning: numerical errors at iteration 0
-
-
-**Total running time of the script:** ( 0 minutes 2.616 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_OT_2D_samples.py <plot_OT_2D_samples.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_OT_2D_samples.ipynb <plot_OT_2D_samples.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb
deleted file mode 100644
index 125d720..0000000
--- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# 2D Optimal transport for different metrics\n\n\n2D OT on empirical distributio with different gound metric.\n\nStole the figure idea from Fig. 1 and 2 in\nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dataset 1 : uniform sampling\n----------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and target distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dataset 1 : Plot OT Matrices\n----------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dataset 2 : Partial circle\n--------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n = 50 # nb samples\nxtot = np.zeros((n + 1, 2))\nxtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\nxtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\nxs = xtot[:n, :]\nxt = xtot[1:, :]\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n\n# Data\npl.figure(4, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(5, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dataset 2 : Plot OT Matrices\n-----------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py
deleted file mode 100644
index 37b429f..0000000
--- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py
+++ /dev/null
@@ -1,208 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-==========================================
-2D Optimal transport for different metrics
-==========================================
-
-2D OT on empirical distributio with different gound metric.
-
-Stole the figure idea from Fig. 1 and 2 in
-https://arxiv.org/pdf/1706.07650.pdf
-
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-import ot.plot
-
-##############################################################################
-# Dataset 1 : uniform sampling
-# ----------------------------
-
-n = 20 # nb samples
-xs = np.zeros((n, 2))
-xs[:, 0] = np.arange(n) + 1
-xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...
-
-xt = np.zeros((n, 2))
-xt[:, 1] = np.arange(n) + 1
-
-a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples
-
-# loss matrix
-M1 = ot.dist(xs, xt, metric='euclidean')
-M1 /= M1.max()
-
-# loss matrix
-M2 = ot.dist(xs, xt, metric='sqeuclidean')
-M2 /= M2.max()
-
-# loss matrix
-Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))
-Mp /= Mp.max()
-
-# Data
-pl.figure(1, figsize=(7, 3))
-pl.clf()
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.axis('equal')
-pl.title('Source and target distributions')
-
-
-# Cost matrices
-pl.figure(2, figsize=(7, 3))
-
-pl.subplot(1, 3, 1)
-pl.imshow(M1, interpolation='nearest')
-pl.title('Euclidean cost')
-
-pl.subplot(1, 3, 2)
-pl.imshow(M2, interpolation='nearest')
-pl.title('Squared Euclidean cost')
-
-pl.subplot(1, 3, 3)
-pl.imshow(Mp, interpolation='nearest')
-pl.title('Sqrt Euclidean cost')
-pl.tight_layout()
-
-##############################################################################
-# Dataset 1 : Plot OT Matrices
-# ----------------------------
-
-
-#%% EMD
-G1 = ot.emd(a, b, M1)
-G2 = ot.emd(a, b, M2)
-Gp = ot.emd(a, b, Mp)
-
-# OT matrices
-pl.figure(3, figsize=(7, 3))
-
-pl.subplot(1, 3, 1)
-ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.axis('equal')
-# pl.legend(loc=0)
-pl.title('OT Euclidean')
-
-pl.subplot(1, 3, 2)
-ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.axis('equal')
-# pl.legend(loc=0)
-pl.title('OT squared Euclidean')
-
-pl.subplot(1, 3, 3)
-ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.axis('equal')
-# pl.legend(loc=0)
-pl.title('OT sqrt Euclidean')
-pl.tight_layout()
-
-pl.show()
-
-
-##############################################################################
-# Dataset 2 : Partial circle
-# --------------------------
-
-n = 50 # nb samples
-xtot = np.zeros((n + 1, 2))
-xtot[:, 0] = np.cos(
- (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
-xtot[:, 1] = np.sin(
- (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
-
-xs = xtot[:n, :]
-xt = xtot[1:, :]
-
-a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples
-
-# loss matrix
-M1 = ot.dist(xs, xt, metric='euclidean')
-M1 /= M1.max()
-
-# loss matrix
-M2 = ot.dist(xs, xt, metric='sqeuclidean')
-M2 /= M2.max()
-
-# loss matrix
-Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))
-Mp /= Mp.max()
-
-
-# Data
-pl.figure(4, figsize=(7, 3))
-pl.clf()
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.axis('equal')
-pl.title('Source and traget distributions')
-
-
-# Cost matrices
-pl.figure(5, figsize=(7, 3))
-
-pl.subplot(1, 3, 1)
-pl.imshow(M1, interpolation='nearest')
-pl.title('Euclidean cost')
-
-pl.subplot(1, 3, 2)
-pl.imshow(M2, interpolation='nearest')
-pl.title('Squared Euclidean cost')
-
-pl.subplot(1, 3, 3)
-pl.imshow(Mp, interpolation='nearest')
-pl.title('Sqrt Euclidean cost')
-pl.tight_layout()
-
-##############################################################################
-# Dataset 2 : Plot OT Matrices
-# -----------------------------
-
-
-#%% EMD
-G1 = ot.emd(a, b, M1)
-G2 = ot.emd(a, b, M2)
-Gp = ot.emd(a, b, Mp)
-
-# OT matrices
-pl.figure(6, figsize=(7, 3))
-
-pl.subplot(1, 3, 1)
-ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.axis('equal')
-# pl.legend(loc=0)
-pl.title('OT Euclidean')
-
-pl.subplot(1, 3, 2)
-ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.axis('equal')
-# pl.legend(loc=0)
-pl.title('OT squared Euclidean')
-
-pl.subplot(1, 3, 3)
-ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])
-pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-pl.axis('equal')
-# pl.legend(loc=0)
-pl.title('OT sqrt Euclidean')
-pl.tight_layout()
-
-pl.show()
diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst
deleted file mode 100644
index 5db4b55..0000000
--- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst
+++ /dev/null
@@ -1,318 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_OT_L1_vs_L2.py:
-
-
-==========================================
-2D Optimal transport for different metrics
-==========================================
-
-2D OT on empirical distributio with different gound metric.
-
-Stole the figure idea from Fig. 1 and 2 in
-https://arxiv.org/pdf/1706.07650.pdf
-
-
-
-
-
-.. code-block:: python
-
-
- # Author: Remi Flamary <remi.flamary@unice.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- import ot.plot
-
-
-
-
-
-
-
-Dataset 1 : uniform sampling
-----------------------------
-
-
-
-.. code-block:: python
-
-
- n = 20 # nb samples
- xs = np.zeros((n, 2))
- xs[:, 0] = np.arange(n) + 1
- xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...
-
- xt = np.zeros((n, 2))
- xt[:, 1] = np.arange(n) + 1
-
- a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples
-
- # loss matrix
- M1 = ot.dist(xs, xt, metric='euclidean')
- M1 /= M1.max()
-
- # loss matrix
- M2 = ot.dist(xs, xt, metric='sqeuclidean')
- M2 /= M2.max()
-
- # loss matrix
- Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))
- Mp /= Mp.max()
-
- # Data
- pl.figure(1, figsize=(7, 3))
- pl.clf()
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.axis('equal')
- pl.title('Source and target distributions')
-
-
- # Cost matrices
- pl.figure(2, figsize=(7, 3))
-
- pl.subplot(1, 3, 1)
- pl.imshow(M1, interpolation='nearest')
- pl.title('Euclidean cost')
-
- pl.subplot(1, 3, 2)
- pl.imshow(M2, interpolation='nearest')
- pl.title('Squared Euclidean cost')
-
- pl.subplot(1, 3, 3)
- pl.imshow(Mp, interpolation='nearest')
- pl.title('Sqrt Euclidean cost')
- pl.tight_layout()
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_001.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_002.png
- :scale: 47
-
-
-
-
-Dataset 1 : Plot OT Matrices
-----------------------------
-
-
-
-.. code-block:: python
-
-
-
- #%% EMD
- G1 = ot.emd(a, b, M1)
- G2 = ot.emd(a, b, M2)
- Gp = ot.emd(a, b, Mp)
-
- # OT matrices
- pl.figure(3, figsize=(7, 3))
-
- pl.subplot(1, 3, 1)
- ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.axis('equal')
- # pl.legend(loc=0)
- pl.title('OT Euclidean')
-
- pl.subplot(1, 3, 2)
- ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.axis('equal')
- # pl.legend(loc=0)
- pl.title('OT squared Euclidean')
-
- pl.subplot(1, 3, 3)
- ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.axis('equal')
- # pl.legend(loc=0)
- pl.title('OT sqrt Euclidean')
- pl.tight_layout()
-
- pl.show()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_005.png
- :align: center
-
-
-
-
-Dataset 2 : Partial circle
---------------------------
-
-
-
-.. code-block:: python
-
-
- n = 50 # nb samples
- xtot = np.zeros((n + 1, 2))
- xtot[:, 0] = np.cos(
- (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
- xtot[:, 1] = np.sin(
- (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
-
- xs = xtot[:n, :]
- xt = xtot[1:, :]
-
- a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples
-
- # loss matrix
- M1 = ot.dist(xs, xt, metric='euclidean')
- M1 /= M1.max()
-
- # loss matrix
- M2 = ot.dist(xs, xt, metric='sqeuclidean')
- M2 /= M2.max()
-
- # loss matrix
- Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))
- Mp /= Mp.max()
-
-
- # Data
- pl.figure(4, figsize=(7, 3))
- pl.clf()
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.axis('equal')
- pl.title('Source and traget distributions')
-
-
- # Cost matrices
- pl.figure(5, figsize=(7, 3))
-
- pl.subplot(1, 3, 1)
- pl.imshow(M1, interpolation='nearest')
- pl.title('Euclidean cost')
-
- pl.subplot(1, 3, 2)
- pl.imshow(M2, interpolation='nearest')
- pl.title('Squared Euclidean cost')
-
- pl.subplot(1, 3, 3)
- pl.imshow(Mp, interpolation='nearest')
- pl.title('Sqrt Euclidean cost')
- pl.tight_layout()
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_007.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_008.png
- :scale: 47
-
-
-
-
-Dataset 2 : Plot OT Matrices
------------------------------
-
-
-
-.. code-block:: python
-
-
-
- #%% EMD
- G1 = ot.emd(a, b, M1)
- G2 = ot.emd(a, b, M2)
- Gp = ot.emd(a, b, Mp)
-
- # OT matrices
- pl.figure(6, figsize=(7, 3))
-
- pl.subplot(1, 3, 1)
- ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.axis('equal')
- # pl.legend(loc=0)
- pl.title('OT Euclidean')
-
- pl.subplot(1, 3, 2)
- ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.axis('equal')
- # pl.legend(loc=0)
- pl.title('OT squared Euclidean')
-
- pl.subplot(1, 3, 3)
- ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])
- pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
- pl.axis('equal')
- # pl.legend(loc=0)
- pl.title('OT sqrt Euclidean')
- pl.tight_layout()
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_011.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 0.958 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_OT_L1_vs_L2.py <plot_OT_L1_vs_L2.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_OT_L1_vs_L2.ipynb <plot_OT_L1_vs_L2.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_UOT_1D.ipynb b/docs/source/auto_examples/plot_UOT_1D.ipynb
deleted file mode 100644
index c695306..0000000
--- a/docs/source/auto_examples/plot_UOT_1D.ipynb
+++ /dev/null
@@ -1,108 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# 1D Unbalanced optimal transport\n\n\nThis example illustrates the computation of Unbalanced Optimal transport\nusing a Kullback-Leibler relaxation.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Hicham Janati <hicham.janati@inria.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# make distributions unbalanced\nb *= 5.\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot distributions and loss matrix\n----------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n# plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve Unbalanced Sinkhorn\n--------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Sinkhorn\n\nepsilon = 0.1 # entropy parameter\nalpha = 1. # Unbalanced KL relaxation parameter\nGs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_UOT_1D.rst b/docs/source/auto_examples/plot_UOT_1D.rst
deleted file mode 100644
index 8e618b4..0000000
--- a/docs/source/auto_examples/plot_UOT_1D.rst
+++ /dev/null
@@ -1,173 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_UOT_1D.py:
-
-
-===============================
-1D Unbalanced optimal transport
-===============================
-
-This example illustrates the computation of Unbalanced Optimal transport
-using a Kullback-Leibler relaxation.
-
-
-
-.. code-block:: python
-
-
- # Author: Hicham Janati <hicham.janati@inria.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- import ot.plot
- from ot.datasets import make_1D_gauss as gauss
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
-
- #%% parameters
-
- n = 100 # nb bins
-
- # bin positions
- x = np.arange(n, dtype=np.float64)
-
- # Gaussian distributions
- a = gauss(n, m=20, s=5) # m= mean, s= std
- b = gauss(n, m=60, s=10)
-
- # make distributions unbalanced
- b *= 5.
-
- # loss matrix
- M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
- M /= M.max()
-
-
-
-
-
-
-
-
-Plot distributions and loss matrix
-----------------------------------
-
-
-
-.. code-block:: python
-
-
- #%% plot the distributions
-
- pl.figure(1, figsize=(6.4, 3))
- pl.plot(x, a, 'b', label='Source distribution')
- pl.plot(x, b, 'r', label='Target distribution')
- pl.legend()
-
- # plot distributions and loss matrix
-
- pl.figure(2, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
-
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_001.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_002.png
- :scale: 47
-
-
-
-
-Solve Unbalanced Sinkhorn
---------------
-
-
-
-.. code-block:: python
-
-
-
- # Sinkhorn
-
- epsilon = 0.1 # entropy parameter
- alpha = 1. # Unbalanced KL relaxation parameter
- Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)
-
- pl.figure(4, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_006.png
- :align: center
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Err
- -------------------
- 0|1.838786e+00|
- 10|1.242379e-01|
- 20|2.581314e-03|
- 30|5.674552e-05|
- 40|1.252959e-06|
- 50|2.768136e-08|
- 60|6.116090e-10|
-
-
-**Total running time of the script:** ( 0 minutes 0.259 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_UOT_1D.py <plot_UOT_1D.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_UOT_1D.ipynb <plot_UOT_1D.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb b/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb
deleted file mode 100644
index e59cdc2..0000000
--- a/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# 1D Wasserstein barycenter demo for Unbalanced distributions\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [10] for Unbalanced inputs.\n\n\n[10] Chizat, L., Peyr\u00e9, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Hicham Janati <hicham.janati@inria.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# make unbalanced dists\na2 *= 3.\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycenter computation\n----------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# non weighted barycenter computation\n\nweight = 0.5 # 0<=weight<=1\nweights = np.array([1 - weight, weight])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nalpha = 1.\n\nbary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycentric interpolation\n-------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# barycenter interpolation\n\nn_weight = 11\nweight_list = np.linspace(0, 1, n_weight)\n\n\nB_l2 = np.zeros((n, n_weight))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_weight):\n weight = weight_list[i]\n weights = np.array([1 - weight, weight])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n\n\n# plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.py b/docs/source/auto_examples/plot_UOT_barycenter_1D.py
deleted file mode 100644
index c8d9d3b..0000000
--- a/docs/source/auto_examples/plot_UOT_barycenter_1D.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-===========================================================
-1D Wasserstein barycenter demo for Unbalanced distributions
-===========================================================
-
-This example illustrates the computation of regularized Wassersyein Barycenter
-as proposed in [10] for Unbalanced inputs.
-
-
-[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.
-
-"""
-
-# Author: Hicham Janati <hicham.janati@inria.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-# necessary for 3d plot even if not used
-from mpl_toolkits.mplot3d import Axes3D # noqa
-from matplotlib.collections import PolyCollection
-
-##############################################################################
-# Generate data
-# -------------
-
-# parameters
-
-n = 100 # nb bins
-
-# bin positions
-x = np.arange(n, dtype=np.float64)
-
-# Gaussian distributions
-a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std
-a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)
-
-# make unbalanced dists
-a2 *= 3.
-
-# creating matrix A containing all distributions
-A = np.vstack((a1, a2)).T
-n_distributions = A.shape[1]
-
-# loss matrix + normalization
-M = ot.utils.dist0(n)
-M /= M.max()
-
-##############################################################################
-# Plot data
-# ---------
-
-# plot the distributions
-
-pl.figure(1, figsize=(6.4, 3))
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-pl.tight_layout()
-
-##############################################################################
-# Barycenter computation
-# ----------------------
-
-# non weighted barycenter computation
-
-weight = 0.5 # 0<=weight<=1
-weights = np.array([1 - weight, weight])
-
-# l2bary
-bary_l2 = A.dot(weights)
-
-# wasserstein
-reg = 1e-3
-alpha = 1.
-
-bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)
-
-pl.figure(2)
-pl.clf()
-pl.subplot(2, 1, 1)
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-
-pl.subplot(2, 1, 2)
-pl.plot(x, bary_l2, 'r', label='l2')
-pl.plot(x, bary_wass, 'g', label='Wasserstein')
-pl.legend()
-pl.title('Barycenters')
-pl.tight_layout()
-
-##############################################################################
-# Barycentric interpolation
-# -------------------------
-
-# barycenter interpolation
-
-n_weight = 11
-weight_list = np.linspace(0, 1, n_weight)
-
-
-B_l2 = np.zeros((n, n_weight))
-
-B_wass = np.copy(B_l2)
-
-for i in range(0, n_weight):
- weight = weight_list[i]
- weights = np.array([1 - weight, weight])
- B_l2[:, i] = A.dot(weights)
- B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)
-
-
-# plot interpolation
-
-pl.figure(3)
-
-cmap = pl.cm.get_cmap('viridis')
-verts = []
-zs = weight_list
-for i, z in enumerate(zs):
- ys = B_l2[:, i]
- verts.append(list(zip(x, ys)))
-
-ax = pl.gcf().gca(projection='3d')
-
-poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])
-poly.set_alpha(0.7)
-ax.add_collection3d(poly, zs=zs, zdir='y')
-ax.set_xlabel('x')
-ax.set_xlim3d(0, n)
-ax.set_ylabel(r'$\alpha$')
-ax.set_ylim3d(0, 1)
-ax.set_zlabel('')
-ax.set_zlim3d(0, B_l2.max() * 1.01)
-pl.title('Barycenter interpolation with l2')
-pl.tight_layout()
-
-pl.figure(4)
-cmap = pl.cm.get_cmap('viridis')
-verts = []
-zs = weight_list
-for i, z in enumerate(zs):
- ys = B_wass[:, i]
- verts.append(list(zip(x, ys)))
-
-ax = pl.gcf().gca(projection='3d')
-
-poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])
-poly.set_alpha(0.7)
-ax.add_collection3d(poly, zs=zs, zdir='y')
-ax.set_xlabel('x')
-ax.set_xlim3d(0, n)
-ax.set_ylabel(r'$\alpha$')
-ax.set_ylim3d(0, 1)
-ax.set_zlabel('')
-ax.set_zlim3d(0, B_l2.max() * 1.01)
-pl.title('Barycenter interpolation with Wasserstein')
-pl.tight_layout()
-
-pl.show()
diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.rst b/docs/source/auto_examples/plot_UOT_barycenter_1D.rst
deleted file mode 100644
index ac17587..0000000
--- a/docs/source/auto_examples/plot_UOT_barycenter_1D.rst
+++ /dev/null
@@ -1,261 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_UOT_barycenter_1D.py:
-
-
-===========================================================
-1D Wasserstein barycenter demo for Unbalanced distributions
-===========================================================
-
-This example illustrates the computation of regularized Wassersyein Barycenter
-as proposed in [10] for Unbalanced inputs.
-
-
-[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Hicham Janati <hicham.janati@inria.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- # necessary for 3d plot even if not used
- from mpl_toolkits.mplot3d import Axes3D # noqa
- from matplotlib.collections import PolyCollection
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- # parameters
-
- n = 100 # nb bins
-
- # bin positions
- x = np.arange(n, dtype=np.float64)
-
- # Gaussian distributions
- a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std
- a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)
-
- # make unbalanced dists
- a2 *= 3.
-
- # creating matrix A containing all distributions
- A = np.vstack((a1, a2)).T
- n_distributions = A.shape[1]
-
- # loss matrix + normalization
- M = ot.utils.dist0(n)
- M /= M.max()
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- # plot the distributions
-
- pl.figure(1, figsize=(6.4, 3))
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
- pl.tight_layout()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_001.png
- :align: center
-
-
-
-
-Barycenter computation
-----------------------
-
-
-
-.. code-block:: python
-
-
- # non weighted barycenter computation
-
- weight = 0.5 # 0<=weight<=1
- weights = np.array([1 - weight, weight])
-
- # l2bary
- bary_l2 = A.dot(weights)
-
- # wasserstein
- reg = 1e-3
- alpha = 1.
-
- bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)
-
- pl.figure(2)
- pl.clf()
- pl.subplot(2, 1, 1)
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
-
- pl.subplot(2, 1, 2)
- pl.plot(x, bary_l2, 'r', label='l2')
- pl.plot(x, bary_wass, 'g', label='Wasserstein')
- pl.legend()
- pl.title('Barycenters')
- pl.tight_layout()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_003.png
- :align: center
-
-
-
-
-Barycentric interpolation
--------------------------
-
-
-
-.. code-block:: python
-
-
- # barycenter interpolation
-
- n_weight = 11
- weight_list = np.linspace(0, 1, n_weight)
-
-
- B_l2 = np.zeros((n, n_weight))
-
- B_wass = np.copy(B_l2)
-
- for i in range(0, n_weight):
- weight = weight_list[i]
- weights = np.array([1 - weight, weight])
- B_l2[:, i] = A.dot(weights)
- B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)
-
-
- # plot interpolation
-
- pl.figure(3)
-
- cmap = pl.cm.get_cmap('viridis')
- verts = []
- zs = weight_list
- for i, z in enumerate(zs):
- ys = B_l2[:, i]
- verts.append(list(zip(x, ys)))
-
- ax = pl.gcf().gca(projection='3d')
-
- poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])
- poly.set_alpha(0.7)
- ax.add_collection3d(poly, zs=zs, zdir='y')
- ax.set_xlabel('x')
- ax.set_xlim3d(0, n)
- ax.set_ylabel(r'$\alpha$')
- ax.set_ylim3d(0, 1)
- ax.set_zlabel('')
- ax.set_zlim3d(0, B_l2.max() * 1.01)
- pl.title('Barycenter interpolation with l2')
- pl.tight_layout()
-
- pl.figure(4)
- cmap = pl.cm.get_cmap('viridis')
- verts = []
- zs = weight_list
- for i, z in enumerate(zs):
- ys = B_wass[:, i]
- verts.append(list(zip(x, ys)))
-
- ax = pl.gcf().gca(projection='3d')
-
- poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])
- poly.set_alpha(0.7)
- ax.add_collection3d(poly, zs=zs, zdir='y')
- ax.set_xlabel('x')
- ax.set_xlim3d(0, n)
- ax.set_ylabel(r'$\alpha$')
- ax.set_ylim3d(0, 1)
- ax.set_zlabel('')
- ax.set_zlim3d(0, B_l2.max() * 1.01)
- pl.title('Barycenter interpolation with Wasserstein')
- pl.tight_layout()
-
- pl.show()
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_005.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_006.png
- :scale: 47
-
-
-
-
-**Total running time of the script:** ( 0 minutes 0.344 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_UOT_barycenter_1D.py <plot_UOT_barycenter_1D.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_UOT_barycenter_1D.ipynb <plot_UOT_barycenter_1D.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb
deleted file mode 100644
index 1661c53..0000000
--- a/docs/source/auto_examples/plot_WDA.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "nbformat_minor": 0,
- "nbformat": 4,
- "cells": [
- {
- "execution_count": null,
- "cell_type": "code",
- "source": [
- "%matplotlib inline"
- ],
- "outputs": [],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "source": [
- "\n# Wasserstein Discriminant Analysis\n\n\nThis example illustrate the use of WDA as proposed in [11].\n\n\n[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).\nWasserstein Discriminant Analysis.\n\n\n"
- ],
- "cell_type": "markdown",
- "metadata": {}
- },
- {
- "execution_count": null,
- "cell_type": "code",
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\n\nfrom ot.dr import wda, fda"
- ],
- "outputs": [],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "source": [
- "Generate data\n-------------\n\n"
- ],
- "cell_type": "markdown",
- "metadata": {}
- },
- {
- "execution_count": null,
- "cell_type": "code",
- "source": [
- "#%% parameters\n\nn = 1000 # nb samples in source and target datasets\nnz = 0.2\n\n# generate circle dataset\nt = np.random.rand(n) * 2 * np.pi\nys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxs = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nt = np.random.rand(n) * 2 * np.pi\nyt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxt = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nnbnoise = 8\n\nxs = np.hstack((xs, np.random.randn(n, nbnoise)))\nxt = np.hstack((xt, np.random.randn(n, nbnoise)))"
- ],
- "outputs": [],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "source": [
- "Plot data\n---------\n\n"
- ],
- "cell_type": "markdown",
- "metadata": {}
- },
- {
- "execution_count": null,
- "cell_type": "code",
- "source": [
- "#%% plot samples\npl.figure(1, figsize=(6.4, 3.5))\n\npl.subplot(1, 2, 1)\npl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Discriminant dimensions')\n\npl.subplot(1, 2, 2)\npl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Other dimensions')\npl.tight_layout()"
- ],
- "outputs": [],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "source": [
- "Compute Fisher Discriminant Analysis\n------------------------------------\n\n"
- ],
- "cell_type": "markdown",
- "metadata": {}
- },
- {
- "execution_count": null,
- "cell_type": "code",
- "source": [
- "#%% Compute FDA\np = 2\n\nPfda, projfda = fda(xs, ys, p)"
- ],
- "outputs": [],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "source": [
- "Compute Wasserstein Discriminant Analysis\n-----------------------------------------\n\n"
- ],
- "cell_type": "markdown",
- "metadata": {}
- },
- {
- "execution_count": null,
- "cell_type": "code",
- "source": [
- "#%% Compute WDA\np = 2\nreg = 1e0\nk = 10\nmaxiter = 100\n\nPwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)"
- ],
- "outputs": [],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "source": [
- "Plot 2D projections\n-------------------\n\n"
- ],
- "cell_type": "markdown",
- "metadata": {}
- },
- {
- "execution_count": null,
- "cell_type": "code",
- "source": [
- "#%% plot samples\n\nxsp = projfda(xs)\nxtp = projfda(xt)\n\nxspw = projwda(xs)\nxtpw = projwda(xt)\n\npl.figure(2)\n\npl.subplot(2, 2, 1)\npl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples FDA')\n\npl.subplot(2, 2, 2)\npl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples FDA')\n\npl.subplot(2, 2, 3)\npl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples WDA')\n\npl.subplot(2, 2, 4)\npl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples WDA')\npl.tight_layout()\n\npl.show()"
- ],
- "outputs": [],
- "metadata": {
- "collapsed": false
- }
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "name": "python2",
- "language": "python"
- },
- "language_info": {
- "mimetype": "text/x-python",
- "nbconvert_exporter": "python",
- "name": "python",
- "file_extension": ".py",
- "version": "2.7.12",
- "pygments_lexer": "ipython2",
- "codemirror_mode": {
- "version": 2,
- "name": "ipython"
- }
- }
- }
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py
deleted file mode 100644
index 93cc237..0000000
--- a/docs/source/auto_examples/plot_WDA.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-=================================
-Wasserstein Discriminant Analysis
-=================================
-
-This example illustrate the use of WDA as proposed in [11].
-
-
-[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).
-Wasserstein Discriminant Analysis.
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-
-from ot.dr import wda, fda
-
-
-##############################################################################
-# Generate data
-# -------------
-
-#%% parameters
-
-n = 1000 # nb samples in source and target datasets
-nz = 0.2
-
-# generate circle dataset
-t = np.random.rand(n) * 2 * np.pi
-ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
-xs = np.concatenate(
- (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)
-xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)
-
-t = np.random.rand(n) * 2 * np.pi
-yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
-xt = np.concatenate(
- (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)
-xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)
-
-nbnoise = 8
-
-xs = np.hstack((xs, np.random.randn(n, nbnoise)))
-xt = np.hstack((xt, np.random.randn(n, nbnoise)))
-
-##############################################################################
-# Plot data
-# ---------
-
-#%% plot samples
-pl.figure(1, figsize=(6.4, 3.5))
-
-pl.subplot(1, 2, 1)
-pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')
-pl.legend(loc=0)
-pl.title('Discriminant dimensions')
-
-pl.subplot(1, 2, 2)
-pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')
-pl.legend(loc=0)
-pl.title('Other dimensions')
-pl.tight_layout()
-
-##############################################################################
-# Compute Fisher Discriminant Analysis
-# ------------------------------------
-
-#%% Compute FDA
-p = 2
-
-Pfda, projfda = fda(xs, ys, p)
-
-##############################################################################
-# Compute Wasserstein Discriminant Analysis
-# -----------------------------------------
-
-#%% Compute WDA
-p = 2
-reg = 1e0
-k = 10
-maxiter = 100
-
-Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)
-
-
-##############################################################################
-# Plot 2D projections
-# -------------------
-
-#%% plot samples
-
-xsp = projfda(xs)
-xtp = projfda(xt)
-
-xspw = projwda(xs)
-xtpw = projwda(xt)
-
-pl.figure(2)
-
-pl.subplot(2, 2, 1)
-pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')
-pl.legend(loc=0)
-pl.title('Projected training samples FDA')
-
-pl.subplot(2, 2, 2)
-pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')
-pl.legend(loc=0)
-pl.title('Projected test samples FDA')
-
-pl.subplot(2, 2, 3)
-pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')
-pl.legend(loc=0)
-pl.title('Projected training samples WDA')
-
-pl.subplot(2, 2, 4)
-pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')
-pl.legend(loc=0)
-pl.title('Projected test samples WDA')
-pl.tight_layout()
-
-pl.show()
diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst
deleted file mode 100644
index 2d83123..0000000
--- a/docs/source/auto_examples/plot_WDA.rst
+++ /dev/null
@@ -1,244 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_WDA.py:
-
-
-=================================
-Wasserstein Discriminant Analysis
-=================================
-
-This example illustrate the use of WDA as proposed in [11].
-
-
-[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).
-Wasserstein Discriminant Analysis.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Remi Flamary <remi.flamary@unice.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
-
- from ot.dr import wda, fda
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- #%% parameters
-
- n = 1000 # nb samples in source and target datasets
- nz = 0.2
-
- # generate circle dataset
- t = np.random.rand(n) * 2 * np.pi
- ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
- xs = np.concatenate(
- (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)
- xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)
-
- t = np.random.rand(n) * 2 * np.pi
- yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
- xt = np.concatenate(
- (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)
- xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)
-
- nbnoise = 8
-
- xs = np.hstack((xs, np.random.randn(n, nbnoise)))
- xt = np.hstack((xt, np.random.randn(n, nbnoise)))
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- #%% plot samples
- pl.figure(1, figsize=(6.4, 3.5))
-
- pl.subplot(1, 2, 1)
- pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')
- pl.legend(loc=0)
- pl.title('Discriminant dimensions')
-
- pl.subplot(1, 2, 2)
- pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')
- pl.legend(loc=0)
- pl.title('Other dimensions')
- pl.tight_layout()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png
- :align: center
-
-
-
-
-Compute Fisher Discriminant Analysis
-------------------------------------
-
-
-
-.. code-block:: python
-
-
- #%% Compute FDA
- p = 2
-
- Pfda, projfda = fda(xs, ys, p)
-
-
-
-
-
-
-
-Compute Wasserstein Discriminant Analysis
------------------------------------------
-
-
-
-.. code-block:: python
-
-
- #%% Compute WDA
- p = 2
- reg = 1e0
- k = 10
- maxiter = 100
-
- Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)
-
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- Compiling cost function...
- Computing gradient of cost function...
- iter cost val grad. norm
- 1 +9.0167295050534191e-01 2.28422652e-01
- 2 +4.8324990550878105e-01 4.89362707e-01
- 3 +3.4613154515357075e-01 2.84117562e-01
- 4 +2.5277108387195002e-01 1.24888750e-01
- 5 +2.4113858393736629e-01 8.07491482e-02
- 6 +2.3642108593032782e-01 1.67612140e-02
- 7 +2.3625721372202199e-01 7.68640008e-03
- 8 +2.3625461994913738e-01 7.42200784e-03
- 9 +2.3624493441436939e-01 6.43534105e-03
- 10 +2.3621901383686217e-01 2.17960585e-03
- 11 +2.3621854258326572e-01 2.03306749e-03
- 12 +2.3621696458678049e-01 1.37118721e-03
- 13 +2.3621569489873540e-01 2.76368907e-04
- 14 +2.3621565599232983e-01 1.41898134e-04
- 15 +2.3621564465487518e-01 5.96602069e-05
- 16 +2.3621564232556647e-01 1.08709521e-05
- 17 +2.3621564230277003e-01 9.17855656e-06
- 18 +2.3621564224857586e-01 1.73728345e-06
- 19 +2.3621564224748123e-01 1.17770019e-06
- 20 +2.3621564224658587e-01 2.16179383e-07
- Terminated - min grad norm reached after 20 iterations, 9.20 seconds.
-
-
-Plot 2D projections
--------------------
-
-
-
-.. code-block:: python
-
-
- #%% plot samples
-
- xsp = projfda(xs)
- xtp = projfda(xt)
-
- xspw = projwda(xs)
- xtpw = projwda(xt)
-
- pl.figure(2)
-
- pl.subplot(2, 2, 1)
- pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')
- pl.legend(loc=0)
- pl.title('Projected training samples FDA')
-
- pl.subplot(2, 2, 2)
- pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')
- pl.legend(loc=0)
- pl.title('Projected test samples FDA')
-
- pl.subplot(2, 2, 3)
- pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')
- pl.legend(loc=0)
- pl.title('Projected training samples WDA')
-
- pl.subplot(2, 2, 4)
- pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')
- pl.legend(loc=0)
- pl.title('Projected test samples WDA')
- pl.tight_layout()
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_WDA_003.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 16.182 seconds)
-
-
-
-.. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_WDA.py <plot_WDA.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_WDA.ipynb <plot_WDA.ipynb>`
-
-.. rst-class:: sphx-glr-signature
-
- `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb
deleted file mode 100644
index fc60e1f..0000000
--- a/docs/source/auto_examples/plot_barycenter_1D.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycenter computation\n----------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycentric interpolation\n-------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py
deleted file mode 100644
index 6864301..0000000
--- a/docs/source/auto_examples/plot_barycenter_1D.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-==============================
-1D Wasserstein barycenter demo
-==============================
-
-This example illustrates the computation of regularized Wassersyein Barycenter
-as proposed in [3].
-
-
-[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).
-Iterative Bregman projections for regularized transportation problems
-SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-# necessary for 3d plot even if not used
-from mpl_toolkits.mplot3d import Axes3D # noqa
-from matplotlib.collections import PolyCollection
-
-##############################################################################
-# Generate data
-# -------------
-
-#%% parameters
-
-n = 100 # nb bins
-
-# bin positions
-x = np.arange(n, dtype=np.float64)
-
-# Gaussian distributions
-a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std
-a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)
-
-# creating matrix A containing all distributions
-A = np.vstack((a1, a2)).T
-n_distributions = A.shape[1]
-
-# loss matrix + normalization
-M = ot.utils.dist0(n)
-M /= M.max()
-
-##############################################################################
-# Plot data
-# ---------
-
-#%% plot the distributions
-
-pl.figure(1, figsize=(6.4, 3))
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-pl.tight_layout()
-
-##############################################################################
-# Barycenter computation
-# ----------------------
-
-#%% barycenter computation
-
-alpha = 0.2 # 0<=alpha<=1
-weights = np.array([1 - alpha, alpha])
-
-# l2bary
-bary_l2 = A.dot(weights)
-
-# wasserstein
-reg = 1e-3
-bary_wass = ot.bregman.barycenter(A, M, reg, weights)
-
-pl.figure(2)
-pl.clf()
-pl.subplot(2, 1, 1)
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-
-pl.subplot(2, 1, 2)
-pl.plot(x, bary_l2, 'r', label='l2')
-pl.plot(x, bary_wass, 'g', label='Wasserstein')
-pl.legend()
-pl.title('Barycenters')
-pl.tight_layout()
-
-##############################################################################
-# Barycentric interpolation
-# -------------------------
-
-#%% barycenter interpolation
-
-n_alpha = 11
-alpha_list = np.linspace(0, 1, n_alpha)
-
-
-B_l2 = np.zeros((n, n_alpha))
-
-B_wass = np.copy(B_l2)
-
-for i in range(0, n_alpha):
- alpha = alpha_list[i]
- weights = np.array([1 - alpha, alpha])
- B_l2[:, i] = A.dot(weights)
- B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)
-
-#%% plot interpolation
-
-pl.figure(3)
-
-cmap = pl.cm.get_cmap('viridis')
-verts = []
-zs = alpha_list
-for i, z in enumerate(zs):
- ys = B_l2[:, i]
- verts.append(list(zip(x, ys)))
-
-ax = pl.gcf().gca(projection='3d')
-
-poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])
-poly.set_alpha(0.7)
-ax.add_collection3d(poly, zs=zs, zdir='y')
-ax.set_xlabel('x')
-ax.set_xlim3d(0, n)
-ax.set_ylabel('$\\alpha$')
-ax.set_ylim3d(0, 1)
-ax.set_zlabel('')
-ax.set_zlim3d(0, B_l2.max() * 1.01)
-pl.title('Barycenter interpolation with l2')
-pl.tight_layout()
-
-pl.figure(4)
-cmap = pl.cm.get_cmap('viridis')
-verts = []
-zs = alpha_list
-for i, z in enumerate(zs):
- ys = B_wass[:, i]
- verts.append(list(zip(x, ys)))
-
-ax = pl.gcf().gca(projection='3d')
-
-poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])
-poly.set_alpha(0.7)
-ax.add_collection3d(poly, zs=zs, zdir='y')
-ax.set_xlabel('x')
-ax.set_xlim3d(0, n)
-ax.set_ylabel('$\\alpha$')
-ax.set_ylim3d(0, 1)
-ax.set_zlabel('')
-ax.set_zlim3d(0, B_l2.max() * 1.01)
-pl.title('Barycenter interpolation with Wasserstein')
-pl.tight_layout()
-
-pl.show()
diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst
deleted file mode 100644
index 66ac042..0000000
--- a/docs/source/auto_examples/plot_barycenter_1D.rst
+++ /dev/null
@@ -1,257 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_barycenter_1D.py:
-
-
-==============================
-1D Wasserstein barycenter demo
-==============================
-
-This example illustrates the computation of regularized Wassersyein Barycenter
-as proposed in [3].
-
-
-[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).
-Iterative Bregman projections for regularized transportation problems
-SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Remi Flamary <remi.flamary@unice.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- # necessary for 3d plot even if not used
- from mpl_toolkits.mplot3d import Axes3D # noqa
- from matplotlib.collections import PolyCollection
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- #%% parameters
-
- n = 100 # nb bins
-
- # bin positions
- x = np.arange(n, dtype=np.float64)
-
- # Gaussian distributions
- a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std
- a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)
-
- # creating matrix A containing all distributions
- A = np.vstack((a1, a2)).T
- n_distributions = A.shape[1]
-
- # loss matrix + normalization
- M = ot.utils.dist0(n)
- M /= M.max()
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- #%% plot the distributions
-
- pl.figure(1, figsize=(6.4, 3))
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
- pl.tight_layout()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png
- :align: center
-
-
-
-
-Barycenter computation
-----------------------
-
-
-
-.. code-block:: python
-
-
- #%% barycenter computation
-
- alpha = 0.2 # 0<=alpha<=1
- weights = np.array([1 - alpha, alpha])
-
- # l2bary
- bary_l2 = A.dot(weights)
-
- # wasserstein
- reg = 1e-3
- bary_wass = ot.bregman.barycenter(A, M, reg, weights)
-
- pl.figure(2)
- pl.clf()
- pl.subplot(2, 1, 1)
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
-
- pl.subplot(2, 1, 2)
- pl.plot(x, bary_l2, 'r', label='l2')
- pl.plot(x, bary_wass, 'g', label='Wasserstein')
- pl.legend()
- pl.title('Barycenters')
- pl.tight_layout()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png
- :align: center
-
-
-
-
-Barycentric interpolation
--------------------------
-
-
-
-.. code-block:: python
-
-
- #%% barycenter interpolation
-
- n_alpha = 11
- alpha_list = np.linspace(0, 1, n_alpha)
-
-
- B_l2 = np.zeros((n, n_alpha))
-
- B_wass = np.copy(B_l2)
-
- for i in range(0, n_alpha):
- alpha = alpha_list[i]
- weights = np.array([1 - alpha, alpha])
- B_l2[:, i] = A.dot(weights)
- B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)
-
- #%% plot interpolation
-
- pl.figure(3)
-
- cmap = pl.cm.get_cmap('viridis')
- verts = []
- zs = alpha_list
- for i, z in enumerate(zs):
- ys = B_l2[:, i]
- verts.append(list(zip(x, ys)))
-
- ax = pl.gcf().gca(projection='3d')
-
- poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])
- poly.set_alpha(0.7)
- ax.add_collection3d(poly, zs=zs, zdir='y')
- ax.set_xlabel('x')
- ax.set_xlim3d(0, n)
- ax.set_ylabel('$\\alpha$')
- ax.set_ylim3d(0, 1)
- ax.set_zlabel('')
- ax.set_zlim3d(0, B_l2.max() * 1.01)
- pl.title('Barycenter interpolation with l2')
- pl.tight_layout()
-
- pl.figure(4)
- cmap = pl.cm.get_cmap('viridis')
- verts = []
- zs = alpha_list
- for i, z in enumerate(zs):
- ys = B_wass[:, i]
- verts.append(list(zip(x, ys)))
-
- ax = pl.gcf().gca(projection='3d')
-
- poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])
- poly.set_alpha(0.7)
- ax.add_collection3d(poly, zs=zs, zdir='y')
- ax.set_xlabel('x')
- ax.set_xlim3d(0, n)
- ax.set_ylabel('$\\alpha$')
- ax.set_ylim3d(0, 1)
- ax.set_zlabel('')
- ax.set_zlim3d(0, B_l2.max() * 1.01)
- pl.title('Barycenter interpolation with Wasserstein')
- pl.tight_layout()
-
- pl.show()
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_005.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_006.png
- :scale: 47
-
-
-
-
-**Total running time of the script:** ( 0 minutes 0.413 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_barycenter_1D.py <plot_barycenter_1D.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_barycenter_1D.ipynb <plot_barycenter_1D.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_barycenter_fgw.ipynb b/docs/source/auto_examples/plot_barycenter_fgw.ipynb
deleted file mode 100644
index 28229b2..0000000
--- a/docs/source/auto_examples/plot_barycenter_fgw.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n=================================\nPlot graphs' barycenter using FGW\n=================================\n\nThis example illustrates the computation barycenter of labeled graphs using FGW\n\nRequires networkx >=2\n\n.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n and Courty Nicolas\n \"Optimal Transport for structured data with application on graphs\"\n International Conference on Machine Learning (ICML). 2019.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Titouan Vayer <titouan.vayer@irisa.fr>\n#\n# License: MIT License\n\n#%% load libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport math\nfrom scipy.sparse.csgraph import shortest_path\nimport matplotlib.colors as mcol\nfrom matplotlib import cm\nfrom ot.gromov import fgw_barycenters\n#%% Graph functions\n\n\ndef find_thresh(C, inf=0.5, sup=3, step=10):\n \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n and the original matrix.\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n inf : float\n The beginning of the linesearch\n sup : float\n The end of the linesearch\n step : integer\n Number of thresholds tested\n \"\"\"\n dist = []\n search = np.linspace(inf, sup, step)\n for thresh in search:\n Cprime = sp_to_adjency(C, 0, thresh)\n SC = shortest_path(Cprime, method='D')\n SC[SC == float('inf')] = 100\n dist.append(np.linalg.norm(SC - C))\n return search[np.argmin(dist)], dist\n\n\ndef sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n threshinf : float\n The minimum value of distance from which the new value is set to 1\n threshsup : float\n The maximum value of distance from which the new value is set to 1\n Returns\n -------\n C : ndarray, shape (n_nodes,n_nodes)\n The threshold matrix. Each element is in {0,1}\n \"\"\"\n H = np.zeros_like(C)\n np.fill_diagonal(H, np.diagonal(C))\n C = C - H\n C = np.minimum(np.maximum(C, threshinf), threshsup)\n C[C == threshsup] = 0\n C[C != 0] = 1\n\n return C\n\n\ndef build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n \"\"\" Create a noisy circular graph\n \"\"\"\n g = nx.Graph()\n g.add_nodes_from(list(range(N)))\n for i in range(N):\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n else:\n g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n g.add_edge(i, i + 1)\n if structure_noise:\n randomint = np.random.randint(0, p)\n if randomint == 0:\n if i <= N - 3:\n g.add_edge(i, i + 2)\n if i == N - 2:\n g.add_edge(i, 0)\n if i == N - 1:\n g.add_edge(i, 1)\n g.add_edge(N, 0)\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n else:\n g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n return g\n\n\ndef graph_colors(nx_graph, vmin=0, vmax=7):\n cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n cpick.set_array([])\n val_map = {}\n for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n val_map[k] = cpick.to_rgba(v)\n colors = []\n for node in nx_graph.nodes():\n colors.append(val_map[node])\n return colors"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% circular dataset\n# We build a dataset of noisy circular graphs.\n# Noise is added on the structures by random connections and on the features by gaussian noise.\n\n\nnp.random.seed(30)\nX0 = []\nfor k in range(9):\n X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Plot graphs\n\nplt.figure(figsize=(8, 10))\nfor i in range(len(X0)):\n plt.subplot(3, 3, i + 1)\n g = X0[i]\n pos = nx.kamada_kawai_layout(g)\n nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\nplt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\nplt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycenter computation\n----------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph\n# Features distances are the euclidean distances\nCs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\nps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\nYs = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\nlambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\nsizebary = 15 # we choose a barycenter with 15 nodes\n\nA, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot Barycenter\n-------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Create the barycenter\nbary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\nfor i, v in enumerate(A.ravel()):\n bary.add_node(i, attr_name=v)\n\n#%%\npos = nx.kamada_kawai_layout(bary)\nnx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\nplt.suptitle('Barycenter', fontsize=20)\nplt.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_barycenter_fgw.py b/docs/source/auto_examples/plot_barycenter_fgw.py
deleted file mode 100644
index 77b0370..0000000
--- a/docs/source/auto_examples/plot_barycenter_fgw.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-=================================
-Plot graphs' barycenter using FGW
-=================================
-
-This example illustrates the computation barycenter of labeled graphs using FGW
-
-Requires networkx >=2
-
-.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
- and Courty Nicolas
- "Optimal Transport for structured data with application on graphs"
- International Conference on Machine Learning (ICML). 2019.
-
-"""
-
-# Author: Titouan Vayer <titouan.vayer@irisa.fr>
-#
-# License: MIT License
-
-#%% load libraries
-import numpy as np
-import matplotlib.pyplot as plt
-import networkx as nx
-import math
-from scipy.sparse.csgraph import shortest_path
-import matplotlib.colors as mcol
-from matplotlib import cm
-from ot.gromov import fgw_barycenters
-#%% Graph functions
-
-
-def find_thresh(C, inf=0.5, sup=3, step=10):
- """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected
- Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested.
- The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix
- and the original matrix.
- Parameters
- ----------
- C : ndarray, shape (n_nodes,n_nodes)
- The structure matrix to threshold
- inf : float
- The beginning of the linesearch
- sup : float
- The end of the linesearch
- step : integer
- Number of thresholds tested
- """
- dist = []
- search = np.linspace(inf, sup, step)
- for thresh in search:
- Cprime = sp_to_adjency(C, 0, thresh)
- SC = shortest_path(Cprime, method='D')
- SC[SC == float('inf')] = 100
- dist.append(np.linalg.norm(SC - C))
- return search[np.argmin(dist)], dist
-
-
-def sp_to_adjency(C, threshinf=0.2, threshsup=1.8):
- """ Thresholds the structure matrix in order to compute an adjency matrix.
- All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0
- Parameters
- ----------
- C : ndarray, shape (n_nodes,n_nodes)
- The structure matrix to threshold
- threshinf : float
- The minimum value of distance from which the new value is set to 1
- threshsup : float
- The maximum value of distance from which the new value is set to 1
- Returns
- -------
- C : ndarray, shape (n_nodes,n_nodes)
- The threshold matrix. Each element is in {0,1}
- """
- H = np.zeros_like(C)
- np.fill_diagonal(H, np.diagonal(C))
- C = C - H
- C = np.minimum(np.maximum(C, threshinf), threshsup)
- C[C == threshsup] = 0
- C[C != 0] = 1
-
- return C
-
-
-def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):
- """ Create a noisy circular graph
- """
- g = nx.Graph()
- g.add_nodes_from(list(range(N)))
- for i in range(N):
- noise = float(np.random.normal(mu, sigma, 1))
- if with_noise:
- g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)
- else:
- g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))
- g.add_edge(i, i + 1)
- if structure_noise:
- randomint = np.random.randint(0, p)
- if randomint == 0:
- if i <= N - 3:
- g.add_edge(i, i + 2)
- if i == N - 2:
- g.add_edge(i, 0)
- if i == N - 1:
- g.add_edge(i, 1)
- g.add_edge(N, 0)
- noise = float(np.random.normal(mu, sigma, 1))
- if with_noise:
- g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)
- else:
- g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))
- return g
-
-
-def graph_colors(nx_graph, vmin=0, vmax=7):
- cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)
- cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')
- cpick.set_array([])
- val_map = {}
- for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():
- val_map[k] = cpick.to_rgba(v)
- colors = []
- for node in nx_graph.nodes():
- colors.append(val_map[node])
- return colors
-
-##############################################################################
-# Generate data
-# -------------
-
-#%% circular dataset
-# We build a dataset of noisy circular graphs.
-# Noise is added on the structures by random connections and on the features by gaussian noise.
-
-
-np.random.seed(30)
-X0 = []
-for k in range(9):
- X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))
-
-##############################################################################
-# Plot data
-# ---------
-
-#%% Plot graphs
-
-plt.figure(figsize=(8, 10))
-for i in range(len(X0)):
- plt.subplot(3, 3, i + 1)
- g = X0[i]
- pos = nx.kamada_kawai_layout(g)
- nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)
-plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)
-plt.show()
-
-##############################################################################
-# Barycenter computation
-# ----------------------
-
-#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph
-# Features distances are the euclidean distances
-Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]
-ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]
-Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]
-lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()
-sizebary = 15 # we choose a barycenter with 15 nodes
-
-A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)
-
-##############################################################################
-# Plot Barycenter
-# -------------------------
-
-#%% Create the barycenter
-bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))
-for i, v in enumerate(A.ravel()):
- bary.add_node(i, attr_name=v)
-
-#%%
-pos = nx.kamada_kawai_layout(bary)
-nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)
-plt.suptitle('Barycenter', fontsize=20)
-plt.show()
diff --git a/docs/source/auto_examples/plot_barycenter_fgw.rst b/docs/source/auto_examples/plot_barycenter_fgw.rst
deleted file mode 100644
index 2c44a65..0000000
--- a/docs/source/auto_examples/plot_barycenter_fgw.rst
+++ /dev/null
@@ -1,268 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_barycenter_fgw.py:
-
-
-=================================
-Plot graphs' barycenter using FGW
-=================================
-
-This example illustrates the computation barycenter of labeled graphs using FGW
-
-Requires networkx >=2
-
-.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain
- and Courty Nicolas
- "Optimal Transport for structured data with application on graphs"
- International Conference on Machine Learning (ICML). 2019.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Titouan Vayer <titouan.vayer@irisa.fr>
- #
- # License: MIT License
-
- #%% load libraries
- import numpy as np
- import matplotlib.pyplot as plt
- import networkx as nx
- import math
- from scipy.sparse.csgraph import shortest_path
- import matplotlib.colors as mcol
- from matplotlib import cm
- from ot.gromov import fgw_barycenters
- #%% Graph functions
-
-
- def find_thresh(C, inf=0.5, sup=3, step=10):
- """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected
- Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested.
- The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix
- and the original matrix.
- Parameters
- ----------
- C : ndarray, shape (n_nodes,n_nodes)
- The structure matrix to threshold
- inf : float
- The beginning of the linesearch
- sup : float
- The end of the linesearch
- step : integer
- Number of thresholds tested
- """
- dist = []
- search = np.linspace(inf, sup, step)
- for thresh in search:
- Cprime = sp_to_adjency(C, 0, thresh)
- SC = shortest_path(Cprime, method='D')
- SC[SC == float('inf')] = 100
- dist.append(np.linalg.norm(SC - C))
- return search[np.argmin(dist)], dist
-
-
- def sp_to_adjency(C, threshinf=0.2, threshsup=1.8):
- """ Thresholds the structure matrix in order to compute an adjency matrix.
- All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0
- Parameters
- ----------
- C : ndarray, shape (n_nodes,n_nodes)
- The structure matrix to threshold
- threshinf : float
- The minimum value of distance from which the new value is set to 1
- threshsup : float
- The maximum value of distance from which the new value is set to 1
- Returns
- -------
- C : ndarray, shape (n_nodes,n_nodes)
- The threshold matrix. Each element is in {0,1}
- """
- H = np.zeros_like(C)
- np.fill_diagonal(H, np.diagonal(C))
- C = C - H
- C = np.minimum(np.maximum(C, threshinf), threshsup)
- C[C == threshsup] = 0
- C[C != 0] = 1
-
- return C
-
-
- def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):
- """ Create a noisy circular graph
- """
- g = nx.Graph()
- g.add_nodes_from(list(range(N)))
- for i in range(N):
- noise = float(np.random.normal(mu, sigma, 1))
- if with_noise:
- g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)
- else:
- g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))
- g.add_edge(i, i + 1)
- if structure_noise:
- randomint = np.random.randint(0, p)
- if randomint == 0:
- if i <= N - 3:
- g.add_edge(i, i + 2)
- if i == N - 2:
- g.add_edge(i, 0)
- if i == N - 1:
- g.add_edge(i, 1)
- g.add_edge(N, 0)
- noise = float(np.random.normal(mu, sigma, 1))
- if with_noise:
- g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)
- else:
- g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))
- return g
-
-
- def graph_colors(nx_graph, vmin=0, vmax=7):
- cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)
- cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')
- cpick.set_array([])
- val_map = {}
- for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():
- val_map[k] = cpick.to_rgba(v)
- colors = []
- for node in nx_graph.nodes():
- colors.append(val_map[node])
- return colors
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- #%% circular dataset
- # We build a dataset of noisy circular graphs.
- # Noise is added on the structures by random connections and on the features by gaussian noise.
-
-
- np.random.seed(30)
- X0 = []
- for k in range(9):
- X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- #%% Plot graphs
-
- plt.figure(figsize=(8, 10))
- for i in range(len(X0)):
- plt.subplot(3, 3, i + 1)
- g = X0[i]
- pos = nx.kamada_kawai_layout(g)
- nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)
- plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)
- plt.show()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_001.png
- :align: center
-
-
-
-
-Barycenter computation
-----------------------
-
-
-
-.. code-block:: python
-
-
- #%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph
- # Features distances are the euclidean distances
- Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]
- ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]
- Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]
- lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()
- sizebary = 15 # we choose a barycenter with 15 nodes
-
- A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)
-
-
-
-
-
-
-
-Plot Barycenter
--------------------------
-
-
-
-.. code-block:: python
-
-
- #%% Create the barycenter
- bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))
- for i, v in enumerate(A.ravel()):
- bary.add_node(i, attr_name=v)
-
- #%%
- pos = nx.kamada_kawai_layout(bary)
- nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)
- plt.suptitle('Barycenter', fontsize=20)
- plt.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_002.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 2.065 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_barycenter_fgw.py <plot_barycenter_fgw.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_barycenter_fgw.ipynb <plot_barycenter_fgw.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb
deleted file mode 100644
index 2199162..0000000
--- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb
+++ /dev/null
@@ -1,108 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n\n\nThis example illustrates the computation of regularized Wasserstein Barycenter\nas proposed in [3] and exact LP barycenters using standard LP solver.\n\nIt reproduces approximately Figure 3.1 and 3.2 from the following paper:\nCuturi, M., & Peyr\u00e9, G. (2016). A smoothed dual approach for variational\nWasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection # noqa\n\n#import ot.lp.cvx as cvx"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Gaussian Data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n\nproblems = []\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dirac Data\n----------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n\na1 = 1.0 * (x > 10) * (x < 50)\na2 = 1.0 * (x > 60) * (x < 80)\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#%% parameters\n\na1 = np.zeros(n)\na2 = np.zeros(n)\n\na1[10] = .25\na1[20] = .5\na1[30] = .25\na2[80] = 1\n\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Final figure\n------------\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% plot\n\nnbm = len(problems)\nnbm2 = (nbm // 2)\n\n\npl.figure(2, (20, 6))\npl.clf()\n\nfor i in range(nbm):\n\n A = problems[i][0]\n bary_l2 = problems[i][1][0]\n bary_wass = problems[i][1][1]\n bary_wass2 = problems[i][1][2]\n\n pl.subplot(2, nbm, 1 + i)\n for j in range(n_distributions):\n pl.plot(x, A[:, j])\n if i == nbm2:\n pl.title('Distributions')\n pl.xticks(())\n pl.yticks(())\n\n pl.subplot(2, nbm, 1 + i + nbm)\n\n pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n if i == nbm - 1:\n pl.legend()\n if i == nbm2:\n pl.title('Barycenters')\n\n pl.xticks(())\n pl.yticks(())"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py
deleted file mode 100644
index b82765e..0000000
--- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py
+++ /dev/null
@@ -1,281 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-=================================================================================
-1D Wasserstein barycenter comparison between exact LP and entropic regularization
-=================================================================================
-
-This example illustrates the computation of regularized Wasserstein Barycenter
-as proposed in [3] and exact LP barycenters using standard LP solver.
-
-It reproduces approximately Figure 3.1 and 3.2 from the following paper:
-Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational
-Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.
-
-[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).
-Iterative Bregman projections for regularized transportation problems
-SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-# necessary for 3d plot even if not used
-from mpl_toolkits.mplot3d import Axes3D # noqa
-from matplotlib.collections import PolyCollection # noqa
-
-#import ot.lp.cvx as cvx
-
-##############################################################################
-# Gaussian Data
-# -------------
-
-#%% parameters
-
-problems = []
-
-n = 100 # nb bins
-
-# bin positions
-x = np.arange(n, dtype=np.float64)
-
-# Gaussian distributions
-# Gaussian distributions
-a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std
-a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)
-
-# creating matrix A containing all distributions
-A = np.vstack((a1, a2)).T
-n_distributions = A.shape[1]
-
-# loss matrix + normalization
-M = ot.utils.dist0(n)
-M /= M.max()
-
-
-#%% plot the distributions
-
-pl.figure(1, figsize=(6.4, 3))
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-pl.tight_layout()
-
-#%% barycenter computation
-
-alpha = 0.5 # 0<=alpha<=1
-weights = np.array([1 - alpha, alpha])
-
-# l2bary
-bary_l2 = A.dot(weights)
-
-# wasserstein
-reg = 1e-3
-ot.tic()
-bary_wass = ot.bregman.barycenter(A, M, reg, weights)
-ot.toc()
-
-
-ot.tic()
-bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)
-ot.toc()
-
-pl.figure(2)
-pl.clf()
-pl.subplot(2, 1, 1)
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-
-pl.subplot(2, 1, 2)
-pl.plot(x, bary_l2, 'r', label='l2')
-pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
-pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
-pl.legend()
-pl.title('Barycenters')
-pl.tight_layout()
-
-problems.append([A, [bary_l2, bary_wass, bary_wass2]])
-
-##############################################################################
-# Dirac Data
-# ----------
-
-#%% parameters
-
-a1 = 1.0 * (x > 10) * (x < 50)
-a2 = 1.0 * (x > 60) * (x < 80)
-
-a1 /= a1.sum()
-a2 /= a2.sum()
-
-# creating matrix A containing all distributions
-A = np.vstack((a1, a2)).T
-n_distributions = A.shape[1]
-
-# loss matrix + normalization
-M = ot.utils.dist0(n)
-M /= M.max()
-
-
-#%% plot the distributions
-
-pl.figure(1, figsize=(6.4, 3))
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-pl.tight_layout()
-
-
-#%% barycenter computation
-
-alpha = 0.5 # 0<=alpha<=1
-weights = np.array([1 - alpha, alpha])
-
-# l2bary
-bary_l2 = A.dot(weights)
-
-# wasserstein
-reg = 1e-3
-ot.tic()
-bary_wass = ot.bregman.barycenter(A, M, reg, weights)
-ot.toc()
-
-
-ot.tic()
-bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)
-ot.toc()
-
-
-problems.append([A, [bary_l2, bary_wass, bary_wass2]])
-
-pl.figure(2)
-pl.clf()
-pl.subplot(2, 1, 1)
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-
-pl.subplot(2, 1, 2)
-pl.plot(x, bary_l2, 'r', label='l2')
-pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
-pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
-pl.legend()
-pl.title('Barycenters')
-pl.tight_layout()
-
-#%% parameters
-
-a1 = np.zeros(n)
-a2 = np.zeros(n)
-
-a1[10] = .25
-a1[20] = .5
-a1[30] = .25
-a2[80] = 1
-
-
-a1 /= a1.sum()
-a2 /= a2.sum()
-
-# creating matrix A containing all distributions
-A = np.vstack((a1, a2)).T
-n_distributions = A.shape[1]
-
-# loss matrix + normalization
-M = ot.utils.dist0(n)
-M /= M.max()
-
-
-#%% plot the distributions
-
-pl.figure(1, figsize=(6.4, 3))
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-pl.tight_layout()
-
-
-#%% barycenter computation
-
-alpha = 0.5 # 0<=alpha<=1
-weights = np.array([1 - alpha, alpha])
-
-# l2bary
-bary_l2 = A.dot(weights)
-
-# wasserstein
-reg = 1e-3
-ot.tic()
-bary_wass = ot.bregman.barycenter(A, M, reg, weights)
-ot.toc()
-
-
-ot.tic()
-bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)
-ot.toc()
-
-
-problems.append([A, [bary_l2, bary_wass, bary_wass2]])
-
-pl.figure(2)
-pl.clf()
-pl.subplot(2, 1, 1)
-for i in range(n_distributions):
- pl.plot(x, A[:, i])
-pl.title('Distributions')
-
-pl.subplot(2, 1, 2)
-pl.plot(x, bary_l2, 'r', label='l2')
-pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
-pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
-pl.legend()
-pl.title('Barycenters')
-pl.tight_layout()
-
-
-##############################################################################
-# Final figure
-# ------------
-#
-
-#%% plot
-
-nbm = len(problems)
-nbm2 = (nbm // 2)
-
-
-pl.figure(2, (20, 6))
-pl.clf()
-
-for i in range(nbm):
-
- A = problems[i][0]
- bary_l2 = problems[i][1][0]
- bary_wass = problems[i][1][1]
- bary_wass2 = problems[i][1][2]
-
- pl.subplot(2, nbm, 1 + i)
- for j in range(n_distributions):
- pl.plot(x, A[:, j])
- if i == nbm2:
- pl.title('Distributions')
- pl.xticks(())
- pl.yticks(())
-
- pl.subplot(2, nbm, 1 + i + nbm)
-
- pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')
- pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
- pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
- if i == nbm - 1:
- pl.legend()
- if i == nbm2:
- pl.title('Barycenters')
-
- pl.xticks(())
- pl.yticks(())
diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst
deleted file mode 100644
index bd1c710..0000000
--- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst
+++ /dev/null
@@ -1,447 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py:
-
-
-=================================================================================
-1D Wasserstein barycenter comparison between exact LP and entropic regularization
-=================================================================================
-
-This example illustrates the computation of regularized Wasserstein Barycenter
-as proposed in [3] and exact LP barycenters using standard LP solver.
-
-It reproduces approximately Figure 3.1 and 3.2 from the following paper:
-Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational
-Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.
-
-[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).
-Iterative Bregman projections for regularized transportation problems
-SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Remi Flamary <remi.flamary@unice.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- # necessary for 3d plot even if not used
- from mpl_toolkits.mplot3d import Axes3D # noqa
- from matplotlib.collections import PolyCollection # noqa
-
- #import ot.lp.cvx as cvx
-
-
-
-
-
-
-
-Gaussian Data
--------------
-
-
-
-.. code-block:: python
-
-
- #%% parameters
-
- problems = []
-
- n = 100 # nb bins
-
- # bin positions
- x = np.arange(n, dtype=np.float64)
-
- # Gaussian distributions
- # Gaussian distributions
- a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std
- a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)
-
- # creating matrix A containing all distributions
- A = np.vstack((a1, a2)).T
- n_distributions = A.shape[1]
-
- # loss matrix + normalization
- M = ot.utils.dist0(n)
- M /= M.max()
-
-
- #%% plot the distributions
-
- pl.figure(1, figsize=(6.4, 3))
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
- pl.tight_layout()
-
- #%% barycenter computation
-
- alpha = 0.5 # 0<=alpha<=1
- weights = np.array([1 - alpha, alpha])
-
- # l2bary
- bary_l2 = A.dot(weights)
-
- # wasserstein
- reg = 1e-3
- ot.tic()
- bary_wass = ot.bregman.barycenter(A, M, reg, weights)
- ot.toc()
-
-
- ot.tic()
- bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)
- ot.toc()
-
- pl.figure(2)
- pl.clf()
- pl.subplot(2, 1, 1)
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
-
- pl.subplot(2, 1, 2)
- pl.plot(x, bary_l2, 'r', label='l2')
- pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
- pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
- pl.legend()
- pl.title('Barycenters')
- pl.tight_layout()
-
- problems.append([A, [bary_l2, bary_wass, bary_wass2]])
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png
- :scale: 47
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- Elapsed time : 0.010712385177612305 s
- Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective
- 1.0 1.0 1.0 - 1.0 1700.336700337
- 0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543
- 0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093
- 0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897
- 0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358
- 0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432
- 7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717
- 5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203
- 1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519
- 6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383
- 2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321
- 1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827
- 3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923
- 1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379
- 5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414
- 1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752
- 5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713
- 3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261
- 1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485
- 5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135
- 1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958
- 6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766
- 1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824
- Optimization terminated successfully.
- Elapsed time : 2.883899211883545 s
-
-
-Dirac Data
-----------
-
-
-
-.. code-block:: python
-
-
- #%% parameters
-
- a1 = 1.0 * (x > 10) * (x < 50)
- a2 = 1.0 * (x > 60) * (x < 80)
-
- a1 /= a1.sum()
- a2 /= a2.sum()
-
- # creating matrix A containing all distributions
- A = np.vstack((a1, a2)).T
- n_distributions = A.shape[1]
-
- # loss matrix + normalization
- M = ot.utils.dist0(n)
- M /= M.max()
-
-
- #%% plot the distributions
-
- pl.figure(1, figsize=(6.4, 3))
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
- pl.tight_layout()
-
-
- #%% barycenter computation
-
- alpha = 0.5 # 0<=alpha<=1
- weights = np.array([1 - alpha, alpha])
-
- # l2bary
- bary_l2 = A.dot(weights)
-
- # wasserstein
- reg = 1e-3
- ot.tic()
- bary_wass = ot.bregman.barycenter(A, M, reg, weights)
- ot.toc()
-
-
- ot.tic()
- bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)
- ot.toc()
-
-
- problems.append([A, [bary_l2, bary_wass, bary_wass2]])
-
- pl.figure(2)
- pl.clf()
- pl.subplot(2, 1, 1)
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
-
- pl.subplot(2, 1, 2)
- pl.plot(x, bary_l2, 'r', label='l2')
- pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
- pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
- pl.legend()
- pl.title('Barycenters')
- pl.tight_layout()
-
- #%% parameters
-
- a1 = np.zeros(n)
- a2 = np.zeros(n)
-
- a1[10] = .25
- a1[20] = .5
- a1[30] = .25
- a2[80] = 1
-
-
- a1 /= a1.sum()
- a2 /= a2.sum()
-
- # creating matrix A containing all distributions
- A = np.vstack((a1, a2)).T
- n_distributions = A.shape[1]
-
- # loss matrix + normalization
- M = ot.utils.dist0(n)
- M /= M.max()
-
-
- #%% plot the distributions
-
- pl.figure(1, figsize=(6.4, 3))
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
- pl.tight_layout()
-
-
- #%% barycenter computation
-
- alpha = 0.5 # 0<=alpha<=1
- weights = np.array([1 - alpha, alpha])
-
- # l2bary
- bary_l2 = A.dot(weights)
-
- # wasserstein
- reg = 1e-3
- ot.tic()
- bary_wass = ot.bregman.barycenter(A, M, reg, weights)
- ot.toc()
-
-
- ot.tic()
- bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)
- ot.toc()
-
-
- problems.append([A, [bary_l2, bary_wass, bary_wass2]])
-
- pl.figure(2)
- pl.clf()
- pl.subplot(2, 1, 1)
- for i in range(n_distributions):
- pl.plot(x, A[:, i])
- pl.title('Distributions')
-
- pl.subplot(2, 1, 2)
- pl.plot(x, bary_l2, 'r', label='l2')
- pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
- pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
- pl.legend()
- pl.title('Barycenters')
- pl.tight_layout()
-
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_003.png
- :scale: 47
-
- *
-
- .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_004.png
- :scale: 47
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- Elapsed time : 0.014938592910766602 s
- Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective
- 1.0 1.0 1.0 - 1.0 1700.336700337
- 0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808
- 0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011
- 0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408
- 0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493
- 0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518
- 0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635
- 4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047
- 8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221
- 3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152
- 7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248
- 8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295
- 3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766
- 1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444
- Optimization terminated successfully.
- Elapsed time : 2.642659902572632 s
- Elapsed time : 0.002908945083618164 s
- Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective
- 1.0 1.0 1.0 - 1.0 1700.336700337
- 0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743
- 0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123
- 0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389
- 6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272
- 2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325
- 1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171
- 3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747
- 7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269
- 1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766
- 4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861
- 2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458
- Optimization terminated successfully.
- Elapsed time : 2.690450668334961 s
-
-
-Final figure
-------------
-
-
-
-
-.. code-block:: python
-
-
- #%% plot
-
- nbm = len(problems)
- nbm2 = (nbm // 2)
-
-
- pl.figure(2, (20, 6))
- pl.clf()
-
- for i in range(nbm):
-
- A = problems[i][0]
- bary_l2 = problems[i][1][0]
- bary_wass = problems[i][1][1]
- bary_wass2 = problems[i][1][2]
-
- pl.subplot(2, nbm, 1 + i)
- for j in range(n_distributions):
- pl.plot(x, A[:, j])
- if i == nbm2:
- pl.title('Distributions')
- pl.xticks(())
- pl.yticks(())
-
- pl.subplot(2, nbm, 1 + i + nbm)
-
- pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')
- pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')
- pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')
- if i == nbm - 1:
- pl.legend()
- if i == nbm2:
- pl.title('Barycenters')
-
- pl.xticks(())
- pl.yticks(())
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_006.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 8.892 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_barycenter_lp_vs_entropic.py <plot_barycenter_lp_vs_entropic.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_barycenter_lp_vs_entropic.ipynb <plot_barycenter_lp_vs_entropic.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb
deleted file mode 100644
index 562eff8..0000000
--- a/docs/source/auto_examples/plot_compute_emd.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# Plot multiple EMD\n\n\nShows how to compute multiple EMD and Sinkhorn with two differnt\nground metrics and plot their values for diffeent distributions.\n\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import make_1D_gauss as gauss"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n\nn = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% plot the distributions\n\npl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute EMD for the different losses\n------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Compute and plot distributions and loss matrix\n\nd_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute Sinkhorn for the different losses\n-----------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%%\nreg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py
deleted file mode 100644
index 7ed2b01..0000000
--- a/docs/source/auto_examples/plot_compute_emd.py
+++ /dev/null
@@ -1,102 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-=================
-Plot multiple EMD
-=================
-
-Shows how to compute multiple EMD and Sinkhorn with two differnt
-ground metrics and plot their values for diffeent distributions.
-
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-from ot.datasets import make_1D_gauss as gauss
-
-
-##############################################################################
-# Generate data
-# -------------
-
-#%% parameters
-
-n = 100 # nb bins
-n_target = 50 # nb target distributions
-
-
-# bin positions
-x = np.arange(n, dtype=np.float64)
-
-lst_m = np.linspace(20, 90, n_target)
-
-# Gaussian distributions
-a = gauss(n, m=20, s=5) # m= mean, s= std
-
-B = np.zeros((n, n_target))
-
-for i, m in enumerate(lst_m):
- B[:, i] = gauss(n, m=m, s=5)
-
-# loss matrix and normalization
-M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')
-M /= M.max()
-M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')
-M2 /= M2.max()
-
-##############################################################################
-# Plot data
-# ---------
-
-#%% plot the distributions
-
-pl.figure(1)
-pl.subplot(2, 1, 1)
-pl.plot(x, a, 'b', label='Source distribution')
-pl.title('Source distribution')
-pl.subplot(2, 1, 2)
-pl.plot(x, B, label='Target distributions')
-pl.title('Target distributions')
-pl.tight_layout()
-
-
-##############################################################################
-# Compute EMD for the different losses
-# ------------------------------------
-
-#%% Compute and plot distributions and loss matrix
-
-d_emd = ot.emd2(a, B, M) # direct computation of EMD
-d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2
-
-
-pl.figure(2)
-pl.plot(d_emd, label='Euclidean EMD')
-pl.plot(d_emd2, label='Squared Euclidean EMD')
-pl.title('EMD distances')
-pl.legend()
-
-##############################################################################
-# Compute Sinkhorn for the different losses
-# -----------------------------------------
-
-#%%
-reg = 1e-2
-d_sinkhorn = ot.sinkhorn2(a, B, M, reg)
-d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)
-
-pl.figure(2)
-pl.clf()
-pl.plot(d_emd, label='Euclidean EMD')
-pl.plot(d_emd2, label='Squared Euclidean EMD')
-pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')
-pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')
-pl.title('EMD distances')
-pl.legend()
-
-pl.show()
diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst
deleted file mode 100644
index 27bca2c..0000000
--- a/docs/source/auto_examples/plot_compute_emd.rst
+++ /dev/null
@@ -1,189 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_compute_emd.py:
-
-
-=================
-Plot multiple EMD
-=================
-
-Shows how to compute multiple EMD and Sinkhorn with two differnt
-ground metrics and plot their values for diffeent distributions.
-
-
-
-
-
-.. code-block:: python
-
-
- # Author: Remi Flamary <remi.flamary@unice.fr>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- from ot.datasets import make_1D_gauss as gauss
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- #%% parameters
-
- n = 100 # nb bins
- n_target = 50 # nb target distributions
-
-
- # bin positions
- x = np.arange(n, dtype=np.float64)
-
- lst_m = np.linspace(20, 90, n_target)
-
- # Gaussian distributions
- a = gauss(n, m=20, s=5) # m= mean, s= std
-
- B = np.zeros((n, n_target))
-
- for i, m in enumerate(lst_m):
- B[:, i] = gauss(n, m=m, s=5)
-
- # loss matrix and normalization
- M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')
- M /= M.max()
- M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')
- M2 /= M2.max()
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- #%% plot the distributions
-
- pl.figure(1)
- pl.subplot(2, 1, 1)
- pl.plot(x, a, 'b', label='Source distribution')
- pl.title('Source distribution')
- pl.subplot(2, 1, 2)
- pl.plot(x, B, label='Target distributions')
- pl.title('Target distributions')
- pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_001.png
- :align: center
-
-
-
-
-Compute EMD for the different losses
-------------------------------------
-
-
-
-.. code-block:: python
-
-
- #%% Compute and plot distributions and loss matrix
-
- d_emd = ot.emd2(a, B, M) # direct computation of EMD
- d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2
-
-
- pl.figure(2)
- pl.plot(d_emd, label='Euclidean EMD')
- pl.plot(d_emd2, label='Squared Euclidean EMD')
- pl.title('EMD distances')
- pl.legend()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_003.png
- :align: center
-
-
-
-
-Compute Sinkhorn for the different losses
------------------------------------------
-
-
-
-.. code-block:: python
-
-
- #%%
- reg = 1e-2
- d_sinkhorn = ot.sinkhorn2(a, B, M, reg)
- d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)
-
- pl.figure(2)
- pl.clf()
- pl.plot(d_emd, label='Euclidean EMD')
- pl.plot(d_emd2, label='Squared Euclidean EMD')
- pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')
- pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')
- pl.title('EMD distances')
- pl.legend()
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_004.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 0.446 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_compute_emd.py <plot_compute_emd.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_compute_emd.ipynb <plot_compute_emd.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb
deleted file mode 100644
index 4981ba3..0000000
--- a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb
+++ /dev/null
@@ -1,90 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# Convolutional Wasserstein Barycenter example\n\n\nThis example is designed to illustrate how the Convolutional Wasserstein Barycenter\nfunction of POT works.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Nicolas Courty <ncourty@irisa.fr>\n#\n# License: MIT License\n\n\nimport numpy as np\nimport pylab as pl\nimport ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\nf2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\nf3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\nf4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n\nA = []\nf1 = f1 / np.sum(f1)\nf2 = f2 / np.sum(f2)\nf3 = f3 / np.sum(f3)\nf4 = f4 / np.sum(f4)\nA.append(f1)\nA.append(f2)\nA.append(f3)\nA.append(f4)\nA = np.array(A)\n\nnb_images = 5\n\n# those are the four corners coordinates that will be interpolated by bilinear\n# interpolation\nv1 = np.array((1, 0, 0, 0))\nv2 = np.array((0, 1, 0, 0))\nv3 = np.array((0, 0, 1, 0))\nv4 = np.array((0, 0, 0, 1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycenter computation and visualization\n----------------------------------------\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(figsize=(10, 10))\npl.title('Convolutional Wasserstein Barycenters in POT')\ncm = 'Blues'\n# regularization parameter\nreg = 0.004\nfor i in range(nb_images):\n for j in range(nb_images):\n pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n tx = float(i) / (nb_images - 1)\n ty = float(j) / (nb_images - 1)\n\n # weights are constructed by bilinear interpolation\n tmp1 = (1 - tx) * v1 + tx * v2\n tmp2 = (1 - tx) * v3 + tx * v4\n weights = (1 - ty) * tmp1 + ty * tmp2\n\n if i == 0 and j == 0:\n pl.imshow(f1, cmap=cm)\n pl.axis('off')\n elif i == 0 and j == (nb_images - 1):\n pl.imshow(f3, cmap=cm)\n pl.axis('off')\n elif i == (nb_images - 1) and j == 0:\n pl.imshow(f2, cmap=cm)\n pl.axis('off')\n elif i == (nb_images - 1) and j == (nb_images - 1):\n pl.imshow(f4, cmap=cm)\n pl.axis('off')\n else:\n # call to barycenter computation\n pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n pl.axis('off')\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.py b/docs/source/auto_examples/plot_convolutional_barycenter.py
deleted file mode 100644
index e74db04..0000000
--- a/docs/source/auto_examples/plot_convolutional_barycenter.py
+++ /dev/null
@@ -1,92 +0,0 @@
-
-#%%
-# -*- coding: utf-8 -*-
-"""
-============================================
-Convolutional Wasserstein Barycenter example
-============================================
-
-This example is designed to illustrate how the Convolutional Wasserstein Barycenter
-function of POT works.
-"""
-
-# Author: Nicolas Courty <ncourty@irisa.fr>
-#
-# License: MIT License
-
-
-import numpy as np
-import pylab as pl
-import ot
-
-##############################################################################
-# Data preparation
-# ----------------
-#
-# The four distributions are constructed from 4 simple images
-
-
-f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]
-f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]
-f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]
-f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]
-
-A = []
-f1 = f1 / np.sum(f1)
-f2 = f2 / np.sum(f2)
-f3 = f3 / np.sum(f3)
-f4 = f4 / np.sum(f4)
-A.append(f1)
-A.append(f2)
-A.append(f3)
-A.append(f4)
-A = np.array(A)
-
-nb_images = 5
-
-# those are the four corners coordinates that will be interpolated by bilinear
-# interpolation
-v1 = np.array((1, 0, 0, 0))
-v2 = np.array((0, 1, 0, 0))
-v3 = np.array((0, 0, 1, 0))
-v4 = np.array((0, 0, 0, 1))
-
-
-##############################################################################
-# Barycenter computation and visualization
-# ----------------------------------------
-#
-
-pl.figure(figsize=(10, 10))
-pl.title('Convolutional Wasserstein Barycenters in POT')
-cm = 'Blues'
-# regularization parameter
-reg = 0.004
-for i in range(nb_images):
- for j in range(nb_images):
- pl.subplot(nb_images, nb_images, i * nb_images + j + 1)
- tx = float(i) / (nb_images - 1)
- ty = float(j) / (nb_images - 1)
-
- # weights are constructed by bilinear interpolation
- tmp1 = (1 - tx) * v1 + tx * v2
- tmp2 = (1 - tx) * v3 + tx * v4
- weights = (1 - ty) * tmp1 + ty * tmp2
-
- if i == 0 and j == 0:
- pl.imshow(f1, cmap=cm)
- pl.axis('off')
- elif i == 0 and j == (nb_images - 1):
- pl.imshow(f3, cmap=cm)
- pl.axis('off')
- elif i == (nb_images - 1) and j == 0:
- pl.imshow(f2, cmap=cm)
- pl.axis('off')
- elif i == (nb_images - 1) and j == (nb_images - 1):
- pl.imshow(f4, cmap=cm)
- pl.axis('off')
- else:
- # call to barycenter computation
- pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)
- pl.axis('off')
-pl.show()
diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.rst b/docs/source/auto_examples/plot_convolutional_barycenter.rst
deleted file mode 100644
index a28db2f..0000000
--- a/docs/source/auto_examples/plot_convolutional_barycenter.rst
+++ /dev/null
@@ -1,151 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_convolutional_barycenter.py:
-
-
-============================================
-Convolutional Wasserstein Barycenter example
-============================================
-
-This example is designed to illustrate how the Convolutional Wasserstein Barycenter
-function of POT works.
-
-
-
-.. code-block:: python
-
-
- # Author: Nicolas Courty <ncourty@irisa.fr>
- #
- # License: MIT License
-
-
- import numpy as np
- import pylab as pl
- import ot
-
-
-
-
-
-
-
-Data preparation
-----------------
-
-The four distributions are constructed from 4 simple images
-
-
-
-.. code-block:: python
-
-
-
- f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]
- f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]
- f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]
- f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]
-
- A = []
- f1 = f1 / np.sum(f1)
- f2 = f2 / np.sum(f2)
- f3 = f3 / np.sum(f3)
- f4 = f4 / np.sum(f4)
- A.append(f1)
- A.append(f2)
- A.append(f3)
- A.append(f4)
- A = np.array(A)
-
- nb_images = 5
-
- # those are the four corners coordinates that will be interpolated by bilinear
- # interpolation
- v1 = np.array((1, 0, 0, 0))
- v2 = np.array((0, 1, 0, 0))
- v3 = np.array((0, 0, 1, 0))
- v4 = np.array((0, 0, 0, 1))
-
-
-
-
-
-
-
-
-Barycenter computation and visualization
-----------------------------------------
-
-
-
-
-.. code-block:: python
-
-
- pl.figure(figsize=(10, 10))
- pl.title('Convolutional Wasserstein Barycenters in POT')
- cm = 'Blues'
- # regularization parameter
- reg = 0.004
- for i in range(nb_images):
- for j in range(nb_images):
- pl.subplot(nb_images, nb_images, i * nb_images + j + 1)
- tx = float(i) / (nb_images - 1)
- ty = float(j) / (nb_images - 1)
-
- # weights are constructed by bilinear interpolation
- tmp1 = (1 - tx) * v1 + tx * v2
- tmp2 = (1 - tx) * v3 + tx * v4
- weights = (1 - ty) * tmp1 + ty * tmp2
-
- if i == 0 and j == 0:
- pl.imshow(f1, cmap=cm)
- pl.axis('off')
- elif i == 0 and j == (nb_images - 1):
- pl.imshow(f3, cmap=cm)
- pl.axis('off')
- elif i == (nb_images - 1) and j == 0:
- pl.imshow(f2, cmap=cm)
- pl.axis('off')
- elif i == (nb_images - 1) and j == (nb_images - 1):
- pl.imshow(f4, cmap=cm)
- pl.axis('off')
- else:
- # call to barycenter computation
- pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)
- pl.axis('off')
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_convolutional_barycenter_001.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 1 minutes 11.608 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_convolutional_barycenter.py <plot_convolutional_barycenter.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_convolutional_barycenter.ipynb <plot_convolutional_barycenter.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_fgw.ipynb b/docs/source/auto_examples/plot_fgw.ipynb
deleted file mode 100644
index 1b150bd..0000000
--- a/docs/source/auto_examples/plot_fgw.ipynb
+++ /dev/null
@@ -1,162 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# Plot Fused-gromov-Wasserstein\n\n\nThis example illustrates the computation of FGW for 1D measures[18].\n\n.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n and Courty Nicolas\n \"Optimal Transport for structured data with application on graphs\"\n International Conference on Machine Learning (ICML). 2019.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Titouan Vayer <titouan.vayer@irisa.fr>\n#\n# License: MIT License\n\nimport matplotlib.pyplot as pl\nimport numpy as np\nimport ot\nfrom ot.gromov import gromov_wasserstein, fused_gromov_wasserstein"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n# We create two 1D random measures\nn = 20 # number of points in the first distribution\nn2 = 30 # number of points in the second distribution\nsig = 1 # std of first distribution\nsig2 = 0.1 # std of second distribution\n\nnp.random.seed(0)\n\nphi = np.arange(n)[:, None]\nxs = phi + sig * np.random.randn(n, 1)\nys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n\nphi2 = np.arange(n2)[:, None]\nxt = phi2 + sig * np.random.randn(n2, 1)\nyt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\nyt = yt[::-1, :]\n\np = ot.unif(n)\nq = ot.unif(n2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% plot the distributions\n\npl.close(10)\npl.figure(10, (7, 7))\n\npl.subplot(2, 1, 1)\n\npl.scatter(ys, xs, c=phi, s=70)\npl.ylabel('Feature value a', fontsize=20)\npl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\npl.xticks(())\npl.yticks(())\npl.subplot(2, 1, 2)\npl.scatter(yt, xt, c=phi2, s=70)\npl.xlabel('coordinates x/y', fontsize=25)\npl.ylabel('Feature value b', fontsize=20)\npl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\npl.yticks(())\npl.tight_layout()\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create structure matrices and across-feature distance matrix\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Structure matrices and across-features distance matrix\nC1 = ot.dist(xs)\nC2 = ot.dist(xt)\nM = ot.dist(ys, yt)\nw1 = ot.unif(C1.shape[0])\nw2 = ot.unif(C2.shape[0])\nGot = ot.emd([], [], M)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot matrices\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%%\ncmap = 'Reds'\npl.close(10)\npl.figure(10, (5, 5))\nfs = 15\nl_x = [0, 5, 10, 15]\nl_y = [0, 5, 10, 15, 20, 25]\ngs = pl.GridSpec(5, 5)\n\nax1 = pl.subplot(gs[3:, :2])\n\npl.imshow(C1, cmap=cmap, interpolation='nearest')\npl.title(\"$C_1$\", fontsize=fs)\npl.xlabel(\"$k$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(l_x)\npl.yticks(l_x)\n\nax2 = pl.subplot(gs[:3, 2:])\n\npl.imshow(C2, cmap=cmap, interpolation='nearest')\npl.title(\"$C_2$\", fontsize=fs)\npl.ylabel(\"$l$\", fontsize=fs)\n#pl.ylabel(\"$l$\",fontsize=fs)\npl.xticks(())\npl.yticks(l_y)\nax2.set_aspect('auto')\n\nax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\npl.imshow(M, cmap=cmap, interpolation='nearest')\npl.yticks(l_x)\npl.xticks(l_y)\npl.ylabel(\"$i$\", fontsize=fs)\npl.title(\"$M_{AB}$\", fontsize=fs)\npl.xlabel(\"$j$\", fontsize=fs)\npl.tight_layout()\nax3.set_aspect('auto')\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute FGW/GW\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Computing FGW and GW\nalpha = 1e-3\n\not.tic()\nGwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\not.toc()\n\n#%reload_ext WGW\nGg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Visualize transport matrices\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% visu OT matrix\ncmap = 'Blues'\nfs = 15\npl.figure(2, (13, 5))\npl.clf()\npl.subplot(1, 3, 1)\npl.imshow(Got, cmap=cmap, interpolation='nearest')\n#pl.xlabel(\"$y$\",fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(())\n\npl.title('Wasserstein ($M$ only)')\n\npl.subplot(1, 3, 2)\npl.imshow(Gg, cmap=cmap, interpolation='nearest')\npl.title('Gromov ($C_1,C_2$ only)')\npl.xticks(())\npl.subplot(1, 3, 3)\npl.imshow(Gwg, cmap=cmap, interpolation='nearest')\npl.title('FGW ($M+C_1,C_2$)')\n\npl.xlabel(\"$j$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\n\npl.tight_layout()\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_fgw.py b/docs/source/auto_examples/plot_fgw.py
deleted file mode 100644
index 43efc94..0000000
--- a/docs/source/auto_examples/plot_fgw.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-==============================
-Plot Fused-gromov-Wasserstein
-==============================
-
-This example illustrates the computation of FGW for 1D measures[18].
-
-.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
- and Courty Nicolas
- "Optimal Transport for structured data with application on graphs"
- International Conference on Machine Learning (ICML). 2019.
-
-"""
-
-# Author: Titouan Vayer <titouan.vayer@irisa.fr>
-#
-# License: MIT License
-
-import matplotlib.pyplot as pl
-import numpy as np
-import ot
-from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein
-
-##############################################################################
-# Generate data
-# ---------
-
-#%% parameters
-# We create two 1D random measures
-n = 20 # number of points in the first distribution
-n2 = 30 # number of points in the second distribution
-sig = 1 # std of first distribution
-sig2 = 0.1 # std of second distribution
-
-np.random.seed(0)
-
-phi = np.arange(n)[:, None]
-xs = phi + sig * np.random.randn(n, 1)
-ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)
-
-phi2 = np.arange(n2)[:, None]
-xt = phi2 + sig * np.random.randn(n2, 1)
-yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)
-yt = yt[::-1, :]
-
-p = ot.unif(n)
-q = ot.unif(n2)
-
-##############################################################################
-# Plot data
-# ---------
-
-#%% plot the distributions
-
-pl.close(10)
-pl.figure(10, (7, 7))
-
-pl.subplot(2, 1, 1)
-
-pl.scatter(ys, xs, c=phi, s=70)
-pl.ylabel('Feature value a', fontsize=20)
-pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)
-pl.xticks(())
-pl.yticks(())
-pl.subplot(2, 1, 2)
-pl.scatter(yt, xt, c=phi2, s=70)
-pl.xlabel('coordinates x/y', fontsize=25)
-pl.ylabel('Feature value b', fontsize=20)
-pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)
-pl.yticks(())
-pl.tight_layout()
-pl.show()
-
-##############################################################################
-# Create structure matrices and across-feature distance matrix
-# ---------
-
-#%% Structure matrices and across-features distance matrix
-C1 = ot.dist(xs)
-C2 = ot.dist(xt)
-M = ot.dist(ys, yt)
-w1 = ot.unif(C1.shape[0])
-w2 = ot.unif(C2.shape[0])
-Got = ot.emd([], [], M)
-
-##############################################################################
-# Plot matrices
-# ---------
-
-#%%
-cmap = 'Reds'
-pl.close(10)
-pl.figure(10, (5, 5))
-fs = 15
-l_x = [0, 5, 10, 15]
-l_y = [0, 5, 10, 15, 20, 25]
-gs = pl.GridSpec(5, 5)
-
-ax1 = pl.subplot(gs[3:, :2])
-
-pl.imshow(C1, cmap=cmap, interpolation='nearest')
-pl.title("$C_1$", fontsize=fs)
-pl.xlabel("$k$", fontsize=fs)
-pl.ylabel("$i$", fontsize=fs)
-pl.xticks(l_x)
-pl.yticks(l_x)
-
-ax2 = pl.subplot(gs[:3, 2:])
-
-pl.imshow(C2, cmap=cmap, interpolation='nearest')
-pl.title("$C_2$", fontsize=fs)
-pl.ylabel("$l$", fontsize=fs)
-#pl.ylabel("$l$",fontsize=fs)
-pl.xticks(())
-pl.yticks(l_y)
-ax2.set_aspect('auto')
-
-ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)
-pl.imshow(M, cmap=cmap, interpolation='nearest')
-pl.yticks(l_x)
-pl.xticks(l_y)
-pl.ylabel("$i$", fontsize=fs)
-pl.title("$M_{AB}$", fontsize=fs)
-pl.xlabel("$j$", fontsize=fs)
-pl.tight_layout()
-ax3.set_aspect('auto')
-pl.show()
-
-##############################################################################
-# Compute FGW/GW
-# ---------
-
-#%% Computing FGW and GW
-alpha = 1e-3
-
-ot.tic()
-Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)
-ot.toc()
-
-#%reload_ext WGW
-Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)
-
-##############################################################################
-# Visualize transport matrices
-# ---------
-
-#%% visu OT matrix
-cmap = 'Blues'
-fs = 15
-pl.figure(2, (13, 5))
-pl.clf()
-pl.subplot(1, 3, 1)
-pl.imshow(Got, cmap=cmap, interpolation='nearest')
-#pl.xlabel("$y$",fontsize=fs)
-pl.ylabel("$i$", fontsize=fs)
-pl.xticks(())
-
-pl.title('Wasserstein ($M$ only)')
-
-pl.subplot(1, 3, 2)
-pl.imshow(Gg, cmap=cmap, interpolation='nearest')
-pl.title('Gromov ($C_1,C_2$ only)')
-pl.xticks(())
-pl.subplot(1, 3, 3)
-pl.imshow(Gwg, cmap=cmap, interpolation='nearest')
-pl.title('FGW ($M+C_1,C_2$)')
-
-pl.xlabel("$j$", fontsize=fs)
-pl.ylabel("$i$", fontsize=fs)
-
-pl.tight_layout()
-pl.show()
diff --git a/docs/source/auto_examples/plot_fgw.rst b/docs/source/auto_examples/plot_fgw.rst
deleted file mode 100644
index aec725d..0000000
--- a/docs/source/auto_examples/plot_fgw.rst
+++ /dev/null
@@ -1,297 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_fgw.py:
-
-
-==============================
-Plot Fused-gromov-Wasserstein
-==============================
-
-This example illustrates the computation of FGW for 1D measures[18].
-
-.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain
- and Courty Nicolas
- "Optimal Transport for structured data with application on graphs"
- International Conference on Machine Learning (ICML). 2019.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Titouan Vayer <titouan.vayer@irisa.fr>
- #
- # License: MIT License
-
- import matplotlib.pyplot as pl
- import numpy as np
- import ot
- from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein
-
-
-
-
-
-
-
-Generate data
----------
-
-
-
-.. code-block:: python
-
-
- #%% parameters
- # We create two 1D random measures
- n = 20 # number of points in the first distribution
- n2 = 30 # number of points in the second distribution
- sig = 1 # std of first distribution
- sig2 = 0.1 # std of second distribution
-
- np.random.seed(0)
-
- phi = np.arange(n)[:, None]
- xs = phi + sig * np.random.randn(n, 1)
- ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)
-
- phi2 = np.arange(n2)[:, None]
- xt = phi2 + sig * np.random.randn(n2, 1)
- yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)
- yt = yt[::-1, :]
-
- p = ot.unif(n)
- q = ot.unif(n2)
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- #%% plot the distributions
-
- pl.close(10)
- pl.figure(10, (7, 7))
-
- pl.subplot(2, 1, 1)
-
- pl.scatter(ys, xs, c=phi, s=70)
- pl.ylabel('Feature value a', fontsize=20)
- pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)
- pl.xticks(())
- pl.yticks(())
- pl.subplot(2, 1, 2)
- pl.scatter(yt, xt, c=phi2, s=70)
- pl.xlabel('coordinates x/y', fontsize=25)
- pl.ylabel('Feature value b', fontsize=20)
- pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)
- pl.yticks(())
- pl.tight_layout()
- pl.show()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_fgw_010.png
- :align: center
-
-
-
-
-Create structure matrices and across-feature distance matrix
----------
-
-
-
-.. code-block:: python
-
-
- #%% Structure matrices and across-features distance matrix
- C1 = ot.dist(xs)
- C2 = ot.dist(xt)
- M = ot.dist(ys, yt)
- w1 = ot.unif(C1.shape[0])
- w2 = ot.unif(C2.shape[0])
- Got = ot.emd([], [], M)
-
-
-
-
-
-
-
-Plot matrices
----------
-
-
-
-.. code-block:: python
-
-
- #%%
- cmap = 'Reds'
- pl.close(10)
- pl.figure(10, (5, 5))
- fs = 15
- l_x = [0, 5, 10, 15]
- l_y = [0, 5, 10, 15, 20, 25]
- gs = pl.GridSpec(5, 5)
-
- ax1 = pl.subplot(gs[3:, :2])
-
- pl.imshow(C1, cmap=cmap, interpolation='nearest')
- pl.title("$C_1$", fontsize=fs)
- pl.xlabel("$k$", fontsize=fs)
- pl.ylabel("$i$", fontsize=fs)
- pl.xticks(l_x)
- pl.yticks(l_x)
-
- ax2 = pl.subplot(gs[:3, 2:])
-
- pl.imshow(C2, cmap=cmap, interpolation='nearest')
- pl.title("$C_2$", fontsize=fs)
- pl.ylabel("$l$", fontsize=fs)
- #pl.ylabel("$l$",fontsize=fs)
- pl.xticks(())
- pl.yticks(l_y)
- ax2.set_aspect('auto')
-
- ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)
- pl.imshow(M, cmap=cmap, interpolation='nearest')
- pl.yticks(l_x)
- pl.xticks(l_y)
- pl.ylabel("$i$", fontsize=fs)
- pl.title("$M_{AB}$", fontsize=fs)
- pl.xlabel("$j$", fontsize=fs)
- pl.tight_layout()
- ax3.set_aspect('auto')
- pl.show()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_fgw_011.png
- :align: center
-
-
-
-
-Compute FGW/GW
----------
-
-
-
-.. code-block:: python
-
-
- #%% Computing FGW and GW
- alpha = 1e-3
-
- ot.tic()
- Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)
- ot.toc()
-
- #%reload_ext WGW
- Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Loss |Relative loss|Absolute loss
- ------------------------------------------------
- 0|4.734462e+01|0.000000e+00|0.000000e+00
- 1|2.508258e+01|8.875498e-01|2.226204e+01
- 2|2.189329e+01|1.456747e-01|3.189297e+00
- 3|2.189329e+01|0.000000e+00|0.000000e+00
- Elapsed time : 0.0016989707946777344 s
- It. |Loss |Relative loss|Absolute loss
- ------------------------------------------------
- 0|4.683978e+04|0.000000e+00|0.000000e+00
- 1|3.860061e+04|2.134468e-01|8.239175e+03
- 2|2.182948e+04|7.682787e-01|1.677113e+04
- 3|2.182948e+04|0.000000e+00|0.000000e+00
-
-
-Visualize transport matrices
----------
-
-
-
-.. code-block:: python
-
-
- #%% visu OT matrix
- cmap = 'Blues'
- fs = 15
- pl.figure(2, (13, 5))
- pl.clf()
- pl.subplot(1, 3, 1)
- pl.imshow(Got, cmap=cmap, interpolation='nearest')
- #pl.xlabel("$y$",fontsize=fs)
- pl.ylabel("$i$", fontsize=fs)
- pl.xticks(())
-
- pl.title('Wasserstein ($M$ only)')
-
- pl.subplot(1, 3, 2)
- pl.imshow(Gg, cmap=cmap, interpolation='nearest')
- pl.title('Gromov ($C_1,C_2$ only)')
- pl.xticks(())
- pl.subplot(1, 3, 3)
- pl.imshow(Gwg, cmap=cmap, interpolation='nearest')
- pl.title('FGW ($M+C_1,C_2$)')
-
- pl.xlabel("$j$", fontsize=fs)
- pl.ylabel("$i$", fontsize=fs)
-
- pl.tight_layout()
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_fgw_004.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 1.468 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_fgw.py <plot_fgw.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_fgw.ipynb <plot_fgw.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_free_support_barycenter.ipynb b/docs/source/auto_examples/plot_free_support_barycenter.ipynb
deleted file mode 100644
index 05a81c8..0000000
--- a/docs/source/auto_examples/plot_free_support_barycenter.ipynb
+++ /dev/null
@@ -1,108 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# 2D free support Wasserstein barycenters of distributions\n\n\nIllustration of 2D Wasserstein barycenters if discributions that are weighted\nsum of diracs.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n -------------\n%% parameters and data generation\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "N = 3\nd = 2\nmeasures_locations = []\nmeasures_weights = []\n\nfor i in range(N):\n\n n_i = np.random.randint(low=1, high=20) # nb samples\n\n mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean\n\n A_i = np.random.rand(d, d)\n cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix\n\n x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations\n b_i = np.random.uniform(0., 1., (n_i,))\n b_i = b_i / np.sum(b_i) # Dirac weights\n\n measures_locations.append(x_i)\n measures_weights.append(b_i)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute free support barycenter\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "k = 10 # number of Diracs of the barycenter\nX_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations\nb = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)\n\nX = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(1)\nfor (x_i, b_i) in zip(measures_locations, measures_weights):\n color = np.random.randint(low=1, high=10 * N)\n pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')\npl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\npl.title('Data measures and their barycenter')\npl.legend(loc=0)\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_free_support_barycenter.py b/docs/source/auto_examples/plot_free_support_barycenter.py
deleted file mode 100644
index b6efc59..0000000
--- a/docs/source/auto_examples/plot_free_support_barycenter.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-====================================================
-2D free support Wasserstein barycenters of distributions
-====================================================
-
-Illustration of 2D Wasserstein barycenters if discributions that are weighted
-sum of diracs.
-
-"""
-
-# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-
-
-##############################################################################
-# Generate data
-# -------------
-#%% parameters and data generation
-N = 3
-d = 2
-measures_locations = []
-measures_weights = []
-
-for i in range(N):
-
- n_i = np.random.randint(low=1, high=20) # nb samples
-
- mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean
-
- A_i = np.random.rand(d, d)
- cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix
-
- x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations
- b_i = np.random.uniform(0., 1., (n_i,))
- b_i = b_i / np.sum(b_i) # Dirac weights
-
- measures_locations.append(x_i)
- measures_weights.append(b_i)
-
-
-##############################################################################
-# Compute free support barycenter
-# -------------
-
-k = 10 # number of Diracs of the barycenter
-X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations
-b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)
-
-X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)
-
-
-##############################################################################
-# Plot data
-# ---------
-
-pl.figure(1)
-for (x_i, b_i) in zip(measures_locations, measures_weights):
- color = np.random.randint(low=1, high=10 * N)
- pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')
-pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')
-pl.title('Data measures and their barycenter')
-pl.legend(loc=0)
-pl.show()
diff --git a/docs/source/auto_examples/plot_free_support_barycenter.rst b/docs/source/auto_examples/plot_free_support_barycenter.rst
deleted file mode 100644
index d1b3b80..0000000
--- a/docs/source/auto_examples/plot_free_support_barycenter.rst
+++ /dev/null
@@ -1,140 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_free_support_barycenter.py:
-
-
-====================================================
-2D free support Wasserstein barycenters of distributions
-====================================================
-
-Illustration of 2D Wasserstein barycenters if discributions that are weighted
-sum of diracs.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
-
-
-
-
-
-
-
-
-Generate data
- -------------
-%% parameters and data generation
-
-
-
-.. code-block:: python
-
- N = 3
- d = 2
- measures_locations = []
- measures_weights = []
-
- for i in range(N):
-
- n_i = np.random.randint(low=1, high=20) # nb samples
-
- mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean
-
- A_i = np.random.rand(d, d)
- cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix
-
- x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations
- b_i = np.random.uniform(0., 1., (n_i,))
- b_i = b_i / np.sum(b_i) # Dirac weights
-
- measures_locations.append(x_i)
- measures_weights.append(b_i)
-
-
-
-
-
-
-
-
-Compute free support barycenter
--------------
-
-
-
-.. code-block:: python
-
-
- k = 10 # number of Diracs of the barycenter
- X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations
- b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)
-
- X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)
-
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- pl.figure(1)
- for (x_i, b_i) in zip(measures_locations, measures_weights):
- color = np.random.randint(low=1, high=10 * N)
- pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')
- pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')
- pl.title('Data measures and their barycenter')
- pl.legend(loc=0)
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_free_support_barycenter_001.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 0.129 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_free_support_barycenter.py <plot_free_support_barycenter.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_free_support_barycenter.ipynb <plot_free_support_barycenter.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb
deleted file mode 100644
index dc1f179..0000000
--- a/docs/source/auto_examples/plot_gromov.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Erwan Vautier <erwan.vautier@gmail.com>\n# Nicolas Courty <ncourty@irisa.fr>\n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sample two Gaussian distributions (2D and 3D)\n---------------------------------------------\n\nThe Gromov-Wasserstein distance allows to compute distances with samples that\ndo not belong to the same metric space. For demonstration purpose, we sample\ntwo Gaussian distributions in 2- and 3-dimensional spaces.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plotting the distributions\n--------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "fig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute distance kernels, normalize them and then display\n---------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute Gromov-Wasserstein plans and distance\n---------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "p = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst
deleted file mode 100644
index 3ed4e11..0000000
--- a/docs/source/auto_examples/plot_gromov.rst
+++ /dev/null
@@ -1,214 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_gromov.py:
-
-
-==========================
-Gromov-Wasserstein example
-==========================
-
-This example is designed to show how to use the Gromov-Wassertsein distance
-computation in POT.
-
-
-
-.. code-block:: python
-
-
- # Author: Erwan Vautier <erwan.vautier@gmail.com>
- # Nicolas Courty <ncourty@irisa.fr>
- #
- # License: MIT License
-
- import scipy as sp
- import numpy as np
- import matplotlib.pylab as pl
- from mpl_toolkits.mplot3d import Axes3D # noqa
- import ot
-
-
-
-
-
-
-
-Sample two Gaussian distributions (2D and 3D)
----------------------------------------------
-
-The Gromov-Wasserstein distance allows to compute distances with samples that
-do not belong to the same metric space. For demonstration purpose, we sample
-two Gaussian distributions in 2- and 3-dimensional spaces.
-
-
-
-.. code-block:: python
-
-
-
- n_samples = 30 # nb samples
-
- mu_s = np.array([0, 0])
- cov_s = np.array([[1, 0], [0, 1]])
-
- mu_t = np.array([4, 4, 4])
- cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
-
-
- xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
- P = sp.linalg.sqrtm(cov_t)
- xt = np.random.randn(n_samples, 3).dot(P) + mu_t
-
-
-
-
-
-
-
-Plotting the distributions
---------------------------
-
-
-
-.. code-block:: python
-
-
-
- fig = pl.figure()
- ax1 = fig.add_subplot(121)
- ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
- ax2 = fig.add_subplot(122, projection='3d')
- ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')
- pl.show()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png
- :align: center
-
-
-
-
-Compute distance kernels, normalize them and then display
----------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
-
- C1 = sp.spatial.distance.cdist(xs, xs)
- C2 = sp.spatial.distance.cdist(xt, xt)
-
- C1 /= C1.max()
- C2 /= C2.max()
-
- pl.figure()
- pl.subplot(121)
- pl.imshow(C1)
- pl.subplot(122)
- pl.imshow(C2)
- pl.show()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png
- :align: center
-
-
-
-
-Compute Gromov-Wasserstein plans and distance
----------------------------------------------
-
-
-
-.. code-block:: python
-
-
- p = ot.unif(n_samples)
- q = ot.unif(n_samples)
-
- gw0, log0 = ot.gromov.gromov_wasserstein(
- C1, C2, p, q, 'square_loss', verbose=True, log=True)
-
- gw, log = ot.gromov.entropic_gromov_wasserstein(
- C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)
-
-
- print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))
- print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))
-
-
- pl.figure(1, (10, 5))
-
- pl.subplot(1, 2, 1)
- pl.imshow(gw0, cmap='jet')
- pl.title('Gromov Wasserstein')
-
- pl.subplot(1, 2, 2)
- pl.imshow(gw, cmap='jet')
- pl.title('Entropic Gromov Wasserstein')
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png
- :align: center
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Loss |Delta loss
- --------------------------------
- 0|4.328711e-02|0.000000e+00
- 1|2.281369e-02|-8.974178e-01
- 2|1.843659e-02|-2.374139e-01
- 3|1.602820e-02|-1.502598e-01
- 4|1.353712e-02|-1.840179e-01
- 5|1.285687e-02|-5.290977e-02
- 6|1.284537e-02|-8.952931e-04
- 7|1.284525e-02|-8.989584e-06
- 8|1.284525e-02|-8.989950e-08
- 9|1.284525e-02|-8.989949e-10
- It. |Err
- -------------------
- 0|7.263293e-02|
- 10|1.737784e-02|
- 20|7.783978e-03|
- 30|3.399419e-07|
- 40|3.751207e-11|
- Gromov-Wasserstein distances: 0.012845252089244688
- Entropic Gromov-Wasserstein distances: 0.013543882352191079
-
-
-**Total running time of the script:** ( 0 minutes 1.916 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_gromov.py <plot_gromov.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_gromov.ipynb <plot_gromov.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_gromov_barycenter.ipynb b/docs/source/auto_examples/plot_gromov_barycenter.ipynb
deleted file mode 100644
index 4c2f28f..0000000
--- a/docs/source/auto_examples/plot_gromov_barycenter.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ],
- "cell_type": "code"
- },
- {
- "metadata": {},
- "source": [
- "\n# Gromov-Wasserstein Barycenter example\n\n\nThis example is designed to show how to use the Gromov-Wasserstein distance\ncomputation in POT.\n\n"
- ],
- "cell_type": "markdown"
- },
- {
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Erwan Vautier <erwan.vautier@gmail.com>\n# Nicolas Courty <ncourty@irisa.fr>\n#\n# License: MIT License\n\n\nimport numpy as np\nimport scipy as sp\n\nimport scipy.ndimage as spi\nimport matplotlib.pylab as pl\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\n\nimport ot"
- ],
- "cell_type": "code"
- },
- {
- "metadata": {},
- "source": [
- "Smacof MDS\n----------\n\nThis function allows to find an embedding of points given a dissimilarity matrix\nthat will be given by the output of the algorithm\n\n"
- ],
- "cell_type": "markdown"
- },
- {
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\n \"\"\"\n Returns an interpolated point cloud following the dissimilarity matrix C\n using SMACOF multidimensional scaling (MDS) in specific dimensionned\n target space\n\n Parameters\n ----------\n C : ndarray, shape (ns, ns)\n dissimilarity matrix\n dim : int\n dimension of the targeted space\n max_iter : int\n Maximum number of iterations of the SMACOF algorithm for a single run\n eps : float\n relative tolerance w.r.t stress to declare converge\n\n Returns\n -------\n npos : ndarray, shape (R, dim)\n Embedded coordinates of the interpolated point cloud (defined with\n one isometry)\n \"\"\"\n\n rng = np.random.RandomState(seed=3)\n\n mds = manifold.MDS(\n dim,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity='precomputed',\n n_init=1)\n pos = mds.fit(C).embedding_\n\n nmds = manifold.MDS(\n 2,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity=\"precomputed\",\n random_state=rng,\n n_init=1)\n npos = nmds.fit_transform(C, init=pos)\n\n return npos"
- ],
- "cell_type": "code"
- },
- {
- "metadata": {},
- "source": [
- "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n"
- ],
- "cell_type": "markdown"
- },
- {
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\nsquare = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\ncross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\ntriangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\nstar = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n\nshapes = [square, cross, triangle, star]\n\nS = 4\nxs = [[] for i in range(S)]\n\n\nfor nb in range(4):\n for i in range(8):\n for j in range(8):\n if shapes[nb][i, j] < 0.95:\n xs[nb].append([j, 8 - i])\n\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\n np.array(xs[2]), np.array(xs[3])])"
- ],
- "cell_type": "code"
- },
- {
- "metadata": {},
- "source": [
- "Barycenter computation\n----------------------\n\n"
- ],
- "cell_type": "markdown"
- },
- {
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "ns = [len(xs[s]) for s in range(S)]\nn_samples = 30\n\n\"\"\"Compute all distances matrices for the four shapes\"\"\"\nCs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\nCs = [cs / cs.max() for cs in Cs]\n\nps = [ot.unif(ns[s]) for s in range(S)]\np = ot.unif(n_samples)\n\n\nlambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\n\nCt01 = [0 for i in range(2)]\nfor i in range(2):\n Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\n [ps[0], ps[1]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt02 = [0 for i in range(2)]\nfor i in range(2):\n Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\n [ps[0], ps[2]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt13 = [0 for i in range(2)]\nfor i in range(2):\n Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\n [ps[1], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt23 = [0 for i in range(2)]\nfor i in range(2):\n Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\n [ps[2], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)"
- ],
- "cell_type": "code"
- },
- {
- "metadata": {},
- "source": [
- "Visualization\n-------------\n\nThe PCA helps in getting consistency between the rotations\n\n"
- ],
- "cell_type": "markdown"
- },
- {
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "clf = PCA(n_components=2)\nnpos = [0, 0, 0, 0]\nnpos = [smacof_mds(Cs[s], 2) for s in range(S)]\n\nnpost01 = [0, 0]\nnpost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\nnpost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\n\nnpost02 = [0, 0]\nnpost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\nnpost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\n\nnpost13 = [0, 0]\nnpost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\nnpost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\n\nnpost23 = [0, 0]\nnpost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\nnpost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\n\n\nfig = pl.figure(figsize=(10, 10))\n\nax1 = pl.subplot2grid((4, 4), (0, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\n\nax2 = pl.subplot2grid((4, 4), (0, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\n\nax3 = pl.subplot2grid((4, 4), (0, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\n\nax4 = pl.subplot2grid((4, 4), (0, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\n\nax5 = pl.subplot2grid((4, 4), (1, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\n\nax6 = pl.subplot2grid((4, 4), (1, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\n\nax7 = pl.subplot2grid((4, 4), (2, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\n\nax8 = pl.subplot2grid((4, 4), (2, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\n\nax9 = pl.subplot2grid((4, 4), (3, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\n\nax10 = pl.subplot2grid((4, 4), (3, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\n\nax11 = pl.subplot2grid((4, 4), (3, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\n\nax12 = pl.subplot2grid((4, 4), (3, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')"
- ],
- "cell_type": "code"
- }
- ],
- "metadata": {
- "language_info": {
- "name": "python",
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "nbconvert_exporter": "python",
- "version": "3.5.2",
- "pygments_lexer": "ipython3",
- "file_extension": ".py",
- "mimetype": "text/x-python"
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3",
- "language": "python"
- }
- },
- "nbformat_minor": 0,
- "nbformat": 4
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_gromov_barycenter.py b/docs/source/auto_examples/plot_gromov_barycenter.py
deleted file mode 100644
index 58fc51a..0000000
--- a/docs/source/auto_examples/plot_gromov_barycenter.py
+++ /dev/null
@@ -1,248 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-=====================================
-Gromov-Wasserstein Barycenter example
-=====================================
-
-This example is designed to show how to use the Gromov-Wasserstein distance
-computation in POT.
-"""
-
-# Author: Erwan Vautier <erwan.vautier@gmail.com>
-# Nicolas Courty <ncourty@irisa.fr>
-#
-# License: MIT License
-
-
-import numpy as np
-import scipy as sp
-
-import scipy.ndimage as spi
-import matplotlib.pylab as pl
-from sklearn import manifold
-from sklearn.decomposition import PCA
-
-import ot
-
-##############################################################################
-# Smacof MDS
-# ----------
-#
-# This function allows to find an embedding of points given a dissimilarity matrix
-# that will be given by the output of the algorithm
-
-
-def smacof_mds(C, dim, max_iter=3000, eps=1e-9):
- """
- Returns an interpolated point cloud following the dissimilarity matrix C
- using SMACOF multidimensional scaling (MDS) in specific dimensionned
- target space
-
- Parameters
- ----------
- C : ndarray, shape (ns, ns)
- dissimilarity matrix
- dim : int
- dimension of the targeted space
- max_iter : int
- Maximum number of iterations of the SMACOF algorithm for a single run
- eps : float
- relative tolerance w.r.t stress to declare converge
-
- Returns
- -------
- npos : ndarray, shape (R, dim)
- Embedded coordinates of the interpolated point cloud (defined with
- one isometry)
- """
-
- rng = np.random.RandomState(seed=3)
-
- mds = manifold.MDS(
- dim,
- max_iter=max_iter,
- eps=1e-9,
- dissimilarity='precomputed',
- n_init=1)
- pos = mds.fit(C).embedding_
-
- nmds = manifold.MDS(
- 2,
- max_iter=max_iter,
- eps=1e-9,
- dissimilarity="precomputed",
- random_state=rng,
- n_init=1)
- npos = nmds.fit_transform(C, init=pos)
-
- return npos
-
-
-##############################################################################
-# Data preparation
-# ----------------
-#
-# The four distributions are constructed from 4 simple images
-
-
-def im2mat(I):
- """Converts and image to matrix (one pixel per line)"""
- return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
-square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256
-cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256
-triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256
-star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256
-
-shapes = [square, cross, triangle, star]
-
-S = 4
-xs = [[] for i in range(S)]
-
-
-for nb in range(4):
- for i in range(8):
- for j in range(8):
- if shapes[nb][i, j] < 0.95:
- xs[nb].append([j, 8 - i])
-
-xs = np.array([np.array(xs[0]), np.array(xs[1]),
- np.array(xs[2]), np.array(xs[3])])
-
-##############################################################################
-# Barycenter computation
-# ----------------------
-
-
-ns = [len(xs[s]) for s in range(S)]
-n_samples = 30
-
-"""Compute all distances matrices for the four shapes"""
-Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]
-Cs = [cs / cs.max() for cs in Cs]
-
-ps = [ot.unif(ns[s]) for s in range(S)]
-p = ot.unif(n_samples)
-
-
-lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]
-
-Ct01 = [0 for i in range(2)]
-for i in range(2):
- Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],
- [ps[0], ps[1]
- ], p, lambdast[i], 'square_loss', # 5e-4,
- max_iter=100, tol=1e-3)
-
-Ct02 = [0 for i in range(2)]
-for i in range(2):
- Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],
- [ps[0], ps[2]
- ], p, lambdast[i], 'square_loss', # 5e-4,
- max_iter=100, tol=1e-3)
-
-Ct13 = [0 for i in range(2)]
-for i in range(2):
- Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],
- [ps[1], ps[3]
- ], p, lambdast[i], 'square_loss', # 5e-4,
- max_iter=100, tol=1e-3)
-
-Ct23 = [0 for i in range(2)]
-for i in range(2):
- Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],
- [ps[2], ps[3]
- ], p, lambdast[i], 'square_loss', # 5e-4,
- max_iter=100, tol=1e-3)
-
-
-##############################################################################
-# Visualization
-# -------------
-#
-# The PCA helps in getting consistency between the rotations
-
-
-clf = PCA(n_components=2)
-npos = [0, 0, 0, 0]
-npos = [smacof_mds(Cs[s], 2) for s in range(S)]
-
-npost01 = [0, 0]
-npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]
-npost01 = [clf.fit_transform(npost01[s]) for s in range(2)]
-
-npost02 = [0, 0]
-npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]
-npost02 = [clf.fit_transform(npost02[s]) for s in range(2)]
-
-npost13 = [0, 0]
-npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]
-npost13 = [clf.fit_transform(npost13[s]) for s in range(2)]
-
-npost23 = [0, 0]
-npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]
-npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]
-
-
-fig = pl.figure(figsize=(10, 10))
-
-ax1 = pl.subplot2grid((4, 4), (0, 0))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')
-
-ax2 = pl.subplot2grid((4, 4), (0, 1))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')
-
-ax3 = pl.subplot2grid((4, 4), (0, 2))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')
-
-ax4 = pl.subplot2grid((4, 4), (0, 3))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')
-
-ax5 = pl.subplot2grid((4, 4), (1, 0))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')
-
-ax6 = pl.subplot2grid((4, 4), (1, 3))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')
-
-ax7 = pl.subplot2grid((4, 4), (2, 0))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')
-
-ax8 = pl.subplot2grid((4, 4), (2, 3))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')
-
-ax9 = pl.subplot2grid((4, 4), (3, 0))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')
-
-ax10 = pl.subplot2grid((4, 4), (3, 1))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')
-
-ax11 = pl.subplot2grid((4, 4), (3, 2))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')
-
-ax12 = pl.subplot2grid((4, 4), (3, 3))
-pl.xlim((-1, 1))
-pl.ylim((-1, 1))
-ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')
diff --git a/docs/source/auto_examples/plot_gromov_barycenter.rst b/docs/source/auto_examples/plot_gromov_barycenter.rst
deleted file mode 100644
index 531ee22..0000000
--- a/docs/source/auto_examples/plot_gromov_barycenter.rst
+++ /dev/null
@@ -1,329 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_gromov_barycenter.py:
-
-
-=====================================
-Gromov-Wasserstein Barycenter example
-=====================================
-
-This example is designed to show how to use the Gromov-Wasserstein distance
-computation in POT.
-
-
-
-.. code-block:: python
-
-
- # Author: Erwan Vautier <erwan.vautier@gmail.com>
- # Nicolas Courty <ncourty@irisa.fr>
- #
- # License: MIT License
-
-
- import numpy as np
- import scipy as sp
-
- import scipy.ndimage as spi
- import matplotlib.pylab as pl
- from sklearn import manifold
- from sklearn.decomposition import PCA
-
- import ot
-
-
-
-
-
-
-
-Smacof MDS
-----------
-
-This function allows to find an embedding of points given a dissimilarity matrix
-that will be given by the output of the algorithm
-
-
-
-.. code-block:: python
-
-
-
- def smacof_mds(C, dim, max_iter=3000, eps=1e-9):
- """
- Returns an interpolated point cloud following the dissimilarity matrix C
- using SMACOF multidimensional scaling (MDS) in specific dimensionned
- target space
-
- Parameters
- ----------
- C : ndarray, shape (ns, ns)
- dissimilarity matrix
- dim : int
- dimension of the targeted space
- max_iter : int
- Maximum number of iterations of the SMACOF algorithm for a single run
- eps : float
- relative tolerance w.r.t stress to declare converge
-
- Returns
- -------
- npos : ndarray, shape (R, dim)
- Embedded coordinates of the interpolated point cloud (defined with
- one isometry)
- """
-
- rng = np.random.RandomState(seed=3)
-
- mds = manifold.MDS(
- dim,
- max_iter=max_iter,
- eps=1e-9,
- dissimilarity='precomputed',
- n_init=1)
- pos = mds.fit(C).embedding_
-
- nmds = manifold.MDS(
- 2,
- max_iter=max_iter,
- eps=1e-9,
- dissimilarity="precomputed",
- random_state=rng,
- n_init=1)
- npos = nmds.fit_transform(C, init=pos)
-
- return npos
-
-
-
-
-
-
-
-
-Data preparation
-----------------
-
-The four distributions are constructed from 4 simple images
-
-
-
-.. code-block:: python
-
-
-
- def im2mat(I):
- """Converts and image to matrix (one pixel per line)"""
- return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
- square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256
- cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256
- triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256
- star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256
-
- shapes = [square, cross, triangle, star]
-
- S = 4
- xs = [[] for i in range(S)]
-
-
- for nb in range(4):
- for i in range(8):
- for j in range(8):
- if shapes[nb][i, j] < 0.95:
- xs[nb].append([j, 8 - i])
-
- xs = np.array([np.array(xs[0]), np.array(xs[1]),
- np.array(xs[2]), np.array(xs[3])])
-
-
-
-
-
-
-
-Barycenter computation
-----------------------
-
-
-
-.. code-block:: python
-
-
-
- ns = [len(xs[s]) for s in range(S)]
- n_samples = 30
-
- """Compute all distances matrices for the four shapes"""
- Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]
- Cs = [cs / cs.max() for cs in Cs]
-
- ps = [ot.unif(ns[s]) for s in range(S)]
- p = ot.unif(n_samples)
-
-
- lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]
-
- Ct01 = [0 for i in range(2)]
- for i in range(2):
- Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],
- [ps[0], ps[1]
- ], p, lambdast[i], 'square_loss', # 5e-4,
- max_iter=100, tol=1e-3)
-
- Ct02 = [0 for i in range(2)]
- for i in range(2):
- Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],
- [ps[0], ps[2]
- ], p, lambdast[i], 'square_loss', # 5e-4,
- max_iter=100, tol=1e-3)
-
- Ct13 = [0 for i in range(2)]
- for i in range(2):
- Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],
- [ps[1], ps[3]
- ], p, lambdast[i], 'square_loss', # 5e-4,
- max_iter=100, tol=1e-3)
-
- Ct23 = [0 for i in range(2)]
- for i in range(2):
- Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],
- [ps[2], ps[3]
- ], p, lambdast[i], 'square_loss', # 5e-4,
- max_iter=100, tol=1e-3)
-
-
-
-
-
-
-
-
-Visualization
--------------
-
-The PCA helps in getting consistency between the rotations
-
-
-
-.. code-block:: python
-
-
-
- clf = PCA(n_components=2)
- npos = [0, 0, 0, 0]
- npos = [smacof_mds(Cs[s], 2) for s in range(S)]
-
- npost01 = [0, 0]
- npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]
- npost01 = [clf.fit_transform(npost01[s]) for s in range(2)]
-
- npost02 = [0, 0]
- npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]
- npost02 = [clf.fit_transform(npost02[s]) for s in range(2)]
-
- npost13 = [0, 0]
- npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]
- npost13 = [clf.fit_transform(npost13[s]) for s in range(2)]
-
- npost23 = [0, 0]
- npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]
- npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]
-
-
- fig = pl.figure(figsize=(10, 10))
-
- ax1 = pl.subplot2grid((4, 4), (0, 0))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')
-
- ax2 = pl.subplot2grid((4, 4), (0, 1))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')
-
- ax3 = pl.subplot2grid((4, 4), (0, 2))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')
-
- ax4 = pl.subplot2grid((4, 4), (0, 3))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')
-
- ax5 = pl.subplot2grid((4, 4), (1, 0))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')
-
- ax6 = pl.subplot2grid((4, 4), (1, 3))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')
-
- ax7 = pl.subplot2grid((4, 4), (2, 0))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')
-
- ax8 = pl.subplot2grid((4, 4), (2, 3))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')
-
- ax9 = pl.subplot2grid((4, 4), (3, 0))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')
-
- ax10 = pl.subplot2grid((4, 4), (3, 1))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')
-
- ax11 = pl.subplot2grid((4, 4), (3, 2))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')
-
- ax12 = pl.subplot2grid((4, 4), (3, 3))
- pl.xlim((-1, 1))
- pl.ylim((-1, 1))
- ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_gromov_barycenter_001.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 5.906 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_gromov_barycenter.py <plot_gromov_barycenter.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_gromov_barycenter.ipynb <plot_gromov_barycenter.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb
deleted file mode 100644
index 107c299..0000000
--- a/docs/source/auto_examples/plot_optim_OTreg.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and\ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\nconditional gradient: analysis of convergence and applications.\narXiv preprint arXiv:1510.06567.\n\n\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.make_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD with Frobenius norm regularization\n--------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD with entropic regularization\n--------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD with Frobenius norm + entropic regularization\n-------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py
deleted file mode 100644
index 2c58def..0000000
--- a/docs/source/auto_examples/plot_optim_OTreg.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-==================================
-Regularized OT with generic solver
-==================================
-
-Illustrates the use of the generic solver for regularized OT with
-user-designed regularization term. It uses Conditional gradient as in [6] and
-generalized Conditional Gradient as proposed in [5][7].
-
-
-[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for
-Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine
-Intelligence , vol.PP, no.99, pp.1-1.
-
-[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
-Regularized discrete optimal transport. SIAM Journal on Imaging Sciences,
-7(3), 1853-1882.
-
-[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized
-conditional gradient: analysis of convergence and applications.
-arXiv preprint arXiv:1510.06567.
-
-
-
-"""
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-import ot.plot
-
-##############################################################################
-# Generate data
-# -------------
-
-#%% parameters
-
-n = 100 # nb bins
-
-# bin positions
-x = np.arange(n, dtype=np.float64)
-
-# Gaussian distributions
-a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std
-b = ot.datasets.make_1D_gauss(n, m=60, s=10)
-
-# loss matrix
-M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
-M /= M.max()
-
-##############################################################################
-# Solve EMD
-# ---------
-
-#%% EMD
-
-G0 = ot.emd(a, b, M)
-
-pl.figure(3, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')
-
-##############################################################################
-# Solve EMD with Frobenius norm regularization
-# --------------------------------------------
-
-#%% Example with Frobenius norm regularization
-
-
-def f(G):
- return 0.5 * np.sum(G**2)
-
-
-def df(G):
- return G
-
-
-reg = 1e-1
-
-Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)
-
-pl.figure(3)
-ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')
-
-##############################################################################
-# Solve EMD with entropic regularization
-# --------------------------------------
-
-#%% Example with entropic regularization
-
-
-def f(G):
- return np.sum(G * np.log(G))
-
-
-def df(G):
- return np.log(G) + 1.
-
-
-reg = 1e-3
-
-Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True)
-
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')
-
-##############################################################################
-# Solve EMD with Frobenius norm + entropic regularization
-# -------------------------------------------------------
-
-#%% Example with Frobenius norm + entropic regularization with gcg
-
-
-def f(G):
- return 0.5 * np.sum(G**2)
-
-
-def df(G):
- return G
-
-
-reg1 = 1e-3
-reg2 = 1e-1
-
-Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)
-
-pl.figure(5, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')
-pl.show()
diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst
deleted file mode 100644
index 844cba0..0000000
--- a/docs/source/auto_examples/plot_optim_OTreg.rst
+++ /dev/null
@@ -1,663 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_optim_OTreg.py:
-
-
-==================================
-Regularized OT with generic solver
-==================================
-
-Illustrates the use of the generic solver for regularized OT with
-user-designed regularization term. It uses Conditional gradient as in [6] and
-generalized Conditional Gradient as proposed in [5][7].
-
-
-[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for
-Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine
-Intelligence , vol.PP, no.99, pp.1-1.
-
-[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
-Regularized discrete optimal transport. SIAM Journal on Imaging Sciences,
-7(3), 1853-1882.
-
-[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized
-conditional gradient: analysis of convergence and applications.
-arXiv preprint arXiv:1510.06567.
-
-
-
-
-
-
-.. code-block:: python
-
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
- import ot.plot
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- #%% parameters
-
- n = 100 # nb bins
-
- # bin positions
- x = np.arange(n, dtype=np.float64)
-
- # Gaussian distributions
- a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std
- b = ot.datasets.make_1D_gauss(n, m=60, s=10)
-
- # loss matrix
- M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
- M /= M.max()
-
-
-
-
-
-
-
-Solve EMD
----------
-
-
-
-.. code-block:: python
-
-
- #%% EMD
-
- G0 = ot.emd(a, b, M)
-
- pl.figure(3, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_003.png
- :align: center
-
-
-
-
-Solve EMD with Frobenius norm regularization
---------------------------------------------
-
-
-
-.. code-block:: python
-
-
- #%% Example with Frobenius norm regularization
-
-
- def f(G):
- return 0.5 * np.sum(G**2)
-
-
- def df(G):
- return G
-
-
- reg = 1e-1
-
- Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)
-
- pl.figure(3)
- ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_004.png
- :align: center
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Loss |Delta loss
- --------------------------------
- 0|1.760578e-01|0.000000e+00
- 1|1.669467e-01|-5.457501e-02
- 2|1.665639e-01|-2.298130e-03
- 3|1.664378e-01|-7.572776e-04
- 4|1.664077e-01|-1.811855e-04
- 5|1.663912e-01|-9.936787e-05
- 6|1.663852e-01|-3.555826e-05
- 7|1.663814e-01|-2.305693e-05
- 8|1.663785e-01|-1.760450e-05
- 9|1.663767e-01|-1.078011e-05
- 10|1.663751e-01|-9.525192e-06
- 11|1.663737e-01|-8.396466e-06
- 12|1.663727e-01|-6.086938e-06
- 13|1.663720e-01|-4.042609e-06
- 14|1.663713e-01|-4.160914e-06
- 15|1.663707e-01|-3.823502e-06
- 16|1.663702e-01|-3.022440e-06
- 17|1.663697e-01|-3.181249e-06
- 18|1.663692e-01|-2.698532e-06
- 19|1.663687e-01|-3.258253e-06
- It. |Loss |Delta loss
- --------------------------------
- 20|1.663682e-01|-2.741118e-06
- 21|1.663678e-01|-2.624135e-06
- 22|1.663673e-01|-2.645179e-06
- 23|1.663670e-01|-1.957237e-06
- 24|1.663666e-01|-2.261541e-06
- 25|1.663663e-01|-1.851305e-06
- 26|1.663660e-01|-1.942296e-06
- 27|1.663657e-01|-2.092896e-06
- 28|1.663653e-01|-1.924361e-06
- 29|1.663651e-01|-1.625455e-06
- 30|1.663648e-01|-1.641123e-06
- 31|1.663645e-01|-1.566666e-06
- 32|1.663643e-01|-1.338514e-06
- 33|1.663641e-01|-1.222711e-06
- 34|1.663639e-01|-1.221805e-06
- 35|1.663637e-01|-1.440781e-06
- 36|1.663634e-01|-1.520091e-06
- 37|1.663632e-01|-1.288193e-06
- 38|1.663630e-01|-1.123055e-06
- 39|1.663628e-01|-1.024487e-06
- It. |Loss |Delta loss
- --------------------------------
- 40|1.663627e-01|-1.079606e-06
- 41|1.663625e-01|-1.172093e-06
- 42|1.663623e-01|-1.047880e-06
- 43|1.663621e-01|-1.010577e-06
- 44|1.663619e-01|-1.064438e-06
- 45|1.663618e-01|-9.882375e-07
- 46|1.663616e-01|-8.532647e-07
- 47|1.663615e-01|-9.930189e-07
- 48|1.663613e-01|-8.728955e-07
- 49|1.663612e-01|-9.524214e-07
- 50|1.663610e-01|-9.088418e-07
- 51|1.663609e-01|-7.639430e-07
- 52|1.663608e-01|-6.662611e-07
- 53|1.663607e-01|-7.133700e-07
- 54|1.663605e-01|-7.648141e-07
- 55|1.663604e-01|-6.557516e-07
- 56|1.663603e-01|-7.304213e-07
- 57|1.663602e-01|-6.353809e-07
- 58|1.663601e-01|-7.968279e-07
- 59|1.663600e-01|-6.367159e-07
- It. |Loss |Delta loss
- --------------------------------
- 60|1.663599e-01|-5.610790e-07
- 61|1.663598e-01|-5.787466e-07
- 62|1.663596e-01|-6.937777e-07
- 63|1.663596e-01|-5.599432e-07
- 64|1.663595e-01|-5.813048e-07
- 65|1.663594e-01|-5.724600e-07
- 66|1.663593e-01|-6.081892e-07
- 67|1.663592e-01|-5.948732e-07
- 68|1.663591e-01|-4.941833e-07
- 69|1.663590e-01|-5.213739e-07
- 70|1.663589e-01|-5.127355e-07
- 71|1.663588e-01|-4.349251e-07
- 72|1.663588e-01|-5.007084e-07
- 73|1.663587e-01|-4.880265e-07
- 74|1.663586e-01|-4.931950e-07
- 75|1.663585e-01|-4.981309e-07
- 76|1.663584e-01|-3.952959e-07
- 77|1.663584e-01|-4.544857e-07
- 78|1.663583e-01|-4.237579e-07
- 79|1.663582e-01|-4.382386e-07
- It. |Loss |Delta loss
- --------------------------------
- 80|1.663582e-01|-3.646051e-07
- 81|1.663581e-01|-4.197994e-07
- 82|1.663580e-01|-4.072764e-07
- 83|1.663580e-01|-3.994645e-07
- 84|1.663579e-01|-4.842721e-07
- 85|1.663578e-01|-3.276486e-07
- 86|1.663578e-01|-3.737346e-07
- 87|1.663577e-01|-4.282043e-07
- 88|1.663576e-01|-4.020937e-07
- 89|1.663576e-01|-3.431951e-07
- 90|1.663575e-01|-3.052335e-07
- 91|1.663575e-01|-3.500538e-07
- 92|1.663574e-01|-3.063176e-07
- 93|1.663573e-01|-3.576367e-07
- 94|1.663573e-01|-3.224681e-07
- 95|1.663572e-01|-3.673221e-07
- 96|1.663572e-01|-3.635561e-07
- 97|1.663571e-01|-3.527236e-07
- 98|1.663571e-01|-2.788548e-07
- 99|1.663570e-01|-2.727141e-07
- It. |Loss |Delta loss
- --------------------------------
- 100|1.663570e-01|-3.127278e-07
- 101|1.663569e-01|-2.637504e-07
- 102|1.663569e-01|-2.922750e-07
- 103|1.663568e-01|-3.076454e-07
- 104|1.663568e-01|-2.911509e-07
- 105|1.663567e-01|-2.403398e-07
- 106|1.663567e-01|-2.439790e-07
- 107|1.663567e-01|-2.634542e-07
- 108|1.663566e-01|-2.452203e-07
- 109|1.663566e-01|-2.852991e-07
- 110|1.663565e-01|-2.165490e-07
- 111|1.663565e-01|-2.450250e-07
- 112|1.663564e-01|-2.685294e-07
- 113|1.663564e-01|-2.821800e-07
- 114|1.663564e-01|-2.237390e-07
- 115|1.663563e-01|-1.992842e-07
- 116|1.663563e-01|-2.166739e-07
- 117|1.663563e-01|-2.086064e-07
- 118|1.663562e-01|-2.435945e-07
- 119|1.663562e-01|-2.292497e-07
- It. |Loss |Delta loss
- --------------------------------
- 120|1.663561e-01|-2.366209e-07
- 121|1.663561e-01|-2.138746e-07
- 122|1.663561e-01|-2.009637e-07
- 123|1.663560e-01|-2.386258e-07
- 124|1.663560e-01|-1.927442e-07
- 125|1.663560e-01|-2.081681e-07
- 126|1.663559e-01|-1.759123e-07
- 127|1.663559e-01|-1.890771e-07
- 128|1.663559e-01|-1.971315e-07
- 129|1.663558e-01|-2.101983e-07
- 130|1.663558e-01|-2.035645e-07
- 131|1.663558e-01|-1.984492e-07
- 132|1.663557e-01|-1.849064e-07
- 133|1.663557e-01|-1.795703e-07
- 134|1.663557e-01|-1.624087e-07
- 135|1.663557e-01|-1.689557e-07
- 136|1.663556e-01|-1.644308e-07
- 137|1.663556e-01|-1.618007e-07
- 138|1.663556e-01|-1.483013e-07
- 139|1.663555e-01|-1.708771e-07
- It. |Loss |Delta loss
- --------------------------------
- 140|1.663555e-01|-2.013847e-07
- 141|1.663555e-01|-1.721217e-07
- 142|1.663554e-01|-2.027911e-07
- 143|1.663554e-01|-1.764565e-07
- 144|1.663554e-01|-1.677151e-07
- 145|1.663554e-01|-1.351982e-07
- 146|1.663553e-01|-1.423360e-07
- 147|1.663553e-01|-1.541112e-07
- 148|1.663553e-01|-1.491601e-07
- 149|1.663553e-01|-1.466407e-07
- 150|1.663552e-01|-1.801524e-07
- 151|1.663552e-01|-1.714107e-07
- 152|1.663552e-01|-1.491257e-07
- 153|1.663552e-01|-1.513799e-07
- 154|1.663551e-01|-1.354539e-07
- 155|1.663551e-01|-1.233818e-07
- 156|1.663551e-01|-1.576219e-07
- 157|1.663551e-01|-1.452791e-07
- 158|1.663550e-01|-1.262867e-07
- 159|1.663550e-01|-1.316379e-07
- It. |Loss |Delta loss
- --------------------------------
- 160|1.663550e-01|-1.295447e-07
- 161|1.663550e-01|-1.283286e-07
- 162|1.663550e-01|-1.569222e-07
- 163|1.663549e-01|-1.172942e-07
- 164|1.663549e-01|-1.399809e-07
- 165|1.663549e-01|-1.229432e-07
- 166|1.663549e-01|-1.326191e-07
- 167|1.663548e-01|-1.209694e-07
- 168|1.663548e-01|-1.372136e-07
- 169|1.663548e-01|-1.338395e-07
- 170|1.663548e-01|-1.416497e-07
- 171|1.663548e-01|-1.298576e-07
- 172|1.663547e-01|-1.190590e-07
- 173|1.663547e-01|-1.167083e-07
- 174|1.663547e-01|-1.069425e-07
- 175|1.663547e-01|-1.217780e-07
- 176|1.663547e-01|-1.140754e-07
- 177|1.663546e-01|-1.160707e-07
- 178|1.663546e-01|-1.101798e-07
- 179|1.663546e-01|-1.114904e-07
- It. |Loss |Delta loss
- --------------------------------
- 180|1.663546e-01|-1.064022e-07
- 181|1.663546e-01|-9.258231e-08
- 182|1.663546e-01|-1.213120e-07
- 183|1.663545e-01|-1.164296e-07
- 184|1.663545e-01|-1.188762e-07
- 185|1.663545e-01|-9.394153e-08
- 186|1.663545e-01|-1.028656e-07
- 187|1.663545e-01|-1.115348e-07
- 188|1.663544e-01|-9.768310e-08
- 189|1.663544e-01|-1.021806e-07
- 190|1.663544e-01|-1.086303e-07
- 191|1.663544e-01|-9.879008e-08
- 192|1.663544e-01|-1.050210e-07
- 193|1.663544e-01|-1.002463e-07
- 194|1.663543e-01|-1.062747e-07
- 195|1.663543e-01|-9.348538e-08
- 196|1.663543e-01|-7.992512e-08
- 197|1.663543e-01|-9.558020e-08
- 198|1.663543e-01|-9.993772e-08
- 199|1.663543e-01|-8.588499e-08
- It. |Loss |Delta loss
- --------------------------------
- 200|1.663543e-01|-8.737134e-08
-
-
-Solve EMD with entropic regularization
---------------------------------------
-
-
-
-.. code-block:: python
-
-
- #%% Example with entropic regularization
-
-
- def f(G):
- return np.sum(G * np.log(G))
-
-
- def df(G):
- return np.log(G) + 1.
-
-
- reg = 1e-3
-
- Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True)
-
- pl.figure(4, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_006.png
- :align: center
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Loss |Delta loss
- --------------------------------
- 0|1.692289e-01|0.000000e+00
- 1|1.617643e-01|-4.614437e-02
- 2|1.612639e-01|-3.102965e-03
- 3|1.611291e-01|-8.371098e-04
- 4|1.610468e-01|-5.110558e-04
- 5|1.610198e-01|-1.672927e-04
- 6|1.610130e-01|-4.232417e-05
- 7|1.610090e-01|-2.513455e-05
- 8|1.610002e-01|-5.443507e-05
- 9|1.609996e-01|-3.657071e-06
- 10|1.609948e-01|-2.998735e-05
- 11|1.609695e-01|-1.569217e-04
- 12|1.609533e-01|-1.010779e-04
- 13|1.609520e-01|-8.043897e-06
- 14|1.609465e-01|-3.415246e-05
- 15|1.609386e-01|-4.898605e-05
- 16|1.609324e-01|-3.837052e-05
- 17|1.609298e-01|-1.617826e-05
- 18|1.609184e-01|-7.080015e-05
- 19|1.609083e-01|-6.273206e-05
- It. |Loss |Delta loss
- --------------------------------
- 20|1.608988e-01|-5.940805e-05
- 21|1.608853e-01|-8.380030e-05
- 22|1.608844e-01|-5.185045e-06
- 23|1.608824e-01|-1.279113e-05
- 24|1.608819e-01|-3.156821e-06
- 25|1.608783e-01|-2.205746e-05
- 26|1.608764e-01|-1.189894e-05
- 27|1.608755e-01|-5.474607e-06
- 28|1.608737e-01|-1.144227e-05
- 29|1.608676e-01|-3.775335e-05
- 30|1.608638e-01|-2.348020e-05
- 31|1.608627e-01|-6.863136e-06
- 32|1.608529e-01|-6.110230e-05
- 33|1.608487e-01|-2.641106e-05
- 34|1.608409e-01|-4.823638e-05
- 35|1.608373e-01|-2.256641e-05
- 36|1.608338e-01|-2.132444e-05
- 37|1.608310e-01|-1.786649e-05
- 38|1.608260e-01|-3.103848e-05
- 39|1.608206e-01|-3.321265e-05
- It. |Loss |Delta loss
- --------------------------------
- 40|1.608201e-01|-3.054747e-06
- 41|1.608195e-01|-4.198335e-06
- 42|1.608193e-01|-8.458736e-07
- 43|1.608159e-01|-2.153759e-05
- 44|1.608115e-01|-2.738314e-05
- 45|1.608108e-01|-3.960032e-06
- 46|1.608081e-01|-1.675447e-05
- 47|1.608072e-01|-5.976340e-06
- 48|1.608046e-01|-1.604130e-05
- 49|1.608020e-01|-1.617036e-05
- 50|1.608014e-01|-3.957795e-06
- 51|1.608011e-01|-1.292411e-06
- 52|1.607998e-01|-8.431795e-06
- 53|1.607964e-01|-2.127054e-05
- 54|1.607947e-01|-1.021878e-05
- 55|1.607947e-01|-3.560621e-07
- 56|1.607900e-01|-2.929781e-05
- 57|1.607890e-01|-5.740229e-06
- 58|1.607858e-01|-2.039550e-05
- 59|1.607836e-01|-1.319545e-05
- It. |Loss |Delta loss
- --------------------------------
- 60|1.607826e-01|-6.378947e-06
- 61|1.607808e-01|-1.145102e-05
- 62|1.607776e-01|-1.941743e-05
- 63|1.607743e-01|-2.087422e-05
- 64|1.607741e-01|-1.310249e-06
- 65|1.607738e-01|-1.682752e-06
- 66|1.607691e-01|-2.913936e-05
- 67|1.607671e-01|-1.288855e-05
- 68|1.607654e-01|-1.002448e-05
- 69|1.607641e-01|-8.209492e-06
- 70|1.607632e-01|-5.588467e-06
- 71|1.607619e-01|-8.050388e-06
- 72|1.607618e-01|-9.417493e-07
- 73|1.607598e-01|-1.210509e-05
- 74|1.607591e-01|-4.392914e-06
- 75|1.607579e-01|-7.759587e-06
- 76|1.607574e-01|-2.760280e-06
- 77|1.607556e-01|-1.146469e-05
- 78|1.607550e-01|-3.689456e-06
- 79|1.607550e-01|-4.065631e-08
- It. |Loss |Delta loss
- --------------------------------
- 80|1.607539e-01|-6.555681e-06
- 81|1.607528e-01|-7.177470e-06
- 82|1.607527e-01|-5.306068e-07
- 83|1.607514e-01|-7.816045e-06
- 84|1.607511e-01|-2.301970e-06
- 85|1.607504e-01|-4.281072e-06
- 86|1.607503e-01|-7.821886e-07
- 87|1.607480e-01|-1.403013e-05
- 88|1.607480e-01|-1.169298e-08
- 89|1.607473e-01|-4.235982e-06
- 90|1.607470e-01|-1.717105e-06
- 91|1.607470e-01|-6.148402e-09
- 92|1.607462e-01|-5.396481e-06
- 93|1.607461e-01|-5.194954e-07
- 94|1.607450e-01|-6.525707e-06
- 95|1.607442e-01|-5.332060e-06
- 96|1.607439e-01|-1.682093e-06
- 97|1.607437e-01|-1.594796e-06
- 98|1.607435e-01|-7.923812e-07
- 99|1.607420e-01|-9.738552e-06
- It. |Loss |Delta loss
- --------------------------------
- 100|1.607419e-01|-1.022448e-07
- 101|1.607419e-01|-4.865999e-07
- 102|1.607418e-01|-7.092012e-07
- 103|1.607408e-01|-5.861815e-06
- 104|1.607402e-01|-3.953266e-06
- 105|1.607395e-01|-3.969572e-06
- 106|1.607390e-01|-3.612075e-06
- 107|1.607377e-01|-7.683735e-06
- 108|1.607365e-01|-7.777599e-06
- 109|1.607364e-01|-2.335096e-07
- 110|1.607364e-01|-4.562036e-07
- 111|1.607360e-01|-2.089538e-06
- 112|1.607356e-01|-2.755355e-06
- 113|1.607349e-01|-4.501960e-06
- 114|1.607347e-01|-1.160544e-06
- 115|1.607346e-01|-6.289450e-07
- 116|1.607345e-01|-2.092146e-07
- 117|1.607336e-01|-5.990866e-06
- 118|1.607330e-01|-3.348498e-06
- 119|1.607328e-01|-1.256222e-06
- It. |Loss |Delta loss
- --------------------------------
- 120|1.607320e-01|-5.418353e-06
- 121|1.607318e-01|-8.296189e-07
- 122|1.607311e-01|-4.381608e-06
- 123|1.607310e-01|-8.913901e-07
- 124|1.607309e-01|-3.808821e-07
- 125|1.607302e-01|-4.608994e-06
- 126|1.607294e-01|-5.063777e-06
- 127|1.607290e-01|-2.532835e-06
- 128|1.607285e-01|-2.870049e-06
- 129|1.607284e-01|-4.892812e-07
- 130|1.607281e-01|-1.760452e-06
- 131|1.607279e-01|-1.727139e-06
- 132|1.607275e-01|-2.220706e-06
- 133|1.607271e-01|-2.516930e-06
- 134|1.607269e-01|-1.201434e-06
- 135|1.607269e-01|-2.183459e-09
- 136|1.607262e-01|-4.223011e-06
- 137|1.607258e-01|-2.530202e-06
- 138|1.607258e-01|-1.857260e-07
- 139|1.607256e-01|-1.401957e-06
- It. |Loss |Delta loss
- --------------------------------
- 140|1.607250e-01|-3.242751e-06
- 141|1.607247e-01|-2.308071e-06
- 142|1.607247e-01|-4.730700e-08
- 143|1.607246e-01|-4.240229e-07
- 144|1.607242e-01|-2.484810e-06
- 145|1.607238e-01|-2.539206e-06
- 146|1.607234e-01|-2.535574e-06
- 147|1.607231e-01|-1.954802e-06
- 148|1.607228e-01|-1.765447e-06
- 149|1.607228e-01|-1.620007e-08
- 150|1.607222e-01|-3.615783e-06
- 151|1.607222e-01|-8.668516e-08
- 152|1.607215e-01|-4.000673e-06
- 153|1.607213e-01|-1.774103e-06
- 154|1.607213e-01|-6.328834e-09
- 155|1.607209e-01|-2.418783e-06
- 156|1.607208e-01|-2.848492e-07
- 157|1.607207e-01|-8.836043e-07
- 158|1.607205e-01|-1.192836e-06
- 159|1.607202e-01|-1.638022e-06
- It. |Loss |Delta loss
- --------------------------------
- 160|1.607202e-01|-3.670914e-08
- 161|1.607197e-01|-3.153709e-06
- 162|1.607197e-01|-2.419565e-09
- 163|1.607194e-01|-2.136882e-06
- 164|1.607194e-01|-1.173754e-09
- 165|1.607192e-01|-8.169238e-07
- 166|1.607191e-01|-9.218755e-07
- 167|1.607189e-01|-9.459255e-07
- 168|1.607187e-01|-1.294835e-06
- 169|1.607186e-01|-5.797668e-07
- 170|1.607186e-01|-4.706272e-08
- 171|1.607183e-01|-1.753383e-06
- 172|1.607183e-01|-1.681573e-07
- 173|1.607183e-01|-2.563971e-10
-
-
-Solve EMD with Frobenius norm + entropic regularization
--------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- #%% Example with Frobenius norm + entropic regularization with gcg
-
-
- def f(G):
- return 0.5 * np.sum(G**2)
-
-
- def df(G):
- return G
-
-
- reg1 = 1e-3
- reg2 = 1e-1
-
- Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)
-
- pl.figure(5, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_008.png
- :align: center
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Loss |Delta loss
- --------------------------------
- 0|1.693084e-01|0.000000e+00
- 1|1.610121e-01|-5.152589e-02
- 2|1.609378e-01|-4.622297e-04
- 3|1.609284e-01|-5.830043e-05
- 4|1.609284e-01|-1.111407e-12
-
-
-**Total running time of the script:** ( 0 minutes 1.990 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_optim_OTreg.py <plot_optim_OTreg.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_optim_OTreg.ipynb <plot_optim_OTreg.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_otda_classes.ipynb b/docs/source/auto_examples/plot_otda_classes.ipynb
deleted file mode 100644
index 643e760..0000000
--- a/docs/source/auto_examples/plot_otda_classes.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# OT for domain adaptation\n\n\nThis example introduces a domain adaptation in a 2D setting and the 4 OTDA\napproaches currently supported in POT.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization l1l2\not_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n verbose=True)\not_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\ntransp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 1 : plots source and target samples\n---------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "param_img = {'interpolation': 'nearest'}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 4, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 4, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 4, 3)\npl.imshow(ot_lpl1.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 4)\npl.imshow(ot_l1l2.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornL1l2Transport')\n\npl.subplot(2, 4, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 4, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornTransport')\n\npl.subplot(2, 4, 7)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 8)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornL1l2Transport')\npl.tight_layout()\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst
deleted file mode 100644
index 19756ff..0000000
--- a/docs/source/auto_examples/plot_otda_classes.rst
+++ /dev/null
@@ -1,263 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_otda_classes.py:
-
-
-========================
-OT for domain adaptation
-========================
-
-This example introduces a domain adaptation in a 2D setting and the 4 OTDA
-approaches currently supported in POT.
-
-
-
-
-.. code-block:: python
-
-
- # Authors: Remi Flamary <remi.flamary@unice.fr>
- # Stanislas Chambon <stan.chambon@gmail.com>
- #
- # License: MIT License
-
- import matplotlib.pylab as pl
- import ot
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- n_source_samples = 150
- n_target_samples = 150
-
- Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)
- Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)
-
-
-
-
-
-
-
-
-Instantiate the different transport algorithms and fit them
------------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- # EMD Transport
- ot_emd = ot.da.EMDTransport()
- ot_emd.fit(Xs=Xs, Xt=Xt)
-
- # Sinkhorn Transport
- ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
- ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-
- # Sinkhorn Transport with Group lasso regularization
- ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)
- ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)
-
- # Sinkhorn Transport with Group lasso regularization l1l2
- ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,
- verbose=True)
- ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)
-
- # transport source samples onto target samples
- transp_Xs_emd = ot_emd.transform(Xs=Xs)
- transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)
- transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)
- transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)
-
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Loss |Delta loss
- --------------------------------
- 0|9.566309e+00|0.000000e+00
- 1|2.169680e+00|-3.409088e+00
- 2|1.914989e+00|-1.329986e-01
- 3|1.860251e+00|-2.942498e-02
- 4|1.838073e+00|-1.206621e-02
- 5|1.827064e+00|-6.025122e-03
- 6|1.820899e+00|-3.386082e-03
- 7|1.817290e+00|-1.985705e-03
- 8|1.814644e+00|-1.458223e-03
- 9|1.812661e+00|-1.093816e-03
- 10|1.810239e+00|-1.338121e-03
- 11|1.809100e+00|-6.296940e-04
- 12|1.807939e+00|-6.420646e-04
- 13|1.806965e+00|-5.389118e-04
- 14|1.806822e+00|-7.889599e-05
- 15|1.806193e+00|-3.482356e-04
- 16|1.805735e+00|-2.536930e-04
- 17|1.805321e+00|-2.292667e-04
- 18|1.804389e+00|-5.170222e-04
- 19|1.803908e+00|-2.661907e-04
- It. |Loss |Delta loss
- --------------------------------
- 20|1.803696e+00|-1.178279e-04
-
-
-Fig 1 : plots source and target samples
----------------------------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(1, figsize=(10, 5))
- pl.subplot(1, 2, 1)
- pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
- pl.xticks([])
- pl.yticks([])
- pl.legend(loc=0)
- pl.title('Source samples')
-
- pl.subplot(1, 2, 2)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
- pl.xticks([])
- pl.yticks([])
- pl.legend(loc=0)
- pl.title('Target samples')
- pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_001.png
- :align: center
-
-
-
-
-Fig 2 : plot optimal couplings and transported samples
-------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- param_img = {'interpolation': 'nearest'}
-
- pl.figure(2, figsize=(15, 8))
- pl.subplot(2, 4, 1)
- pl.imshow(ot_emd.coupling_, **param_img)
- pl.xticks([])
- pl.yticks([])
- pl.title('Optimal coupling\nEMDTransport')
-
- pl.subplot(2, 4, 2)
- pl.imshow(ot_sinkhorn.coupling_, **param_img)
- pl.xticks([])
- pl.yticks([])
- pl.title('Optimal coupling\nSinkhornTransport')
-
- pl.subplot(2, 4, 3)
- pl.imshow(ot_lpl1.coupling_, **param_img)
- pl.xticks([])
- pl.yticks([])
- pl.title('Optimal coupling\nSinkhornLpl1Transport')
-
- pl.subplot(2, 4, 4)
- pl.imshow(ot_l1l2.coupling_, **param_img)
- pl.xticks([])
- pl.yticks([])
- pl.title('Optimal coupling\nSinkhornL1l2Transport')
-
- pl.subplot(2, 4, 5)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.3)
- pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
- pl.xticks([])
- pl.yticks([])
- pl.title('Transported samples\nEmdTransport')
- pl.legend(loc="lower left")
-
- pl.subplot(2, 4, 6)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.3)
- pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
- pl.xticks([])
- pl.yticks([])
- pl.title('Transported samples\nSinkhornTransport')
-
- pl.subplot(2, 4, 7)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.3)
- pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
- pl.xticks([])
- pl.yticks([])
- pl.title('Transported samples\nSinkhornLpl1Transport')
-
- pl.subplot(2, 4, 8)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.3)
- pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
- pl.xticks([])
- pl.yticks([])
- pl.title('Transported samples\nSinkhornL1l2Transport')
- pl.tight_layout()
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_003.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 1.423 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_otda_classes.py <plot_otda_classes.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_otda_classes.ipynb <plot_otda_classes.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_otda_color_images.ipynb b/docs/source/auto_examples/plot_otda_color_images.ipynb
deleted file mode 100644
index 103bdec..0000000
--- a/docs/source/auto_examples/plot_otda_color_images.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# OT for image color adaptation\n\n\nThis example presents a way of transferring colors between two images\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot original image\n-------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Scatter plot of colors\n----------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot new images\n---------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(3, figsize=(8, 4))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(2, 3, 2)\npl.imshow(I1t)\npl.axis('off')\npl.title('Image 1 Adapt')\n\npl.subplot(2, 3, 3)\npl.imshow(I1te)\npl.axis('off')\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\n\npl.subplot(2, 3, 5)\npl.imshow(I2t)\npl.axis('off')\npl.title('Image 2 Adapt')\n\npl.subplot(2, 3, 6)\npl.imshow(I2te)\npl.axis('off')\npl.title('Image 2 Adapt (reg)')\npl.tight_layout()\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst
deleted file mode 100644
index ab0406e..0000000
--- a/docs/source/auto_examples/plot_otda_color_images.rst
+++ /dev/null
@@ -1,262 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_otda_color_images.py:
-
-
-=============================
-OT for image color adaptation
-=============================
-
-This example presents a way of transferring colors between two images
-with Optimal Transport as introduced in [6]
-
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).
-Regularized discrete optimal transport.
-SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
-
-
-
-.. code-block:: python
-
-
- # Authors: Remi Flamary <remi.flamary@unice.fr>
- # Stanislas Chambon <stan.chambon@gmail.com>
- #
- # License: MIT License
-
- import numpy as np
- from scipy import ndimage
- import matplotlib.pylab as pl
- import ot
-
-
- r = np.random.RandomState(42)
-
-
- def im2mat(I):
- """Converts an image to matrix (one pixel per line)"""
- return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
- def mat2im(X, shape):
- """Converts back a matrix to an image"""
- return X.reshape(shape)
-
-
- def minmax(I):
- return np.clip(I, 0, 1)
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- # Loading images
- I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
- I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
-
- X1 = im2mat(I1)
- X2 = im2mat(I2)
-
- # training samples
- nb = 1000
- idx1 = r.randint(X1.shape[0], size=(nb,))
- idx2 = r.randint(X2.shape[0], size=(nb,))
-
- Xs = X1[idx1, :]
- Xt = X2[idx2, :]
-
-
-
-
-
-
-
-
-Plot original image
--------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(1, figsize=(6.4, 3))
-
- pl.subplot(1, 2, 1)
- pl.imshow(I1)
- pl.axis('off')
- pl.title('Image 1')
-
- pl.subplot(1, 2, 2)
- pl.imshow(I2)
- pl.axis('off')
- pl.title('Image 2')
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_001.png
- :align: center
-
-
-
-
-Scatter plot of colors
-----------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(2, figsize=(6.4, 3))
-
- pl.subplot(1, 2, 1)
- pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)
- pl.axis([0, 1, 0, 1])
- pl.xlabel('Red')
- pl.ylabel('Blue')
- pl.title('Image 1')
-
- pl.subplot(1, 2, 2)
- pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)
- pl.axis([0, 1, 0, 1])
- pl.xlabel('Red')
- pl.ylabel('Blue')
- pl.title('Image 2')
- pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_003.png
- :align: center
-
-
-
-
-Instantiate the different transport algorithms and fit them
------------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- # EMDTransport
- ot_emd = ot.da.EMDTransport()
- ot_emd.fit(Xs=Xs, Xt=Xt)
-
- # SinkhornTransport
- ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
- ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-
- # prediction between images (using out of sample prediction as in [6])
- transp_Xs_emd = ot_emd.transform(Xs=X1)
- transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)
-
- transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)
- transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)
-
- I1t = minmax(mat2im(transp_Xs_emd, I1.shape))
- I2t = minmax(mat2im(transp_Xt_emd, I2.shape))
-
- I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
- I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))
-
-
-
-
-
-
-
-
-Plot new images
----------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(3, figsize=(8, 4))
-
- pl.subplot(2, 3, 1)
- pl.imshow(I1)
- pl.axis('off')
- pl.title('Image 1')
-
- pl.subplot(2, 3, 2)
- pl.imshow(I1t)
- pl.axis('off')
- pl.title('Image 1 Adapt')
-
- pl.subplot(2, 3, 3)
- pl.imshow(I1te)
- pl.axis('off')
- pl.title('Image 1 Adapt (reg)')
-
- pl.subplot(2, 3, 4)
- pl.imshow(I2)
- pl.axis('off')
- pl.title('Image 2')
-
- pl.subplot(2, 3, 5)
- pl.imshow(I2t)
- pl.axis('off')
- pl.title('Image 2 Adapt')
-
- pl.subplot(2, 3, 6)
- pl.imshow(I2te)
- pl.axis('off')
- pl.title('Image 2 Adapt (reg)')
- pl.tight_layout()
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_005.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 3 minutes 55.541 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_otda_color_images.py <plot_otda_color_images.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_otda_color_images.ipynb <plot_otda_color_images.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb
deleted file mode 100644
index b9002ee..0000000
--- a/docs/source/auto_examples/plot_otda_d2.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# OT for domain adaptation on empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(M, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Matrix of pairwise distances')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(2, figsize=(10, 6))\n\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_lpl1.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 3, 4)\not.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nEMDTransport')\n\npl.subplot(2, 3, 5)\not.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\not.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 3 : plot transported samples\n--------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# display transported samples\npl.figure(4, figsize=(10, 4))\npl.subplot(1, 3, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornLpl1Transport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst
deleted file mode 100644
index 80cc34c..0000000
--- a/docs/source/auto_examples/plot_otda_d2.rst
+++ /dev/null
@@ -1,269 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_otda_d2.py:
-
-
-===================================================
-OT for domain adaptation on empirical distributions
-===================================================
-
-This example introduces a domain adaptation in a 2D setting. It explicits
-the problem of domain adaptation and introduces some optimal transport
-approaches to solve it.
-
-Quantities such as optimal couplings, greater coupling coefficients and
-transported samples are represented in order to give a visual understanding
-of what the transport methods are doing.
-
-
-
-.. code-block:: python
-
-
- # Authors: Remi Flamary <remi.flamary@unice.fr>
- # Stanislas Chambon <stan.chambon@gmail.com>
- #
- # License: MIT License
-
- import matplotlib.pylab as pl
- import ot
- import ot.plot
-
-
-
-
-
-
-
-generate data
--------------
-
-
-
-.. code-block:: python
-
-
- n_samples_source = 150
- n_samples_target = 150
-
- Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)
- Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)
-
- # Cost matrix
- M = ot.dist(Xs, Xt, metric='sqeuclidean')
-
-
-
-
-
-
-
-
-Instantiate the different transport algorithms and fit them
------------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- # EMD Transport
- ot_emd = ot.da.EMDTransport()
- ot_emd.fit(Xs=Xs, Xt=Xt)
-
- # Sinkhorn Transport
- ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
- ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-
- # Sinkhorn Transport with Group lasso regularization
- ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)
- ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)
-
- # transport source samples onto target samples
- transp_Xs_emd = ot_emd.transform(Xs=Xs)
- transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)
- transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)
-
-
-
-
-
-
-
-
-Fig 1 : plots source and target samples + matrix of pairwise distance
----------------------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(1, figsize=(10, 10))
- pl.subplot(2, 2, 1)
- pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
- pl.xticks([])
- pl.yticks([])
- pl.legend(loc=0)
- pl.title('Source samples')
-
- pl.subplot(2, 2, 2)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
- pl.xticks([])
- pl.yticks([])
- pl.legend(loc=0)
- pl.title('Target samples')
-
- pl.subplot(2, 2, 3)
- pl.imshow(M, interpolation='nearest')
- pl.xticks([])
- pl.yticks([])
- pl.title('Matrix of pairwise distances')
- pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_001.png
- :align: center
-
-
-
-
-Fig 2 : plots optimal couplings for the different methods
----------------------------------------------------------
-
-
-
-.. code-block:: python
-
- pl.figure(2, figsize=(10, 6))
-
- pl.subplot(2, 3, 1)
- pl.imshow(ot_emd.coupling_, interpolation='nearest')
- pl.xticks([])
- pl.yticks([])
- pl.title('Optimal coupling\nEMDTransport')
-
- pl.subplot(2, 3, 2)
- pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')
- pl.xticks([])
- pl.yticks([])
- pl.title('Optimal coupling\nSinkhornTransport')
-
- pl.subplot(2, 3, 3)
- pl.imshow(ot_lpl1.coupling_, interpolation='nearest')
- pl.xticks([])
- pl.yticks([])
- pl.title('Optimal coupling\nSinkhornLpl1Transport')
-
- pl.subplot(2, 3, 4)
- ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])
- pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
- pl.xticks([])
- pl.yticks([])
- pl.title('Main coupling coefficients\nEMDTransport')
-
- pl.subplot(2, 3, 5)
- ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])
- pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
- pl.xticks([])
- pl.yticks([])
- pl.title('Main coupling coefficients\nSinkhornTransport')
-
- pl.subplot(2, 3, 6)
- ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])
- pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
- pl.xticks([])
- pl.yticks([])
- pl.title('Main coupling coefficients\nSinkhornLpl1Transport')
- pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_003.png
- :align: center
-
-
-
-
-Fig 3 : plot transported samples
---------------------------------
-
-
-
-.. code-block:: python
-
-
- # display transported samples
- pl.figure(4, figsize=(10, 4))
- pl.subplot(1, 3, 1)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
- pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
- pl.title('Transported samples\nEmdTransport')
- pl.legend(loc=0)
- pl.xticks([])
- pl.yticks([])
-
- pl.subplot(1, 3, 2)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
- pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
- pl.title('Transported samples\nSinkhornTransport')
- pl.xticks([])
- pl.yticks([])
-
- pl.subplot(1, 3, 3)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
- pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
- pl.title('Transported samples\nSinkhornLpl1Transport')
- pl.xticks([])
- pl.yticks([])
-
- pl.tight_layout()
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_006.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 35.515 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_otda_d2.py <plot_otda_d2.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_otda_d2.ipynb <plot_otda_d2.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb
deleted file mode 100644
index 027b6cb..0000000
--- a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb
+++ /dev/null
@@ -1,180 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# Linear OT mapping estimation\n\n\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport pylab as pl\nimport ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n = 1000\nd = 2\nsigma = .1\n\n# source samples\nangles = np.random.rand(n, 1) * 2 * np.pi\nxs = np.concatenate((np.sin(angles), np.cos(angles)),\n axis=1) + sigma * np.random.randn(n, 2)\nxs[:n // 2, 1] += 2\n\n\n# target samples\nanglet = np.random.rand(n, 1) * 2 * np.pi\nxt = np.concatenate((np.sin(anglet), np.cos(anglet)),\n axis=1) + sigma * np.random.randn(n, 2)\nxt[:n // 2, 1] += 2\n\n\nA = np.array([[1.5, .7], [.7, 1.5]])\nb = np.array([[4, 2]])\nxt = xt.dot(A) + b"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(1, (5, 5))\npl.plot(xs[:, 0], xs[:, 1], '+')\npl.plot(xt[:, 0], xt[:, 1], 'o')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Estimate linear mapping and transport\n-------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "Ae, be = ot.da.OT_mapping_linear(xs, xt)\n\nxst = xs.dot(Ae) + be"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot transported samples\n------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(1, (5, 5))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+')\npl.plot(xt[:, 0], xt[:, 1], 'o')\npl.plot(xst[:, 0], xst[:, 1], '+')\n\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load image data\n---------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)\n\n\n# Loading images\nI1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Estimate mapping and adapt\n----------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "mapping = ot.da.LinearTransport()\n\nmapping.fit(Xs=X1, Xt=X2)\n\n\nxst = mapping.transform(Xs=X1)\nxts = mapping.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(xst, I1.shape))\nI2t = minmax(mat2im(xts, I2.shape))\n\n# %%"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot transformed images\n-----------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(2, figsize=(10, 7))\n\npl.subplot(2, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 2, 3)\npl.imshow(I1t)\npl.axis('off')\npl.title('Mapping Im. 1')\n\npl.subplot(2, 2, 4)\npl.imshow(I2t)\npl.axis('off')\npl.title('Inverse mapping Im. 2')"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.rst b/docs/source/auto_examples/plot_otda_linear_mapping.rst
deleted file mode 100644
index 8e2e0cf..0000000
--- a/docs/source/auto_examples/plot_otda_linear_mapping.rst
+++ /dev/null
@@ -1,260 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_otda_linear_mapping.py:
-
-
-============================
-Linear OT mapping estimation
-============================
-
-
-
-
-
-.. code-block:: python
-
-
- # Author: Remi Flamary <remi.flamary@unice.fr>
- #
- # License: MIT License
-
- import numpy as np
- import pylab as pl
- import ot
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- n = 1000
- d = 2
- sigma = .1
-
- # source samples
- angles = np.random.rand(n, 1) * 2 * np.pi
- xs = np.concatenate((np.sin(angles), np.cos(angles)),
- axis=1) + sigma * np.random.randn(n, 2)
- xs[:n // 2, 1] += 2
-
-
- # target samples
- anglet = np.random.rand(n, 1) * 2 * np.pi
- xt = np.concatenate((np.sin(anglet), np.cos(anglet)),
- axis=1) + sigma * np.random.randn(n, 2)
- xt[:n // 2, 1] += 2
-
-
- A = np.array([[1.5, .7], [.7, 1.5]])
- b = np.array([[4, 2]])
- xt = xt.dot(A) + b
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- pl.figure(1, (5, 5))
- pl.plot(xs[:, 0], xs[:, 1], '+')
- pl.plot(xt[:, 0], xt[:, 1], 'o')
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png
- :align: center
-
-
-
-
-Estimate linear mapping and transport
--------------------------------------
-
-
-
-.. code-block:: python
-
-
- Ae, be = ot.da.OT_mapping_linear(xs, xt)
-
- xst = xs.dot(Ae) + be
-
-
-
-
-
-
-
-
-Plot transported samples
-------------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(1, (5, 5))
- pl.clf()
- pl.plot(xs[:, 0], xs[:, 1], '+')
- pl.plot(xt[:, 0], xt[:, 1], 'o')
- pl.plot(xst[:, 0], xst[:, 1], '+')
-
- pl.show()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png
- :align: center
-
-
-
-
-Load image data
----------------
-
-
-
-.. code-block:: python
-
-
-
- def im2mat(I):
- """Converts and image to matrix (one pixel per line)"""
- return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
- def mat2im(X, shape):
- """Converts back a matrix to an image"""
- return X.reshape(shape)
-
-
- def minmax(I):
- return np.clip(I, 0, 1)
-
-
- # Loading images
- I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256
- I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-
- X1 = im2mat(I1)
- X2 = im2mat(I2)
-
-
-
-
-
-
-
-Estimate mapping and adapt
-----------------------------
-
-
-
-.. code-block:: python
-
-
- mapping = ot.da.LinearTransport()
-
- mapping.fit(Xs=X1, Xt=X2)
-
-
- xst = mapping.transform(Xs=X1)
- xts = mapping.inverse_transform(Xt=X2)
-
- I1t = minmax(mat2im(xst, I1.shape))
- I2t = minmax(mat2im(xts, I2.shape))
-
- # %%
-
-
-
-
-
-
-
-
-Plot transformed images
------------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(2, figsize=(10, 7))
-
- pl.subplot(2, 2, 1)
- pl.imshow(I1)
- pl.axis('off')
- pl.title('Im. 1')
-
- pl.subplot(2, 2, 2)
- pl.imshow(I2)
- pl.axis('off')
- pl.title('Im. 2')
-
- pl.subplot(2, 2, 3)
- pl.imshow(I1t)
- pl.axis('off')
- pl.title('Mapping Im. 1')
-
- pl.subplot(2, 2, 4)
- pl.imshow(I2t)
- pl.axis('off')
- pl.title('Inverse mapping Im. 2')
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_004.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 0.635 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_otda_linear_mapping.py <plot_otda_linear_mapping.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_otda_linear_mapping.ipynb <plot_otda_linear_mapping.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb
deleted file mode 100644
index 898466d..0000000
--- a/docs/source/auto_examples/plot_otda_mapping.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# OT mapping estimation for domain adaptation\n\n\nThis example presents how to use MappingTransport to estimate at the same\ntime both the coupling transport and approximate the transport map with either\na linear or a kernelized mapping as introduced in [8].\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n \"Mapping estimation for discrete optimal transport\",\n Neural Information Processing Systems (NIPS), 2016.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source_samples = 100\nn_target_samples = 100\ntheta = 2 * np.pi / 20\nnoise_level = 0.1\n\nXs, ys = ot.datasets.make_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXs_new, _ = ot.datasets.make_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXt, yt = ot.datasets.make_data_classif(\n 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n\n# one of the target mode changes its variance (no linear mapping)\nXt[yt == 2] *= 3\nXt = Xt + 4"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n---------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot transported samples\n------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(2)\npl.clf()\npl.subplot(2, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n label='Mapped source samples')\npl.title(\"Bary. mapping (linear)\")\npl.legend(loc=0)\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n c=ys, marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (linear)\")\n\npl.subplot(2, 2, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n marker='+', label='barycentric mapping')\npl.title(\"Bary. mapping (kernel)\")\n\npl.subplot(2, 2, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (kernel)\")\npl.tight_layout()\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst
deleted file mode 100644
index 1d95fc6..0000000
--- a/docs/source/auto_examples/plot_otda_mapping.rst
+++ /dev/null
@@ -1,235 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_otda_mapping.py:
-
-
-===========================================
-OT mapping estimation for domain adaptation
-===========================================
-
-This example presents how to use MappingTransport to estimate at the same
-time both the coupling transport and approximate the transport map with either
-a linear or a kernelized mapping as introduced in [8].
-
-[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
- "Mapping estimation for discrete optimal transport",
- Neural Information Processing Systems (NIPS), 2016.
-
-
-
-.. code-block:: python
-
-
- # Authors: Remi Flamary <remi.flamary@unice.fr>
- # Stanislas Chambon <stan.chambon@gmail.com>
- #
- # License: MIT License
-
- import numpy as np
- import matplotlib.pylab as pl
- import ot
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- n_source_samples = 100
- n_target_samples = 100
- theta = 2 * np.pi / 20
- noise_level = 0.1
-
- Xs, ys = ot.datasets.make_data_classif(
- 'gaussrot', n_source_samples, nz=noise_level)
- Xs_new, _ = ot.datasets.make_data_classif(
- 'gaussrot', n_source_samples, nz=noise_level)
- Xt, yt = ot.datasets.make_data_classif(
- 'gaussrot', n_target_samples, theta=theta, nz=noise_level)
-
- # one of the target mode changes its variance (no linear mapping)
- Xt[yt == 2] *= 3
- Xt = Xt + 4
-
-
-
-
-
-
-
-Plot data
----------
-
-
-
-.. code-block:: python
-
-
- pl.figure(1, (10, 5))
- pl.clf()
- pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
- pl.legend(loc=0)
- pl.title('Source and target distributions')
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_001.png
- :align: center
-
-
-
-
-Instantiate the different transport algorithms and fit them
------------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- # MappingTransport with linear kernel
- ot_mapping_linear = ot.da.MappingTransport(
- kernel="linear", mu=1e0, eta=1e-8, bias=True,
- max_iter=20, verbose=True)
-
- ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
-
- # for original source samples, transform applies barycentric mapping
- transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)
-
- # for out of source samples, transform applies the linear mapping
- transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)
-
-
- # MappingTransport with gaussian kernel
- ot_mapping_gaussian = ot.da.MappingTransport(
- kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1,
- max_iter=10, verbose=True)
- ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
-
- # for original source samples, transform applies barycentric mapping
- transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)
-
- # for out of source samples, transform applies the gaussian mapping
- transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)
-
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Loss |Delta loss
- --------------------------------
- 0|4.299275e+03|0.000000e+00
- 1|4.290443e+03|-2.054271e-03
- 2|4.290040e+03|-9.389994e-05
- 3|4.289876e+03|-3.830707e-05
- 4|4.289783e+03|-2.157428e-05
- 5|4.289724e+03|-1.390941e-05
- 6|4.289706e+03|-4.051054e-06
- It. |Loss |Delta loss
- --------------------------------
- 0|4.326465e+02|0.000000e+00
- 1|4.282533e+02|-1.015416e-02
- 2|4.279473e+02|-7.145955e-04
- 3|4.277941e+02|-3.580104e-04
- 4|4.277069e+02|-2.039229e-04
- 5|4.276462e+02|-1.418698e-04
- 6|4.276011e+02|-1.054172e-04
- 7|4.275663e+02|-8.145802e-05
- 8|4.275405e+02|-6.028774e-05
- 9|4.275191e+02|-5.005886e-05
- 10|4.275019e+02|-4.021935e-05
-
-
-Plot transported samples
-------------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(2)
- pl.clf()
- pl.subplot(2, 2, 1)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=.2)
- pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',
- label='Mapped source samples')
- pl.title("Bary. mapping (linear)")
- pl.legend(loc=0)
-
- pl.subplot(2, 2, 2)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=.2)
- pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],
- c=ys, marker='+', label='Learned mapping')
- pl.title("Estim. mapping (linear)")
-
- pl.subplot(2, 2, 3)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=.2)
- pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,
- marker='+', label='barycentric mapping')
- pl.title("Bary. mapping (kernel)")
-
- pl.subplot(2, 2, 4)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=.2)
- pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,
- marker='+', label='Learned mapping')
- pl.title("Estim. mapping (kernel)")
- pl.tight_layout()
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_003.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 0.795 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_otda_mapping.py <plot_otda_mapping.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_otda_mapping.ipynb <plot_otda_mapping.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb
deleted file mode 100644
index baffef4..0000000
--- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# OT for image color adaptation with mapping estimation\n\n\nOT for domain adaptation with image color adaptation [6] with mapping\nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Domain adaptation for pixel distribution transfer\n-------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\nImage_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\nImage_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n\not_mapping_linear = ot.da.MappingTransport(\n mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\nX1tl = ot_mapping_linear.transform(Xs=X1)\nImage_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n\not_mapping_gaussian = ot.da.MappingTransport(\n mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\nX1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\nImage_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot original images\n--------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(1, figsize=(6.4, 3))\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot pixel values distribution\n------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(2, figsize=(6.4, 5))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot transformed images\n-----------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(2, figsize=(10, 5))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 3, 2)\npl.imshow(Image_emd)\npl.axis('off')\npl.title('EmdTransport')\n\npl.subplot(2, 3, 5)\npl.imshow(Image_sinkhorn)\npl.axis('off')\npl.title('SinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(Image_mapping_linear)\npl.axis('off')\npl.title('MappingTransport (linear)')\n\npl.subplot(2, 3, 6)\npl.imshow(Image_mapping_gaussian)\npl.axis('off')\npl.title('MappingTransport (gaussian)')\npl.tight_layout()\n\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py
deleted file mode 100644
index a20eca8..0000000
--- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-=====================================================
-OT for image color adaptation with mapping estimation
-=====================================================
-
-OT for domain adaptation with image color adaptation [6] with mapping
-estimation [8].
-
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized
- discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),
- 1853-1882.
-[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
- discrete optimal transport", Neural Information Processing Systems (NIPS),
- 2016.
-
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-# Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import numpy as np
-from scipy import ndimage
-import matplotlib.pylab as pl
-import ot
-
-r = np.random.RandomState(42)
-
-
-def im2mat(I):
- """Converts and image to matrix (one pixel per line)"""
- return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
-def mat2im(X, shape):
- """Converts back a matrix to an image"""
- return X.reshape(shape)
-
-
-def minmax(I):
- return np.clip(I, 0, 1)
-
-
-##############################################################################
-# Generate data
-# -------------
-
-# Loading images
-I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-
-X1 = im2mat(I1)
-X2 = im2mat(I2)
-
-# training samples
-nb = 1000
-idx1 = r.randint(X1.shape[0], size=(nb,))
-idx2 = r.randint(X2.shape[0], size=(nb,))
-
-Xs = X1[idx1, :]
-Xt = X2[idx2, :]
-
-
-##############################################################################
-# Domain adaptation for pixel distribution transfer
-# -------------------------------------------------
-
-# EMDTransport
-ot_emd = ot.da.EMDTransport()
-ot_emd.fit(Xs=Xs, Xt=Xt)
-transp_Xs_emd = ot_emd.transform(Xs=X1)
-Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape))
-
-# SinkhornTransport
-ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)
-Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
-
-ot_mapping_linear = ot.da.MappingTransport(
- mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)
-ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
-
-X1tl = ot_mapping_linear.transform(Xs=X1)
-Image_mapping_linear = minmax(mat2im(X1tl, I1.shape))
-
-ot_mapping_gaussian = ot.da.MappingTransport(
- mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)
-ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
-
-X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping
-Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))
-
-
-##############################################################################
-# Plot original images
-# --------------------
-
-pl.figure(1, figsize=(6.4, 3))
-pl.subplot(1, 2, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Image 1')
-
-pl.subplot(1, 2, 2)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Image 2')
-pl.tight_layout()
-
-
-##############################################################################
-# Plot pixel values distribution
-# ------------------------------
-
-pl.figure(2, figsize=(6.4, 5))
-
-pl.subplot(1, 2, 1)
-pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)
-pl.axis([0, 1, 0, 1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 1')
-
-pl.subplot(1, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)
-pl.axis([0, 1, 0, 1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 2')
-pl.tight_layout()
-
-
-##############################################################################
-# Plot transformed images
-# -----------------------
-
-pl.figure(2, figsize=(10, 5))
-
-pl.subplot(2, 3, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Im. 1')
-
-pl.subplot(2, 3, 4)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Im. 2')
-
-pl.subplot(2, 3, 2)
-pl.imshow(Image_emd)
-pl.axis('off')
-pl.title('EmdTransport')
-
-pl.subplot(2, 3, 5)
-pl.imshow(Image_sinkhorn)
-pl.axis('off')
-pl.title('SinkhornTransport')
-
-pl.subplot(2, 3, 3)
-pl.imshow(Image_mapping_linear)
-pl.axis('off')
-pl.title('MappingTransport (linear)')
-
-pl.subplot(2, 3, 6)
-pl.imshow(Image_mapping_gaussian)
-pl.axis('off')
-pl.title('MappingTransport (gaussian)')
-pl.tight_layout()
-
-pl.show()
diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst
deleted file mode 100644
index 2afdc8a..0000000
--- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst
+++ /dev/null
@@ -1,310 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_otda_mapping_colors_images.py:
-
-
-=====================================================
-OT for image color adaptation with mapping estimation
-=====================================================
-
-OT for domain adaptation with image color adaptation [6] with mapping
-estimation [8].
-
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized
- discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),
- 1853-1882.
-[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
- discrete optimal transport", Neural Information Processing Systems (NIPS),
- 2016.
-
-
-
-
-.. code-block:: python
-
-
- # Authors: Remi Flamary <remi.flamary@unice.fr>
- # Stanislas Chambon <stan.chambon@gmail.com>
- #
- # License: MIT License
-
- import numpy as np
- from scipy import ndimage
- import matplotlib.pylab as pl
- import ot
-
- r = np.random.RandomState(42)
-
-
- def im2mat(I):
- """Converts and image to matrix (one pixel per line)"""
- return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
- def mat2im(X, shape):
- """Converts back a matrix to an image"""
- return X.reshape(shape)
-
-
- def minmax(I):
- return np.clip(I, 0, 1)
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- # Loading images
- I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
- I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-
- X1 = im2mat(I1)
- X2 = im2mat(I2)
-
- # training samples
- nb = 1000
- idx1 = r.randint(X1.shape[0], size=(nb,))
- idx2 = r.randint(X2.shape[0], size=(nb,))
-
- Xs = X1[idx1, :]
- Xt = X2[idx2, :]
-
-
-
-
-
-
-
-
-Domain adaptation for pixel distribution transfer
--------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- # EMDTransport
- ot_emd = ot.da.EMDTransport()
- ot_emd.fit(Xs=Xs, Xt=Xt)
- transp_Xs_emd = ot_emd.transform(Xs=X1)
- Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape))
-
- # SinkhornTransport
- ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
- ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
- transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)
- Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
-
- ot_mapping_linear = ot.da.MappingTransport(
- mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)
- ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
-
- X1tl = ot_mapping_linear.transform(Xs=X1)
- Image_mapping_linear = minmax(mat2im(X1tl, I1.shape))
-
- ot_mapping_gaussian = ot.da.MappingTransport(
- mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)
- ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
-
- X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping
- Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))
-
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- It. |Loss |Delta loss
- --------------------------------
- 0|3.680534e+02|0.000000e+00
- 1|3.592501e+02|-2.391854e-02
- 2|3.590682e+02|-5.061555e-04
- 3|3.589745e+02|-2.610227e-04
- 4|3.589167e+02|-1.611644e-04
- 5|3.588768e+02|-1.109242e-04
- 6|3.588482e+02|-7.972733e-05
- 7|3.588261e+02|-6.166174e-05
- 8|3.588086e+02|-4.871697e-05
- 9|3.587946e+02|-3.919056e-05
- 10|3.587830e+02|-3.228124e-05
- 11|3.587731e+02|-2.744744e-05
- 12|3.587648e+02|-2.334451e-05
- 13|3.587576e+02|-1.995629e-05
- 14|3.587513e+02|-1.761058e-05
- 15|3.587457e+02|-1.542568e-05
- 16|3.587408e+02|-1.366315e-05
- 17|3.587365e+02|-1.221732e-05
- 18|3.587325e+02|-1.102488e-05
- 19|3.587303e+02|-6.062107e-06
- It. |Loss |Delta loss
- --------------------------------
- 0|3.784871e+02|0.000000e+00
- 1|3.646491e+02|-3.656142e-02
- 2|3.642975e+02|-9.642655e-04
- 3|3.641626e+02|-3.702413e-04
- 4|3.640888e+02|-2.026301e-04
- 5|3.640419e+02|-1.289607e-04
- 6|3.640097e+02|-8.831646e-05
- 7|3.639861e+02|-6.487612e-05
- 8|3.639679e+02|-4.994063e-05
- 9|3.639536e+02|-3.941436e-05
- 10|3.639419e+02|-3.209753e-05
-
-
-Plot original images
---------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(1, figsize=(6.4, 3))
- pl.subplot(1, 2, 1)
- pl.imshow(I1)
- pl.axis('off')
- pl.title('Image 1')
-
- pl.subplot(1, 2, 2)
- pl.imshow(I2)
- pl.axis('off')
- pl.title('Image 2')
- pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_001.png
- :align: center
-
-
-
-
-Plot pixel values distribution
-------------------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(2, figsize=(6.4, 5))
-
- pl.subplot(1, 2, 1)
- pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)
- pl.axis([0, 1, 0, 1])
- pl.xlabel('Red')
- pl.ylabel('Blue')
- pl.title('Image 1')
-
- pl.subplot(1, 2, 2)
- pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)
- pl.axis([0, 1, 0, 1])
- pl.xlabel('Red')
- pl.ylabel('Blue')
- pl.title('Image 2')
- pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_003.png
- :align: center
-
-
-
-
-Plot transformed images
------------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(2, figsize=(10, 5))
-
- pl.subplot(2, 3, 1)
- pl.imshow(I1)
- pl.axis('off')
- pl.title('Im. 1')
-
- pl.subplot(2, 3, 4)
- pl.imshow(I2)
- pl.axis('off')
- pl.title('Im. 2')
-
- pl.subplot(2, 3, 2)
- pl.imshow(Image_emd)
- pl.axis('off')
- pl.title('EmdTransport')
-
- pl.subplot(2, 3, 5)
- pl.imshow(Image_sinkhorn)
- pl.axis('off')
- pl.title('SinkhornTransport')
-
- pl.subplot(2, 3, 3)
- pl.imshow(Image_mapping_linear)
- pl.axis('off')
- pl.title('MappingTransport (linear)')
-
- pl.subplot(2, 3, 6)
- pl.imshow(Image_mapping_gaussian)
- pl.axis('off')
- pl.title('MappingTransport (gaussian)')
- pl.tight_layout()
-
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_004.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 3 minutes 14.206 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_otda_mapping_colors_images.py <plot_otda_mapping_colors_images.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_otda_mapping_colors_images.ipynb <plot_otda_mapping_colors_images.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb
deleted file mode 100644
index e3192da..0000000
--- a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# OTDA unsupervised vs semi-supervised setting\n\n\nThis example introduces a semi supervised domain adaptation in a 2D setting.\nIt explicits the problem of semi supervised domain adaptation and introduces\nsome optimal transport approaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n-------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Transport source samples onto target samples\n--------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# unsupervised domain adaptation\not_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n\n# semi-supervised domain adaptation\not_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\ntransp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n\n# semi supervised DA uses available labaled target samples to modify the cost\n# matrix involved in the OT problem. The cost of transporting a source sample\n# of class A onto a target sample of class B != A is set to infinite, or a\n# very large value\n\n# note that in the present case we consider that all the target samples are\n# labeled. For daily applications, some target sample might not have labels,\n# in this case the element of yt corresponding to these samples should be\n# filled with -1.\n\n# Warning: we recall that -1 cannot be used as a class label"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - unsupervised DA')\n\npl.subplot(2, 2, 4)\npl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - semisupervised DA')\n\npl.tight_layout()\n\n# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n# similar\" to the cost matrix, (block diagonal matrix)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(2, figsize=(8, 4))\n\npl.subplot(1, 2, 1)\npl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nUnsupervised DA')\n\npl.subplot(1, 2, 2)\npl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSemi-supervised DA')\n\npl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 3 : plot transported samples\n--------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# display transported samples\npl.figure(4, figsize=(8, 4))\npl.subplot(1, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.rst b/docs/source/auto_examples/plot_otda_semi_supervised.rst
deleted file mode 100644
index 2ed7819..0000000
--- a/docs/source/auto_examples/plot_otda_semi_supervised.rst
+++ /dev/null
@@ -1,245 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_otda_semi_supervised.py:
-
-
-============================================
-OTDA unsupervised vs semi-supervised setting
-============================================
-
-This example introduces a semi supervised domain adaptation in a 2D setting.
-It explicits the problem of semi supervised domain adaptation and introduces
-some optimal transport approaches to solve it.
-
-Quantities such as optimal couplings, greater coupling coefficients and
-transported samples are represented in order to give a visual understanding
-of what the transport methods are doing.
-
-
-
-.. code-block:: python
-
-
- # Authors: Remi Flamary <remi.flamary@unice.fr>
- # Stanislas Chambon <stan.chambon@gmail.com>
- #
- # License: MIT License
-
- import matplotlib.pylab as pl
- import ot
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-
-.. code-block:: python
-
-
- n_samples_source = 150
- n_samples_target = 150
-
- Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)
- Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)
-
-
-
-
-
-
-
-
-Transport source samples onto target samples
---------------------------------------------
-
-
-
-.. code-block:: python
-
-
-
- # unsupervised domain adaptation
- ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)
- ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt)
- transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)
-
- # semi-supervised domain adaptation
- ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)
- ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)
- transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)
-
- # semi supervised DA uses available labaled target samples to modify the cost
- # matrix involved in the OT problem. The cost of transporting a source sample
- # of class A onto a target sample of class B != A is set to infinite, or a
- # very large value
-
- # note that in the present case we consider that all the target samples are
- # labeled. For daily applications, some target sample might not have labels,
- # in this case the element of yt corresponding to these samples should be
- # filled with -1.
-
- # Warning: we recall that -1 cannot be used as a class label
-
-
-
-
-
-
-
-
-Fig 1 : plots source and target samples + matrix of pairwise distance
----------------------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(1, figsize=(10, 10))
- pl.subplot(2, 2, 1)
- pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
- pl.xticks([])
- pl.yticks([])
- pl.legend(loc=0)
- pl.title('Source samples')
-
- pl.subplot(2, 2, 2)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
- pl.xticks([])
- pl.yticks([])
- pl.legend(loc=0)
- pl.title('Target samples')
-
- pl.subplot(2, 2, 3)
- pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')
- pl.xticks([])
- pl.yticks([])
- pl.title('Cost matrix - unsupervised DA')
-
- pl.subplot(2, 2, 4)
- pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')
- pl.xticks([])
- pl.yticks([])
- pl.title('Cost matrix - semisupervised DA')
-
- pl.tight_layout()
-
- # the optimal coupling in the semi-supervised DA case will exhibit " shape
- # similar" to the cost matrix, (block diagonal matrix)
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_001.png
- :align: center
-
-
-
-
-Fig 2 : plots optimal couplings for the different methods
----------------------------------------------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(2, figsize=(8, 4))
-
- pl.subplot(1, 2, 1)
- pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')
- pl.xticks([])
- pl.yticks([])
- pl.title('Optimal coupling\nUnsupervised DA')
-
- pl.subplot(1, 2, 2)
- pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')
- pl.xticks([])
- pl.yticks([])
- pl.title('Optimal coupling\nSemi-supervised DA')
-
- pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_003.png
- :align: center
-
-
-
-
-Fig 3 : plot transported samples
---------------------------------
-
-
-
-.. code-block:: python
-
-
- # display transported samples
- pl.figure(4, figsize=(8, 4))
- pl.subplot(1, 2, 1)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
- pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
- pl.title('Transported samples\nEmdTransport')
- pl.legend(loc=0)
- pl.xticks([])
- pl.yticks([])
-
- pl.subplot(1, 2, 2)
- pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
- pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
- pl.title('Transported samples\nSinkhornTransport')
- pl.xticks([])
- pl.yticks([])
-
- pl.tight_layout()
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_006.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 0.256 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_otda_semi_supervised.py <plot_otda_semi_supervised.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_otda_semi_supervised.ipynb <plot_otda_semi_supervised.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_stochastic.ipynb b/docs/source/auto_examples/plot_stochastic.ipynb
deleted file mode 100644
index 7f6ff3d..0000000
--- a/docs/source/auto_examples/plot_stochastic.ipynb
+++ /dev/null
@@ -1,295 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# Stochastic examples\n\n\nThis example is designed to show how to use the stochatic optimization\nalgorithms for descrete and semicontinous measures from the POT library.\n\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Kilian Fatras <kilian.fatras@gmail.com>\n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport numpy as np\nimport ot\nimport ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n############################################################################\n############################################################################\n DISCRETE CASE:\n\n Sample two discrete measures for the discrete case\n ---------------------------------------------\n\n Define 2 discrete measures a and b, the points where are defined the source\n and the target measures and finally the cost matrix c.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Call the \"SAG\" method to find the transportation matrix in the discrete case\n---------------------------------------------\n\nDefine the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "method = \"SAG\"\nsag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax)\nprint(sag_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "SEMICONTINOUS CASE:\n\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\ncase\n---------------------------------------------\n\nDefine the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "method = \"ASGD\"\nasgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax, log=log)\nprint(log_asgd['alpha'], log_asgd['beta'])\nprint(asgd_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "PLOT TRANSPORTATION MATRIX\n#############################################################################\n\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot SAG results\n----------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot ASGD results\n-----------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot Sinkhorn results\n---------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n############################################################################\n############################################################################\n SEMICONTINOUS CASE:\n\n Sample one general measure a, one discrete measures b for the semicontinous\n case\n ---------------------------------------------\n\n Define one general measure a, one discrete measures b, the points where\n are defined the source and the target measures and finally the cost matrix c.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 100000\nlr = 0.1\nbatch_size = 3\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Call the \"SGD\" dual method to find the transportation matrix in the\nsemicontinous case\n---------------------------------------------\n\nCall ot.solve_dual_entropic and plot the results.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n batch_size, numItermax,\n lr, log=log)\nprint(log_sgd['alpha'], log_sgd['beta'])\nprint(sgd_dual_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot SGD results\n-----------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\npl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot Sinkhorn results\n---------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\npl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-} \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_stochastic.py b/docs/source/auto_examples/plot_stochastic.py
deleted file mode 100644
index 742f8d9..0000000
--- a/docs/source/auto_examples/plot_stochastic.py
+++ /dev/null
@@ -1,208 +0,0 @@
-"""
-==========================
-Stochastic examples
-==========================
-
-This example is designed to show how to use the stochatic optimization
-algorithms for descrete and semicontinous measures from the POT library.
-
-"""
-
-# Author: Kilian Fatras <kilian.fatras@gmail.com>
-#
-# License: MIT License
-
-import matplotlib.pylab as pl
-import numpy as np
-import ot
-import ot.plot
-
-
-#############################################################################
-# COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM
-#############################################################################
-#############################################################################
-# DISCRETE CASE:
-#
-# Sample two discrete measures for the discrete case
-# ---------------------------------------------
-#
-# Define 2 discrete measures a and b, the points where are defined the source
-# and the target measures and finally the cost matrix c.
-
-n_source = 7
-n_target = 4
-reg = 1
-numItermax = 1000
-
-a = ot.utils.unif(n_source)
-b = ot.utils.unif(n_target)
-
-rng = np.random.RandomState(0)
-X_source = rng.randn(n_source, 2)
-Y_target = rng.randn(n_target, 2)
-M = ot.dist(X_source, Y_target)
-
-#############################################################################
-#
-# Call the "SAG" method to find the transportation matrix in the discrete case
-# ---------------------------------------------
-#
-# Define the method "SAG", call ot.solve_semi_dual_entropic and plot the
-# results.
-
-method = "SAG"
-sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,
- numItermax)
-print(sag_pi)
-
-#############################################################################
-# SEMICONTINOUS CASE:
-#
-# Sample one general measure a, one discrete measures b for the semicontinous
-# case
-# ---------------------------------------------
-#
-# Define one general measure a, one discrete measures b, the points where
-# are defined the source and the target measures and finally the cost matrix c.
-
-n_source = 7
-n_target = 4
-reg = 1
-numItermax = 1000
-log = True
-
-a = ot.utils.unif(n_source)
-b = ot.utils.unif(n_target)
-
-rng = np.random.RandomState(0)
-X_source = rng.randn(n_source, 2)
-Y_target = rng.randn(n_target, 2)
-M = ot.dist(X_source, Y_target)
-
-#############################################################################
-#
-# Call the "ASGD" method to find the transportation matrix in the semicontinous
-# case
-# ---------------------------------------------
-#
-# Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the
-# results.
-
-method = "ASGD"
-asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,
- numItermax, log=log)
-print(log_asgd['alpha'], log_asgd['beta'])
-print(asgd_pi)
-
-#############################################################################
-#
-# Compare the results with the Sinkhorn algorithm
-# ---------------------------------------------
-#
-# Call the Sinkhorn algorithm from POT
-
-sinkhorn_pi = ot.sinkhorn(a, b, M, reg)
-print(sinkhorn_pi)
-
-
-##############################################################################
-# PLOT TRANSPORTATION MATRIX
-##############################################################################
-
-##############################################################################
-# Plot SAG results
-# ----------------
-
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')
-pl.show()
-
-
-##############################################################################
-# Plot ASGD results
-# -----------------
-
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')
-pl.show()
-
-
-##############################################################################
-# Plot Sinkhorn results
-# ---------------------
-
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')
-pl.show()
-
-
-#############################################################################
-# COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM
-#############################################################################
-#############################################################################
-# SEMICONTINOUS CASE:
-#
-# Sample one general measure a, one discrete measures b for the semicontinous
-# case
-# ---------------------------------------------
-#
-# Define one general measure a, one discrete measures b, the points where
-# are defined the source and the target measures and finally the cost matrix c.
-
-n_source = 7
-n_target = 4
-reg = 1
-numItermax = 100000
-lr = 0.1
-batch_size = 3
-log = True
-
-a = ot.utils.unif(n_source)
-b = ot.utils.unif(n_target)
-
-rng = np.random.RandomState(0)
-X_source = rng.randn(n_source, 2)
-Y_target = rng.randn(n_target, 2)
-M = ot.dist(X_source, Y_target)
-
-#############################################################################
-#
-# Call the "SGD" dual method to find the transportation matrix in the
-# semicontinous case
-# ---------------------------------------------
-#
-# Call ot.solve_dual_entropic and plot the results.
-
-sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,
- batch_size, numItermax,
- lr, log=log)
-print(log_sgd['alpha'], log_sgd['beta'])
-print(sgd_dual_pi)
-
-#############################################################################
-#
-# Compare the results with the Sinkhorn algorithm
-# ---------------------------------------------
-#
-# Call the Sinkhorn algorithm from POT
-
-sinkhorn_pi = ot.sinkhorn(a, b, M, reg)
-print(sinkhorn_pi)
-
-##############################################################################
-# Plot SGD results
-# -----------------
-
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')
-pl.show()
-
-
-##############################################################################
-# Plot Sinkhorn results
-# ---------------------
-
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')
-pl.show()
diff --git a/docs/source/auto_examples/plot_stochastic.rst b/docs/source/auto_examples/plot_stochastic.rst
deleted file mode 100644
index d531045..0000000
--- a/docs/source/auto_examples/plot_stochastic.rst
+++ /dev/null
@@ -1,446 +0,0 @@
-
-
-.. _sphx_glr_auto_examples_plot_stochastic.py:
-
-
-==========================
-Stochastic examples
-==========================
-
-This example is designed to show how to use the stochatic optimization
-algorithms for descrete and semicontinous measures from the POT library.
-
-
-
-
-.. code-block:: python
-
-
- # Author: Kilian Fatras <kilian.fatras@gmail.com>
- #
- # License: MIT License
-
- import matplotlib.pylab as pl
- import numpy as np
- import ot
- import ot.plot
-
-
-
-
-
-
-
-
-COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM
-############################################################################
-############################################################################
- DISCRETE CASE:
-
- Sample two discrete measures for the discrete case
- ---------------------------------------------
-
- Define 2 discrete measures a and b, the points where are defined the source
- and the target measures and finally the cost matrix c.
-
-
-
-.. code-block:: python
-
-
- n_source = 7
- n_target = 4
- reg = 1
- numItermax = 1000
-
- a = ot.utils.unif(n_source)
- b = ot.utils.unif(n_target)
-
- rng = np.random.RandomState(0)
- X_source = rng.randn(n_source, 2)
- Y_target = rng.randn(n_target, 2)
- M = ot.dist(X_source, Y_target)
-
-
-
-
-
-
-
-Call the "SAG" method to find the transportation matrix in the discrete case
----------------------------------------------
-
-Define the method "SAG", call ot.solve_semi_dual_entropic and plot the
-results.
-
-
-
-.. code-block:: python
-
-
- method = "SAG"
- sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,
- numItermax)
- print(sag_pi)
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- [[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06]
- [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03]
- [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07]
- [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04]
- [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01]
- [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01]
- [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]]
-
-
-SEMICONTINOUS CASE:
-
-Sample one general measure a, one discrete measures b for the semicontinous
-case
----------------------------------------------
-
-Define one general measure a, one discrete measures b, the points where
-are defined the source and the target measures and finally the cost matrix c.
-
-
-
-.. code-block:: python
-
-
- n_source = 7
- n_target = 4
- reg = 1
- numItermax = 1000
- log = True
-
- a = ot.utils.unif(n_source)
- b = ot.utils.unif(n_target)
-
- rng = np.random.RandomState(0)
- X_source = rng.randn(n_source, 2)
- Y_target = rng.randn(n_target, 2)
- M = ot.dist(X_source, Y_target)
-
-
-
-
-
-
-
-Call the "ASGD" method to find the transportation matrix in the semicontinous
-case
----------------------------------------------
-
-Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the
-results.
-
-
-
-.. code-block:: python
-
-
- method = "ASGD"
- asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,
- numItermax, log=log)
- print(log_asgd['alpha'], log_asgd['beta'])
- print(asgd_pi)
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- [3.98220325 7.76235856 3.97645524 2.72051681 1.23219313 3.07696856
- 2.84476972] [-2.65544161 -2.50838395 -0.9397765 6.10360206]
- [[2.34528761e-02 1.00491956e-01 1.89058354e-02 6.47543413e-06]
- [1.16616747e-01 1.32074516e-02 1.45653361e-03 1.15764107e-02]
- [3.16154850e-03 7.42892944e-02 6.54061055e-02 1.94426150e-07]
- [2.33152216e-02 3.27486992e-02 8.61986263e-02 5.94595747e-04]
- [6.34131496e-03 5.31975896e-04 8.12724003e-03 1.27856612e-01]
- [1.41744829e-02 6.49096245e-04 1.42704389e-03 1.26606520e-01]
- [3.73127657e-02 2.62526499e-02 7.57727161e-02 3.51901117e-03]]
-
-
-Compare the results with the Sinkhorn algorithm
----------------------------------------------
-
-Call the Sinkhorn algorithm from POT
-
-
-
-.. code-block:: python
-
-
- sinkhorn_pi = ot.sinkhorn(a, b, M, reg)
- print(sinkhorn_pi)
-
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]
- [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]
- [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]
- [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]
- [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]
- [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]
- [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]
-
-
-PLOT TRANSPORTATION MATRIX
-#############################################################################
-
-
-Plot SAG results
-----------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(4, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')
- pl.show()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_stochastic_004.png
- :align: center
-
-
-
-
-Plot ASGD results
------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(4, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')
- pl.show()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_stochastic_005.png
- :align: center
-
-
-
-
-Plot Sinkhorn results
----------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(4, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')
- pl.show()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_stochastic_006.png
- :align: center
-
-
-
-
-COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM
-############################################################################
-############################################################################
- SEMICONTINOUS CASE:
-
- Sample one general measure a, one discrete measures b for the semicontinous
- case
- ---------------------------------------------
-
- Define one general measure a, one discrete measures b, the points where
- are defined the source and the target measures and finally the cost matrix c.
-
-
-
-.. code-block:: python
-
-
- n_source = 7
- n_target = 4
- reg = 1
- numItermax = 100000
- lr = 0.1
- batch_size = 3
- log = True
-
- a = ot.utils.unif(n_source)
- b = ot.utils.unif(n_target)
-
- rng = np.random.RandomState(0)
- X_source = rng.randn(n_source, 2)
- Y_target = rng.randn(n_target, 2)
- M = ot.dist(X_source, Y_target)
-
-
-
-
-
-
-
-Call the "SGD" dual method to find the transportation matrix in the
-semicontinous case
----------------------------------------------
-
-Call ot.solve_dual_entropic and plot the results.
-
-
-
-.. code-block:: python
-
-
- sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,
- batch_size, numItermax,
- lr, log=log)
- print(log_sgd['alpha'], log_sgd['beta'])
- print(sgd_dual_pi)
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- [0.92449986 2.75486107 1.07923806 0.02741145 0.61355413 1.81961594
- 0.12072562] [0.33831611 0.46806842 1.5640451 4.96947652]
- [[2.20001105e-02 9.26497883e-02 1.08654588e-02 9.78995555e-08]
- [1.55669974e-02 1.73279561e-03 1.19120878e-04 2.49058251e-05]
- [3.48198483e-03 8.04151063e-02 4.41335396e-02 3.45115752e-09]
- [3.14927954e-02 4.34760520e-02 7.13338154e-02 1.29442395e-05]
- [6.81836550e-02 5.62182457e-03 5.35386584e-02 2.21568095e-02]
- [8.04671052e-02 3.62163462e-03 4.96331605e-03 1.15837801e-02]
- [4.88644009e-02 3.37903481e-02 6.07955004e-02 7.42743505e-05]]
-
-
-Compare the results with the Sinkhorn algorithm
----------------------------------------------
-
-Call the Sinkhorn algorithm from POT
-
-
-
-.. code-block:: python
-
-
- sinkhorn_pi = ot.sinkhorn(a, b, M, reg)
- print(sinkhorn_pi)
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out::
-
- [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]
- [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]
- [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]
- [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]
- [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]
- [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]
- [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]
-
-
-Plot SGD results
------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(4, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')
- pl.show()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_stochastic_007.png
- :align: center
-
-
-
-
-Plot Sinkhorn results
----------------------
-
-
-
-.. code-block:: python
-
-
- pl.figure(4, figsize=(5, 5))
- ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')
- pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_stochastic_008.png
- :align: center
-
-
-
-
-**Total running time of the script:** ( 0 minutes 20.889 seconds)
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: plot_stochastic.py <plot_stochastic.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: plot_stochastic.ipynb <plot_stochastic.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/searchindex b/docs/source/auto_examples/searchindex
deleted file mode 100644
index 2cad500..0000000
--- a/docs/source/auto_examples/searchindex
+++ /dev/null
Binary files differ
diff --git a/docs/source/conf.py b/docs/source/conf.py
index d29b829..384bf40 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -34,7 +34,9 @@ class Mock(MagicMock):
@classmethod
def __getattr__(cls, name):
return MagicMock()
-MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cupy','autograd.numpy','pymanopt.manifolds','pymanopt.solvers']
+
+
+MOCK_MODULES = [ 'cupy']
# 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers',
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
# !!!!
@@ -57,6 +59,7 @@ sys.path.insert(0, os.path.abspath("../.."))
# ones.
extensions = [
'sphinx.ext.autodoc',
+ 'sphinx.ext.autosummary',
'sphinx.ext.doctest',
'sphinx.ext.intersphinx',
'sphinx.ext.todo',
@@ -65,9 +68,12 @@ extensions = [
'sphinx.ext.ifconfig',
'sphinx.ext.viewcode',
'sphinx.ext.napoleon',
- #'sphinx_gallery.gen_gallery',
+ 'sphinx_gallery.gen_gallery',
]
+autosummary_generate = True
+
+
napoleon_numpy_docstring = True
# Add any paths that contain templates here, relative to this directory.
@@ -86,7 +92,7 @@ master_doc = 'index'
# General information about the project.
project = u'POT Python Optimal Transport'
-copyright = u'2016-2019, Rémi Flamary, Nicolas Courty'
+copyright = u'2016-2020, Rémi Flamary, Nicolas Courty'
author = u'Rémi Flamary, Nicolas Courty'
# The version info for the project you're documenting, acts as replacement for
@@ -248,17 +254,17 @@ htmlhelp_basename = 'POTdoc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
-# The paper size ('letterpaper' or 'a4paper').
-#'papersize': 'letterpaper',
+ # The paper size ('letterpaper' or 'a4paper').
+ #'papersize': 'letterpaper',
-# The font size ('10pt', '11pt' or '12pt').
-#'pointsize': '10pt',
+ # The font size ('10pt', '11pt' or '12pt').
+ #'pointsize': '10pt',
-# Additional stuff for the LaTeX preamble.
-#'preamble': '',
+ # Additional stuff for the LaTeX preamble.
+ #'preamble': '',
-# Latex figure (float) alignment
-#'figure_align': 'htbp',
+ # Latex figure (float) alignment
+ #'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
@@ -295,7 +301,7 @@ latex_documents = [
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
- (master_doc, 'pot', u'POT Python Optimal Transport library Documentation',
+ (master_doc, 'pot', u'POT Python Optimal Transport',
[author], 1)
]
@@ -309,8 +315,8 @@ man_pages = [
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
- (master_doc, 'POT', u'POT Python Optimal Transport library Documentation',
- author, 'POT', 'Python Optimal Transport librar.',
+ (master_doc, 'POT', u'POT Python Optimal Transport',
+ author, 'POT', 'Python Optimal Transport',
'Miscellaneous'),
]
@@ -331,13 +337,14 @@ texinfo_documents = [
intersphinx_mapping = {'python': ('https://docs.python.org/3', None),
'numpy': ('http://docs.scipy.org/doc/numpy/', None),
'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
- 'matplotlib': ('http://matplotlib.sourceforge.net/', None)}
+ 'matplotlib': ('http://matplotlib.org/', None)}
sphinx_gallery_conf = {
- 'examples_dirs': ['../../examples','../../examples/da'],
+ 'examples_dirs': ['../../examples', '../../examples/da'],
'gallery_dirs': 'auto_examples',
- 'backreferences_dir': '../modules/generated/',
+ 'backreferences_dir': 'gen_modules/backreferences',
+ 'inspect_global_variables' : True,
+ 'doc_module' : ('ot','numpy','scipy','pylab'),
'reference_url': {
- 'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1',
- 'scipy': 'http://docs.scipy.org/doc/scipy-0.17.0/reference'}
+ 'ot': None}
}
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 9078d35..be01343 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -10,15 +10,17 @@ Contents
--------
.. toctree::
- :maxdepth: 2
+ :maxdepth: 1
self
quickstart
all
auto_examples/index
+ releases
.. include:: readme.rst
- :start-line: 5
+ :start-line: 2
+
Indices and tables
diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst
index 978eaff..d56f812 100644
--- a/docs/source/quickstart.rst
+++ b/docs/source/quickstart.rst
@@ -645,6 +645,53 @@ implemented the main function :any:`ot.barycenter_unbalanced`.
- :any:`auto_examples/plot_UOT_barycenter_1D`
+Partial optimal transport
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Partial OT is a variant of the optimal transport problem when only a fixed amount of mass m
+is to be transported. The partial OT metric between two histograms a and b is defined as [28]_:
+
+.. math::
+ \gamma = \arg\min_\gamma <\gamma,M>_F
+
+ s.t.
+ \gamma\geq 0 \\
+ \gamma 1 \leq a\\
+ \gamma^T 1 \leq b\\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\}
+
+
+Interestingly the problem can be casted into a regular OT problem by adding reservoir points
+in which the surplus mass is sent [29]_. We provide a solver for partial OT
+in :any:`ot.partial`. The exact resolution of the problem is computed in :any:`ot.partial.partial_wasserstein`
+and :any:`ot.partial.partial_wasserstein2` that return respectively the OT matrix and the value of the
+linear term. The entropic solution of the problem is computed in :any:`ot.partial.entropic_partial_wasserstein`
+(see [3]_).
+
+The partial Gromov-Wasserstein formulation of the problem
+
+.. math::
+ GW = \min_\gamma \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*\gamma_{i,j}*\gamma_{k,l}
+
+ s.t.
+ \gamma\geq 0 \\
+ \gamma 1 \leq a\\
+ \gamma^T 1 \leq b\\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\}
+
+is computed in :any:`ot.partial.partial_gromov_wasserstein` and in
+:any:`ot.partial.entropic_partial_gromov_wasserstein` when considering the entropic
+regularization of the problem.
+
+
+.. hint::
+
+ Examples of the use of :any:`ot.partial` are available in :
+
+ - :any:`auto_examples/plot_partial`
+
+
+
Gromov-Wasserstein
^^^^^^^^^^^^^^^^^^
@@ -921,3 +968,20 @@ References
.. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. :
Learning with a Wasserstein Loss, Advances in Neural Information
Processing Systems (NIPS) 2015
+
+.. [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). Screening Sinkhorn
+ Algorithm for Regularized Optimal Transport <https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport>,
+ Advances in Neural Information Processing Systems 33 (NeurIPS).
+
+.. [27] Redko I., Courty N., Flamary R., Tuia D. (2019). Optimal Transport for Multi-source
+ Domain Adaptation under Target Shift <http://proceedings.mlr.press/v89/redko19a.html>,
+ Proceedings of the Twenty-Second International Conference on Artificial Intelligence
+ and Statistics (AISTATS) 22, 2019.
+
+.. [28] Caffarelli, L. A., McCann, R. J. (2020). Free boundaries in optimal transport and
+ Monge-Ampere obstacle problems <http://www.math.toronto.edu/~mccann/papers/annals2010.pdf>,
+ Annals of mathematics, 673-730.
+
+.. [29] Chapel, L., Alaya, M., Gasso, G. (2019). Partial Gromov-Wasserstein with
+ Applications on Positive-Unlabeled Learning <https://arxiv.org/abs/2002.08276>,
+ arXiv preprint arXiv:2002.08276.
diff --git a/docs/source/readme.rst b/docs/source/readme.rst
index 0871779..b8cb48c 100644
--- a/docs/source/readme.rst
+++ b/docs/source/readme.rst
@@ -1,57 +1,116 @@
POT: Python Optimal Transport
=============================
-|PyPI version| |Anaconda Cloud| |Build Status| |Documentation Status|
+|PyPI version| |Anaconda Cloud| |Build Status| |Codecov Status|
|Downloads| |Anaconda downloads| |License|
This open source Python library provide several solvers for optimization
problems related to Optimal Transport for signal, image processing and
machine learning.
-It provides the following solvers:
-
-- OT Network Flow solver for the linear program/ Earth Movers Distance
- [1].
-- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2],
- stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU
- implementation (requires cupy).
+Website and documentation: https://PythonOT.github.io/
+
+Source Code (MIT): https://github.com/PythonOT/POT
+
+POT provides the following generic OT solvers (links to examples):
+
+- `OT Network Simplex
+ solver <auto_examples/plot_OT_1D.html>`__
+ for the linear program/ Earth Movers Distance [1] .
+- `Conditional
+ gradient <auto_examples/plot_optim_OTreg.html>`__
+ [6] and `Generalized conditional
+ gradient <auto_examples/plot_optim_OTreg.html>`__
+ for regularized OT [7].
+- Entropic regularization OT solver with `Sinkhorn Knopp
+ Algorithm <auto_examples/plot_OT_1D.html>`__
+ [2] , stabilized version [9] [10], greedy Sinkhorn [22] and
+ `Screening Sinkhorn
+ [26] <auto_examples/plot_screenkhorn_1D.html>`__
+ with optional GPU implementation (requires cupy).
+- Bregman projections for `Wasserstein
+ barycenter <auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html>`__
+ [3], `convolutional
+ barycenter <auto_examples/barycenters/plot_convolutional_barycenter.html>`__
+ [21] and unmixing [4].
- Sinkhorn divergence [23] and entropic regularization OT from
empirical data.
-- Smooth optimal transport solvers (dual and semi-dual) for KL and
- squared L2 regularizations [17].
-- Non regularized Wasserstein barycenters [16] with LP solver (only
- small scale).
-- Bregman projections for Wasserstein barycenter [3], convolutional
- barycenter [21] and unmixing [4].
-- Optimal transport for domain adaptation with group lasso
- regularization [5]
-- Conditional gradient [6] and Generalized conditional gradient for
- regularized OT [7].
-- Linear OT [14] and Joint OT matrix and mapping estimation [8].
-- Wasserstein Discriminant Analysis [11] (requires autograd +
- pymanopt).
-- Gromov-Wasserstein distances and barycenters ([13] and regularized
- [12])
-- Stochastic Optimization for Large-scale Optimal Transport (semi-dual
- problem [18] and dual problem [19])
-- Non regularized free support Wasserstein barycenters [20].
-- Unbalanced OT with KL relaxation distance and barycenter [10, 25].
-
-Some demonstrations (both in Python and Jupyter Notebook format) are
-available in the examples folder.
+- `Smooth optimal transport
+ solvers <auto_examples/plot_OT_1D_smooth.html>`__
+ (dual and semi-dual) for KL and squared L2 regularizations [17].
+- Non regularized `Wasserstein barycenters
+ [16] <auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html>`__)
+ with LP solver (only small scale).
+- `Gromov-Wasserstein
+ distances <auto_examples/gromov/plot_gromov.html>`__
+ and `GW
+ barycenters <auto_examples/gromov/plot_gromov_barycenter.html>`__
+ (exact [13] and regularized [12])
+- `Fused-Gromov-Wasserstein distances
+ solver <auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py>`__
+ and `FGW
+ barycenters <auto_examples/gromov/plot_barycenter_fgw.html>`__
+ [24]
+- `Stochastic
+ solver <auto_examples/plot_stochastic.html>`__
+ for Large-scale Optimal Transport (semi-dual problem [18] and dual
+ problem [19])
+- Non regularized `free support Wasserstein
+ barycenters <auto_examples/barycenters/plot_free_support_barycenter.html>`__
+ [20].
+- `Unbalanced
+ OT <auto_examples/unbalanced-partial/plot_UOT_1D.html>`__
+ with KL relaxation and
+ `barycenter <auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html>`__
+ [10, 25].
+- `Partial Wasserstein and
+ Gromov-Wasserstein <auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html>`__
+ (exact [29] and entropic [3] formulations).
+
+POT provides the following Machine Learning related solvers:
+
+- `Optimal transport for domain
+ adaptation <auto_examples/domain-adaptation/plot_otda_classes.html>`__
+ with `group lasso
+ regularization <auto_examples/domain-adaptation/plot_otda_classes.html>`__,
+ `Laplacian
+ regularization <auto_examples/domain-adaptation/plot_otda_laplacian.html>`__
+ [5] [30] and `semi supervised
+ setting <auto_examples/domain-adaptation/plot_otda_semi_supervised.html>`__.
+- `Linear OT
+ mapping <auto_examples/domain-adaptation/plot_otda_linear_mapping.html>`__
+ [14] and `Joint OT mapping
+ estimation <auto_examples/domain-adaptation/plot_otda_mapping.html>`__
+ [8].
+- `Wasserstein Discriminant
+ Analysis <auto_examples/others/plot_WDA.html>`__
+ [11] (requires autograd + pymanopt).
+- `JCPOT algorithm for multi-source domain adaptation with target
+ shift <auto_examples/domain-adaptation/plot_otda_jcpot.html>`__
+ [27].
+
+Some other examples are available in the
+`documentation <auto_examples/index.html>`__.
Using and citing the toolbox
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If you use this toolbox in your research and find it useful, please cite
-POT using the following bibtex reference:
+POT using the following reference:
+
+::
+
+ Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library,
+ Website: https://pythonot.github.io/, 2017
+
+In Bibtex format:
::
@misc{flamary2017pot,
title={POT Python Optimal Transport library},
author={Flamary, R{'e}mi and Courty, Nicolas},
- url={https://github.com/rflamary/POT},
+ url={https://pythonot.github.io/},
year={2017}
}
@@ -59,10 +118,10 @@ Installation
------------
The library has been tested on Linux, MacOSX and Windows. It requires a
-C++ compiler for using the EMD solver and relies on the following Python
-modules:
+C++ compiler for building/installing the EMD solver and relies on the
+following Python modules:
-- Numpy (>=1.11)
+- Numpy (>=1.16)
- Scipy (>=1.0)
- Cython (>=0.23)
- Matplotlib (>=1.5)
@@ -83,11 +142,11 @@ You can install the toolbox through PyPI with:
pip install POT
-or get the very latest version by downloading it and then running:
+or get the very latest version by running:
::
- python setup.py install --user # for user install (no root)
+ pip install -U https://github.com/PythonOT/POT/archive/master.zip # with --user for user install (no root)
Anaconda installation with conda-forge
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -174,42 +233,8 @@ Examples and Notebooks
~~~~~~~~~~~~~~~~~~~~~~
The examples folder contain several examples and use case for the
-library. The full documentation is available on
-`Readthedocs <http://pot.readthedocs.io/>`__.
-
-Here is a list of the Python notebooks available
-`here <https://github.com/rflamary/POT/blob/master/notebooks/>`__ if you
-want a quick look:
-
-- `1D optimal
- transport <https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb>`__
-- `OT Ground
- Loss <https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb>`__
-- `Multiple EMD
- computation <https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb>`__
-- `2D optimal transport on empirical
- distributions <https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb>`__
-- `1D Wasserstein
- barycenter <https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb>`__
-- `OT with user provided
- regularization <https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb>`__
-- `Domain adaptation with optimal
- transport <https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb>`__
-- `Color transfer in
- images <https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb>`__
-- `OT mapping estimation for domain
- adaptation <https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb>`__
-- `OT mapping estimation for color transfer in
- images <https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb>`__
-- `Wasserstein Discriminant
- Analysis <https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb>`__
-- `Gromov
- Wasserstein <https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb>`__
-- `Gromov Wasserstein
- Barycenter <https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb>`__
-
-You can also see the notebooks with `Jupyter
-nbviewer <https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/>`__.
+library. The full documentation with examples and output is available on
+https://PythonOT.github.io/.
Acknowledgements
----------------
@@ -221,22 +246,29 @@ This toolbox has been created and is maintained by
The contributors to this library are
-- `Alexandre Gramfort <http://alexandre.gramfort.net/>`__
+- `Alexandre Gramfort <http://alexandre.gramfort.net/>`__ (CI,
+ documentation)
- `Laetitia Chapel <http://people.irisa.fr/Laetitia.Chapel/>`__
+ (Partial OT)
- `Michael Perrot <http://perso.univ-st-etienne.fr/pem82055/>`__
(Mapping estimation)
- `Léo Gautheron <https://github.com/aje>`__ (GPU implementation)
- `Nathalie
Gayraud <https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1>`__
-- `Stanislas Chambon <https://slasnista.github.io/>`__
-- `Antoine Rolet <https://arolet.github.io/>`__
+ (DA classes)
+- `Stanislas Chambon <https://slasnista.github.io/>`__ (DA classes)
+- `Antoine Rolet <https://arolet.github.io/>`__ (EMD solver debug)
- Erwan Vautier (Gromov-Wasserstein)
-- `Kilian Fatras <https://kilianfatras.github.io/>`__
+- `Kilian Fatras <https://kilianfatras.github.io/>`__ (Stochastic
+ solvers)
- `Alain
Rakotomamonjy <https://sites.google.com/site/alainrakotomamonjy/home>`__
-- `Vayer Titouan <https://tvayer.github.io/>`__
+- `Vayer Titouan <https://tvayer.github.io/>`__ (Gromov-Wasserstein -,
+ Fused-Gromov-Wasserstein)
- `Hicham Janati <https://hichamjanati.github.io/>`__ (Unbalanced OT)
- `Romain Tavenard <https://rtavenar.github.io/>`__ (1d Wasserstein)
+- `Mokhtar Z. Alaya <http://mzalaya.github.io/>`__ (Screenkhorn)
+- `Ievgen Redko <https://ievred.github.io/>`__ (Laplacian DA, JCPOT)
This toolbox benefit a lot from open source research and we would like
to thank the following persons for providing some code (in various
@@ -387,21 +419,48 @@ and Statistics, (AISTATS) 21, 2018
graphs <http://proceedings.mlr.press/v97/titouan19a.html>`__ Proceedings
of the 36th International Conference on Machine Learning (ICML).
-[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019).
+[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015).
`Learning with a Wasserstein Loss <http://cbcl.mit.edu/wasserstein/>`__
Advances in Neural Information Processing Systems (NIPS).
+[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019).
+`Screening Sinkhorn Algorithm for Regularized Optimal
+Transport <https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport>`__,
+Advances in Neural Information Processing Systems 33 (NeurIPS).
+
+[27] Redko I., Courty N., Flamary R., Tuia D. (2019). `Optimal Transport
+for Multi-source Domain Adaptation under Target
+Shift <http://proceedings.mlr.press/v89/redko19a.html>`__, Proceedings
+of the Twenty-Second International Conference on Artificial Intelligence
+and Statistics (AISTATS) 22, 2019.
+
+[28] Caffarelli, L. A., McCann, R. J. (2010). `Free boundaries in
+optimal transport and Monge-Ampere obstacle
+problems <http://www.math.toronto.edu/~mccann/papers/annals2010.pdf>`__,
+Annals of mathematics, 673-730.
+
+[29] Chapel, L., Alaya, M., Gasso, G. (2019). `Partial
+Gromov-Wasserstein with Applications on Positive-Unlabeled
+Learning <https://arxiv.org/abs/2002.08276>`__, arXiv preprint
+arXiv:2002.08276.
+
+[30] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). `Optimal
+transport with Laplacian regularization: Applications to domain
+adaptation and shape
+matching <https://remi.flamary.com/biblio/flamary2014optlaplace.pdf>`__,
+NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014.
+
.. |PyPI version| image:: https://badge.fury.io/py/POT.svg
:target: https://badge.fury.io/py/POT
.. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg
:target: https://anaconda.org/conda-forge/pot
-.. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master
- :target: https://travis-ci.org/rflamary/POT
-.. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest
- :target: http://pot.readthedocs.io/en/latest/?badge=latest
+.. |Build Status| image:: https://github.com/PythonOT/POT/workflows/build/badge.svg
+ :target: https://github.com/PythonOT/POT/actions
+.. |Codecov Status| image:: https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg
+ :target: https://codecov.io/gh/PythonOT/POT
.. |Downloads| image:: https://pepy.tech/badge/pot
:target: https://pepy.tech/project/pot
.. |Anaconda downloads| image:: https://anaconda.org/conda-forge/pot/badges/downloads.svg
:target: https://anaconda.org/conda-forge/pot
.. |License| image:: https://anaconda.org/conda-forge/pot/badges/license.svg
- :target: https://github.com/rflamary/POT/blob/master/LICENSE
+ :target: https://github.com/PythonOT/POT/blob/master/LICENSE
diff --git a/docs/source/releases.rst b/docs/source/releases.rst
new file mode 100644
index 0000000..5a357f3
--- /dev/null
+++ b/docs/source/releases.rst
@@ -0,0 +1,341 @@
+Releases
+========
+
+0.7.0
+-----
+
+*May 2020*
+
+This is the new stable release for POT. We made a lot of changes in the
+documentation and added several new features such as Partial OT,
+Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes.
+One important change is that we have created the GitHub organization
+`PythonOT <https://github.com/PythonOT>`__ that now owns the main POT
+repository https://github.com/PythonOT/POT and the repository for the
+new documentation is now hosted at https://PythonOT.github.io/.
+
+This is the first release where the Python 2.7 tests have been removed.
+Most of the toolbox should still work but we do not offer support for
+Python 2.7 and will close related Issues.
+
+A lot of changes have been done to the documentation that is now hosted
+on https://PythonOT.github.io/ instead of readthedocs. It was a hard
+choice but readthedocs did not allow us to run sphinx-gallery to update
+our beautiful examples and it was a huge amount of work to maintain. The
+documentation is now automatically compiled and updated on merge. We
+also removed the notebooks from the repository for space reason and also
+because they are all available in the `example
+gallery <auto_examples/index.html>`__. Note
+that now the output of the documentation build for each commit in the PR
+is available to check that the doc builds correctly before merging which
+was not possible with readthedocs.
+
+The CI framework has also been changed with a move from Travis to Github
+Action which allows to get faster tests on Windows, MacOS and Linux. We
+also now report our coverage on
+`Codecov.io <https://codecov.io/gh/PythonOT/POT>`__ and we have a
+reasonable 92% coverage. We also now generate wheels for a number of OS
+and Python versions at each merge in the master branch. They are
+available as outputs of this
+`action <https://github.com/PythonOT/POT/actions?query=workflow%3A%22Build+dist+and+wheels%22>`__.
+This will allow simpler multi-platform releases from now on.
+
+In terms of new features we now have `OTDA Classes for unbalanced
+OT <https://pythonot.github.io/gen_modules/ot.da.html#ot.da.UnbalancedSinkhornTransport>`__,
+a new Domain adaptation class form `multi domain problems
+(JCPOT) <auto_examples/domain-adaptation/plot_otda_jcpot.html#sphx-glr-auto-examples-domain-adaptation-plot-otda-jcpot-py>`__,
+and several solvers to solve the `Partial Optimal
+Transport <auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html#sphx-glr-auto-examples-unbalanced-partial-plot-partial-wass-and-gromov-py>`__
+problems.
+
+This release is also the moment to thank all the POT contributors (old
+and new) for helping making POT such a nice toolbox. A lot of changes
+(also in the API) are comming for the next versions.
+
+Features
+^^^^^^^^
+
+- New documentation on https://PythonOT.github.io/ (PR #160, PR #143,
+ PR #144)
+- Documentation build on CircleCI with sphinx-gallery (PR #145,PR #146,
+ #155)
+- Run sphinx gallery in CI (PR #146)
+- Remove notebooks from repo because available in doc (PR #156)
+- Build wheels in CI (#157)
+- Move from travis to GitHub Action for Windows, MacOS and Linux (PR
+ #148, PR #150)
+- Partial Optimal Transport (PR#141 and PR #142)
+- Laplace regularized OTDA (PR #140)
+- Multi source DA with target shift (PR #137)
+- Screenkhorn algorithm (PR #121)
+
+Closed issues
+^^^^^^^^^^^^^
+
+- Bug in Unbalanced OT example (Issue #127)
+- Clean Cython output when calling setup.py clean (Issue #122)
+- Various Macosx compilation problems (Issue #113, Issue #118, PR#130)
+- EMD dimension mismatch (Issue #114, Fixed in PR #116)
+- 2D barycenter bug for non square images (Issue #124, fixed in PR
+ #132)
+- Bad value in EMD 1D (Issue #138, fixed in PR #139)
+- Log bugs for Gromov-Wassertein solver (Issue #107, fixed in PR #108)
+- Weight issues in barycenter function (PR #106)
+
+0.6.0
+-----
+
+*July 2019*
+
+This is the first official stable release of POT and this means a jump
+to 0.6! The library has been used in the wild for a while now and we
+have reached a state where a lot of fundamental OT solvers are available
+and tested. It has been quite stable in the last months but kept the
+beta flag in its Pypi classifiers until now.
+
+Note that this release will be the last one supporting officially Python
+2.7 (See https://python3statement.org/ for more reasons). For next
+release we will keep the travis tests for Python 2 but will make them
+non necessary for merge in 2020.
+
+The features are never complete in a toolbox designed for solving
+mathematical problems and research but with the new contributions we now
+implement algorithms and solvers from 24 scientific papers (listed in
+the README.md file). New features include a direct implementation of the
+`empirical Sinkhorn
+divergence <all.html#ot.bregman.empirical_sinkhorn_divergence>`__
+, a new efficient (Cython implementation) solver for `EMD in
+1D <all.html#ot.lp.emd_1d>`__ and
+corresponding `Wasserstein
+1D <all.html#ot.lp.wasserstein_1d>`__.
+We now also have implementations for `Unbalanced
+OT <auto_examples/plot_UOT_1D.html>`__
+and a solver for `Unbalanced OT
+barycenters <auto_examples/plot_UOT_barycenter_1D.html>`__.
+A new variant of Gromov-Wasserstein divergence called `Fused
+Gromov-Wasserstein <all.html?highlight=fused_#ot.gromov.fused_gromov_wasserstein>`__
+has been also contributed with exemples of use on `structured
+data <auto_examples/plot_fgw.html>`__
+and computing `barycenters of labeld
+graphs <auto_examples/plot_barycenter_fgw.html>`__.
+
+A lot of work has been done on the documentation with several new
+examples corresponding to the new features and a lot of corrections for
+the docstrings. But the most visible change is a new `quick start
+guide <quickstart.html>`__ for POT
+that gives several pointers about which function or classes allow to
+solve which specific OT problem. When possible a link is provided to
+relevant examples.
+
+We will also provide with this release some pre-compiled Python wheels
+for Linux 64bit on github and pip. This will simplify the install
+process that before required a C compiler and numpy/cython already
+installed.
+
+Finally we would like to acknowledge and thank the numerous contributors
+of POT that has helped in the past build the foundation and are still
+contributing to bring new features and solvers to the library.
+
+Features
+^^^^^^^^
+
+- Add compiled manylinux 64bits wheels to pip releases (PR #91)
+- Add quick start guide (PR #88)
+- Make doctest work on travis (PR #90)
+- Update documentation (PR #79, PR #84)
+- Solver for EMD in 1D (PR #89)
+- Solvers for regularized unbalanced OT (PR #87, PR#99)
+- Solver for Fused Gromov-Wasserstein (PR #86)
+- Add empirical Sinkhorn and empirical Sinkhorn divergences (PR #80)
+
+Closed issues
+^^^^^^^^^^^^^
+
+- Issue #59 fail when using "pip install POT" (new details in doc+
+ hopefully wheels)
+- Issue #85 Cannot run gpu modules
+- Issue #75 Greenkhorn do not return log (solved in PR #76)
+- Issue #82 Gromov-Wasserstein fails when the cost matrices are
+ slightly different
+- Issue #72 Macosx build problem
+
+0.5.0
+-----
+
+*Sep 2018*
+
+POT is 2 years old! This release brings numerous new features to the
+toolbox as listed below but also several bug correction.
+
+| Among the new features, we can highlight a `non-regularized
+ Gromov-Wasserstein
+ solver <auto_examples/plot_gromov.html>`__,
+ a new `greedy variant of
+ sinkhorn <all.html#ot.bregman.greenkhorn>`__,
+| `non-regularized <all.html#ot.lp.barycenter>`__,
+ `convolutional
+ (2D) <auto_examples/plot_convolutional_barycenter.html>`__
+ and `free
+ support <auto_examples/plot_free_support_barycenter.html>`__
+ Wasserstein barycenters and
+ `smooth <https://github.com/rflamary/POT/blob/prV0.5/notebooks/plot_OT_1D_smooth.html>`__
+ and
+ `stochastic <all.html#ot.stochastic.sgd_entropic_regularization>`__
+ implementation of entropic OT.
+
+POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework
+instead of the unmaintained cudamat. Note that while we tried to keed
+changes to the minimum, the OTDA classes were deprecated. If you are
+happy with the cudamat implementation, we recommend you stay with stable
+release 0.4 for now.
+
+The code quality has also improved with 92% code coverage in tests that
+is now printed to the log in the Travis builds. The documentation has
+also been greatly improved with new modules and examples/notebooks.
+
+This new release is so full of new stuff and corrections thanks to the
+old and new POT contributors (you can see the list in the
+`readme <https://github.com/rflamary/POT/blob/master/README.md>`__).
+
+Features
+^^^^^^^^
+
+- Add non regularized Gromov-Wasserstein solver (PR #41)
+- Linear OT mapping between empirical distributions and 90% test
+ coverage (PR #42)
+- Add log parameter in class EMDTransport and SinkhornLpL1Transport (PR
+ #44)
+- Add Markdown format for Pipy (PR #45)
+- Test for Python 3.5 and 3.6 on Travis (PR #46)
+- Non regularized Wasserstein barycenter with scipy linear solver
+ and/or cvxopt (PR #47)
+- Rename dataset functions to be more sklearn compliant (PR #49)
+- Smooth and sparse Optimal transport implementation with entropic and
+ quadratic regularization (PR #50)
+- Stochastic OT in the dual and semi-dual (PR #52 and PR #62)
+- Free support barycenters (PR #56)
+- Speed-up Sinkhorn function (PR #57 and PR #58)
+- Add convolutional Wassersein barycenters for 2D images (PR #64)
+- Add Greedy Sinkhorn variant (Greenkhorn) (PR #66)
+- Big ot.gpu update with cupy implementation (instead of un-maintained
+ cudamat) (PR #67)
+
+Deprecation
+^^^^^^^^^^^
+
+Deprecated OTDA Classes were removed from ot.da and ot.gpu for version
+0.5 (PR #48 and PR #67). The deprecation message has been for a year
+here since 0.4 and it is time to pull the plug.
+
+Closed issues
+^^^^^^^^^^^^^
+
+- Issue #35 : remove import plot from ot/\ **init**.py (See PR #41)
+- Issue #43 : Unusable parameter log for EMDTransport (See PR #44)
+- Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip
+
+0.4
+---
+
+*15 Sep 2017*
+
+This release contains a lot of contribution from new contributors.
+
+Features
+^^^^^^^^
+
+- Automatic notebooks and doc update (PR #27)
+- Add gromov Wasserstein solver and Gromov Barycenters (PR #23)
+- emd and emd2 can now return dual variables and have max\_iter (PR #29
+ and PR #25)
+- New domain adaptation classes compatible with scikit-learn (PR #22)
+- Proper tests with pytest on travis (PR #19)
+- PEP 8 tests (PR #13)
+
+Closed issues
+^^^^^^^^^^^^^
+
+- emd convergence problem du to fixed max iterations (#24)
+- Semi supervised DA error (#26)
+
+0.3.1
+-----
+
+*11 Jul 2017*
+
+- Correct bug in emd on windows
+
+0.3
+---
+
+*7 Jul 2017*
+
+- emd\* and sinkhorn\* are now performed in parallel for multiple
+ target distributions
+- emd and sinkhorn are for OT matrix computation
+- emd2 and sinkhorn2 are for OT loss computation
+- new notebooks for emd computation and Wasserstein Discriminant
+ Analysis
+- relocate notebooks
+- update documentation
+- clean\_zeros(a,b,M) for removimg zeros in sparse distributions
+- GPU implementations for sinkhorn and group lasso regularization
+
+V0.2
+----
+
+*7 Apr 2017*
+
+- New dimensionality reduction method (WDA)
+- Efficient method emd2 returns only tarnsport (in paralell if several
+ histograms given)
+
+0.1.11
+------
+
+*5 Jan 2017*
+
+- Add sphinx gallery for better documentation
+- Small efficiency tweak in sinkhorn
+- Add simple tic() toc() functions for timing
+
+0.1.10
+------
+
+*7 Nov 2016* \* numerical stabilization for sinkhorn (log domain and
+epsilon scaling)
+
+0.1.9
+-----
+
+*4 Nov 2016*
+
+- Update classes and examples for domain adaptation
+- Joint OT matrix and mapping estimation
+
+0.1.7
+-----
+
+*31 Oct 2016*
+
+- Original Domain adaptation classes
+
+0.1.3
+-----
+
+- pipy works
+
+First pre-release
+-----------------
+
+*28 Oct 2016*
+
+It provides the following solvers: \* OT solver for the linear program/
+Earth Movers Distance. \* Entropic regularization OT solver with
+Sinkhorn Knopp Algorithm. \* Bregman projections for Wasserstein
+barycenter [3] and unmixing. \* Optimal transport for domain adaptation
+with group lasso regularization \* Conditional gradient and Generalized
+conditional gradient for regularized OT.
+
+Some demonstrations (both in Python and Jupyter Notebook format) are
+available in the examples folder.
diff --git a/examples/README.txt b/examples/README.txt
index b08d3f1..69a9f84 100644
--- a/examples/README.txt
+++ b/examples/README.txt
@@ -1,4 +1,8 @@
-POT Examples
-============
+Examples gallery
+================
This is a gallery of all the POT example files.
+
+
+OT and regularized OT
+--------------------- \ No newline at end of file
diff --git a/examples/barycenters/README.txt b/examples/barycenters/README.txt
new file mode 100644
index 0000000..8461f7f
--- /dev/null
+++ b/examples/barycenters/README.txt
@@ -0,0 +1,4 @@
+
+
+Wasserstein barycenters
+----------------------- \ No newline at end of file
diff --git a/examples/plot_barycenter_1D.py b/examples/barycenters/plot_barycenter_1D.py
index 6864301..63dc460 100644
--- a/examples/plot_barycenter_1D.py
+++ b/examples/barycenters/plot_barycenter_1D.py
@@ -18,6 +18,8 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 4
+
import numpy as np
import matplotlib.pylab as pl
import ot
diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/barycenters/plot_barycenter_lp_vs_entropic.py
index d7c72d0..57a6bac 100644
--- a/examples/plot_barycenter_lp_vs_entropic.py
+++ b/examples/barycenters/plot_barycenter_lp_vs_entropic.py
@@ -21,6 +21,8 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 4
+
import numpy as np
import matplotlib.pylab as pl
import ot
diff --git a/examples/plot_convolutional_barycenter.py b/examples/barycenters/plot_convolutional_barycenter.py
index e74db04..cbcd4a1 100644
--- a/examples/plot_convolutional_barycenter.py
+++ b/examples/barycenters/plot_convolutional_barycenter.py
@@ -26,10 +26,10 @@ import ot
# The four distributions are constructed from 4 simple images
-f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]
-f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]
-f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]
-f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]
+f1 = 1 - pl.imread('../../data/redcross.png')[:, :, 2]
+f2 = 1 - pl.imread('../../data/duck.png')[:, :, 2]
+f3 = 1 - pl.imread('../../data/heart.png')[:, :, 2]
+f4 = 1 - pl.imread('../../data/tooth.png')[:, :, 2]
A = []
f1 = f1 / np.sum(f1)
diff --git a/examples/plot_free_support_barycenter.py b/examples/barycenters/plot_free_support_barycenter.py
index 64b89e4..27ddc8e 100644
--- a/examples/plot_free_support_barycenter.py
+++ b/examples/barycenters/plot_free_support_barycenter.py
@@ -4,7 +4,7 @@
2D free support Wasserstein barycenters of distributions
====================================================
-Illustration of 2D Wasserstein barycenters if discributions that are weighted
+Illustration of 2D Wasserstein barycenters if distributions are weighted
sum of diracs.
"""
@@ -21,7 +21,7 @@ import ot
##############################################################################
# Generate data
# -------------
-#%% parameters and data generation
+
N = 3
d = 2
measures_locations = []
@@ -46,7 +46,7 @@ for i in range(N):
##############################################################################
# Compute free support barycenter
-# -------------
+# -------------------------------
k = 10 # number of Diracs of the barycenter
X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations
diff --git a/examples/domain-adaptation/README.txt b/examples/domain-adaptation/README.txt
new file mode 100644
index 0000000..81dd8d2
--- /dev/null
+++ b/examples/domain-adaptation/README.txt
@@ -0,0 +1,5 @@
+
+
+
+Domain adaptation examples
+-------------------------- \ No newline at end of file
diff --git a/examples/plot_otda_classes.py b/examples/domain-adaptation/plot_otda_classes.py
index c311fbd..f028022 100644
--- a/examples/plot_otda_classes.py
+++ b/examples/domain-adaptation/plot_otda_classes.py
@@ -17,7 +17,6 @@ approaches currently supported in POT.
import matplotlib.pylab as pl
import ot
-
##############################################################################
# Generate data
# -------------
diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/examples/domain-adaptation/plot_otda_color_images.py
index 62383a2..929365e 100644
--- a/docs/source/auto_examples/plot_otda_color_images.py
+++ b/examples/domain-adaptation/plot_otda_color_images.py
@@ -17,8 +17,9 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 2
+
import numpy as np
-from scipy import ndimage
import matplotlib.pylab as pl
import ot
@@ -45,8 +46,8 @@ def minmax(I):
# -------------
# Loading images
-I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
+I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256
+I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256
X1 = im2mat(I1)
X2 = im2mat(I2)
diff --git a/docs/source/auto_examples/plot_otda_d2.py b/examples/domain-adaptation/plot_otda_d2.py
index cf22c2f..d8b2a93 100644
--- a/docs/source/auto_examples/plot_otda_d2.py
+++ b/examples/domain-adaptation/plot_otda_d2.py
@@ -18,12 +18,14 @@ of what the transport methods are doing.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 2
+
import matplotlib.pylab as pl
import ot
import ot.plot
##############################################################################
-# generate data
+# Generate data
# -------------
n_samples_source = 150
diff --git a/examples/domain-adaptation/plot_otda_jcpot.py b/examples/domain-adaptation/plot_otda_jcpot.py
new file mode 100644
index 0000000..c495690
--- /dev/null
+++ b/examples/domain-adaptation/plot_otda_jcpot.py
@@ -0,0 +1,171 @@
+# -*- coding: utf-8 -*-
+"""
+========================
+OT for multi-source target shift
+========================
+
+This example introduces a target shift problem with two 2D source and 1 target domain.
+
+"""
+
+# Authors: Remi Flamary <remi.flamary@unice.fr>
+# Ievgen Redko <ievgen.redko@univ-st-etienne.fr>
+#
+# License: MIT License
+
+import pylab as pl
+import numpy as np
+import ot
+from ot.datasets import make_data_classif
+
+##############################################################################
+# Generate data
+# -------------
+n = 50
+sigma = 0.3
+np.random.seed(1985)
+
+p1 = .2
+dec1 = [0, 2]
+
+p2 = .9
+dec2 = [0, -2]
+
+pt = .4
+dect = [4, 0]
+
+xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1)
+xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2)
+xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect)
+
+all_Xr = [xs1, xs2]
+all_Yr = [ys1, ys2]
+# %%
+
+da = 1.5
+
+
+def plot_ax(dec, name):
+ pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5)
+ pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5)
+ pl.text(dec[0] - .5, dec[1] + 2, name)
+
+
+##############################################################################
+# Fig 1 : plots source and target samples
+# ---------------------------------------
+
+pl.figure(1)
+pl.clf()
+plot_ax(dec1, 'Source 1')
+plot_ax(dec2, 'Source 2')
+plot_ax(dect, 'Target')
+pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9,
+ label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1))
+pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9,
+ label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2))
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9,
+ label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt))
+pl.title('Data')
+
+pl.legend()
+pl.axis('equal')
+pl.axis('off')
+
+##############################################################################
+# Instantiate Sinkhorn transport algorithm and fit them for all source domains
+# ----------------------------------------------------------------------------
+ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean')
+
+
+def print_G(G, xs, ys, xt):
+ for i in range(G.shape[0]):
+ for j in range(G.shape[1]):
+ if G[i, j] > 5e-4:
+ if ys[i]:
+ c = 'b'
+ else:
+ c = 'r'
+ pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2)
+
+
+##############################################################################
+# Fig 2 : plot optimal couplings and transported samples
+# ------------------------------------------------------
+pl.figure(2)
+pl.clf()
+plot_ax(dec1, 'Source 1')
+plot_ax(dec2, 'Source 2')
+plot_ax(dect, 'Target')
+print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt)
+print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt)
+pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)
+pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)
+
+pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')
+pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')
+
+pl.title('Independent OT')
+
+pl.legend()
+pl.axis('equal')
+pl.axis('off')
+
+##############################################################################
+# Instantiate JCPOT adaptation algorithm and fit it
+# ----------------------------------------------------------------------------
+otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True)
+otda.fit(all_Xr, all_Yr, xt)
+
+ws1 = otda.proportions_.dot(otda.log_['D2'][0])
+ws2 = otda.proportions_.dot(otda.log_['D2'][1])
+
+pl.figure(3)
+pl.clf()
+plot_ax(dec1, 'Source 1')
+plot_ax(dec2, 'Source 2')
+plot_ax(dect, 'Target')
+print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)
+print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)
+pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)
+pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)
+
+pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')
+pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')
+
+pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1]))
+
+pl.legend()
+pl.axis('equal')
+pl.axis('off')
+
+##############################################################################
+# Run oracle transport algorithm with known proportions
+# ----------------------------------------------------------------------------
+h_res = np.array([1 - pt, pt])
+
+ws1 = h_res.dot(otda.log_['D2'][0])
+ws2 = h_res.dot(otda.log_['D2'][1])
+
+pl.figure(4)
+pl.clf()
+plot_ax(dec1, 'Source 1')
+plot_ax(dec2, 'Source 2')
+plot_ax(dect, 'Target')
+print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)
+print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)
+pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)
+pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)
+
+pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')
+pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')
+
+pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1]))
+
+pl.legend()
+pl.axis('equal')
+pl.axis('off')
+pl.show()
diff --git a/docs/source/auto_examples/plot_otda_classes.py b/examples/domain-adaptation/plot_otda_laplacian.py
index c311fbd..67c8f67 100644
--- a/docs/source/auto_examples/plot_otda_classes.py
+++ b/examples/domain-adaptation/plot_otda_laplacian.py
@@ -1,23 +1,21 @@
# -*- coding: utf-8 -*-
"""
-========================
-OT for domain adaptation
-========================
+======================================================
+OT with Laplacian regularization for domain adaptation
+======================================================
-This example introduces a domain adaptation in a 2D setting and the 4 OTDA
-approaches currently supported in POT.
+This example introduces a domain adaptation in a 2D setting and OTDA
+approach with Laplacian regularization.
"""
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-# Stanislas Chambon <stan.chambon@gmail.com>
-#
+# Authors: Ievgen Redko <ievgen.redko@univ-st-etienne.fr>
+
# License: MIT License
import matplotlib.pylab as pl
import ot
-
##############################################################################
# Generate data
# -------------
@@ -38,24 +36,17 @@ ot_emd = ot.da.EMDTransport()
ot_emd.fit(Xs=Xs, Xt=Xt)
# Sinkhorn Transport
-ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
+ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01)
ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-# Sinkhorn Transport with Group lasso regularization
-ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)
-ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)
-
-# Sinkhorn Transport with Group lasso regularization l1l2
-ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,
- verbose=True)
-ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)
+# EMD Transport with Laplacian regularization
+ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)
+ot_emd_laplace.fit(Xs=Xs, Xt=Xt)
# transport source samples onto target samples
transp_Xs_emd = ot_emd.transform(Xs=Xs)
transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)
-transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)
-transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)
-
+transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs)
##############################################################################
# Fig 1 : plots source and target samples
@@ -85,31 +76,26 @@ pl.tight_layout()
param_img = {'interpolation': 'nearest'}
pl.figure(2, figsize=(15, 8))
-pl.subplot(2, 4, 1)
+pl.subplot(2, 3, 1)
pl.imshow(ot_emd.coupling_, **param_img)
pl.xticks([])
pl.yticks([])
pl.title('Optimal coupling\nEMDTransport')
-pl.subplot(2, 4, 2)
+pl.figure(2, figsize=(15, 8))
+pl.subplot(2, 3, 2)
pl.imshow(ot_sinkhorn.coupling_, **param_img)
pl.xticks([])
pl.yticks([])
pl.title('Optimal coupling\nSinkhornTransport')
-pl.subplot(2, 4, 3)
-pl.imshow(ot_lpl1.coupling_, **param_img)
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSinkhornLpl1Transport')
-
-pl.subplot(2, 4, 4)
-pl.imshow(ot_l1l2.coupling_, **param_img)
+pl.subplot(2, 3, 3)
+pl.imshow(ot_emd_laplace.coupling_, **param_img)
pl.xticks([])
pl.yticks([])
-pl.title('Optimal coupling\nSinkhornL1l2Transport')
+pl.title('Optimal coupling\nEMDLaplaceTransport')
-pl.subplot(2, 4, 5)
+pl.subplot(2, 3, 4)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
label='Target samples', alpha=0.3)
pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,
@@ -119,7 +105,7 @@ pl.yticks([])
pl.title('Transported samples\nEmdTransport')
pl.legend(loc="lower left")
-pl.subplot(2, 4, 6)
+pl.subplot(2, 3, 5)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
label='Target samples', alpha=0.3)
pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
@@ -128,23 +114,14 @@ pl.xticks([])
pl.yticks([])
pl.title('Transported samples\nSinkhornTransport')
-pl.subplot(2, 4, 7)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.3)
-pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
-pl.xticks([])
-pl.yticks([])
-pl.title('Transported samples\nSinkhornLpl1Transport')
-
-pl.subplot(2, 4, 8)
+pl.subplot(2, 3, 6)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
label='Target samples', alpha=0.3)
-pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,
+pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys,
marker='+', label='Transp samples', s=30)
pl.xticks([])
pl.yticks([])
-pl.title('Transported samples\nSinkhornL1l2Transport')
+pl.title('Transported samples\nEMDLaplaceTransport')
pl.tight_layout()
pl.show()
diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.py b/examples/domain-adaptation/plot_otda_linear_mapping.py
index c65bd4f..dbf16b8 100644
--- a/docs/source/auto_examples/plot_otda_linear_mapping.py
+++ b/examples/domain-adaptation/plot_otda_linear_mapping.py
@@ -12,6 +12,8 @@ Linear OT mapping estimation
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 2
+
import numpy as np
import pylab as pl
import ot
@@ -92,8 +94,8 @@ def minmax(I):
# Loading images
-I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
+I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256
+I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256
X1 = im2mat(I1)
diff --git a/docs/source/auto_examples/plot_otda_mapping.py b/examples/domain-adaptation/plot_otda_mapping.py
index 5880adf..d21d3c9 100644
--- a/docs/source/auto_examples/plot_otda_mapping.py
+++ b/examples/domain-adaptation/plot_otda_mapping.py
@@ -9,8 +9,8 @@ time both the coupling transport and approximate the transport map with either
a linear or a kernelized mapping as introduced in [8].
[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
- "Mapping estimation for discrete optimal transport",
- Neural Information Processing Systems (NIPS), 2016.
+"Mapping estimation for discrete optimal transport",
+Neural Information Processing Systems (NIPS), 2016.
"""
# Authors: Remi Flamary <remi.flamary@unice.fr>
@@ -18,6 +18,8 @@ a linear or a kernelized mapping as introduced in [8].
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 2
+
import numpy as np
import matplotlib.pylab as pl
import ot
diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py
index a20eca8..ee5c8b0 100644
--- a/examples/plot_otda_mapping_colors_images.py
+++ b/examples/domain-adaptation/plot_otda_mapping_colors_images.py
@@ -8,11 +8,10 @@ OT for domain adaptation with image color adaptation [6] with mapping
estimation [8].
[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized
- discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),
- 1853-1882.
+discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
+
[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
- discrete optimal transport", Neural Information Processing Systems (NIPS),
- 2016.
+discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
"""
@@ -21,8 +20,9 @@ estimation [8].
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 3
+
import numpy as np
-from scipy import ndimage
import matplotlib.pylab as pl
import ot
@@ -48,8 +48,8 @@ def minmax(I):
# -------------
# Loading images
-I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
+I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256
+I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256
X1 = im2mat(I1)
diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.py b/examples/domain-adaptation/plot_otda_semi_supervised.py
index 8a67720..478c3b8 100644
--- a/docs/source/auto_examples/plot_otda_semi_supervised.py
+++ b/examples/domain-adaptation/plot_otda_semi_supervised.py
@@ -18,6 +18,8 @@ of what the transport methods are doing.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 3
+
import matplotlib.pylab as pl
import ot
diff --git a/examples/gromov/README.txt b/examples/gromov/README.txt
new file mode 100644
index 0000000..9cc9c64
--- /dev/null
+++ b/examples/gromov/README.txt
@@ -0,0 +1,4 @@
+
+
+Gromov and Fused-Gromov-Wasserstein
+----------------------------------- \ No newline at end of file
diff --git a/examples/plot_barycenter_fgw.py b/examples/gromov/plot_barycenter_fgw.py
index 77b0370..3f81765 100644
--- a/examples/plot_barycenter_fgw.py
+++ b/examples/gromov/plot_barycenter_fgw.py
@@ -4,14 +4,15 @@
Plot graphs' barycenter using FGW
=================================
-This example illustrates the computation barycenter of labeled graphs using FGW
+This example illustrates the computation barycenter of labeled graphs using
+FGW [18].
Requires networkx >=2
-.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
- and Courty Nicolas
- "Optimal Transport for structured data with application on graphs"
- International Conference on Machine Learning (ICML). 2019.
+[18] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain
+and Courty Nicolas
+"Optimal Transport for structured data with application on graphs"
+International Conference on Machine Learning (ICML). 2019.
"""
diff --git a/examples/plot_fgw.py b/examples/gromov/plot_fgw.py
index 43efc94..97fe619 100644
--- a/examples/plot_fgw.py
+++ b/examples/gromov/plot_fgw.py
@@ -4,12 +4,12 @@
Plot Fused-gromov-Wasserstein
==============================
-This example illustrates the computation of FGW for 1D measures[18].
+This example illustrates the computation of FGW for 1D measures [18].
-.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
- and Courty Nicolas
- "Optimal Transport for structured data with application on graphs"
- International Conference on Machine Learning (ICML). 2019.
+[18] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain
+and Courty Nicolas
+"Optimal Transport for structured data with application on graphs"
+International Conference on Machine Learning (ICML). 2019.
"""
@@ -17,6 +17,8 @@ This example illustrates the computation of FGW for 1D measures[18].
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 3
+
import matplotlib.pyplot as pl
import numpy as np
import ot
@@ -60,14 +62,14 @@ pl.subplot(2, 1, 1)
pl.scatter(ys, xs, c=phi, s=70)
pl.ylabel('Feature value a', fontsize=20)
-pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)
+pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, y=1)
pl.xticks(())
pl.yticks(())
pl.subplot(2, 1, 2)
pl.scatter(yt, xt, c=phi2, s=70)
pl.xlabel('coordinates x/y', fontsize=25)
pl.ylabel('Feature value b', fontsize=20)
-pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)
+pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, y=1)
pl.yticks(())
pl.tight_layout()
pl.show()
diff --git a/docs/source/auto_examples/plot_gromov.py b/examples/gromov/plot_gromov.py
index deb2f86..deb2f86 100644
--- a/docs/source/auto_examples/plot_gromov.py
+++ b/examples/gromov/plot_gromov.py
diff --git a/examples/plot_gromov_barycenter.py b/examples/gromov/plot_gromov_barycenter.py
index 58fc51a..f6f031a 100755
--- a/examples/plot_gromov_barycenter.py
+++ b/examples/gromov/plot_gromov_barycenter.py
@@ -17,7 +17,6 @@ computation in POT.
import numpy as np
import scipy as sp
-import scipy.ndimage as spi
import matplotlib.pylab as pl
from sklearn import manifold
from sklearn.decomposition import PCA
@@ -90,10 +89,10 @@ def im2mat(I):
return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256
-cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256
-triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256
-star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256
+square = pl.imread('../../data/square.png').astype(np.float64)[:, :, 2]
+cross = pl.imread('../../data/cross.png').astype(np.float64)[:, :, 2]
+triangle = pl.imread('../../data/triangle.png').astype(np.float64)[:, :, 2]
+star = pl.imread('../../data/star.png').astype(np.float64)[:, :, 2]
shapes = [square, cross, triangle, star]
diff --git a/examples/others/README.txt b/examples/others/README.txt
new file mode 100644
index 0000000..df4c697
--- /dev/null
+++ b/examples/others/README.txt
@@ -0,0 +1,5 @@
+
+
+
+Other OT problems
+----------------- \ No newline at end of file
diff --git a/examples/plot_WDA.py b/examples/others/plot_WDA.py
index 93cc237..bdfa57d 100644
--- a/examples/plot_WDA.py
+++ b/examples/others/plot_WDA.py
@@ -16,6 +16,8 @@ Wasserstein Discriminant Analysis.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 2
+
import numpy as np
import matplotlib.pylab as pl
@@ -31,6 +33,8 @@ from ot.dr import wda, fda
n = 1000 # nb samples in source and target datasets
nz = 0.2
+np.random.seed(1)
+
# generate circle dataset
t = np.random.rand(n) * 2 * np.pi
ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
@@ -86,7 +90,11 @@ reg = 1e0
k = 10
maxiter = 100
-Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)
+P0 = np.random.randn(xs.shape[1], p)
+
+P0 /= np.sqrt(np.sum(P0**2, 0, keepdims=True))
+
+Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter, P0=P0)
##############################################################################
diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py
index f33e2a4..15ead96 100644
--- a/examples/plot_OT_1D.py
+++ b/examples/plot_OT_1D.py
@@ -12,6 +12,7 @@ and their visualization.
# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 3
import numpy as np
import matplotlib.pylab as pl
diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py
index b690751..75cd295 100644
--- a/examples/plot_OT_1D_smooth.py
+++ b/examples/plot_OT_1D_smooth.py
@@ -13,6 +13,8 @@ and their visualization.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 6
+
import numpy as np
import matplotlib.pylab as pl
import ot
diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py
index 63126ba..1544e82 100644
--- a/examples/plot_OT_2D_samples.py
+++ b/examples/plot_OT_2D_samples.py
@@ -14,6 +14,8 @@ sum of diracs. The OT matrix is plotted with the samples.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 4
+
import numpy as np
import matplotlib.pylab as pl
import ot
diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py
index 37b429f..60353ab 100644
--- a/examples/plot_OT_L1_vs_L2.py
+++ b/examples/plot_OT_L1_vs_L2.py
@@ -16,6 +16,8 @@ https://arxiv.org/pdf/1706.07650.pdf
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 3
+
import numpy as np
import matplotlib.pylab as pl
import ot
diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py
index 7ed2b01..527a847 100644
--- a/examples/plot_compute_emd.py
+++ b/examples/plot_compute_emd.py
@@ -4,8 +4,8 @@
Plot multiple EMD
=================
-Shows how to compute multiple EMD and Sinkhorn with two differnt
-ground metrics and plot their values for diffeent distributions.
+Shows how to compute multiple EMD and Sinkhorn with two different
+ground metrics and plot their values for different distributions.
"""
@@ -14,6 +14,8 @@ ground metrics and plot their values for diffeent distributions.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 3
+
import numpy as np
import matplotlib.pylab as pl
import ot
diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py
deleted file mode 100644
index deb2f86..0000000
--- a/examples/plot_gromov.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-==========================
-Gromov-Wasserstein example
-==========================
-
-This example is designed to show how to use the Gromov-Wassertsein distance
-computation in POT.
-"""
-
-# Author: Erwan Vautier <erwan.vautier@gmail.com>
-# Nicolas Courty <ncourty@irisa.fr>
-#
-# License: MIT License
-
-import scipy as sp
-import numpy as np
-import matplotlib.pylab as pl
-from mpl_toolkits.mplot3d import Axes3D # noqa
-import ot
-
-#############################################################################
-#
-# Sample two Gaussian distributions (2D and 3D)
-# ---------------------------------------------
-#
-# The Gromov-Wasserstein distance allows to compute distances with samples that
-# do not belong to the same metric space. For demonstration purpose, we sample
-# two Gaussian distributions in 2- and 3-dimensional spaces.
-
-
-n_samples = 30 # nb samples
-
-mu_s = np.array([0, 0])
-cov_s = np.array([[1, 0], [0, 1]])
-
-mu_t = np.array([4, 4, 4])
-cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
-
-
-xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
-P = sp.linalg.sqrtm(cov_t)
-xt = np.random.randn(n_samples, 3).dot(P) + mu_t
-
-#############################################################################
-#
-# Plotting the distributions
-# --------------------------
-
-
-fig = pl.figure()
-ax1 = fig.add_subplot(121)
-ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-ax2 = fig.add_subplot(122, projection='3d')
-ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')
-pl.show()
-
-#############################################################################
-#
-# Compute distance kernels, normalize them and then display
-# ---------------------------------------------------------
-
-
-C1 = sp.spatial.distance.cdist(xs, xs)
-C2 = sp.spatial.distance.cdist(xt, xt)
-
-C1 /= C1.max()
-C2 /= C2.max()
-
-pl.figure()
-pl.subplot(121)
-pl.imshow(C1)
-pl.subplot(122)
-pl.imshow(C2)
-pl.show()
-
-#############################################################################
-#
-# Compute Gromov-Wasserstein plans and distance
-# ---------------------------------------------
-
-p = ot.unif(n_samples)
-q = ot.unif(n_samples)
-
-gw0, log0 = ot.gromov.gromov_wasserstein(
- C1, C2, p, q, 'square_loss', verbose=True, log=True)
-
-gw, log = ot.gromov.entropic_gromov_wasserstein(
- C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)
-
-
-print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))
-print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))
-
-
-pl.figure(1, (10, 5))
-
-pl.subplot(1, 2, 1)
-pl.imshow(gw0, cmap='jet')
-pl.title('Gromov Wasserstein')
-
-pl.subplot(1, 2, 2)
-pl.imshow(gw, cmap='jet')
-pl.title('Entropic Gromov Wasserstein')
-
-pl.show()
diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py
index 2c58def..5eb15bd 100644
--- a/examples/plot_optim_OTreg.py
+++ b/examples/plot_optim_OTreg.py
@@ -6,7 +6,7 @@ Regularized OT with generic solver
Illustrates the use of the generic solver for regularized OT with
user-designed regularization term. It uses Conditional gradient as in [6] and
-generalized Conditional Gradient as proposed in [5][7].
+generalized Conditional Gradient as proposed in [5,7].
[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for
@@ -14,8 +14,8 @@ Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine
Intelligence , vol.PP, no.99, pp.1-1.
[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
-Regularized discrete optimal transport. SIAM Journal on Imaging Sciences,
-7(3), 1853-1882.
+Regularized discrete optimal transport. SIAM Journal on Imaging
+Sciences, 7(3), 1853-1882.
[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized
conditional gradient: analysis of convergence and applications.
@@ -24,6 +24,7 @@ arXiv preprint arXiv:1510.06567.
"""
+# sphinx_gallery_thumbnail_number = 4
import numpy as np
import matplotlib.pylab as pl
diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py
deleted file mode 100644
index 62383a2..0000000
--- a/examples/plot_otda_color_images.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-=============================
-OT for image color adaptation
-=============================
-
-This example presents a way of transferring colors between two images
-with Optimal Transport as introduced in [6]
-
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).
-Regularized discrete optimal transport.
-SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-# Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import numpy as np
-from scipy import ndimage
-import matplotlib.pylab as pl
-import ot
-
-
-r = np.random.RandomState(42)
-
-
-def im2mat(I):
- """Converts an image to matrix (one pixel per line)"""
- return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
-def mat2im(X, shape):
- """Converts back a matrix to an image"""
- return X.reshape(shape)
-
-
-def minmax(I):
- return np.clip(I, 0, 1)
-
-
-##############################################################################
-# Generate data
-# -------------
-
-# Loading images
-I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-X1 = im2mat(I1)
-X2 = im2mat(I2)
-
-# training samples
-nb = 1000
-idx1 = r.randint(X1.shape[0], size=(nb,))
-idx2 = r.randint(X2.shape[0], size=(nb,))
-
-Xs = X1[idx1, :]
-Xt = X2[idx2, :]
-
-
-##############################################################################
-# Plot original image
-# -------------------
-
-pl.figure(1, figsize=(6.4, 3))
-
-pl.subplot(1, 2, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Image 1')
-
-pl.subplot(1, 2, 2)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Image 2')
-
-
-##############################################################################
-# Scatter plot of colors
-# ----------------------
-
-pl.figure(2, figsize=(6.4, 3))
-
-pl.subplot(1, 2, 1)
-pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)
-pl.axis([0, 1, 0, 1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 1')
-
-pl.subplot(1, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)
-pl.axis([0, 1, 0, 1])
-pl.xlabel('Red')
-pl.ylabel('Blue')
-pl.title('Image 2')
-pl.tight_layout()
-
-
-##############################################################################
-# Instantiate the different transport algorithms and fit them
-# -----------------------------------------------------------
-
-# EMDTransport
-ot_emd = ot.da.EMDTransport()
-ot_emd.fit(Xs=Xs, Xt=Xt)
-
-# SinkhornTransport
-ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-
-# prediction between images (using out of sample prediction as in [6])
-transp_Xs_emd = ot_emd.transform(Xs=X1)
-transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)
-
-transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)
-transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)
-
-I1t = minmax(mat2im(transp_Xs_emd, I1.shape))
-I2t = minmax(mat2im(transp_Xt_emd, I2.shape))
-
-I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
-I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))
-
-
-##############################################################################
-# Plot new images
-# ---------------
-
-pl.figure(3, figsize=(8, 4))
-
-pl.subplot(2, 3, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Image 1')
-
-pl.subplot(2, 3, 2)
-pl.imshow(I1t)
-pl.axis('off')
-pl.title('Image 1 Adapt')
-
-pl.subplot(2, 3, 3)
-pl.imshow(I1te)
-pl.axis('off')
-pl.title('Image 1 Adapt (reg)')
-
-pl.subplot(2, 3, 4)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Image 2')
-
-pl.subplot(2, 3, 5)
-pl.imshow(I2t)
-pl.axis('off')
-pl.title('Image 2 Adapt')
-
-pl.subplot(2, 3, 6)
-pl.imshow(I2te)
-pl.axis('off')
-pl.title('Image 2 Adapt (reg)')
-pl.tight_layout()
-
-pl.show()
diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py
deleted file mode 100644
index cf22c2f..0000000
--- a/examples/plot_otda_d2.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-===================================================
-OT for domain adaptation on empirical distributions
-===================================================
-
-This example introduces a domain adaptation in a 2D setting. It explicits
-the problem of domain adaptation and introduces some optimal transport
-approaches to solve it.
-
-Quantities such as optimal couplings, greater coupling coefficients and
-transported samples are represented in order to give a visual understanding
-of what the transport methods are doing.
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-# Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import matplotlib.pylab as pl
-import ot
-import ot.plot
-
-##############################################################################
-# generate data
-# -------------
-
-n_samples_source = 150
-n_samples_target = 150
-
-Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)
-Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)
-
-# Cost matrix
-M = ot.dist(Xs, Xt, metric='sqeuclidean')
-
-
-##############################################################################
-# Instantiate the different transport algorithms and fit them
-# -----------------------------------------------------------
-
-# EMD Transport
-ot_emd = ot.da.EMDTransport()
-ot_emd.fit(Xs=Xs, Xt=Xt)
-
-# Sinkhorn Transport
-ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-
-# Sinkhorn Transport with Group lasso regularization
-ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)
-ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)
-
-# transport source samples onto target samples
-transp_Xs_emd = ot_emd.transform(Xs=Xs)
-transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)
-transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)
-
-
-##############################################################################
-# Fig 1 : plots source and target samples + matrix of pairwise distance
-# ---------------------------------------------------------------------
-
-pl.figure(1, figsize=(10, 10))
-pl.subplot(2, 2, 1)
-pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.xticks([])
-pl.yticks([])
-pl.legend(loc=0)
-pl.title('Source samples')
-
-pl.subplot(2, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.xticks([])
-pl.yticks([])
-pl.legend(loc=0)
-pl.title('Target samples')
-
-pl.subplot(2, 2, 3)
-pl.imshow(M, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Matrix of pairwise distances')
-pl.tight_layout()
-
-
-##############################################################################
-# Fig 2 : plots optimal couplings for the different methods
-# ---------------------------------------------------------
-pl.figure(2, figsize=(10, 6))
-
-pl.subplot(2, 3, 1)
-pl.imshow(ot_emd.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nEMDTransport')
-
-pl.subplot(2, 3, 2)
-pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSinkhornTransport')
-
-pl.subplot(2, 3, 3)
-pl.imshow(ot_lpl1.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSinkhornLpl1Transport')
-
-pl.subplot(2, 3, 4)
-ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])
-pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.xticks([])
-pl.yticks([])
-pl.title('Main coupling coefficients\nEMDTransport')
-
-pl.subplot(2, 3, 5)
-ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])
-pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.xticks([])
-pl.yticks([])
-pl.title('Main coupling coefficients\nSinkhornTransport')
-
-pl.subplot(2, 3, 6)
-ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])
-pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.xticks([])
-pl.yticks([])
-pl.title('Main coupling coefficients\nSinkhornLpl1Transport')
-pl.tight_layout()
-
-
-##############################################################################
-# Fig 3 : plot transported samples
-# --------------------------------
-
-# display transported samples
-pl.figure(4, figsize=(10, 4))
-pl.subplot(1, 3, 1)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
-pl.title('Transported samples\nEmdTransport')
-pl.legend(loc=0)
-pl.xticks([])
-pl.yticks([])
-
-pl.subplot(1, 3, 2)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
-pl.title('Transported samples\nSinkhornTransport')
-pl.xticks([])
-pl.yticks([])
-
-pl.subplot(1, 3, 3)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
-pl.title('Transported samples\nSinkhornLpl1Transport')
-pl.xticks([])
-pl.yticks([])
-
-pl.tight_layout()
-pl.show()
diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py
deleted file mode 100644
index c65bd4f..0000000
--- a/examples/plot_otda_linear_mapping.py
+++ /dev/null
@@ -1,144 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-============================
-Linear OT mapping estimation
-============================
-
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import pylab as pl
-import ot
-
-##############################################################################
-# Generate data
-# -------------
-
-n = 1000
-d = 2
-sigma = .1
-
-# source samples
-angles = np.random.rand(n, 1) * 2 * np.pi
-xs = np.concatenate((np.sin(angles), np.cos(angles)),
- axis=1) + sigma * np.random.randn(n, 2)
-xs[:n // 2, 1] += 2
-
-
-# target samples
-anglet = np.random.rand(n, 1) * 2 * np.pi
-xt = np.concatenate((np.sin(anglet), np.cos(anglet)),
- axis=1) + sigma * np.random.randn(n, 2)
-xt[:n // 2, 1] += 2
-
-
-A = np.array([[1.5, .7], [.7, 1.5]])
-b = np.array([[4, 2]])
-xt = xt.dot(A) + b
-
-##############################################################################
-# Plot data
-# ---------
-
-pl.figure(1, (5, 5))
-pl.plot(xs[:, 0], xs[:, 1], '+')
-pl.plot(xt[:, 0], xt[:, 1], 'o')
-
-
-##############################################################################
-# Estimate linear mapping and transport
-# -------------------------------------
-
-Ae, be = ot.da.OT_mapping_linear(xs, xt)
-
-xst = xs.dot(Ae) + be
-
-
-##############################################################################
-# Plot transported samples
-# ------------------------
-
-pl.figure(1, (5, 5))
-pl.clf()
-pl.plot(xs[:, 0], xs[:, 1], '+')
-pl.plot(xt[:, 0], xt[:, 1], 'o')
-pl.plot(xst[:, 0], xst[:, 1], '+')
-
-pl.show()
-
-##############################################################################
-# Load image data
-# ---------------
-
-
-def im2mat(I):
- """Converts and image to matrix (one pixel per line)"""
- return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
-
-
-def mat2im(X, shape):
- """Converts back a matrix to an image"""
- return X.reshape(shape)
-
-
-def minmax(I):
- return np.clip(I, 0, 1)
-
-
-# Loading images
-I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
-
-
-X1 = im2mat(I1)
-X2 = im2mat(I2)
-
-##############################################################################
-# Estimate mapping and adapt
-# ----------------------------
-
-mapping = ot.da.LinearTransport()
-
-mapping.fit(Xs=X1, Xt=X2)
-
-
-xst = mapping.transform(Xs=X1)
-xts = mapping.inverse_transform(Xt=X2)
-
-I1t = minmax(mat2im(xst, I1.shape))
-I2t = minmax(mat2im(xts, I2.shape))
-
-# %%
-
-
-##############################################################################
-# Plot transformed images
-# -----------------------
-
-pl.figure(2, figsize=(10, 7))
-
-pl.subplot(2, 2, 1)
-pl.imshow(I1)
-pl.axis('off')
-pl.title('Im. 1')
-
-pl.subplot(2, 2, 2)
-pl.imshow(I2)
-pl.axis('off')
-pl.title('Im. 2')
-
-pl.subplot(2, 2, 3)
-pl.imshow(I1t)
-pl.axis('off')
-pl.title('Mapping Im. 1')
-
-pl.subplot(2, 2, 4)
-pl.imshow(I2t)
-pl.axis('off')
-pl.title('Inverse mapping Im. 2')
diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py
deleted file mode 100644
index 5880adf..0000000
--- a/examples/plot_otda_mapping.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-===========================================
-OT mapping estimation for domain adaptation
-===========================================
-
-This example presents how to use MappingTransport to estimate at the same
-time both the coupling transport and approximate the transport map with either
-a linear or a kernelized mapping as introduced in [8].
-
-[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
- "Mapping estimation for discrete optimal transport",
- Neural Information Processing Systems (NIPS), 2016.
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-# Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-
-
-##############################################################################
-# Generate data
-# -------------
-
-n_source_samples = 100
-n_target_samples = 100
-theta = 2 * np.pi / 20
-noise_level = 0.1
-
-Xs, ys = ot.datasets.make_data_classif(
- 'gaussrot', n_source_samples, nz=noise_level)
-Xs_new, _ = ot.datasets.make_data_classif(
- 'gaussrot', n_source_samples, nz=noise_level)
-Xt, yt = ot.datasets.make_data_classif(
- 'gaussrot', n_target_samples, theta=theta, nz=noise_level)
-
-# one of the target mode changes its variance (no linear mapping)
-Xt[yt == 2] *= 3
-Xt = Xt + 4
-
-##############################################################################
-# Plot data
-# ---------
-
-pl.figure(1, (10, 5))
-pl.clf()
-pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.legend(loc=0)
-pl.title('Source and target distributions')
-
-
-##############################################################################
-# Instantiate the different transport algorithms and fit them
-# -----------------------------------------------------------
-
-# MappingTransport with linear kernel
-ot_mapping_linear = ot.da.MappingTransport(
- kernel="linear", mu=1e0, eta=1e-8, bias=True,
- max_iter=20, verbose=True)
-
-ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
-
-# for original source samples, transform applies barycentric mapping
-transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)
-
-# for out of source samples, transform applies the linear mapping
-transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)
-
-
-# MappingTransport with gaussian kernel
-ot_mapping_gaussian = ot.da.MappingTransport(
- kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1,
- max_iter=10, verbose=True)
-ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
-
-# for original source samples, transform applies barycentric mapping
-transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)
-
-# for out of source samples, transform applies the gaussian mapping
-transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)
-
-
-##############################################################################
-# Plot transported samples
-# ------------------------
-
-pl.figure(2)
-pl.clf()
-pl.subplot(2, 2, 1)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=.2)
-pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',
- label='Mapped source samples')
-pl.title("Bary. mapping (linear)")
-pl.legend(loc=0)
-
-pl.subplot(2, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=.2)
-pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],
- c=ys, marker='+', label='Learned mapping')
-pl.title("Estim. mapping (linear)")
-
-pl.subplot(2, 2, 3)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=.2)
-pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,
- marker='+', label='barycentric mapping')
-pl.title("Bary. mapping (kernel)")
-
-pl.subplot(2, 2, 4)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=.2)
-pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,
- marker='+', label='Learned mapping')
-pl.title("Estim. mapping (kernel)")
-pl.tight_layout()
-
-pl.show()
diff --git a/examples/plot_otda_semi_supervised.py b/examples/plot_otda_semi_supervised.py
deleted file mode 100644
index 8a67720..0000000
--- a/examples/plot_otda_semi_supervised.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-============================================
-OTDA unsupervised vs semi-supervised setting
-============================================
-
-This example introduces a semi supervised domain adaptation in a 2D setting.
-It explicits the problem of semi supervised domain adaptation and introduces
-some optimal transport approaches to solve it.
-
-Quantities such as optimal couplings, greater coupling coefficients and
-transported samples are represented in order to give a visual understanding
-of what the transport methods are doing.
-"""
-
-# Authors: Remi Flamary <remi.flamary@unice.fr>
-# Stanislas Chambon <stan.chambon@gmail.com>
-#
-# License: MIT License
-
-import matplotlib.pylab as pl
-import ot
-
-
-##############################################################################
-# Generate data
-# -------------
-
-n_samples_source = 150
-n_samples_target = 150
-
-Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)
-Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)
-
-
-##############################################################################
-# Transport source samples onto target samples
-# --------------------------------------------
-
-
-# unsupervised domain adaptation
-ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt)
-transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)
-
-# semi-supervised domain adaptation
-ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)
-ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)
-transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)
-
-# semi supervised DA uses available labaled target samples to modify the cost
-# matrix involved in the OT problem. The cost of transporting a source sample
-# of class A onto a target sample of class B != A is set to infinite, or a
-# very large value
-
-# note that in the present case we consider that all the target samples are
-# labeled. For daily applications, some target sample might not have labels,
-# in this case the element of yt corresponding to these samples should be
-# filled with -1.
-
-# Warning: we recall that -1 cannot be used as a class label
-
-
-##############################################################################
-# Fig 1 : plots source and target samples + matrix of pairwise distance
-# ---------------------------------------------------------------------
-
-pl.figure(1, figsize=(10, 10))
-pl.subplot(2, 2, 1)
-pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.xticks([])
-pl.yticks([])
-pl.legend(loc=0)
-pl.title('Source samples')
-
-pl.subplot(2, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.xticks([])
-pl.yticks([])
-pl.legend(loc=0)
-pl.title('Target samples')
-
-pl.subplot(2, 2, 3)
-pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Cost matrix - unsupervised DA')
-
-pl.subplot(2, 2, 4)
-pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Cost matrix - semisupervised DA')
-
-pl.tight_layout()
-
-# the optimal coupling in the semi-supervised DA case will exhibit " shape
-# similar" to the cost matrix, (block diagonal matrix)
-
-
-##############################################################################
-# Fig 2 : plots optimal couplings for the different methods
-# ---------------------------------------------------------
-
-pl.figure(2, figsize=(8, 4))
-
-pl.subplot(1, 2, 1)
-pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nUnsupervised DA')
-
-pl.subplot(1, 2, 2)
-pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSemi-supervised DA')
-
-pl.tight_layout()
-
-
-##############################################################################
-# Fig 3 : plot transported samples
-# --------------------------------
-
-# display transported samples
-pl.figure(4, figsize=(8, 4))
-pl.subplot(1, 2, 1)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
-pl.title('Transported samples\nEmdTransport')
-pl.legend(loc=0)
-pl.xticks([])
-pl.yticks([])
-
-pl.subplot(1, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
- label='Target samples', alpha=0.5)
-pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,
- marker='+', label='Transp samples', s=30)
-pl.title('Transported samples\nSinkhornTransport')
-pl.xticks([])
-pl.yticks([])
-
-pl.tight_layout()
-pl.show()
diff --git a/examples/plot_UOT_1D.py b/examples/plot_screenkhorn_1D.py
index 2ea8b05..785642a 100644
--- a/examples/plot_UOT_1D.py
+++ b/examples/plot_screenkhorn_1D.py
@@ -1,28 +1,30 @@
# -*- coding: utf-8 -*-
"""
===============================
-1D Unbalanced optimal transport
+1D Screened optimal transport
===============================
-This example illustrates the computation of Unbalanced Optimal transport
-using a Kullback-Leibler relaxation.
+This example illustrates the computation of Screenkhorn [26].
+
+[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019).
+Screening Sinkhorn Algorithm for Regularized Optimal Transport,
+Advances in Neural Information Processing Systems 33 (NeurIPS).
"""
-# Author: Hicham Janati <hicham.janati@inria.fr>
+# Author: Mokhtar Z. Alaya <mokhtarzahdi.alaya@gmail.com>
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
-import ot
import ot.plot
from ot.datasets import make_1D_gauss as gauss
+from ot.bregman import screenkhorn
##############################################################################
# Generate data
# -------------
-
#%% parameters
n = 100 # nb bins
@@ -34,14 +36,10 @@ x = np.arange(n, dtype=np.float64)
a = gauss(n, m=20, s=5) # m= mean, s= std
b = gauss(n, m=60, s=10)
-# make distributions unbalanced
-b *= 5.
-
# loss matrix
M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
M /= M.max()
-
##############################################################################
# Plot distributions and loss matrix
# ----------------------------------
@@ -58,19 +56,16 @@ pl.legend()
pl.figure(2, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
-
##############################################################################
-# Solve Unbalanced Sinkhorn
-# --------------
-
+# Solve Screenkhorn
+# -----------------------
-# Sinkhorn
-
-epsilon = 0.1 # entropy parameter
-alpha = 1. # Unbalanced KL relaxation parameter
-Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)
+# Screenkhorn
+lambd = 2e-03 # entropy parameter
+ns_budget = 30 # budget number of points to be keeped in the source distribution
+nt_budget = 30 # budget number of points to be keeped in the target distribution
+G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)
pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')
-
+ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn')
pl.show()
diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py
index 742f8d9..3a1ef31 100644
--- a/examples/plot_stochastic.py
+++ b/examples/plot_stochastic.py
@@ -1,10 +1,18 @@
"""
-==========================
+===================
Stochastic examples
-==========================
+===================
This example is designed to show how to use the stochatic optimization
-algorithms for descrete and semicontinous measures from the POT library.
+algorithms for discrete and semi-continuous measures from the POT library.
+
+[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F.
+Stochastic Optimization for Large-scale Optimal Transport.
+Advances in Neural Information Processing Systems (2016).
+
+[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A. &
+Blondel, M. Large-scale Optimal Transport and Mapping Estimation.
+International Conference on Learning Representation (2018)
"""
@@ -19,16 +27,14 @@ import ot.plot
#############################################################################
-# COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM
-#############################################################################
-#############################################################################
-# DISCRETE CASE:
+# Compute the Transportation Matrix for the Semi-Dual Problem
+# -----------------------------------------------------------
#
-# Sample two discrete measures for the discrete case
-# ---------------------------------------------
+# Discrete case
+# `````````````
#
-# Define 2 discrete measures a and b, the points where are defined the source
-# and the target measures and finally the cost matrix c.
+# Sample two discrete measures for the discrete case and compute their cost
+# matrix c.
n_source = 7
n_target = 4
@@ -44,12 +50,7 @@ Y_target = rng.randn(n_target, 2)
M = ot.dist(X_source, Y_target)
#############################################################################
-#
# Call the "SAG" method to find the transportation matrix in the discrete case
-# ---------------------------------------------
-#
-# Define the method "SAG", call ot.solve_semi_dual_entropic and plot the
-# results.
method = "SAG"
sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,
@@ -57,14 +58,12 @@ sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,
print(sag_pi)
#############################################################################
-# SEMICONTINOUS CASE:
+# Semi-Continuous Case
+# ````````````````````
#
# Sample one general measure a, one discrete measures b for the semicontinous
-# case
-# ---------------------------------------------
-#
-# Define one general measure a, one discrete measures b, the points where
-# are defined the source and the target measures and finally the cost matrix c.
+# case, the points where source and target measures are defined and compute the
+# cost matrix.
n_source = 7
n_target = 4
@@ -81,13 +80,8 @@ Y_target = rng.randn(n_target, 2)
M = ot.dist(X_source, Y_target)
#############################################################################
-#
# Call the "ASGD" method to find the transportation matrix in the semicontinous
-# case
-# ---------------------------------------------
-#
-# Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the
-# results.
+# case.
method = "ASGD"
asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,
@@ -96,23 +90,17 @@ print(log_asgd['alpha'], log_asgd['beta'])
print(asgd_pi)
#############################################################################
-#
# Compare the results with the Sinkhorn algorithm
-# ---------------------------------------------
-#
-# Call the Sinkhorn algorithm from POT
sinkhorn_pi = ot.sinkhorn(a, b, M, reg)
print(sinkhorn_pi)
##############################################################################
-# PLOT TRANSPORTATION MATRIX
-##############################################################################
-
-##############################################################################
-# Plot SAG results
-# ----------------
+# Plot Transportation Matrices
+# ````````````````````````````
+#
+# For SAG
pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')
@@ -120,8 +108,7 @@ pl.show()
##############################################################################
-# Plot ASGD results
-# -----------------
+# For ASGD
pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')
@@ -129,8 +116,7 @@ pl.show()
##############################################################################
-# Plot Sinkhorn results
-# ---------------------
+# For Sinkhorn
pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')
@@ -138,17 +124,14 @@ pl.show()
#############################################################################
-# COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM
-#############################################################################
-#############################################################################
-# SEMICONTINOUS CASE:
+# Compute the Transportation Matrix for the Dual Problem
+# ------------------------------------------------------
#
-# Sample one general measure a, one discrete measures b for the semicontinous
-# case
-# ---------------------------------------------
+# Semi-continuous case
+# ````````````````````
#
-# Define one general measure a, one discrete measures b, the points where
-# are defined the source and the target measures and finally the cost matrix c.
+# Sample one general measure a, one discrete measures b for the semi-continuous
+# case and compute the cost matrix c.
n_source = 7
n_target = 4
@@ -169,10 +152,7 @@ M = ot.dist(X_source, Y_target)
#############################################################################
#
# Call the "SGD" dual method to find the transportation matrix in the
-# semicontinous case
-# ---------------------------------------------
-#
-# Call ot.solve_dual_entropic and plot the results.
+# semi-continuous case
sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,
batch_size, numItermax,
@@ -183,7 +163,7 @@ print(sgd_dual_pi)
#############################################################################
#
# Compare the results with the Sinkhorn algorithm
-# ---------------------------------------------
+# ```````````````````````````````````````````````
#
# Call the Sinkhorn algorithm from POT
@@ -191,8 +171,10 @@ sinkhorn_pi = ot.sinkhorn(a, b, M, reg)
print(sinkhorn_pi)
##############################################################################
-# Plot SGD results
-# -----------------
+# Plot Transportation Matrices
+# ````````````````````````````
+#
+# For SGD
pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')
@@ -200,8 +182,7 @@ pl.show()
##############################################################################
-# Plot Sinkhorn results
-# ---------------------
+# For Sinkhorn
pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')
diff --git a/examples/unbalanced-partial/README.txt b/examples/unbalanced-partial/README.txt
new file mode 100644
index 0000000..2f404f0
--- /dev/null
+++ b/examples/unbalanced-partial/README.txt
@@ -0,0 +1,3 @@
+
+Unbalanced and Partial OT
+------------------------- \ No newline at end of file
diff --git a/docs/source/auto_examples/plot_UOT_1D.py b/examples/unbalanced-partial/plot_UOT_1D.py
index 2ea8b05..2ea8b05 100644
--- a/docs/source/auto_examples/plot_UOT_1D.py
+++ b/examples/unbalanced-partial/plot_UOT_1D.py
diff --git a/examples/plot_UOT_barycenter_1D.py b/examples/unbalanced-partial/plot_UOT_barycenter_1D.py
index c8d9d3b..931798b 100644
--- a/examples/plot_UOT_barycenter_1D.py
+++ b/examples/unbalanced-partial/plot_UOT_barycenter_1D.py
@@ -16,6 +16,8 @@ as proposed in [10] for Unbalanced inputs.
#
# License: MIT License
+# sphinx_gallery_thumbnail_number = 2
+
import numpy as np
import matplotlib.pylab as pl
import ot
@@ -77,7 +79,7 @@ bary_l2 = A.dot(weights)
reg = 1e-3
alpha = 1.
-bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)
+bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)
pl.figure(2)
pl.clf()
@@ -111,7 +113,7 @@ for i in range(0, n_weight):
weight = weight_list[i]
weights = np.array([1 - weight, weight])
B_l2[:, i] = A.dot(weights)
- B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)
+ B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)
# plot interpolation
diff --git a/examples/unbalanced-partial/plot_partial_wass_and_gromov.py b/examples/unbalanced-partial/plot_partial_wass_and_gromov.py
new file mode 100755
index 0000000..0c5cbf9
--- /dev/null
+++ b/examples/unbalanced-partial/plot_partial_wass_and_gromov.py
@@ -0,0 +1,165 @@
+# -*- coding: utf-8 -*-
+"""
+==================================================
+Partial Wasserstein and Gromov-Wasserstein example
+==================================================
+
+This example is designed to show how to use the Partial (Gromov-)Wassertsein
+distance computation in POT.
+"""
+
+# Author: Laetitia Chapel <laetitia.chapel@irisa.fr>
+# License: MIT License
+
+# sphinx_gallery_thumbnail_number = 2
+
+# necessary for 3d plot even if not used
+from mpl_toolkits.mplot3d import Axes3D # noqa
+import scipy as sp
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+
+
+#############################################################################
+#
+# Sample two 2D Gaussian distributions and plot them
+# --------------------------------------------------
+#
+# For demonstration purpose, we sample two Gaussian distributions in 2-d
+# spaces and add some random noise.
+
+
+n_samples = 20 # nb samples (gaussian)
+n_noise = 20 # nb of samples (noise)
+
+mu = np.array([0, 0])
+cov = np.array([[1, 0], [0, 2]])
+
+xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))
+xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))
+
+M = sp.spatial.distance.cdist(xs, xt)
+
+fig = pl.figure()
+ax1 = fig.add_subplot(131)
+ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+ax2 = fig.add_subplot(132)
+ax2.scatter(xt[:, 0], xt[:, 1], color='r')
+ax3 = fig.add_subplot(133)
+ax3.imshow(M)
+pl.show()
+
+#############################################################################
+#
+# Compute partial Wasserstein plans and distance
+# ----------------------------------------------
+
+p = ot.unif(n_samples + n_noise)
+q = ot.unif(n_samples + n_noise)
+
+w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True)
+w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5,
+ log=True)
+
+print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist']))
+print('Entropic partial Wasserstein distance (m = 0.5): ' +
+ str(log['partial_w_dist']))
+
+pl.figure(1, (10, 5))
+pl.subplot(1, 2, 1)
+pl.imshow(w0, cmap='jet')
+pl.title('Partial Wasserstein')
+pl.subplot(1, 2, 2)
+pl.imshow(w, cmap='jet')
+pl.title('Entropic partial Wasserstein')
+pl.show()
+
+
+#############################################################################
+#
+# Sample one 2D and 3D Gaussian distributions and plot them
+# ---------------------------------------------------------
+#
+# The Gromov-Wasserstein distance allows to compute distances with samples that
+# do not belong to the same metric space. For demonstration purpose, we sample
+# two Gaussian distributions in 2- and 3-dimensional spaces.
+
+n_samples = 20 # nb samples
+n_noise = 10 # nb of samples (noise)
+
+p = ot.unif(n_samples + n_noise)
+q = ot.unif(n_samples + n_noise)
+
+mu_s = np.array([0, 0])
+cov_s = np.array([[1, 0], [0, 1]])
+
+mu_t = np.array([0, 0, 0])
+cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+
+
+xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
+xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)
+P = sp.linalg.sqrtm(cov_t)
+xt = np.random.randn(n_samples, 3).dot(P) + mu_t
+xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)
+
+fig = pl.figure()
+ax1 = fig.add_subplot(121)
+ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+ax2 = fig.add_subplot(122, projection='3d')
+ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')
+pl.show()
+
+
+#############################################################################
+#
+# Compute partial Gromov-Wasserstein plans and distance
+# -----------------------------------------------------
+
+C1 = sp.spatial.distance.cdist(xs, xs)
+C2 = sp.spatial.distance.cdist(xt, xt)
+
+# transport 100% of the mass
+print('-----m = 1')
+m = 1
+res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)
+res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,
+ m=m, log=True)
+
+print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))
+print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist']))
+
+pl.figure(1, (10, 5))
+pl.title("mass to be transported m = 1")
+pl.subplot(1, 2, 1)
+pl.imshow(res0, cmap='jet')
+pl.title('Wasserstein')
+pl.subplot(1, 2, 2)
+pl.imshow(res, cmap='jet')
+pl.title('Entropic Wasserstein')
+pl.show()
+
+# transport 2/3 of the mass
+print('-----m = 2/3')
+m = 2 / 3
+res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)
+res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,
+ m=m, log=True)
+
+print('Partial Wasserstein distance (m = 2/3): ' +
+ str(log0['partial_gw_dist']))
+print('Entropic partial Wasserstein distance (m = 2/3): ' +
+ str(log['partial_gw_dist']))
+
+pl.figure(1, (10, 5))
+pl.title("mass to be transported m = 2/3")
+pl.subplot(1, 2, 1)
+pl.imshow(res0, cmap='jet')
+pl.title('Partial Wasserstein')
+pl.subplot(1, 2, 2)
+pl.imshow(res, cmap='jet')
+pl.title('Entropic partial Wasserstein')
+pl.show()
diff --git a/notebooks/plot_OT_1D.ipynb b/notebooks/plot_OT_1D.ipynb
deleted file mode 100644
index bf9f9cd..0000000
--- a/notebooks/plot_OT_1D.ipynb
+++ /dev/null
@@ -1,248 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# 1D optimal transport\n",
- "\n",
- "\n",
- "This example illustrates the computation of EMD and Sinkhorn transport plans\n",
- "and their visualization.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "import ot.plot\n",
- "from ot.datasets import make_1D_gauss as gauss"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n",
- "\n",
- "n = 100 # nb bins\n",
- "\n",
- "# bin positions\n",
- "x = np.arange(n, dtype=np.float64)\n",
- "\n",
- "# Gaussian distributions\n",
- "a = gauss(n, m=20, s=5) # m= mean, s= std\n",
- "b = gauss(n, m=60, s=10)\n",
- "\n",
- "# loss matrix\n",
- "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n",
- "M /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot distributions and loss matrix\n",
- "----------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x216 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot the distributions\n",
- "\n",
- "pl.figure(1, figsize=(6.4, 3))\n",
- "pl.plot(x, a, 'b', label='Source distribution')\n",
- "pl.plot(x, b, 'r', label='Target distribution')\n",
- "pl.legend()\n",
- "\n",
- "#%% plot distributions and loss matrix\n",
- "\n",
- "pl.figure(2, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% EMD\n",
- "\n",
- "G0 = ot.emd(a, b, M)\n",
- "\n",
- "pl.figure(3, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve Sinkhorn\n",
- "--------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Err \n",
- "-------------------\n",
- " 0|8.187970e-02|\n",
- " 10|3.460174e-02|\n",
- " 20|6.633335e-03|\n",
- " 30|9.797798e-04|\n",
- " 40|1.389606e-04|\n",
- " 50|1.959016e-05|\n",
- " 60|2.759079e-06|\n",
- " 70|3.885166e-07|\n",
- " 80|5.470605e-08|\n",
- " 90|7.702918e-09|\n",
- " 100|1.084609e-09|\n",
- " 110|1.527180e-10|\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% Sinkhorn\n",
- "\n",
- "lambd = 1e-3\n",
- "Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n",
- "\n",
- "pl.figure(4, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_OT_1D_smooth.ipynb b/notebooks/plot_OT_1D_smooth.ipynb
deleted file mode 100644
index 69e71da..0000000
--- a/notebooks/plot_OT_1D_smooth.ipynb
+++ /dev/null
@@ -1,302 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# 1D smooth optimal transport\n",
- "\n",
- "\n",
- "This example illustrates the computation of EMD, Sinkhorn and smooth OT plans\n",
- "and their visualization.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "import ot.plot\n",
- "from ot.datasets import make_1D_gauss as gauss"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n",
- "\n",
- "n = 100 # nb bins\n",
- "\n",
- "# bin positions\n",
- "x = np.arange(n, dtype=np.float64)\n",
- "\n",
- "# Gaussian distributions\n",
- "a = gauss(n, m=20, s=5) # m= mean, s= std\n",
- "b = gauss(n, m=60, s=10)\n",
- "\n",
- "# loss matrix\n",
- "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n",
- "M /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot distributions and loss matrix\n",
- "----------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x216 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot the distributions\n",
- "\n",
- "pl.figure(1, figsize=(6.4, 3))\n",
- "pl.plot(x, a, 'b', label='Source distribution')\n",
- "pl.plot(x, b, 'r', label='Target distribution')\n",
- "pl.legend()\n",
- "\n",
- "#%% plot distributions and loss matrix\n",
- "\n",
- "pl.figure(2, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% EMD\n",
- "\n",
- "G0 = ot.emd(a, b, M)\n",
- "\n",
- "pl.figure(3, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve Sinkhorn\n",
- "--------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Err \n",
- "-------------------\n",
- " 0|7.958844e-02|\n",
- " 10|5.921715e-03|\n",
- " 20|1.238266e-04|\n",
- " 30|2.469780e-06|\n",
- " 40|4.919966e-08|\n",
- " 50|9.800197e-10|\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% Sinkhorn\n",
- "\n",
- "lambd = 2e-3\n",
- "Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n",
- "\n",
- "pl.figure(4, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n",
- "\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve Smooth OT\n",
- "--------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% Smooth OT with KL regularization\n",
- "\n",
- "lambd = 2e-3\n",
- "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n",
- "\n",
- "pl.figure(5, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n",
- "\n",
- "pl.show()\n",
- "\n",
- "\n",
- "#%% Smooth OT with KL regularization\n",
- "\n",
- "lambd = 1e-1\n",
- "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n",
- "\n",
- "pl.figure(6, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_OT_2D_samples.ipynb b/notebooks/plot_OT_2D_samples.ipynb
deleted file mode 100644
index cd1b541..0000000
--- a/notebooks/plot_OT_2D_samples.ipynb
+++ /dev/null
@@ -1,366 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# 2D Optimal transport between empirical distributions\n",
- "\n",
- "\n",
- "Illustration of 2D optimal transport between discributions that are weighted\n",
- "sum of diracs. The OT matrix is plotted with the samples.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n",
- "# Kilian Fatras <kilian.fatras@irisa.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "import ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters and data generation\n",
- "\n",
- "n = 50 # nb samples\n",
- "\n",
- "mu_s = np.array([0, 0])\n",
- "cov_s = np.array([[1, 0], [0, 1]])\n",
- "\n",
- "mu_t = np.array([4, 4])\n",
- "cov_t = np.array([[1, -.8], [-.8, 1]])\n",
- "\n",
- "xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\n",
- "xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n",
- "\n",
- "a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n",
- "\n",
- "# loss matrix\n",
- "M = ot.dist(xs, xt)\n",
- "M /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5,1,'Cost matrix M')"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot samples\n",
- "\n",
- "pl.figure(1)\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('Source and target distributions')\n",
- "\n",
- "pl.figure(2)\n",
- "pl.imshow(M, interpolation='nearest')\n",
- "pl.title('Cost matrix M')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute EMD\n",
- "-----------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5,1,'OT matrix with samples')"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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+fHmlJ18brF27FmazGRcvXsSlS5dqEPj48ePxwQcfqJI1Tp48CQC4dOkSunTpgt/97neYOnUqTp06VetzO4NG7BpcIiEB2LkTCAgAbsWpyMwEvv4a8PQEAr5eAt/Ha0o4ZS8swbffArm5wNy5QLt29TN3De7h1VfrZ5zCwkIsWLAAffr0QVBQEBISEvDaa6+hUaNG+PLLLzFr1iwEBgbCw8MDzz//vOXcr+KFF17AwIED4enp6XDsp59+Gg8//DCCgoIQHByMlStXokGDBli3bh3+/Oc/Izg4GCEhIZVBUFv86U9/QmBgIPr27Ythw4YhODgYv/71r/H1118jODgY586dc7k6sIeHH34YgwYNwsSJE/HRRx9V8foBIDIyEkajEUFBQQgICEBkZCQAQK/Xo2/fvggJCUF8fDyeeuqpWp/bGe5Iz9OBAwdKrdHGvYHUVJJyu3bAU08BXrUU727eBL76ikv9hQvte+EVFcDKlcyCmT0bqOb01C+WLWNqpa2UYzBQs3cRH7gXcPbsWfTu3ftOT+MXgYULF2Ly5MkIDw+/LePbe5ZCiBgppUvZW/PYNThETg6wahXg6wvMmVN7Us/OBr75hj8/9ZR9Ujebge++A5KTgalTbzOpAz9fKQQNGu4gtOqOGuyipIRetNlMvbtJk9p9PieHnr7JBCxYANjbkyYlsGULcPYsMH48EBxcP3N3CjWPfqYOadMj4Lc5StvJquGW8NVXX93pKTiE5rFrqAGTiVyXk0NPvbZBzNxckrrRyMyWhx6yf9zOncDJk8CoUcCQIXWft7s485CCg0ER8PtiKczPaTtZNdx/0IhdQxVICXz/PfXuxx8HOnWq3efz8kjqZWUk9bZt7R938CAzbUJDActGw58FJ08CMf80IDQmChUvR8LjY20nq4b7Dxqxa6iCffuAuDiSbVBQ7T6bn09NvaQEmDfPcWbLiRP01vv2BSZOtG6Qud04cgQ49R8DdOt18P5OD6+3alEKQYOGewgasWuoxKlTwJ491Lot5TTcRmEhSb2wkJp8hw72j0tIoK7erRswbdrPQ+pS8rp+/BHoVxFNUh+r7WTVcP9CC55qAABcvgxs3gz4+3NnaW0It6iI8kt+Pj11R8X8Ll1iBoyfH51kJynL9QYpSehHjwIhIUDfyCXwqO7OaDtZ6w1ZWVl45JFHAADp6enw9PSEWs312LFjaNCgQb2f88SJE8jIyMCECRPqfezaoKKiAq1atUJu7p1vR6ERuwZkZQFr1gDNm9eecIuL6ann5tJTf/hh+8dduwasXs1A7Ny5wC326K0VzGbGC2JjGZwdN+7nk33uCdyGnP6WLVsiNjYWAKsz+vj44KWXXnL78yaTyekmJXs4ceIE4uPj7zix303QpJhfOIqLWXBLCBJzbWoRlZSwTEB2Nsna39/+cZmZPIePDz36eq53ZBcVFcC6dST10aNvkdTv97Z8P3NO/5QpUzBgwAAEBATgs88+A0Avt3nz5vj973+PoKAgHDt2DJs3b0bPnj0xYMAA/Pa3v8W0adMAcFfrwoULK0vgfv/99ygpKcHrr7+Ob7/9FiEhIVi3bl2Vc54+fRqhoaGV5XEvXbrkci4vvvgiAgICMH78eBw9ehRhYWHo0qULtm7dCgD47LPPMH36dISFhaF79+5444037F7vP/7xDwwaNAhBQUF4/fXXAQAFBQWYOHEigoOD0bdv3xrzrTe4UwKyvv9pZXvvDhiNUn7+uZRvvCHl1au1+2xJiZQffyzl0qVSXrjg+LicHCn/9S8p//lPKbOz6zbfGnjnnZrle3fvlsY335HffCPla69JeeRIHca3lAgu2Lxbms3ynigZXOuyveo1RUbW+7W9+uqr8t133618nZWVJaVkudzevXvL7OxsaTQaJQC5fv36yvc6dOggU1JSpNlsluHh4XLq1KlSSin/9Kc/yVWrVkkppczOzpbdu3eXJSUl8tNPP60spVsdzz//vFy9erWUUsrS0tLKMr3O5rJjxw4ppZSTJ0+WEyZMkEajUR4/fryyrO+nn34q27dvL7Ozs2VhYaHs3bu3PHnypDQajbJZs2ZSSil/+OEHGRERIc1mszSZTHL8+PHy4MGDcvXq1fL555+vnF9ubq7D+3c3lO3VcI9BSmDjRuDqVQYx/fzc/2xpKYt13bjBEgDdutk5aNkylGw1YPly5rPPmwc8GFvP3q7F45S7DSgqAmAwQOp02JEbWrmTdfDgOoyvKLj2nh4ec3S49vR92pZPUdiVaunt70713nvvITg4GEOHDkVqamplw4oGDRpg+vTpAICEhAT07NkTnTp1ghACc+fOrfz8jh078OabbyIkJASKolSWwHWGYcOG4Y033sCyZctw9erVyloujubSuHFjjB07FgBL9I4ePRpeXl41yvWOHz8eDz74IJo2bYpp06bhwIEDVc67Y8cObNu2Df369UP//v2RlJSE8+fPIygoCNu3b8df/vIXHDx4EM2aNavbTXUAjdh/odi9GzhzBnj0URb3chdlZZRV0tLIc9272z+uPDgUmK3Dg7EGzJ4NtEm4Dct8RUH5Cj3KpumQEP4KzLN0+H6eHjG+CmbNYrC0LoiPB75IVpAwmpuZ6kx8d6O0YzCwK9Vt7k61c+dO7Nu3D0eOHEFcXByCgoIqS/Q2bty4Rrlbe5BSYuPGjYiNjUVsbCyuXLlSWd/dEebPn48NGzagYcOGmDBhAvbt2+d0LrbB3bqW6/3b3/5WOdekpCQsXLgQvXv3xvHjxxEQEIC//OUveOut29O6ol6IXQiRIoQ4LYSIFUJo1b3ucpw8CRw4wPK7w4a5/7nycpYZuHYNCA93XNfFaGSf0rXhesz+ToeM51+BKbz+vd2MDODj8wqO9Y9A6PaliB4YgfjWCp54AqhrHaxjx4D164GBBQYMOFZPxHe31alRz6/XA6/f3pz+vLw8tGjRAo0bN8aZM2cQ7SC9tE+fPkhMTMTVq1chpcSaNWsq31NL4KpQS+D6+vqioKDA7niXLl1Ct27d8MILL2Dy5Mk4deqU23Nxhh07diA3NxfFxcXYtGkThg8fXuX98ePH4/PPP0dRUREAIDU1FTdv3sS1a9fg4+OD+fPn449//CNOnDhR63O7g/r02BUpZYh0o/KYhjuHS5eYR961K/DYY+4HFI1GFgS7ehWYOdMxcZrNJMTLl4G+v1VwYnAEBv24FKZn6neZHx8PfPYZVwLDTkXhyNhIBB6IwtNdDeja9dbHlZK8tm0bMLLCgAlf6tjByR7x1dYDt+TMy1k6ZP3mLpB2oqOrnv825vRPmjQJxcXF6NOnD/72t79hsAONrEmTJvjvf/+LRx99FAMHDkTz5s0r5YpXX30VRUVFCAwMREBAAF6zdAkZM2YM4uLi0K9fvxrByJUrVyIgIAAhISE4f/485s2b5/ZcnCE0NBRTp05FcHAw5s6di5Bqy8PHHnsM4eHhGDJkCAIDA6HT6VBYWIi4uLjKYO5bb72Fv/71r7U+t1twR4h39Q9ACoBW7h6vBU/vDG7ckPLtt9mz1BJDcgvl5VJ+842Uf/+7lKdOOT7ObJZywwYGLY8elXLn/+6WhU1aydwX6i8wZzJJuX07z/HDn3bLigdbyVXP7pbvvitl1rq6BTdNJim3bOHYGzdKafqH/eBsZV/X6sFUN4Kr169LeXic836vdcGt9jy9m1BQUCCllNJsNstnnnlGvv/++3d4RlXhLFhbn7gbgqcSwA4hRIwQ4ll7BwghnhVCHBdCHM/MzKyn02pwF4WFlFG8vYEnngCq9QNwiIoKOnGXLjEYGRho/zh1I5BajqDhIQOGvKfDlXf1aPbv+lnmFxUxvfLIEaoXQzyisWamHmm9FCxaBLSYeeseZ0UFVxrHjwPDh7NOjsefl9T0pm37uioK5Bo9jDN0uLLQtQd+6hSw628GBB2MQsHv7fd71QBERUUhJCQEffr0QUlJCZ555pk7PaV7DvXSaEMI0UFKeU0I8RCAnwD8VkrpsFOt1mjj54XRyGYXmZlsdtG+vXufU0n9wgUSnZO2ldi3jxw1eDD7oV7+zTI0GhmKEZGKVe6pw+aX1FTOpaQEmDyZOfFr1gAPPMBiY3VJLigr41jJycDYse7HHUwm4IcfgGb/egVh+5ZC/i0SYunrNY4zm4EdO4Abqw2YvV4HuUaPxo8pVTXuepJjtEYb9w/ueKMNKeU1y/8ZADYAGFQf42qoO9RGFtevUxt3l9RNJm7wuXABmDTJOalHR5OjgoKAQYOADRuAizOXYMjLSlUN39bbrQViYmiYPD2BxYu56li5EmjRgoaqLqSulkNISWHap7ukXlLC7KCc7wwYGhdFUv+opgeurjKOHgUGe0TDe4OF1IHbpmnXh7Om4c6irs+wziUFhBBNAXhIKQssP48DUNNt0XBH8NNPwLlzbGThbncik4myRGIiqy8OdOIfxMcDW7cCPXrwWLX3wOzZdS8bUFHBsU+eZLB35kzOafNm5t3XRlKyh9xc5uPn5bHuvIvMuUpkZ1s6Sx034MnNOnhtsHjcY5QqHvj161wJFBfTaPQKtmPU6rlOTaNGjZCVlYWWLVu6lUKo4e6DlBJZWVk1+qfWBvVRK6YNgA2WPyIvACullNvrYVwNdUR0NPXoQYPcb2RhNtPjVrsaDXKy9kpK4rGdOpF0f/iBm5aeeAJ48MG6zT0vj/x4/TowciR1+2PHqON37Ur+rEs9qYwMkrraDMRRjZvquHKFZC0l8ETbaHitt59VEtdCwZYt7Dy1aJH7K6W6ws/PD6mpqdDiWPc2GjVqBL/a7BqshjoTu5TyEoCfo6mZhlrgwgWm7PXoQYJ2B2Yzd6OqG5ecGYOrV8lhDz1Ebzc2lsHB0aMdb1pyF5cuccVgMlmbW+/bx9K7vXsDM2bUvv+qLa5cocft5UUpp00bJwfbFMo6fRrYtAkIyDBg3IPRaPp2TQ/cNErBT2UKjm5k7ZzwcKBpUzcmVU8Fuby9vdG5c2e3j9dwf0LbeXofIj2d+nibNvSka5SptQO1c9Lp08CYMcwMcYQbN6hx+/qycFhGBj3pHj1qX8e9+hwOHqQn3bQp8MwzJPUdO0jqISEkyrqQ+vnz1LybNAF+9SsXpA4AoaGQOh2ilxnw3XdAaKEB01bp0HR0zU1FVfT0wSyj4BapW84DnQ55G++SzUsa7mloZXvvM+Tnk3QbNaIk4o5coZJ6bCwQFkbpwxFycki83t6UMABg7VoGMKdPv/WyuGVl1M4TEoA+fZha6eXF38XGkijHj69b2d24OHrcbdvSILlDuhUjFex+Wo/hf9fBNCICg09EccNSNV28up5e28bccrSChFf18H9Sh8wFEWi9VmuyreHWoRH7fYSyMkoMZWXMHvH1df0ZKa0BypEjSeyOUFhIj9Rkom7s68ta7GVl9E5vNdZz8yZJMSuL6YZDh1oDuAkJnFNYWN1I/fBhev6dO1PesZQAcYqiIuDzz4GcRgp8hkZg2I6ltGbV5JLrm6LxRasl8PHhfXfUEtARjEbLailLQfjECARELWUJA43UNdwiNGK/T6Bu5VeDly4lBpDUt2/nppxhw8gjjshTrehYWAg89RTQujU/e+UKNW93zmcP584xAOvlRc7s3Jk1afR64OJF1lEfOvTWxlavcdcuSjx9+nBV4Y6Uk54OfPkl59I/jymNmD+fNyEkBLmLX4TvcQNMM3X4aboeHfvVQk+3QW4ujVp6OjC9uQF99trUpdE6O2m4RWjEfh9ASgZK1Zxzu2V07Xxmxw5mmgwZwmCpI1I3GinvZGbSaPj5UYtXtWRHu1GdwWymjHzgADNGdDrKOaWlPFdqqutNUe6cQ5WYBgxgbRx34g3x8cz9lxJ4xMOA4ct1lfKLDA4BXnoJl7+JRY+L26CfqUfbuQrGjnVvbFskJ1PGMpuBX3UxwO9Fm81KinLna8louGehEft9gCNHrF63s5xzFaoXq6ZCOusuZDKRW1JT6ZF27cpVwebNTBG0lK6uFYqLSZwXL5K4H3uMXnRRER3ijAyeq0+f2o+twmi05uKPGsVsHXeknJ07AfnOMnTqEIrev1YwaI+lUBYA09vLsKnnEnQNjEVw3HLsHx2Jfi8qCAqq3dyk5L0WMzUVAAAgAElEQVT/6SegVStKQy0/d1KQSyN2DbWERuz3OM6do+fduze9bndgMFCaGDAAmDDBMeFJyWBjUhK38ffpQ496zRrq6bNm1b4hdVoa+aqggGMOGMDf5+VRv8/LY5s9d1YdjlBayv6qly/z+twp3icld5JevAh0fTgU8zbq4LlAz1RDgwHmWTpsW6BHQaIB3ZK24cjYSAyLiYJnlgLAfeKt1NNPA716MdDasCHspzRqUoyGW4SW7ngP49o1eqUdOrifkbJ3L7B/Pz3lSZOck/q2bSSgRx4hAUtJPTwvjyqBj0/t5hsXB3zxBaWHRYuspJ6VRT27sJAydl1IvbCQu1+vXqX27w6pl5QA779PUn/gAWD6+wo81+lRMVOH4pdYS37dLD2ys4HwtToc+J0eQRtfh+e62hU2y83l9Z8+bVVaGv7nLmi+cTc2ANFQJ2jEfo8iN5cZMD4+3CDkzvb9/fuZDx4cDEyZ4twQ7N1LFWDoUGtO+759zAMfPx7o2NH9uZpMzLzZuJH6/LPP0hgB1iCl0QgsWOD+DlB7yM4mcarNtd3R/tPTgffe4/3s3Bl44QXmuO8RCg4GRqDJv5biUFAEzndQ0C41Ggmv6jH2TQVNmqBWtV6Sk4FPPgF6bFyGxZ0NGDXKcv9DQ+m2P/ccD7wT+et3WwMQDXWGJsXcg1ADjBUVJEN3POdDh9gOLzCQQUlnpH70KIk9JIQauhAMzO7Zw0Jftfm+FxQwQHj1Ko3Eo49ag4xXr/I6GjSgp96qlfvjVkd6OvV5s5lZO+7sxj51isZGSsYnxo5lFszGjUDJVgN0J6Kwd1QkBh6LQkpnBW3/b0lNY+FCLrHV01u2BPo/F4pmz+iQ94AeTScr8AIghYDp29XwaN0GHh//DPnr1Xe5Kgrw8suQkycjVfdH+G2xn6uv4d6BRuz3GEwmEmVWFnPHW7d2/RmVWAIC6Bw6y944dYppjL16Wb367GwGO9u0oS7ubj755cvcAVtWxmCobW/VS5eog/v6ktSbN3dvTHtISeFYDRvS0LlzT7ZtY0aQENa55eRwnCZHDdCt10E/U4/LXRRkBCh4Yo0OnvP1qKue7u2tIP6qHp2f1CF1ZgQe3hqFHxZvwAMxBoS9+TPlr1s89OyP9Gg2TYHYY4Bp6dtI7DkTfb9aisI/RMJHI/V7GpoUcw9BShbaunSJpOtOSRC1cJZaY8UZqZ8/T2+1c2drKQKjsTIpxO2KjVLS6//mG3rjTz9dldTPnrWW3V20qG6kfu4cPXVfX24OckXqFRWUa44dA0YeWYbf9jUgIIBSyaefAg/EGDDy6LtYG65HSmcF/v7ApH9Sc69NeV17enpREfX/9dkKkidEwH85ZZ6iImD46dvfUFpF2TAFx17So+FTOiTPfwVl03TYPehldEvahqIXI+GzXGsAcq9D89jvIRw4YN0hWq3Fol3ExNAz7dnTdc2Yy5e5EmjXjgTu5UWC3rKldhUbbb3Unj3ppdruSFW39XfowDEbN3Y9piOcPMlztW/PsZo0cX58QQF17sJCGpURvw+F9zwdzqbrsfamgt7pBkxapcM6nR7J/kpV6agWGSrJyVypmEzU+rt3ZzrqTz9xrDltDfD/F2WeoUfex7CY/8Bj48bbnr8uJY3q9u1AQakCERaB0FVLcWbAfDwS/TY8N+ohxijAZC2H/l6HRuz3COLjrRq5O9+1kydJyt27U2pwlpaYns5AbLNmrKGibrePjq5dxcacHKZC3rjBOY4cWVW2OXqUpNKlC43HrZbdVYuF7drlfgnflBR69iYTi5XNmQOYzQoOvaBHyJ90GDssAkGHorBOp8eVrgpmOGkD6GxeR48y/bRlS57Dy4vnvXSJcx1lMqDVb3RYM0sP/0UKvFbcgId+jXWQ25S/npvLAPaFC8z86Z7KXa5JcyPRZ9P/sfPTGC2H/n6BRuz3AK5coUTy8MOuA58Ad1pu3mwlPWdb6LOySDwNG1LrVr3eK1dqV7HxwgXq8ACNg23KopTMyDEYqDXPnHnrFRrVHbNHjgB9+3JF4CqX/tAhessAr0VRKIusWgVcMymoGBiBsJ1LsXdUJLKDFTw9h4XCagOjkYb01Cle49SplIm2b2dAd9IkplWeXxqNYwv0UP6iMLNo1MfA3DlVSbQ2+esuyv2aTLxXe/fy3jVrBjwYa8D09TqUfKNHt5kKYLB46P363docNNx1qDeNXQjhKYQ4KYTYUl9jamDgcvVqfiFVicQZ1Jrhqlfs7Pj8fG4KkrJq31A1k6V5c9f58VKSNFau5OeffbYmqf/0E7kmOJibmm6V1E0mGji1mfWMGc5JXQ00qxKITkeuSkujlH3tGuCfbMDA45RFBp+MwvM9DbUm9bw8pmyqq5vHHmO+/6ZNwKMnluHprgYkJHDFlfPMEjz+ONBxlU2OuG3LwNrmlNukKpaXo0qq4tWrlJ527mTmVEUFDdDY5tFotEnP5t/q+W9Diz4Ndw716bG/AOAsgAfqccxfNIqLSZiAexrymTMkFH9/17ntJSX01EtKmEmiphqqvU7dqdhYWsrznT/PNMjJk6ue02ymF3vyJEsXONvl6gpGI0n6wgX7Mk91qL1MMzO5Glm8mE1B4uM5Z7OZpB6+lpuP2j+poIGHAo85tdOWU1I4L1VPNxppNIxG5vu36RwKn1/p4DFbj0kRCgbkGyBm66wR6epQiVqdg23Da3tQFJSv0ENO0yF2aAQGxUShfLkeOwoVnPiCMYyGDSmTDRzIWvuNG2u7XO931IvHLoTwAzAJwGf1MZ4Geld6PbXROXOo2TrD2bPchdqxIwnGGamXl3P7fHY2x7Zt27ZjB2WYKVOcV2zMyGAWSVISe50ylc/6vlp29+RJyh91IfWSEq4s1CJnlZt7HOD6deCDD0jqLVsCv/sds2V27eKczGYe1/5aNL6brceAlyxFvB5x33NV89O/+YYGd948euzr1jEw+6tfWTJjkhXs+JUeM/U6+L77ipXUHZGoxXs2T56MnMefqkny1Tz3pCTgw7MKjoREYPCPS5E+LQLvn1bQ+INl6HPDgJISGu1nngEmNTGg8Qd2PH9t5+l9h/ry2P8NYAkANyqAa3AFtfHF5cuUG1ztxkxMJKGomSbOAokVFQxwXr9OzrBNmTx9mmmArio2xsdTw1fzxqvPT02RTEripp9hw1xfsyPk59MIZWVRxnFVGEyNL0hJSWj2bBqZVatoGKpcx2NLMHduNT3dDc/VaGTaaVwcM3/69rU22Rgzhr/bsIFB6R49gMQrClqFRmCE3nWeupRAbHMFXj1mIvD75TA9MR+edjz34mIa4bg4IDjbgOGnoxA3NRLdVkah01MKrrUPxaxvdAj6lx4tpyloddqJ51/bVYKGux51JnYhxGQAGVLKGCHEaCfHPQvgWQB4uC77xn8B2LuX3p+iuM7MuHCB37927apmtNiD2qj60iUGYXv1sr6nVmzs1MlxxUazmXrt4cNcGcyaVbOZR2kpSVT1+vv3d++a7SEri556SQmvzVnevtnM1M7jx/l6+HDWuMnJ4Xxu3qx6fOfOzBZyJW9VR14eSTwtDRgxgqmT69dzdfPEEyTzzz5jHKF3b66k+uUaMOyU6zrrxcU06KXbDJidtA0l4fPRaNUKGM2A985tgF7PTktneK0lJcAUHwP6vqfDqhl6XO2mwL+1gqnf6BD3Vz0wfQY6vzgdR1b9DiPio+CxVs+AzerVwMcfV71/02agYroOxQsi0Hyl1r3pXkd9eOzDATwuhHgMQCMADwghVkgp59keJKX8BMAnADBw4EBZD+e9LxEXZ93O76xFHcCiVWvWkFRc6eHq5qaEBBK3bZ1ztWJj48aOUyOLirgqSEmhgzd+fM3jqpfdtd2UVFtcv05PHeCqwFYuqo7iYpJ3aiolmqlTGai9eJH6d3l51eOHDSPp17Z+uq2ePno0cOIEA80jR3KVs307VzN+ftac8bFeBgxdoYNY67zO+oULDLa2STDgiU065Hymx6p0BaMvAMGrlwPz56NgoIKtembbtGvH1UHJ69FYNU2PzD4KjEVAYaiCwkf06LA/GhsazsGs8uUYtXcpTH+N5IlWr+ZNmjOn0js3hevw/Xw9Hgxug7D3te5N9wOElPXHsRaP/SUp5WRnxw0cOFAeV10rDZVISaGH2qkTPVRnGR+XLpHMWrYk8bna6LNrFzc4jRhBUlMhJce5eBFYuNB+ca/UVBJacTEDpPb6eaoZNrm55C138t6dXduaNVbt2ll8Qa0RU1RECWrePBLr0aNM1/TwsGrqnp6MBfTtW7v5SGndwduiBY3M6dOc17RpPGb9enrzQUEMJldU8F4FbXeejmg0UlI5fpzB3XnXl+Fq21Csz1YwddtzCIhfDY+pU2Fe9x3WzPseZjMw1CsaceOX4PRpGvOyMq7Uxoyhkd+6lSuw4eUGKO9Ph7msHB4eAp4NvZhWBMAUrkPyeJY0WD1dj0aNgJlrdPD8nwiuKjSP/a6EECJGSumy64KWx36XQO372aIFidEZqaekkIxbtGDBK1ekfugQSX3AAH75bbF3L73FiRPtk7q6e1Xdsm+vn2d2NoOIpaUk1k6dXF6uQ6iZPS1bcixnfVttM1yaN+e98PW1NsAWwkrqvr40lrVt4Werp3fsSA/99Gl2nVIUGhCDgZt+goN53tatKVO1bg0gyHEGyrVrnH9WFscbNgz44YclSEwERlYYEHhmNaQQ+LH9ItyYvQhzV0yDh6fA2ic2IOkMDVlpKc87YgQlsq1bea0LHjagwx90+DZ8A3qnGxC6fSng2YRB38YKjIERGLWKufudOgGj/p8OYr3Wvel+Qb0Su5RyD4A99TnmLwFFRZQdPD1JPs4klStXmAKpEpkrjfjkSeZx9+nD/GrbbJILF0jswcE1KzZWVFibXHftyk1F9gzIjRvWXPgFC2rfyNkW0dE8p5rZ48hg2Wr9AA3JnDkk4a+/5goD4JwA6umzZtW+fIGtnu7nx2qUzZvzOlu2pKqRnMwAaWkpST04mPfZWQDbbOaGLeOby9CmWygm/UmBlxe1+ZanDFjYNBod/YCzb21ETAww4786XJ4YASkEYnvOxpWuCsylLPEwcSJXSV9+Sc19yBD+S/l1NPZO06PTw8DALdT35X/+g6Q3V+N8RyA8OgoHx0Ri+MkoeJX+CLz8co1qj3j3XY3Y71FoHvsdhtFIgigspBTirCBWaioNwAMPkNRdNU4+d47BuC5duNHIVlNWKza2bVuz4UZeHp2169epH48ebV+PVufj7c353GrZXSlZ633PHko4s2Y5TtcsKaEslJzM12rDkPR03seioqrH11VPLy/n/U5N5Ypn7FhmK330Ed8bNozefFmZez1as7PppaemAsrwUIT/V4f4XnpsyFXQN9OAqZt1yP9Uj8+zFPjrl8HcIRSxQyIwfP1S7B8diWR/BaGGZWjy2hJ07kxdv/2KZQgICUX/Pyrw8ODqqVmDUIzPWY02P3wHoWeZhCOZCqZ8OQ2zsQb7/rABQ15W4BWj8I/j73+37jx97jneTItsA6CKfKTh7odG7HcQUvK7k5rKla/afMIerl2jlty0qVVycAa1EFWHDjV3oNpWbNTpqpLopUvUi00mfs42c8YW9VV2V+3UFB1tbQDiSIa6cYMSVF4eX48bR+9U3W0rhNVL9/BgqmhtA7iqnr59Oz18k4m/e/JJbvzauZPyS5s2fH34MCWx+fOdyzxSMtj644+8vpkzgc6dFews02PYyzpMHBWBgdFROBWpx6Z4eskNOoRi9uppAAQOKJEIPfQ+Bh/7D0pXbkR0EXeVNmgADAkPRY+/6ZDcS4/VNxR0u2rAzM06eIbPgFyjx34vBYavALRT0DVwDh7uCIx9S6ExVxRgwwbIVathmqmDx68j4KEGWFVo6Y/3HDRiv4PYtcuapdK7t+Pj0tJI6o0bUwZ4wMXe3uvXSbotWtTMa1dz5G/cIFmpFRulpBa/a5dNg2UHQctz52g0WrYkodW2RZ4KtURAfDybcKhNPewhIYGerslEIzVzJiWQn34iuXp5UT4COJ9582qvp1dUcKdsXByDkSUlDIZOmMCVwOefc2XQvz/lj6NHGYidPNl5mmlREXX/8+cpC02bRs89KgoobqSgwcAIhG1fiugJkdiaR8KtvA9CQEoJb2+ggSwDpCd++gmIb01DOL6BAQ1PRePYH/Xo+zsdxgyPwKATUfBYp0fRIKUykwlg0Lfzjx+jRYuq8yserOD7DAVtE9tUqQkvdTrcmBGBNt9Va7zhoj6NhjsPjdjvEGJirA2lhw51fFx6OjVsdTOQWs/FEW7epDyiZpRU15WPHaOHO3q0taZLWRmJJyGBWvzUqY414voqu1teTgfw4kWWxlXb71WH2UzOOHCAZNekidUgrVrFTVCenlZS9/enc1kXPV0IjqnTccUSG8tVhZcX53rsGMl60iQ+P2e7YBMTeW/LypgiGhpKfX3vXq4qOl0yYJDaqWlfFBLaKLjcRYGUQIfr0VgzewP8kw0I27EUKSPno82xTegZuxohXyvoeoVNtrcv0iPaR0H5oAiM/GkpCn4fiZv+CvT/pfYvBJ/3iBE1JamLF2lcHzrDXHv5t0iIqChkBytIHhSBAZ8sRc5vI/GgLYnbbGiSo9moQ/Po7y5oxH4HcPEiMy26dasZ0LRFRgZJ3dubpO5K7sjL4/FCkNSre/ZXrjC1zrZio5qNk5VFj3noUMfzOXaMBNe5M4OVt1p2V62Bc/26c126pIRxgKQkvn7oIRqT8nIGGrOyOFeTie/fqp5++TJXOGVlfN2rF0nb05Pnj4+nwejYkYW8mjVjyQBngeLycsouJ05w5aCmpC5fzvMBQI9rBkxZp4M+XI/U7gpSOisIX6vDd7P1uNRJweGRS+CfbMDgk1E4oESi39EoXPvVq+i58m3E/a4NKg5HYb1Oj/MPKPC/yGJmhX+IRMMvorAvR0FpZwUtWjBmUb2wmdFIWenYMSA4x4DHN+ng8Z0e5jAF8a0VdHtyGgKEQGZEJFqvigKmK1WCqxUr9TBP1+H8IxEI2Ke10rvboBH7z4wbN+jYPPQQN/E4IqHMTAbBPD1JCq6aXBQVkTTKyhiErS6j2KvYeO4c5Q0vL0oqjnZ2SkmPefdubpcPD7/1Co2q8cnLo9zTs6f94zIySLY5OXzdowfll8uXrTEAdW5qeztX5QbsXZeany4lV0WTJlFeuXbNmps+ciSf2/79lMwef9x55tLVq7yvOTlciYwezXl/+aXVeLRoAbQ4EI2Nc/VI8VOACuBKVwXrZunhdz0alzopCMwyYPxaHdbM1MNrrIJuzyjwe06HhM4TMXArUxWTOip4+IIBczboULaCm5oaTaOBOPOKHv0jlBrPKj2dBiszk8XZxsVGw2OdHjcDFWz8AvA+AfT0FBCzZ6P1h68Ds6qmP6akAN+fUxAUHIGw75bC/L+REBqp31Wo1w1K7uKXukGpoICeppRsF+dIK8/KYgs1oGrlRUcoK6MRyMiwn0duMvH9tDSet1Urq7zRvj2/s44kHinp2R06RL358cdd1z93hMxMxgrKypjO6Cjf/exZa366ycQA6aOPsujWzp0k1dJSHqsGkx96qHZzqajgORIS+LpLF2rfPj68L2pu+qhRJPT8fAZrBw1yvKIxmZjds38/Pzt9utXLP3iQx3h58V9pKQ2JSvTqmF5evO62bYFOa5Yht3so+vxGQVkZDVDo/v9D2K5IHBryRww8HoVNT+jxSLNolAWGYsU1BUYj5ar5fga0vVpV85aS8Yjdu7l6mDoV6PbdMsiBoTjaRMGuXTz/09HPoUULQHxiU3bAYIDxUDS2By3BiRNA4E0Dpq7UwfM32oamnxPaBqW7DOXl1IRLStjn0xGpZ2czF9tspuftitQrKujZpqVRHrFHlmrFxpkzSVwrV1IO6tePUpAj79tspmR04gRl1YkTb71CY2oqz+vpyeuyV/NcShLq/v1Wgps0iUHCTZsYG2jY0ErqDz/Ma66tnp6by3ucm8v5TJzIgGhhIVcTycn0/tu14/Wrm7OcZS3dvElDcf065zthAkn788/5O4BGqKiI1yAE31d3xvr40PA3bcrfX78O+P1xCcaEktAvXAAG5BswbN/bWDl3C1I6KygapGDOlzoc/r0eO1NIqr17k7AbNlRg23g7P59aenKytVF5kyZAQa9QeE/VIXGmHp3HKZjWzIAm//yuhl5+tq2CrY0UFJ1klcgBX0yGWLoUePFF64aml1/mH6QWQL3j0Ij9Z4DZzKVvejqJyJE2m5NDwqmoIPm5asxsNlMuSEmhd9ijR81jTp2yVmxs2ZKldgsKmMkxYIDjsU0mEtWZM5QiFOXWST0piTzh40PJx56sZFvb3cuLhDtnDu/BV1+R6Ly8rB7u4MH0oGurp6vljU0mevlz51KeOn+exsNoJClfvswMoR496Mk7Mh5SshzAjh2MhagVKBMT+czLy3nfvLxI6k2aVM21b9iQz7GoiPPIzaUBmTSJK7fPPuPfQ3Aw0DgqGutm6ZHir2DUKCAoSMF3xXo02xsNr9EKpk+3L0edOcNsH5OJhK7GNI4fB3bEK+g0W49Zq3XIbxCBJhuqet8FBYyrnD3LWMHcuUD7FdHA0qXA228D/foxgPryy8ArrzDlyl1o2TW3DRqx/wzYsYNf9IkT7ZMvYPUiy8spv7iSFtS0xXPnSERBQTWPSU/nMZ068Uv5xRcklkWLnHuftk0tnGWsuIPTpy1ZFw8xm8VeamRmJgO42dkkah8fEkhZGQ2RmtlRUcH/Z868tfz0TZuY1QMwQ2TMGJLd9u3W3PRRoyj35Oa6DiYXFDDjJSmJu3OnTuX93brVWtLd25v3s3Fjzr+42Pr5hx6ifNa0KZ97aSkJvXdvkumZM3xOLVpY5j1sCaWnmST9Dz8EzA8paB+i4A9P1tyFXFbGceLigImnl6HnvFA0668gL4/zNu8yYFR2NAyhSxA3NAJDP7VJdZTcdbxjB+f9yCO8F56eqCRdU1A/mKbrcH5MBAL2R0F8/33t5BitXPBtg0bstxlHj/Lf4MHUZ+0hP99aa+Wpp1z321TbzcXGkogGD655TEkJvx+NG9ND3ryZmR3h4c53rJaVUTK6fNm1V+8KavNqdcu/vYBjYiI9aPW61A1VFy5QBvH2tqYyNm7MlUxt9fT8fAYuc3M5h3nzeJ6bN3nu9HQ+mxYt6GU3acLzOKsuffYsjabRSIMdGsrxP/rIWiLY09Mqs+TnWz/brh1fZ2ZyPkVFNMxjx3JlEhXF5zdiBCWz06f5ue7d+UxUSUUIx8bnyhWugPLy+DcyYHgoPObqEJusx7ZSBR2TDJi1Toe14XqEmQ0YEmstK5w/QMGGXAZJO3Wil189GH/hArAtQUFwcATCNrB6pGdtNXZFQVaUHk2n6iAiItDwC02rry9oxH4bcf489dGePSkb2ENBAT314mLKFM7K06o4eJBBsNBQZlxUh5TWL3WrVjQAQ4fS+3YmXRQXM7h54wa94tpWQbQ9v6qVO2perfZK3buXRFpcTC98yhR+9uhREmJhIY9v1473p7Z6+smTlCHMZhq2efN4D06etOamh4fTwGzfTs97+nTHxq+sjMfFxnJOM2bwHsfHk3BNJqtu7uvLZ6Beg48PpaXkZEowUvI8Oh2fuzpumzZcTfz0k3W1Mm4cj/nwQ85BLStRnXBNJt7TAwcYEF+0iAHcnBwFhjl6jP+bDoWDI9DvaBS2PKWHMgR4+CV6yeYwBWdaKejyhA7eT+gxJUJBv341y038+CMNW1CWASPiaRA8o6KAR103KbEdx2AA4s4oGDsoAsOWaeWC6xMasd8mpKVxd2bbtvzy2yPUwkKSemGh1Yt0hZgYar99+zoOZqoVG9Vel+7URrctuztnzq2X3bUNuPbrRw+z+rWXldHwJCZayXvkSGa/6PUsV9CkiZUQBwxgkLc2enppKe//xYu8R+PHc/zSUs5PzU0fOZIEn5XlupeqrRc8ciQQFkZyXr+e4wH8rIcHDVBurvWzAQH8fHIy36+o4PmGDWN6ZFQUn8GIEdwfoErVahPzU6dIqADv65QpNeeZlcUVx/XrrOc/YQLH2rOH2TqylYLWgyIw0rAUibpITPk/ttCDXo+0Xgo2fwqk5ygY9Uc9ZnpHo2F/K8maTMxK2ruX1zy9uQGB/9VBrKtdRciyMhqdI0c4zuSmBvSPc92EREPtoBH7bUBeHjNAmjShVmxvI09REeWX/Hxqz/ZK5lZHQoJ1Y9O0afYJKDGRXz6A558zx7V0kZ1NUi8urlvZ3YoKEsvZs1YNu/oc1Q1RN2/SWy0u5rW0a8dAYW4u5RdVi54+3X78wBmSkki2paWUOhYsoIFNTbXmpo8ZQ6OyahWPeeopEr09mEwkx4MHGeBUZZqbN/kMCwqsxzZvTmOqykd+fvSuz5yxpol26UKj7OtLr/zYMWt5hkOHrBuy+vblimzlSj4jb28+zy5dqs6veh0aNYCbm8tGSTk5fA7+yQYMiI5C1m8i0XNNFHBEgfEPS7BnD3D4U+vqoXfvqhk1ly/z7y4zk6vPCROA5p9EVyVxS69WREfbJWazmXPcs4d/+4GBwDhvA3wW2xgDrVxwvUHLY69nlJUxSJmXxxQ5e6RaXExPPTvbWlzKFS5e5Be8QwcSgL3qhxkZ/CKbzfS4Z8xwvpFG/czy5SSvefPck4LsoayMaZcpKVbvuDrOnyfxq/DwoDdaWsrfC8EgopS8vsWLXccbbGG72xOgsVRr5Rw8aM1NnzqVHnBsLO+9mgZqD5mZ1oymfv14bQ0bWksMq1BrxahZOw0aMPYRF2fV1318SOi9e6OyFnt2NvX9oCAavIICkvOUKXwmP/zA59mxo/3nXlRE7z4x0VqH5oEHbLx0SbnJz7KJSej1aDBeqeyctOkJPU63UtC/P/V621FbPagAACAASURBVL+XwkIGkuPiuHKYONHxhjJHkJKrx59+oiHs1MkqK2lZMbWHu3nsGrHXI8xmazeiJ5+kXlsdJSX08m7epDdf3fuyh9RUfqZFC3qL9sg6I4MZJBUVJNVx41ynJ6oVI729SRqu0isdobCQ9WkyMkia1T1sKam3GwwkiMJCerZz59KTVQlXJcAWLYBnnnFtlGxx+bJVJgGsPU8LC/n75GTKIUOHMpCckcGgYliYfYlH3ZW6cydJesoUxguMRj5jtWwwwGtSzwvQG/X2poFRK04OHkz+8vTkiurgQauRyc+3ZKmYUVkCYMcOa4BUrWJZHUlJzPQpKeG1DhnCeXzzjdVLl5Lzm5+2DC0nkERLSjh+7gYDuuVEo/2/l1TZdWw2U/LbvZvGcvhwSk+OSik7QloaCT05mSuSsWOZFXarabMafsYNSkKIRgD2AWhoGW+dlPLVuo57r0FKenBJSdSV7ZF6aSm948xM+0tqe8jIoKeuViy0R3bnz9PbM5u5dA8Lcz1ucjI97KZNHeeWu4OcHBqH/Hz72nxZGYOK585Z0/v8/elZ7thBeUnN3wYoIYSHu//lr6ggAR0+bM0XnzGDXvH58zx3RQWJ2cuLKyVvb95Le88I4LVs2kStv3t37rb18aEhVMs2APTSKyqspO7ry3u/b5/VSLVty3O3a0evf+NGBqf79WMw+8cfuXoAGEvo1YsZPOXllk5IC2oGSI1GEmZ0tKWdnqWSpcEAmN5ehmbtQ5HfTYHJBAwcyL6rDcoBOVrB2QT+nRYXA8PmKRgUplQh7GvX+P7161wBPPZY7evsVwZG4ygHTpzIa7vVHcsaao/60NjLAIyRUhYKIbwBHBBCbJNSHqmHse8ZHD5ML2f4cPspgmVl1oyT2bOtlRWdITeXn/H0tF8e17ZBBUCPzR1ST0xknro77eec4cYNzq+ighp19ThBVhYNTmYmCS49nUG9ESNoVNLTaVhUUnck4TjC9ev0xtX0QnUV8OCDDIgeO0bCmzaNm3FiYqiNz5zpeOev7WYetXojQOOxf7/1ODWTR8XQodayvwCNx7hx3NEK8DmpGUBz5/J+fPYZDaOXF2MJFy9aG3gHBnLe1VcT6emME9y8yVXAo49Svnn/faD398tg9vRC+FpWfOwVoSDgx/8DIiNRtGYLvl/DZ9+uHZ+7rcxVUsJrPH6cf2fqXoHaeNfVA6PDh/NZ12blpaF+UGdil9RyLPkL8Lb8+/n1nTuIhAR6UAEBVRtFqygr4xc2LY3LbEeblGyhbm83Gim/VPeobXdqAvQsHaVU2uLUKXqN7dtTLrrVsrtXrlCS8PZmSl31WMKFCyQgISjxpKfz3vj5MQZRXs7PFhWRvBYt4nvuwLYmi5pGqcYUCgutddMHDyaxbtjA18OHM2hqT3opLaUxOHWKcYzp02n4SkroQWdm8rgGDTh3ldQffJDkun27NYjapw/w+LllaFgQipvZCjZupCc8WhowtCQaKViCDz6gIWjdmsHITZvo5duuOGyh1svfvdtakrlLFxr1/fv5fnrHUMxYo0PK3JcxebkO5/ZPhDy9Aldf+CdWxtODHzuWxlO9B1Lymnfs4LUOHsxVX23IuHpgNCiIstOtNl/RUHfUS1aMEMITQAyAbgD+n5TyqJ1jngXwLAA87Gznxz2G1FQSh58f9dLqHk55OaWU1FRKDI46EtmitJSGoKDAfmeejAx6wjk5lAOaNiUZuPKu1ICfvz9lE2fNIZwhMZGphM2akWBsv8BSUj/etYvEaDRynrNmkTiWL+d81c5ETZoAv/mN696tKm7coGFKT7d6zWr7vrg4a2763Lkkzs8/J4nNnevYoKakcMz8fK54Ro7kKikxkQkaakNsb28+T4D3esQInn/tWv5O1cy7dAHk7lAYZ+iwfYYe2T0VPNXRAP8/63Dsj3psX8XjBw3ivVixgvfioYfsr8zy8ji/lBQS/uTJ/Bt5/32udtTnnttPwdneevT5uw7pLXsj+NRyXBgyHyubv4jO7fk52yYbGRkMzl65wr/fSZOAtt8sAxq6F9C0Fxh94olbD8BrqD/UC7FLKU0AQoQQzQFsEEL0lVLGVzvmEwCfAAye1sd57zRycui1+vqSKKsHl9RA29WrJF53ysqqn8nIIBlVlzfOnKF316ABvb2cHEo7rjys/fvp7fXoQZK91bK7sbEM9LVrxy+x7Uae8nLOLSGBxiMtzVoS+PRpLvNbtqREA9AzXrzYvfx0s5keq8HAa1f1bZ2OWvB33/He+PtTEz9yhFKMnx8Nqr3qlbb6fIsWnIufH8+1Zg3jAgBJ3mTiswFoyB55hF56URGJdfhwGgUvL5LtpqsKMFUPnV6HnNkRaPduFDY/qUdsGcvoTpnCOaalcUxHq4n4eGtmzOOPs2aMwUDjKaV1boGBNHo/FCvw7joRwaeW4/LDI9Hh9DY8sciAbvOVSgNQXk5Z6MgR3ke1fowQqNzmL9foIcY43uaflkYvPyWFz3TOnNsUGNUyZ24J9ZrHLqXMFUIYAEwAEO/q+HsZJSX0xM3mmgQHWKsuqgW63NnFaTLRE1YrMdrq8GYzMzQOHybZt2xJkp0503meupT0ng8e5Jd/6tRbD2IdOkTvrEsXftdtPf6cHF5vZia9ynPnaHimTWOA8PJlyhYqqQ8axKCaO8jKsvaGbduWBPbggzRo5eVM8VRz0/v04T28ft1a7tfe9d64wZXWjRvU0ceNo8FQC2+pFSRtG3kAlCpKS61lENq1o+Fo0aJqPrmUQPNBCo4lRyDsczaijn1QwUMPkUQ3b+a4jRrxOqqnvNpKQ35+/Bsym+ml5+VZCbRZM/4tqGUHhhz6PwSdWoG4oPnolbwN+N+X0f1/dUB3djs6e5bzy8+3BnCrrJYUBTc+0OOBqToYfxWBB76tus0/L4/G8NSpnykwqtWTuSXUR1ZMawBGC6k3BjAWwDt1ntldDJOJf1fZ2fRGq2cNVFTQ47t0yX76nz1Iae2L+dhjVQ1BUREqe1eGhnKpu2kTicuZwTCbKb3ExDA7wlm3JldzU2uyBwSQrG09/osXOT8pKTWdPUujNGoU70N+PklAbZoxYwaNjDvnVVMOvbxIfikp1NOnTeN1qSmUixfzPqn17rnRxv6YR47Q2DVqVFWiMRio3QPWVEE1G9jHh9LL3r006l5evJ8hITy2oID55BcuUJIpKACan2T3o72jIjHwWBS8xiq46KtU7iDt1Imrp+pOgZq6aSsN7d7N+w9Ya9B06cLVYHY2f9/nhgGK4RXsffyfaPvOi2iYZqgsp1u8NxobritISqK0Fx5eczWYn897fTpRwfihERjyH+s2/zsWGFUUFHyuR4NpOpQvjoDvCq2ejDuoD4+9HYCvLTq7BwC9lHJLPYx7V0JKZj6kpJBcqntaJhM116QkLnFDQtwbU017UxSSt4rUVI6n7tBs04a6cadO9LYcwbZRtKNdoO7AbCZhxcbSOEycWDXwZtsA29eXpB4ayuwTta2fhwfn7+nJ/HR3mkzn5tJ4paRQaikp4c8jR9JDXLfOmps+caK1fk67diRLe+mbtlp1z558Pmr9848+qloCQO3MJCVXARUVlF4AGpaZM60rFlUuMRrp9efnA494GDBgNbsfXeuhoNk0BcGROpwP10N0UfDIIywnYPtMqu9wXbyY53j/fY6pzkfNYrp0if83bsz3ml+Ixom/f48hf1BIuL0UmFbpkbI2Gqs6LoHnFQZqQ0OrSj4VFbx3+/fzeU/xMaDfSW7zl1FRSGyn4PtCBcXFP29gNDubc4qLU6D0j8DIf2v1ZNyFtkGplti3j55dWFjNAlyqlHLuHANRA11uIyD27uUXevBgpvypX/aYGC7HfX3peDVvbt2E9OyzjndL2pbdfeQREvutwGjk9Zw/z+sNC0MVnfb770lq3bvTQ71xg/MvLuZ9ss1P9/UFfv1r1x6eWi5W9WpDQ2lUysspR3h6WnPTJ04k6a9fTwM4cCDPby9+cPq0VaueMMGqKZ88yZVSdXh4cJz+/SmvlJeTQGfNsrYQLC7miujMGWt5Xj8/fibnr8twtU0oigYpaN3aEndINqDzzWh0jlpSw1u+eZNxgrQ0zm3sWN7DI0es85GSBlTN0PH05OsbNyjHTJlStRxEUhLnl5PDld24cVVTW9Xg5/btPKZXL+Cxxgb4/ooa+wU/BQn/z4Cxn+mw/3/0CHpB+VkCozdvktBPn+Y1PuppQOg/dfD4tdatSdt5ehtw+jS/fEFBNWu1qE0vEhJIOI5K9FaHmqkSHGzNqqmo4O9OnuQmmpkzSYjqrtaFCx3XlrEtu1sb41IdpaUc58oVSg62q4icHEosN27wOs+e5XmnTuU9OneuapC0UyfmubsKktrWN/f3J4Hu3UsDER5Ogldz08PDOY8NG6wNJOzJUiUlvJfx8VatukULkrDawMMWajqjWpDt2jX+378/76d6DWpjjpISa5XGsWNJSgcO8JjevSmVqMXMevbkPbJNMZWSBvzHH2kcpkzhamPVqqpeuo8PDYmaodO+PT1adWfoqFFWg2ZbgbFlSz6/6pvhsrJI6ElJNA4TJlg2bC1bhqwuodhSpFQGRh/3NaBjejTEn+shWOkkGJq5aAn27eOz8vLi3+7ICgOaLHSgsf8CyV1rjVfPuHyZX2S1PnV1Ulf7Z6p9Md3B6dMknZ49mfEgBL+Uej0JR03j8/CgR3/hAr+kjki9uJhpkunp7uvY9lBQwHEyM2uW7710yaqnjxrFJXzjxiTanTuZzePrayV1d8obSMkv89atNGrjxnEcg4Fa/f9n782jqzqvu/99JCEhicEIBIh5njGzJGYkgwGbwQaMsevEdpI6b/K2TYe0aX5NYpw06+2w+rZZSePE6dvWSezY2MaJJ2wmMVmAQAwSZhQICaF5nqd7z++Pj3efc6+upCsxGs53LRZI3HvOc86957v38332891Ll0L4WpuenExGd+gQJP/EE213Z+pYf/97tPekJGYuISEEnm3bjH4uwu/DwwlQEydyr71e9PunnzYL1M7mFXpNCxYwrjffNJ2exo6FWENC+BOoX2pdnVlXGTuWcsTDh826oGbpPXua4NCvH0EkLw9yX7fOSFseD3bH+/bxvuRkNk45ZzBNTWYmEBZmxhUaKtLww3+S4zJP9n6WJFFRfNdmV6VI6IljIjeD1EX+ZzG05bVtEro8SUL2p4h302Y58CfbZP/PCW4LFzLu6GgR+aeumY25AG7GHgTKytC1o6JEvvpV34zL64XwMzK61m3o0iWqSIYPZ6NQjx4Q0TvvQG6PP25q3i9eJINzZvX+qKlB09aa8WA2QQWCOj3W1VGtodvuddFx1y5IdNIk0wx7wQJkDq0e0VrvYJwZ6+p477lzZNQrVnCOvDyIuF8/MsuwMOMC+c47BNpZs5gd+ZeZtrYSZI4eZawbNjDO5mbuudPnRYSMtayMRc+QEOOzomsTiuxszq2t7caOJdPVgNrcTCCwLCNB9etH0POXMC5ehNQbG7nmoUMJDErgIr7NrsPDCXKXLvFzUhLBRGcQTgfGCRO4L/77CzIyuC+1taz9PPQQMwFdGC14PUUef2OznH9pmwx4IklGXrn52XFTk8iFX6TIuL/bLCUbvyGD3n1Z3tywTfInJklCAolAsHsa7ke4GftNQn09ZY2WRebmP41+/32z6Bksqefm8qwMHEj9b1iY2dQzYADPklbalJczGxg8GCkgEKlXVGD8VF8fvFtkIBQUQFBeLx4lKke0tHCdmZkQemQkRDBpEouk77wDQTQ2GjfB557r3F/+/HmO29REUBw2jLUBlXWysjjPqFGQc0kJpY3NzZD8jBltj1lYiFxWUkJyuGIFxK82Cs7SxV69yIZLS8l6i4r4/YAB3EclxpYWSFPb6vXuzWcxfjyzCpVe1DZBS/+0cYizLLSlhfrv48c559NPo+Hrwqw26dBm13qcigpmhGPHcm5dHK6rIxCqA+OWLW0dGPPzmWXk5fGZbNnC314vie++fXx3HlyTJCWLtsmUP9ssxz74hgw/87KEvHVzSL2xERntyBGRhoYkeXj+N2T+r38knyZ/X0Y+lySbE7q/C9pFW7jE3gG0Fr2qCqJz7trT6phTp5AKliwJ7phFRWTfffqwa9OykDbOnv18K/o6QwTNzWRxIpB9e1a9arv75S8H16wjEK5eZVyRkYxLA0tlJWMoLEQaun4dQl6wADLYuZP7oiV3vXqJfO1rgTcEKZw12oMHk9nn5hKc+vaFjFNSTG36/PmQ5/79jOvZZ9s6UTo3MEVFQczjxhk7B/U4V0yezHqFLooWFUGqy5eTNWoAvXqVINzQwP8vXkwm39xMaWVBAb/v25d7pLLHmjXo8s5AXFBA0Ckt5ZrGjydpcDa3Vtg2gW7wYNZawsMJZg8+yDH9HRgXLeI76PyO1NWRLJw8iayxfr0JhhcukL3rjtH4eAL3q7lJ8nDiN2TJ7h+J/b0br0BpbGTmdOQI/46KYhF5xuGXJefL35cFH70slp0kEunKKjcTLrG3A21+fO1a25pfdXI8cYIHKhjjLRHIz2mT29AAaZaVte1dqYGjuBiSClS+d/06pBUa2r1eoIpz58i6Y2IgdTXIys4m6Oji5JEjjPXhh3nPtWsQmpK6esV3ZFWQlYUEUVvLfZs/n+Bw4gTZ6JAh6OJam64LiVeuQGqPPtq2cUllJbOa3Fyz5T4qijFu326aXogww+jZk/+LjjakOngwNe167S0t3JMLF/h51CgCUJ8+jOWNN3hNz54ED/WJ0YVeZ0mnM+hER3Oec+cIZCJmgVRfGx1NcDl9msx+2jQkH613z89nBtGeA6PHw/tSUhijmsP17Nl2x+j69dy3t9/me/lY3xR58PP+p9bLL4skJ3WL3BsaDKE3NRl5ZXhWimz8/Wax39kmI1cmiaS4zTVuBVxibwcpKSzoPfSQb1s522bafPw4WWuw9eE1NZC6x4PhVWEhZKRb7p1+2CJMWzMz+a4HcoLUDPtGbXfT0yGJoUON1GTbPJQ7d/LwL13KNXs8kMiBAxBzz57GsnbmTMi/vcqX5maOl55Otr1lC5LGb3+LRDBvHkHs4EHu95o1ZNG/+AWZns+298+hurE2vNCMVD1ptM5bhGCwdCnXpc0s6ur4++GHOb8eW20blLg3bCC79nq5D0c/d0LSwBAZyTlnzOD+OANPZSWBKieHGdm0aRzb6QyppB4SwneqsZFMu08f3w1U/g6MGzZwPOc9yc5mNlRSQiXMqlXc76oqM0uKiuKatVxT7X2TJEUin72xjkYNDSwAp6X5Ero23o7fd0zC3nUXQ2813MXTANDaZv/ekraNnnn4cNua847Q0EBpXUUFJHzpEgQ2ZAjPjb9skZuLb/i4cRCg/zkuXuRZiInheN2x3bVt5I29eznPE09ASE49eeJEdPQPPoBk5s41Xi2Njab0ThtBtHcvcnIgt8pKiCspicxx2zYe/vh4MnatTZ8xwzgZ9uvHPfLf1FRfz7jOnSMLf/xx7uOZMxCnU0ufOROCSU0lkGoGP3Qo1633v66OTDwvj5+dvVa13WFxselrqn/bttmF6oTWzts2pHb1KkEjEPRe79vHuebNI6nQptcZGXz36utNtaBzT0BlJYHz3DlmDStXcszmZt8dowkJ5l5oA/Hk5M9lxhvwZamvN4Q+dy8lkxeGJEmvXqw9za1JkbCTQfi7uN4wHcJdPO0msrMhjDFjfBcr1XPl8GG+d8GSenMzmXVZGaWD+/ej7c6aBRn4b6apqWGR74EHICv/c2RmQpKDByPRdKeCQHe6Hj2KvLFuHZlrVRXSUEEBem1ICCQ5fDhBaNcu301HISGQbnvt0lpaIOcjRyDo55+HhNPTyRT79kXi+PRTrmfjRq7njTcIfoEWH0VM56D6evmfHZx1dQTP3Fzzuqgo7uGePcyQRCD10FAy2TlzjF598KBp1NyvH/dWSyjPnmV2pe/1eMjWq6uRvzZt8tX8nQ2zhw8nUO3ebfxnnIiJITDqLGHAACQolf6Ki7lXOTlo7n/0R1QGOe/xp5/yR8Q0yLYsMntdGJ0+nWMePkyCMXKkqcb5HwQizk6aS9fXEySOHTPrFflD58kTr22WCz/cJmO+miR9T6SIPB2kv4vrDXNT4GbsDpSUUNbYpw8PlzMjUh+ROXPar07xh8cDSV2+DAEdPw5xq3FSoNe/+iok9LWvtdXMjx+HMEaOZIreHdtdjwcCycz0nXXk5PDstLYiaVy4QJY4dSpZdVaWL6lHRjJbcJKME9rTs6yMZ3X5cgLBjh1k5yNGMJMpKTENIwoK0Hrr6hjX3Lm+99nZOSg2FtIePNjs0NUZhAizgNhYpBOv18gdw4cjYWjFS24uWnp1NePTenc9344dzOBETHbepw9BcPZsAoRzwfLqVa67poZMtbTUuEQ60aMH1xwRQZBtajILs2FhbR0Yly/3laJsm+x8507GMnUqRN2nDzM658Lo9Olcw/Xr3JPly5GWbsSJsa7OEHpLi5Gj+vZl3ST0QIpM3rpZrq/9hkzY07XdorXvp0j4M5vl8sPfkMn77u+dpv5wM/YuoraWqXZYGFqzk9T374fUZ80KntS9XjLrrCym6Pv2kUE+/3z7lSs7d7IgGcix8dAhMs8JE8gQu9p/UgSyUB+b5GRDYGlpkEu/flRe7NoF4SUk8NqyMsaupD5gAKQeqAuRx8P9OnQIiehLX2L2U1PD85mXh/Rz9SqSzlNPQTJHjkBGGlT9676d3ZI0EFRXU/6oZYoifG5btpCBp6WZ3/tr6TU1ZMJKuv6LpyUlBGVdGBbhehobITD/jVsej7HTjYnh/h46ZEoWnZg9m3Hs2cP9HTaMmcnAgYawP/64fQfG4mL+Pzub9zz7rLFJVi+c/v1JILKymIH27m1sf4OxSW4PtbUQ+vHjJAERERC7Bp/wcL4/1fVJ0uvRb8ikN4P3d8nPZ0bx2WdJsmzWN2TJ21TmWC6pdxkusQtfzDfe4Ev73HO+GzsOHYKUZ8xou+O0Pdg2mZ5uYz91igdv06a2Tn6KjAyIyN+x0Wm7O20axNsdi1S1Gb5+neuYPZsH88MPGd+ECcgv27eTAS5axMPr9XI+XewbN47rCDRbcPb0nDmTrLtnT4KV6unDhkE2o0eTcYeFIf9cuIDGvH69b1D1ek2poy4Uq3yzd6/v+adPZ4Hyd7/zJdThw7lvMTFc85EjfKYeD9fmnB2oV436yiiGDIF4nDa9ipIS7lthIWOor+cz88eQIdz7nBy6SIn4mnKVl/O9ac+BsbHRyM0RERD33LkEqXffNQujycnGMiA8nNliQkL3kgFFbS333Eno2ihl5UoC465dLFgPGiTydFyKDPoZ1TXy8svtSjq2zfWmpppgv7pnisw5c+OVOQFxn2j49z2x2zYPxfXr7LR0ZtPqXDh9utnyHwz27eMB6N2bDHX+fCNFBEJhIRt1/B0bbRuCSU/3XcjrKqqrqT4pL2excPJkfrdtG9e9ZAlk+dvfcvw5c3iIe/UyZXwiPA+rVrUdg9fL6/ftY0ru3CSjenp0NIR9/brRxQsLmUFUV0MOCQm+91i9YK5dQ2p49FHG89Of+joxhoYSJM6fN3X/urC5fLk57qVLjEXfO2wYmbcG8sZGPoezZ82xdRNTfj7yzooVZl3EtuGDXbsgzfh4goI25FD07Amh9+/P8a9fJ0CuWYN00drKjPDQIRNo4uPNffZ6Cb579hA05syBvEND4SRdGE1MNH1wvV6ue/HiG9vJWVPDZ5ueTiAMD2e8vXvzfRw/nnG///7npLyahdKQLR1X13g8yIGHDxsbiuXLRebVIsPIW+2/94Zwn2j4973GvnMnX66HH4aAFUeOIE9MnYomGyyh6vvCwiCT9et9yyX90dAQ2LHRqYUvXAgZdkcTLS2l9K+xEalh1Ciz87WlhUy2sZHpekwMMsv580gS1dXmOKtWQRSBjq89PadO5WGPimL8O3ZACLqB6YEHINKhQyHEnTu53k2bfPud2jZE9vHHXPOjjxKM1CbAicGDCUzvv8+91Kx76FCubcAAzv3JJ2jPSvgrVvh6t6g9svOaR4+G0C2LwO70d6+tpXLq0iUCstdLAHJCOystXMh37NAhSH7VKlOm2JkD47Vr3MeCAtYlVq1CftEeo/X1vC8mhnva0EAikpTU/RJYEe6DErrXa3x0YmO535Mnm16/NTVIRg89pP4u7WfFjX/2N5KebspOBw4kyE+b9vlM9BZn1F6vSM6rKRL3Z5ulOUAjkbsdrrtjENDFyHnzyDT0IVfHxcmTIaJgpY9TpyBjER60J5/svLuROjY6mzm3trKIeOHCjdnu6gamkBCqKZwLjQ88wPgyMniAR45Egy8oML1ERchEN25sW/mite579vAaZ3MQrey5ds0ECK1NtywI8exZ0zDDmVHW1RFkzp8nCD32GO9//fW2VSWJiYwzI4OftQQxKYkgrZnw4cP8v9fr26har0PtHBTh4dyPS5d4/aZNvvLchQtcQ3Mz9+X8ed/yShHev2EDs4O8P/snufTAPOmzPklWruR66z5IkctvHJN3x/9NQAfGmhoCWUYGRL9iBffw0iXfhdExY5glVFYSiFasaH9BOxhUVRGATp7kfmmf10GDDKGXlJhKnbg4xt5ZI/KqKr4v6ekcb/RoCH3s2FvQTi8A6usJhseO8X1adfgHkvDJ5/r/D3946wdwk3DbFk8tyxouIr8WkUEiYovIK7Zt/+RGj3urodPy8ePJgpwe6Oq42BVS13I1EY65YUPn3uP79xvHRn0wmppMSz1/u9yu4PJlZAnVpfv0Ias9eRIZYN06CP7cObTtvDyyvR49eAgsi/c+/XRboqio4FpzctDm1641Mw3V0/VYDQ2ca+ZMtPe33uL9y5e3bTThb4w1dy5SjH9VSc+eSAyHDpmt/l4vNqfvCAAAIABJREFUgUuz9DNnTDYZFsb/JyeTPevsS7VppymYVutcumQ2oOl3wLnJasAA7ql/XXpkJAFz8GAI+PhxkalD58kzv9ssoc9uE09Ekpz5aYqM+dvNkrFl2/+UJ6q84/Ew6ztwgH8vWsS16sxLF0aXLeO+pKRAun/0RzdGklVVLDifPEmw69GD8+sGtQkT+G5+8glrQT17Eqhnzep4NltUhKR55gzHnTqV672R4NMVFBURUDIzCfSjR4tsjEmR4UHo/zcVt1nbvxkae6uI/JVt2ycsy+otIumWZe2ybftsZ2+8UygsJCPWBSr9Yp48SbY4fjy/D5bUz5wxPTAXL+az6+wBu3gRYp8xw3imNzSQYefnB+eM2B4++4zFvNhYHngRyijVMXHePIJHfj5Z9tmzLIZ5vSbzHDTIt0pExPT03LnTyEwzZphrPXGCGZCSQmwswbF/f/5vxw6y1eeeg0AVTsIcOJBAVF4u8s//7GsHIEIA7NUL0hYxbeK0TLG42NSzq6lU//4Q/uDB5jhZWdyjhgZ+DgnhWtQL/Omn+R4o8vN5fVkZM4mcHF/bX8uCbBct4tg//zmZITbDSRKycZs0P75ZTi/4hkzZ/7Ic/ott8uhfJfnIJZcuQZxlZRDpypVc34cfmoXRRYsYy7596PNO/5juoLISQj91yhi4tbTw2S1danY9nz5NoKqrMxp/e9q9bRMsU1NJMHr04DuXmHh7Oi95vTxfaWmMIyyMe5SQIDLwMz/Hyput4beH26zt3zCx27ZdICIFn/+7xrKscyIyVETuSmLXaX1EBMSl279PnyZbHDuW+x2oC08gHD1qtGDd4t0ZysshCadjo1oOlJWR8bW36aczqIw0YgTXV1JiKlLUt/w//5OsfOJEiKxXL1+7WA1szq3x1dVk/FlZTP/XrTM7Np16umqxWpKoZZ8ZGdzbxx/3rQy6fp17UV6OfDJ/vgk6Cs3Ip07l/Hl55nfaMLt3b+PfEx4OqTc2QoTLlpkg7fGYjWZKhv36UbFy8iT3beNGE9D8F4YHDCBrdmL4cO61+vKfOUOAeuIJAlFJichvTiXJnBnfkKU7fiSl3/y+PPT3hkScawD9+xNURowwO0ZF4IWGBqPT6xpBsN9Tf1RUaNs5X0IfPBhCHzOG+1NYyH29di3wBiknPB6ShNRU3hcdTQCYO/f2ODc2NvIZpqURsPr04Ts4e7bj/P99h/zdk5Kk4dVt0mPjZrG//g3p8R+3Vtu/qRq7ZVmjROSAiEyzbbva7/9eEJEXRERGjBgxJycn56adN1g0N4v813/xID3/vMngMjN5IEeN4gENpizMuXszJIRaYmcW2tEY/t//gyhfeAFSqagwHuhbtrT1jQkGts0MYP9+IyOpj4rauVZVMVMJDydzysszerouOsbHkyk6+5pmZkLcHo+RSJQUnfXpISEEzMceI+MsLkZ6KSszTZmdlR4HDvCnd28Iv6CATDxQA4x+/fh/EUjI40HzXbiQhzklhQdb7Xc1S3dqv+XlzKycQWP6dI5bWurb2ETE11xs4ECux4mICO7ryJEQ5M6dfL660ciyCCKpqTgaPrl9s4T9yTck7Fc81M0Lk+TgQYJMaCjXM28ex9KF0alTOY9aBickcOzuEqXpI2oWkltbuYalS3kGLMv40qSnc67ly03jbn80NRFQjx7lOzZgAAH6wQe7H3i6gtJSzn36NMFpxAju06RJN1azf6NoaeG7c/kyZaBFRSLL9v5Alh7ovrZ/2xdPLcvqJSL7ReTHtm1v7+i1d2Lx1OslE8zKMptiRJAt3nmHL4M2vOgMjY0Q1pUrPJBf/WpwmqGWVmZmGlvZkhJIvbWV33XHdtfrhXiPH+fhe+QRgk56ummtl5nJzKJ/f85VWWn6dKqcoSWHCudCprMWXJGXh45fV8e1aW16795M7T/8EFLauNE3WJWVmRLT6dMh59de8y2t1LHFxEAWHo/J0gcOZCxNTVxTURH3v76e18bHQ0TOzzIjgxmHSjsqD6SlMcYNG8zipQazjz4yHYyqfdIUs2u3spJ7dOUK92jtWmYR168T8KqrRcbmpsiWdzdL2DtkaPbeFPFs2izvbtkmZwclyYwZZLYa2MrK+D7GxUFWjY3IRElJHdshd4SyMgg9I8OX0EePNuWueu0nTxKQGhoC+9IoamrMgmhjI2NesICgfqsXRLX+PS2Nv0ND+S7Fx98+/T7QmIqLDZHn5HCPQ0K4N7OrUmTKS/RutX7RvYz9tu48tSyrh4i8IyKvdUbqdwLqyKgLlUrqalc7fDjT32BIvbiYAFFRwZfpK18J/ovk79iYn4/8ciO2u62tSB2ffcZDlZhIoLh2DcJctgyySEsjaGgjZMuCOLUsc/Nm365L585BWE1NZOmJib7Zj+rpts2fhx7ifK2tLKzqpqyNG83Cqvb33LmTa96wAQ30F7/wvSbtHBQdbXZ+qkfLokUs2O3dyzX36UNmduEC//7yl32DSHMzBK0ZrwifV79+ZNKjRzMOHWNDA9elx66uNh2hRAg0zz7L648cYaZgWXyv5s5l3O+9Z2wI4uJENoce+x9SLywU2ZGTJCHrtsmkomOS+N0kCQ0l0OnCaEIC9z83l+/JQw/5rg90Bc7G0EroHg9BbMkS3w1Q169zr/LzIaJHHmlrvibCdyg1lWN6vVTKzJ/feWXMzUBTE59lWhrBqlcvnqc5c9rf/HcrUVsLiV+5AqGrpDlgAGMaO5bZUPinn2vqb39O5sm3Vtu/4YzdsixLRF4VkXLbtv88mPfc7oxda8vnz6dOWAQi2LYNbfWZZ4LzXdHKF11g1F2QwSAnB//t8eN5aJ9/nlLHqCiO48yEg0VTE9dw5QrkO3IkGXRjIxr4+PEErkuXeFBzc31LGUND+fmpp0xwamgg+8/M5HePPeYbcJx6ugjkp1pyaSkzmeLitrJGbS0Z88WLkMqcOQQk52YedZdUvVfESET9+5tdm4cO8bsZMwhgxcWQ/cqVvp+jes84bQHmzuV+VVSYxU4dY3Y2Y6qp4TjO8krLMs0zioog7/x8guEjj3AfMjJIIPR9y5ZBnpbFPd+7l4AYGQlZjx6N5KILo1Oncn3Fxdz75cvbNqEOFiUlEPqZM5zfsvjsdIexc2aoO2VPnIAoV6wg+/W3SM7JgdAvXeIzmjmTZ6o7392uoqICMj95ku/90KEEwClTurcTu7tobTXyyuXLxs4iMhISHzOGv9vYbdykqpjbJsVYlrVIRA6KSKaI6Cbs/8+27Y/ae8/tJHbdjThpEgFSdyC++SbZyJe+1HlZotdLRcDhwyabfPJJ05O0M9TU4GkSESHyx3/Ml+Dv/x6duz3Plc5QV8cicEEBJK67VHv3ZmyRkQQOJYn8fEPqamblX/ly6RLkW1fHw79oke9DU1vLMVWj1tr0nj0JBB98wAP/+OO+HvLOFnjJyQQ2tcbVTFwXcFVuETH/TkwkcOzejfQxaRIZ99GjXNPatb6zDaefvH69IyIIJvqejRsJhCI8rCkpkFbPnm3r5UeO5J726MEaRmoq93f1aojFWdctwvE3bzYbl7TphVoUz5/P73RhdNo0iCsnh+9EcnJbn/VgUVLCusWZM6au3+Phni1e7OvB49+FKSEBnd0ZHL1ePq/UVPMdio+Ho251b1KtrklLIxELCeE7Fx9/e2YHOoaSEkPk/vKKEnlc3O2px79tUoxt24dE5DZcUtehJWpDhjDdtixT3z1wIJl6Z6ReV0fWd/WqWehcvz54Uvd4yGKbm5EJtPlDbCzn787DUVmJhFNVBYFcuULgHzMGwqqooG1bczNjzs8nG66v5+/mZohw40ZTxaJdjGJjfTN4RV4egaShATJes4aM2eOB0NPTmdZv2mQChdY9nzyJlDB+POSsZKsLaxERZgrrfDj69iXBOX0aEoyNJYilpxMsnDtdFfX1ZN3a9FmE7C46GnIaN47Ao+9x+ryEhfmSelgY1zNxIg/0++8z/Z8501gLaMDXrH/0aO5rdDTfmR07CK66eSgvT+SVVxjnpEnGLiAy0njWdGfBsbiYoHP2LGPRoDhlCoTuL+Vcu0YwKixkbKtX+1oPNzczrsOH+b7FxFDBNWPGjXnOBIOWFmYxR4/y+URFkWjMndu93gNdRV2dkVb85ZXZsyHyUaPadvK6m3DP7jytrKT6JDQUC9xevYj+r7/OtP7LX+6cVHWbeX095JCT09Z6oDN89BGkW1TEXgh/vPiiyNatwR+vuBhSVzuAw4cZl/rRXLgAUfXsCek2NxvbWs3YnZUvV68iL1VVodEvW9aWWI4fNwuJ/ftTCaJb9d96C3Lw38xz7Rq6cUUF2d3582ZxVC1enbYFOosQ4e85cxjHsWMQibYf3Lu37U5XxdWr5vNSzJrF515djfyh7Qdtm0xQA43/ztHJkwkiIrwmPZ1ses0aAuj588aBUbP8JUsYZ00N6xqffWZ6uIaGcpyyMgJg376GhBMTWZ/oLMkIhKIiCP3cOc5h23ze06ZB6P7rNrW1jOP0aUhy5UrIXwNqXR33Re0Jhg3js5048dZXmFRVcd4TJzj34MHMIqZNu7XVNU555coV490fGWky8jFjur9wfTNxX1sKNDZS1lhVRcVKbCzk99prPJzPPtv5QotuvT90SOR//S8ehEWLIIdgkZEBuSUm8gApnD0uu4Jr14y18MqVpqPOunV8+XVrvM4sVDYS4Xrr69llGx9PYNizh6woJoYg4VxIE4Hs3n3X7K6cPZvMLiwMUnrvPa7lscdM3b3TtrdvXwKANpIOCyPLaWiAVJTUnfJHnz6UyZ08CcnMmkWmtmsXxD1+PNKLM3PzetGqDx40vwsPNz7kvXv7+tHU1DD2rKy2n4VW8YwfD3l/9BFkmJhI0KutNQ6MfftyT8PCmBGOGsWsQMexcCGEkJLC2GNiGMP589z/mTM5ZnekuMJC7vP586aqyba5d4sX+/ZA1XuUlsZ9amkhwC1ZYrLOsjLGfvo0n+HEiRB6MCW8NwLbhlTT0ghOIsxkEhI4962QN5zyypUrfDYqrwwfDpGPHUtguZPlkoFw3/qxq/RRWkr5YGwsX5zXXuNB/PKXOyb11lYe5pMn+XC//30eAt1tFyycjo0rVtz4dV26xEJpnz5m4bFXL6pyBg6EqE6dMoZbSpaWxb9bWsi0J0xgJvL73/Mwx8cTrPynlTU17OAsL4e4Nm7kgfN4TN9Pfx+V0lJmCwUFEFpurnFS1DrwyEgelupqzun1muAzaRK/P3SIY2/ZwntefZX/V2sC58Pu7PqkGDSI2Ul6Osdct87UfZ8/z73SHadOUp8+HbmhpYXv0NmzHGvLFsb/6aeQtuqrubnGIbKwUOTf/53rnTyZQJCeToIRFcWxs7MJ9hMmcM+7UwVVUAChX7gAoauGPnMmiYd64DiRk8N3uriY7/Tq1eZ1165B6BogZszg++4fGG42WltZBzh6lHvXsyfnjY+/NZmxyisqsejssX9/I6+MHNm95jV3I+4pYtcFxCtXeJjHjIHEXnvNlMJpWVsgVFVBnvn5ZD1KWKrlBps9NDRANpGRvpYFihdf7Np1ZWQglwwcaFrUjRpljv3b35J1PPAARKw9SdUmICyMax8wwHi7ByoNVFy5wiJpayuBUfuqVlay3nD9OhmVSgxO+9qwMGYMupYQHc19KC7mgdXm1zqr0NfExUEu0dGsYYwZg3Z/6RLXun592+3o584xo2hpMZn3xIl8fqWlZnZiWUhSH39syhCdiIyEnNVQa9cujpmcTNZ69SoyWnk5mXxNjWlEMmsW47x8mXv15JN85379a847ZQpySWamCYS6aNsV5OdD6BcvGkL3ejn/okWBnRxVEsrM5N7rjmZt5pGayljVeyc+vuPn42agpgZp7/hxZjuxschbDz54c7X71laClmblGvh79jTSypgxt8fi4E7gnpJivvIVHhqVTP7qr/jiqD9JoCnv1q38yc6GtFpbIa5///e2rw1GD7dt5JIrV3wdG7sLLdUcPpyHWQnl4Ych2tdfhyB79jS2tV4v11pTYypf6ushwfZKAxV79pAxi/Cgq0HahQtk+bZN0JwyhdfU1BB0Ll9mtlBRwWssCxK8fNnsFG1tZZwhIUYHHzoUEm5pMVUZly4RoFtbWTdw2uuK+DbcFjHVHxMnmmbOmzaZCpDr18nANag4MWMGQVvLMa9e5Tu0di1E88knZO79+/Paw4e5v6tXk2mmpZk1gJAQKlLq61mkbWjg3NpRyallB4vr141ZnEoulkWWuXBhYGLyeMiE9+/n3wsXGofQ06e5BrVRTkzk+3CrFwKvX2dMn33GNUyYwOc9evTNkVtsm++Rs3qlpcXIK87qlbtNXukK7juN/bPP0Jnffhu9s7CQB/vf/g1Sb296Z1lksLt3k9Fu3uw7De2qHq69UW/EmVGEc+7dC8mOHo1sUlcH4cyYwRf3zTd5cHVXptZ+x8aiIU6YQAVIWhoPeaDSQEVLC5LH9esQ8ZNPQk4eD+NITUVzfOIJU7d89izZanMzpKMbeQYNgsBzcnx9aHS7v4jxc6mq4oFbtYrxffQRn6W/va6ipIQZmJK0ZRHEtKmJswG22hbs39/2eqOiTPZ8+DDac2gos5AZM5iB7NvHMRYtMk2jBw7ke3b0KJ/HzJnMKA4e5DMaOpRry87mHEuXIp11tdb62jXT+FxLQkNCWG9YuLB9Xf7KFdYASkuNkVjPnlxPWhpBJy6OmciUKbeW5NQ75uhRvlcREdyv+PibU/teX+9bveKUV5TIR426d+QVkftMY9d+mCJM2YuL2X0pwkJpe6Su2u6uXXzJ1627sS/BxYsQycyZxrGxO/B6IcyTJ01jjOho0ws0IwOdWEsX9W8RQ54qE/zmN8bFcfXqwJVABQWQelMTQe3553lddTWB8to1rmflSki/qQnyOH2a+6XBJSyMh/bECYglNBRSDw9n/ErqWg0THW10/0uX0PQbGtra64q0rU3Xax46lFlCYaHZQGRZ/O611yBbf8ycyb0oK6PJSWEh2f4jjzAL+tWv+A6NH88C4549ZPITJnA9e/cyE1uxgnWN3/8eoho/nkXVsDDet2BB179PubkQutpVKBITOV575X5VVdybs2eRZZ56igB/+DDfo9ZWxrdgAcHsVtZc19WxvnDsGPcrJob7PWPGjT1fra0EbyVyp7yi0srYsfeuvNIVfOEz9q1bRV56qfPXOWWU9t4TSGpRqaYzlJdTnxwTAzF2Vy9sbWW36PnzkNb16zyITzwB2e7bR/DQ0kX1VOnRA8IsL4eAvV4IKTycBcH2ujilphoL3DlzjNtkVhbBsrUVwpw+ndfk5PD7qirf2czEiZwrM9M30AwcCIFqf1ERCHvxYhbLdDH21CmCkr+9rgjrBb/7HaQnYsolR42CcAcMIPseNMiUMX78cdtr1Y1DQ4ZwHw8fJrisXs1i6J49jKNvX2YQPXvyWTQ08Fnk5jIDWbAAUsnM5JhDh5rKitmzydK7Wm+dkwOhZ2ebDF0D5fz57Wvfra1cx8GDXPvixdyXo0eRpSwL/Xr+/O4t1nYFhYXG+9zjgWQTEpj5dSeQOOUVrV5ReWXYMFO98kWXV7qC+06KEWkrmwQjo3S39NAJdWysqcGxsbsZQ2MjPjQ5Oaa6Zd48iNq20bLPnDEVL7oJpW9fU7O+ahWZdE4OZLtmTWBSaG1Fn1ci2biRag5n6aBazw4YwPFTUpAjnLtDo6LYRHTokClfVI19wADjTRMWxjmnTUM379uXc//hD7xPfW38JYvsbO6Jzkxsm9f06UNWrRq5bsD61a98+6Eq1BwtLw8tvaLCtHM7dw5Sb26GABcvJtvcs4fra23lz9y5XPuxY1zfqFEQfF0d1TcPPdT1apKrVyH0q1cNoffoASEmJnZcwZWVxcypvJzPesIEZnM5OWTGc+dynFu5qcfrJQk5epTA16MHn0lCQvcqa5zyypUr5jsVE2OI/F6TV7oCl9gD/BzMe7oK26bE78wZdpKOHdu949TWIh0UF0PcTU1kz7NmQRxvvAEpaTcgJdahQ817Zs5ksTUkhCy0vQYMhYVUbegmoa9+1Sy2bt8OySgR/vjHIt/8Jr9XKUWRkAAZpab6kn2fPgSe5mZzfwcNYkwjR5J17d5NZh3IXlfv60cfUT0hwuvKynjAGxog2kceYZwiBKP2tHTdULVrF7JEv35Gh//oI2ZFo0ZxvF69kFYuXjQzj7Fjuc9ayTFyJIRTUcHC3PLlXav3tm1D6Dk5htDDwyHzxMSObXkrK5mRXLjA/ZgwAZIvLeXeJyYyc7iV5NfQYFrNVVWRzMTH833tykYrj8e3ekXtKnr2ZG1JK1hupH/rvYT7SmNX+JcRBlNW2NXSQ38cPQqpJyV1n9TVj12zk9BQ5Bx1Y3z9dfN/WvUiYjTd2FiI4ODBtk0wnLBtiF89zydNIiMPCSEzfucdAsr69RCmbSNZOXeFikAmK1eS0fp7lCsBKyIi0MznzOE4eXlIOeXlge11RSCuV1/l77AwzldcbNYPnDOJsjLsE3SDkzNQz5oFWV+8SBlrXR0zg4QE7tXx4xC/NkgpKKCsURfhoqJ47dmzEM/gwWS/OTlcp5YPBiszqPfJ/v1ktzo7CQtjlpCQ0DEptrQQRLVqaexY7seRI9ybxx9HcruVpljFxXznMzIIrqNGMUucMCE4OcS2+cyUyLOzTbnq8OHM2saORS67X+SVW4F7KmO/3XA6Nj75ZPd0xMJCMvXGRh6UESMgrV69+PK/9ZYpFVQ5IyQE3fLiRcimogKyf/hhCDTQOJwe8pZlary9Xkhu/37I6oknIM7qal7/ta+ZNQbLgohDQggOWnqnD2CPHr5eK84Waq2tnENr6NevD1xDf/w48oL6rtfXm5lFRQXH1JZx27aRtfpDHSv79iUjP3+e+7R2LVntzp0cV832IiJ8A15YGEGhqAgC7tcPSSQvj89l6VIy4mCJx7a57/v3k51qht6zJ3p9fHzH2bVt81l//LHxbampgRDHjOEY2vHoVkDPf/SoaTU3fTqBKJCtrz/q63mfLno65RVn9Up3LBXuN9yXUszthL9jY3e+lDk5ZONaVTJ3LoQbGmr8WdTCVjPRXr1Mezbd5DNiBETZXgnZtWssPjY0MM4vfYmMqK4OieXKFR7UNWuQA775zcC+Nn/xF2SE6syopYz+rfWGDkVGUiOxwkLkjaKi9mvoW1vZaKUOiZMnQ9qRkfyfbUPM06YZL3iduWjAEzG2B6dPQ9QeD1nguHEEjJwc3/E1NRFYr13j/ePGcbzz5zm37lwOD4dA588PvuZbm0EcOMA9U0KPjGTmMG9e58cqLze9BCIijLw1bRpj6a5PezBobGQxOS2N71mfPox59uyOfZY8Ht/qFZVXIiJ8q1dceaXrcIn9FsLjQSooLCSj7U61wfnzlBLq7X/0UR4YrxdCOnLEEIFiyBAIrLiY7NjrZcEuISFw9uhfxx0XZxwlc3KQXurrIcLZsyG5Dz4w3jAiZOsnT0ImH3zA+Xv04BhVVb4+LxERXIdaznq9yAad1dBfvUrgaW4myx48GFJ/4AEy1Lg4ql5aW3mdc3FUA56WTkZGsjiak2NkgsxMKkfCw00PTMti0XT7do4bHc0MQitJ4uKQZrxeZglLlwbfyMG2IeL9+yE1/RyjoyH0uXM7r5pqaeGzS001zUzCwxl7YuKtNaQqKzOt5pqbkUi01Vwgmce2CUBK5FevmgDkrF5x5ZUbx32psd8ufPIJGd6mTd0j9ZMnjYGWLu79x39AiNu301EoOdlX19YmDLpjU5s4O61WnXDWoIuQaeku0kOHqMXu14/ANHiw2XnrdEZUEj592jRwHjaMzLu2lmMpqc+bB2lqBlpaSpZ+/Xpge10Rru+DD8jARZg1FBVB6mphEB8Pqb7/PsFQoWWetg3ZrVxpdlv26EEQiYw06xMzZzK+6GhmLm+/bWwPRo0iWJ45QzZfXs59mzKFzyGQ/0ogqGRx4IAhdBHGsWgR4+yM0G2bwPrRR8bPJjoaMp8799bJFbYNKaelmV2u06Zx/50e7oqGBl/vFadVxIMPuvLKncbNao33nyKyRkSKbdue1tnrv8g4fZpKgMTE9mvDO8KhQyw6ipAVbtkCib30Ej+ra9/y5WR5lgVppqcbTTspybfzjz8uXGCBsqmJ16xfz8Pm9CrXDVk//jHZmHMCFRnJ/3k8nOvqVchl2DCOrRYBIsg/Tz9tyE83Eu3ZA4lt3NjWXlcE8nz1VUg3IoJM9tNPuUYl7c2bed2//IuRXbQbUEsLgeLpp/n5P/+ToDBlCp/NwYNc58CBjGHECONwuHOn0bgjIri+2FiuS/cNLF8evB2EbXNfDhwgy1dCj45mUXTmzOBsZ/PzmUVpx6cHHiCoTZ9+6xZEm5tNq7nSUsa8dClBxFkm65RXrlzhPolw/0aP5vs4Zszt6abkonPcrIz9v0XkZyLy65t0vLsShYVkmN1xbLRtNN5jx/h55kxki7Aws4POWWGiWuyUKTx0ImjrGze2r6u2tkJaeo7evU3TDPWWr61Fepk3DyL80Y98N2Bp0+R332WGsHQpQaGw0CxU6gLuI48gUygqK6lLb89eV5GaanzQR49mfHv3QtRNTRBqYiL3y9ngWssPNUtfsQIyPXIEEtq0CXL69a8Z38MPG5kqN5esv7SUY0VHs8YQEQEZlZSYJiPjxwe3EGnbzCL27+deKvn26sXO0xkzgiPk0lLut2rRMTGM/VY2hfZvNTdkiG9VTaDqFae8snSpKQN15ZW7DzeF2G3bPmBZ1qibcay7FZ05NnYEtRJWYlQfGf8dsD/4gfm3ku3SpRDt/PnIAu1lfiUlyAsaHEaNosIlMhJ9efduU7MeF2d+p+jdmwe7ulrkZz+DvB94AOJOSfGVhYYNw6pBx6Kd7T/5hJ8D2evqPfztbyGwkBBb99AkAAAgAElEQVTkkwsXTKs5bQJSWMi1OKFeNNHRkG9TE4vXlZWmafDu3WS7U6dCjGpdsHs3OruI0eS16qa4mGtftw4iDrZk79w5goqT0Pv0gdCDzbALCwleuqNWG3l0t2w2mHFfvWpazan7pLaaa2zk90rmupbxwANc09ixBGJXXrn7cdMWTz8n9g+CkWK+aIunXi+Ldt1xbGxpwZO7oICM8+mnfW1bs7Mp2/vbv+V1OTmQ+r/+q1mcfOqp9jfAKKlqhyPtEbpiBUT4hz+QVU6ciCTT3Awp/+EPbY+1dq0pl5w/n4xWHRRFIKsnnySjVdTUkAl3ZK8r4rtQqRnphx9yDssicx45krE6v5Iqy4iQpScnQ9TqPZ+cTJ352bP8/MgjEFBrK5n8gQO+DbNDQ5n5FBUZCSgxMTgLCNvmPPv3E0h1UTQmhgA8bVrngUHJNSXFrH9ERUHokyd3PobuoKWFwJaWxnVHRvI5z57N5+esXrFtI69o9Yorr9w9uOsWTy3LekFEXhARGXGr27LcZOzfT9nao48GT+pbt9J8/Je/JGt84AGCgrryqafJJ5+Ysq+cHOPJUlUFUT79dPuk09gIqZ49azy6H38c6SQ/n1lCdbWRJM6cgUznzKHsMDZW5H//b7OY6vUyllWrCBROm9tRoyiTdBKXHq+11df73ImWFmQG7Y6TkABRvPmmqeyJjWWc+hpFaCjv1yy9ooIyzIYGSg+joliE9nqZ1SxYwHu05ruiwvfexcaS0ZeUMI4lS4LrOev1sqB54IAxNxPhXi1dGpxLoh7j00/NDt6wMMadmHhr5IzqamS59HTu2cCBBMIePQguv/iFkVeGDuV+jBnDv2/lJicXtx63jdht235FRF4RIWO/Xee9Ueii2MyZvnpyZ3jpJVN3PGoU3ZxUunB2aRoxAi0zOZksKTMTsli+nGyyPVy7xkJbdTWk0KsX2fTgwTzMn3wCIT73HBnqO++YHpsinGP8eIh9924e7sWLmSH87nfmPKGhSDra+k4EycRpr/vYY4F9QZwNsHv25DinTyM/qF4eHW38ZBRal+7xEICWLuV8Fy+iBT/0ENm4OjCuXg3JlpVB6FlZZKW6yBodzfFKSsiqk5ODq6H2egleBw5wbL13/fszpsmTO9fAm5up+jlyxBiniXBdy5d3r5l5R7Bt7vvRo3zetm2aeRcXE8BFfOWVUaM6tjBw8cWDW+7YAcrLyTbj4rrWQenSJf5ubvZtViHC4uW2bRCzNqLo1YvjZ2eTTf3mN237jyq0NjwlhcBh20gYmzZBiO+8A+GOG0f2ri3b6up4/+DBEGFGBiWWS5dSE71hA6TobDE3fDhZujPrvXjRtJYLZK8rAiHv2gW5iEAey5cjxZSU+PrK1NX5bjAKCeHfWvGSny/y85/z+mXLyMLfe4+Zj27pb242tf8hIabpiP67ro6guXx54NK9QPc4MxNCLy831zdwIPcrGBuB2lqu//hxZlZaBhoXx8wvmHF0Ba2tfO5HjvCZh4aaBeLr1zn/6NHManRz0K207nVxZ3FTNHbLsn4nIstEZICIFInIi7Zt/7/2Xv9F0Ni749jYmR1wfj4ShBJNVhYPWGUlBB0TQ4bdnhtfdTWB5upVY9ur3ZSKi5FeKiog3Ph4Sg7T0sy2/+Rks81e69UTEznvjh1G2w4JQfOdNcucu6mpc3tdEaQK7eqklTPR0Yxbd9jqAqYSptfruxlr1iw0/vffJwCOHk1WefiwcWBcsoSAk5HBjKO21rf1nmrzgwZB6GPHdk5kXi/HO3DAjN/rhYyXLQuuWqa0lMXgjAyuR3cHR0UxjkCLyjeC2lpKO0+d8jVdsyyCh24OcuWVewO3VWO3bfupm3GcuwW2DakUF7NTMxhSt20eft21+PWv+y4CZmaSaUZGQohZWTzs2vdz/Hiy7va2mF+4wIJnSwvHaG6GXB98EElnxw6y02ef5Ri//KWphz5xgsXY1FTOK8IsYfVqsv8jR8x5YmPb9oZ12usuWhTYXte2TaWNbUNoTz+N9PLBB75Zuu4U1VlESAgkGBVFFn71KuPXlnOXLjFDUQfG2FiC5I4dxr8lJMR3TSAyktc++GDn+rXHYwi9stK8Xgm9s6Bg21S2pKYyowkLMxu5KiuNJ83NkjsaG9HNT5wwn7EI93TiRFO94sor9y9cKSYA1LExOTm40rOmJjLSCxcomVuzBmIXgcz27mXRLC6OTFnL/bSD0/z5xlzLH87a9L59jS/5l76E1vv730NKY8ZA9CdPYmFr2xDjww8zW5g71xDrtGnMCpyWBiKQ2JIlhsT87XW/8pXAi8dVVcwWdNPKnDkcR2cXeh9EOG9dHX+U7L1eMtlZs1iMLS5m+3rPnqapyOOPownX1xN0T5zgPoSH+/rUhIdz7oSEzjcFeTwEnoMHfQl96FDuRWf9ONWLPDWVa4+MpNIkP5+F8BEjCJ436ufi9XL8rCzkFqd75gMPELwefJCZlyuvuBBxib0NcnIg0okTTQPgjlBairxSVuZbGfLii2RW27eTcY4dS1bn8RjfD/Vcb6+NXkkJmnlREeRQWEjWumkTBPcf/8FrdHfi66/zGhE09vnzkV1EIIfwcM6VkeG78ad3b2YmTnuEYOx1bRti1MqY8HA2UEVFMTb/zUW9extSUi+ZyEgWVS9coNyzd2/kocxMrnHuXFPJkZZGkGxpMR41St66Q3fZss4zVY8H6eLgQYKSEvqIEdzLUaM6fn9LC+8/fJgZV79+3J/SUgJOr14mEHWXaCsqTBlidrZJAkS49gcfZKw3e/HVxb0B1wTMAXVs7NkTD5XONmJcvAhxa+WIkxDKymiOUVaGzHLxIg95WJjJVDdvhoD9obXp3/42waJ3b7JY1dMzMyHT8HAIpLISAlfyXruWDPqHP2x7bN3wpJg1i+Ci0kqw9rp1dUhLFy/y8/DhBJwLF3z1ehEWd/PyfPV1EWY3U6aY0spp05B7cnN9HRizszlmSYkhdKf5WFwc97+zSpfWVu6rdnvSsYwZw33prAq3ro6Z07FjBJ2hQwme9fVIRY2NfEbLlnW9yUVjI7MbJXOV6Hr0MO6WI0ey+BnszlgX9x7uujr2ux0eD9Uqzc1ozB2Rum2T7aWkkElv2eLrtpeVRaa9axc6s5Kf9v/UTUeBvKydtekpKcg65eXILFOmmCbXI0eiIe/YYeSOSZMg9agoCKFXLzK9738fklc5RIQAsHmzr9QUjL2uCOT9+98zVvWuSUggw3cadfXvzzjUileJNDKSKpzMTEor+/cnu/3sM8alTamrqvhMzp0zswX1kmlsJBitWWO6KLWH1lYy6UOHCN5KimPHQuid7U0oL0duOX2aY02YAMFalqkkGjUK2SWQKVygvrleL5KNEnlenpnJaeloRYWxT0hIaN/wzYULf7jE/jk++YSHqzPHxqYmSO38eabDa9YY0nF2KIqJoSpl8WIIaOJEyHrIEEjdvw/p1q1s93/nHchHjbMsi41NERFU6RQVIRHFxSF3tLTwf48/bkr/VIN2BhsnqY8Zw3WqZOFvr/vUU4HtdZ2VMSLMJDZvhoz+9V9NBh0Swrb+s2d9bYdt29ROv/suJYlTpnDfMzONA2N4OGM5dIix6QzHueA6eDDyUUdWui0thtDVjVKEjHfp0s5LDvPyIPRz5/gMtSl0VJTZ/dq7N/LT1KntZ9EvvcTnW1Fh3BCzs839GjIE+amlhSQgP5/PbsUKAqy7COqiq3CJXYxj4/z57Ts2bt0q8qd/ip5eWookkphoHubWVrLpf/s3Fja1Hjw2FhLKzCSj3rChrVbt9ZoWdIcPQ56Kv/xL/iQnk0Fv3gyhaHs0dWmMiPDVxXv3JuONjITERDi+9lHVcQdjrytC1r19u+l+M2UKQU1NuBQjRhAA1JtF0bMn4zx5kvMNGkRmevascWAcPhwS/eQTzqNmVNHRELPa2C5ZwjW1V+3S0kLVyKFDxrJAhMC3dGnHi5lqvZuaiiTUsyeBNCGB+3LsGDOplhZq+Jcs6bhZhnr3/PSnpoKlTx82N40dy79Pn6bevaWFmdiqVYzVNddy0V3c9xp7QQGWr8OGtd0y74Rlifyf/8P/b9pE1quoqYHwr19vO+VWPPMMroOBsrqjRwkSb78NWar8oc0tjh9nfPHxaOtNTWYn55gxvg01tH47MpLf66LbgAFk4ur74W+v+8gjge11W1tZsDx82NTDP/II5Pjb3xqyDQuD7JXQnV+radPIStVMbNQo5KOQEPTo+HgkKpWVnI0p6uogTm32vHFj+5VKLS3cq0OH0L1V+pk6lZlTR23cWlsh2MOHGUvfvgT6WbM4b04O4ysq4vyrVgXebatob0/DX/+1yD/8A3JdWhrZe2ioaTV3Kzsiufjiw+2gFAQaGkReeQXS+PrX25/WnznDg/fyy+jpzrr2vDxIvakJkn3qKXzGk5PJQF96iSw5kB1BRxuavvUtYy713e8SPLSz0dSpLGr26AEJbd9uSihFCALqGChCxpmUZP4/WHvdggKOrVa3sbG8NjXVV0uPi4NUS0t9F0d79mSWkZ7OfYqL455XVhoHxn/6JzJf3Uhl27xPN9vExqL96+Kseu040dwMoX/6qS+hT58OoXekTTc0kIWnpRFE4uLQz9X/paYGaS0zE7JfuZKZV7CLl7r5yrb5jmirOZ1Vaau5YLszubi/4RJ7JwjGsbGznaSnT6NnHzpkLGv9kZUVXC28kxDPnYN4d+8mo/7HfzR+K5s3U6Vi2+jH2vhZM+HaWkPE2p1J7Qn87XVXrQq8E9LrhSRTUkxZ4pw5BLR9+4xublkEM806nXr65MlktJ9+Ssbbvz8zGnVgHD0akpszx8xyevRgjK2tEGtZGRlyYiLau/+mqOZmSPnTT83MQYRqm8WLO+58VFGBhHTyJEFp3DgIfdQorsvjMR2ZPB7+b/Hi4Fwg/WFZVP7o7tBhw8jOJ092d4O66BrcqphOEIxjo7OawUm8Xi/keOQIRLB9u9GlLYuuRFFREFlXvLWdHiuDB2NlsGePWWTcsAEicDahFiGLnTaN8egiqVN7FwneXresDA08L4+MtUcPSO34cd+69Oho5Bd/Ug8PZ7Zy/DgBKi6OQFNUhOyycCEzgV/9ytTch4RwrOZmZhDjxhnZZvPmtna2TU1kvampZgHSsghSixZ1bDObn8/7zp7lPdOnc33OBfPsbIi4tJTxrFrVdeta2+bzSUtj1nX8uGk1N3Ro147lwkVXcV9m7BcuUGM+cybkF8y0Wom9oYHKlcuXmUavXGmm2keOQBKvvBK48qUj/O3fQmjXryNzvPFG29e8+CJE9/vfk2WGhEBk+fnGKiA0FNJW+18RX3vd5cvb2utu3cqxjx9nc5buBh0yBMJ1yjoiZMJlZcahUTFhAvJCejqBLTwc2UUdGMPCmIX85CemwbYTf/ZnfB6HDhHYnnjCl1Cbmgh6hw/7Evrs2dyH9qwfbJv7k5qK/BQRwUwhIcFX2qmqIrB+9pmxLw5UHdQRmpvZAJaWRt19dDTnmju3fQ8gFy6ChSvFtIOyMrLFmBgkmGCn1lu3YnH7xhuQ1aOPQigikOCOHRDjmTPsAO3KlP3iRapZdu6E4E+fhri0+kP12W3bTJY+bhwLe++9ZxZIBw/Ga0UJLlh7XcvCUfLyZbP4OmwYQcb59QgJgRQbGnxJPTycgHbiBNUsAwaQ7fbpAzmOHw8hHzhAZj9tGlJIbi739cwZqmm2b4d4Z80iEOg9bGw0hK7XGhICYS5c6FvW6YTHgzaemgrJ9ukDmc+Z41uf72zKYdsEiYULg+tTqqisRBY6cYLxxsVxrqlTu3YcFy46givFBEBzM+QYEkLm2xXyffpp6sZ79MBoS3cpNjVRzZKVBbn94AfBL6x5PFScpKYiBezdC5kPGsQmKZUHzpxBc1c72/XrCQZvvWWOtWwZGrAukAZjryuC7KB/W5Zp2JGXx+91puKfneu/x43j//btY4YSHs7CoJYC5uTQ0KGsDAkoLIzAFRUFeW/dSlb7yisQ4mOPoZGL8PORI8bVUYTxzZ3L8dvLgBsbCbJpachHAwdy3GnT2mraWVmUl5aVsSi6cmVwpm8ixvzr6FGzmKyt5oYPd3eHurhzuG+IvTuOjfq+Tz9F646LIyPWDLGqiuy8pIQyxa404qiuRtLJzYUMtO59wQIqWMLCyLjXruV1IhDGtGkQuurd2l1IdVt/e91nnglcQtdev1W1HIiMJCjYttG/1SpYhAA3Z46ZXfTuzZhGjjQWBe+8Q4Dp1w9JIyuL3y9eDDGHh2NT/OtfM4N65hnG3NAAoR85Ygg9LAzpa8GC9iWuqirec+IE7xszBmknkDtjZSXrJOfPc+4/+qPA9g6B0NrKTODoUdNqbsECxtfe7MGFi9uJ+0aKOXKEBzk5GWIJBi0tZL1nzkCo69aZLD8/n6qalha04K4skl6+jOzQ0oLf+C9+0fY1zz1natRjYggop0+T3SumTmVMukHGaa+7cGFge10nrl6FfL/9bbNIHBFB9Y3TAlcdGXVhduRIsv/sbEi2rg7Sf/hhNtYcOkSWHRJC5pqXx7XOmsWYeveGvN99l8XcqVMJYB6PIXTtUxoWhqQxf377JYGFhZzvzBkC0bRpvD4uru1rW1sJ1IcOQfZLllB1E4xcUl3NTCA9nQA3cCBjmz69e9UyLlx0Fa4U48Cf/znkGKxjowjE9uabZNIPPQRRatZ3/jyEGB3NpqaOLAic8HpZNDxwgNrqzZvRo19+2TSeyM2F9LX5RnIymeSbb5qdi6GhRloQCd5e1zmOffvwu3Fi8mSCjrZws21mNtqtPiyMcyqBqmWu+o1nZdGtqaaG8+sW+kmTuIeq71+/bmYdq1dD7AcPMn4l9B49INzExMA7YbXqJDWVv3v0YByJie3Pxpy9UKdOZct+Zxm2bTNebTXn9fI9SkgwpZEuXNxtuFkdlFaJyE9EJFRE/sO27X/o6PW3M2Ovrubh/elPg3NsFIFct22DZDZuNJURWvmycyfSx5YtwVe+1NYSDK5epRrnkUd8s7zaWjJZzZwHDOD4mZm+FSSDBqH3azVHMPa6TlRVibz2mm+f0fR0kW9+07SyE4Eca2tNy7qhQyG1ggIj0wwZguwSEsLicW4usottEwyGDYM8dT3Ctk0/1t69ka+ysyF0PU94ONl2QkJgjxSPh8Xgw4fJ1Hv1Mgui7XmqlJdD6JcucV9Xr/bdORwIep6jR5mdRUQw44iPD65fqgsXtwK3LWO3LCtURP5dRFaISJ6IHLMs6z3bts/e6LFvFF6vWWB88sngSP3ECUoDH3iARVLdtej1UmGSnk5m+/jjwU+/s7Mh9aYmpBNnyzndCJOSgr5tWcgV773H2LWjvQgSUlISr/G31/3ylwPb6zpx/LjZ0CRCJrxqFcTrJPVBg8x5Q0OparlwwSys2jaEPmkSmb82vYiJgUT792c24tyh2dSEPcKZM4xzwABmIUroERHMiubNC/w5NTWZptBaebNuHTJIezJKSwszgdRUxr5iBUGgI3mqtpbP+Phx/t2/P0F4xoyOPWFcuLibcDOkmHgRybJt+4qIiGVZb4jIehG5o8TuvzioconuGvWHx0MmnpaGXr5xo8kAm5og2cuXIZ+HHgpuCq72vvv2QXpf+pKvX0lWFkSrEsvGjWybz8oS+Zd/MeOMjGRxTxdIg7XXVdTXU86oG4Isi+uYPJnfa014TAx6+RtvEEAGDWIR8vx5UwY5cyby0LlzyC6NjaavZ1iYMRlzkmdxMTOgsjKCSG6uqcZRk625cwNfQ02NaQrd1GQWZzvyJLdtxvzJJ8xQpk+H1DuqIy8oMJ2zPB7kr4SE4HqlunBxt+FmEPtQEbnm+DlPRBL8X2RZ1gsi8oKIyIjOOhrcBLS3azQQ6uspWczORqNdscKUBjorX9auNbXrnaGuDonk8mWI5dFHDXFVVEA6Fy6Y8yQmkhW+/z5Tf8X48SzO9ugRvL2uwr8PqQgSxJo1BI9f/cq8VglXhOM//zyykWbD/fqRudo2Uk5REUTZ1ERmu3QplSH+Wa3aLqiBmJ4jMpIZyJw5gTPh4mLGnpHBOSdP5vid7dosLUV2uXyZYP7ccwSDQPB6CVBpaYwrPNx4n3dkR+DCxd2O27Z4atv2KyLyigga++06b2coKiJDranxraEW8a18eeaZznVZRW4ugaK+3hiAWRbZ76FDprLFskxdelERRL1vnzmOBqaLF0X+5E+Cs9dVFBSQJevCpza3GDMGV0bNmPv1g5yVcJXQrl41ZLxiBbLK3r3ozhERxtpgzhxI3X+tobWVCp0zZ3x/Hx0Noc+eHbjVXk4O9+fSJYLKnDlo7p3p2s3NLEofPsxxV61C1glUu19fj6xz7BiyTr9+zHpmzgxOrnPh4m7HzSD26yIy3PHzsM9/d9fgxRcD//78eSpQIiLI7JyVJOfO8X9dqXyxbUhpzx40+q9+lZI724bgdu2CSLQefOpUSOvjj1kEffRR9PVhw1jotW1jr/vLX0JYGzcGttdV1NczG8jIML/T0seaGtwUdfem2ueKEFACBZW/+RtI+uc/Nw2ym5qovU9ODpzZ5uYSLJ3GXL16UVo4a1ZbTVwz59RUgmlUFOOdN6/znp62TbDZuZPrmzkTqSzQonZREfcyM5NrGj2aADl+vOt97uLewg1XxViWFSYiF0XkIYHQj4nI07Ztf9bee+60V4xtk93t28fU/sknjf6q8sWuXV2rfGloIKO+eBHZYN06sr/CQog7J4fqnLo6st3VqyHhvXtNZtzaSi24ZpoVFcHZ64pAjseOIbvoguSAAcw0+vZlkXX3bn4fGQm56ianQYOYNXz6KSS5dSvX4fEw9qoqs/N0xAgy+EDllFVVyC6XL5vf9e5NRj9jRltCb27GXfHIEWYWMTEEuhkzgluYLi42Hu5xcdzT4cN9X+P1ci1pacxSwsLohJSQEHyZqgsXdwtuW1WMbdutlmX9iYh8IpQ7/mdHpH6n0dwMAZ87xwO+dq0hHI8HokhPJyN97LHgCCYvD+mlpgYJID6eRcUPP+RYPXuy+7OwEL132TKqYHJzId2qKtNFaOBAgssLL1DfLkKQCGSvq7hyhXPpIqxlmXG0tLABSqtc4uIYh8bzhx/m7//+b65/2TJ+PnoUgtZ707cvmfCECW0NxP78zwmUJ0+a3/fuzQLsgw+2rUKprYVojx8nIA4fjhQycWJwC5VNTQTltDQCjvr2OLPuxkbGk5ZG0OjTh1LQWbM6nwW4cPFFx03R2G3b/khEProZx7qVqKxEIiguJuucP98QSWMj5NyVyheVSXbtgsi+8hWIMz2dTLyxESLMy+OcDz0EEb3+Ou/X9nWJifyfZtHvv0+N+JAh7dvrivguwioGDUI6+ud/Rib53e8g7JAQMvL583ld//5IKQcOQPrjxvFzRgbkrhp8ZCQ/z5zZVq6oqDAt/TRQRERA0jNmtH19aSmzodOnGdOkSSyI+mfZHd3vzEzud20tZP7QQ75EXVoKmZ86RVDTGcakSa7c4uL+wX2x81SE6fpbb0EoTz/t6wtSWQnZlpUFX/nS2Eit+blzkPdjj0Her7wCUY4YQZZ45gy18OvXQ2rZ2ZCq7ix95hljR+C019WMO1Bw0UXYTz/1bVKtwcrjgXD1verj8skn/P/ixfz81luM8YknyIJ/8xsy6GXLCDKLFhF0/Gct5eWUcZ4+zc9K6vHxkLo/gebmop9rLfzMmYyjK5UnhYVmE5RKZFoho7a8aWnGj2b6dMYTyFbAhYt7Hfc0sWvJ47FjaMX9+lF54iSU69fJaltbqRUPpvKloABSrKyETKdOZfPSmTNmyn/yJCQUH8/53n4bAlJL24kTkVh0ITUYe11dhP3kE7R6hV5XbCzeMzojEGF94L33kEVEIO2jRyHyBQvQ7nfu5JpU6587l4VOf2+W8nIy/J/8JPBC64svonOLEHAuXIDQ8/LI/Jcs4X50pQ1cYyOzn+PHOcbataYZd1MTwSUtjaDcqxfXN3eu22rOxf2Ne9oEzLKQNdLTIbANG3zL2bTypVcvsviOemOKQKzp6QSJqCh2n+blkb16vRBlaCjkFx0NwWdkIO/ExpIlazY+ezbjc9rraoehQJJBQQHkr3a6irlzjU7+/PPUmAeDb32LRU1n9czUqUgb/qWFZWVcU2Ym1zdjBnX96qfe1GRq0VtaTFPo8nJkpPnzydK7snPTtpFTdu/m3syda1wnKyog85MnOffQoSyGTpnitppzcW/jvjcB04w2PR2yTE42hOmsfBk2jGl9Zxmec0v82LFmM1FFBfptYiKZZW4uVTGjRkHEHg8a8rVryAIbNoj87GeULAZjr1tXx3FPnDC/syxIcv16zvWtb1G6N348ZL16NQ07tm4lgKxbhzTz4oscp6GBxVsl9REjkFCGDPE9d2kphH7mDISZkMDi70cfGUvhrVsZS329aQpdX8+xNm1ifF3VtvPzOcf169y7Rx7hHqmvjG7smjKFMXVkeObCxf2Iey5j76wBtccDaZw4EXzlS1ER0kt5OURSWoqWO2AAhFhXxzFFyCqvXGGDzeDBSAmVlQSXpCTjt/Kv/9qxva7HA1GmpBhPcjXfGjHCzD527YJgf/hDApZeZyDbBBGy+wUL+Hf//ox/3Dijx2unKCV0dU2cP59AsHs3WfgTTxCovvMdJJZTp5iNTJjA8UeM6PpWfC3/TE8n0K5YQWDIzITQi4uZKc2Zw5jcVnMu7jfctxl7R1YCjY0Q9JUrLAwmJ3dMPioHfPQR1R5Tp0IwYWEQ5PTpLOidPUtmOWUK2/FbWsieL19G5nn2WTL41lZeL8Ix2rPXvXyZ15WVQewrV0KwdXWmXd7ly8wgqqvNtTobWX/veyI/+pGxA/7v/xb5r/+CdKOiIM0HH/TNpouLzaJrjx4EnfnzCTp/+AMbuoaUYdcAABBOSURBVCZNYqZQVsa9jI5GEtGm0J3JWYHg9RJotZIoIYGZRkYG6wkNDQTJ9euZ6bit5ly46Bj3zSPirHzxd1gMhOZmCP30aTLzhgYyWN3ZqBUwdXUEiZISSGjwYMjy0iUIds0aMm3/mcSf/il/nKZkzvJFDTj796PVR0QQIAYNQpP/t3/ztfP9/vfNvzdvNv/W7PzqVchcG0v4z1Kysow+v2gRhB4VZRaKq6p4f0wMJaM5OYxpwQKIuLvZc14e97mgAJln5kzunTYfmTSJ43dnBuDCxf2Ke5rY1Uqgq5UvJSWQmTZALi1FM96yBWLds4fKkgEDqPJITSUQTJ8OKXm9ZJczZvhKHCoFhYX5ziSam429rP4+Ls5kpidPUoqYk4OjYkND+8ZWIpz/1Ck0/LQ0pJ45cwhIgTzL/YPO8uX8/cd/DKFq67eTJ7kXffsyi5g1q2NXyY5QV4esc+oUs5o5cyD5P/wBiWn+fO6t22rOhYuu457T2P1x9iwui8FWvmRksChq25BwdDSEOHMmWvv27RD+rFnIBufOkaX37UumPXQo+ndMTPvnUInIf8ONCFnylSuB2+UtXUpZ4/r1vgutejwNIj/5Cdm/CAu9a9YE3+NVywg//JB7ERPDz3V1nHPBghurPvF6KV3UtYMhQ5hFNTTw2WirOdf73IWLtrhvNXaFGnLt3h1c5UtLC7r2yZOQm2UhWSxdCsmkpqIBR0UhZ6SnQ35/9VcQ8cWL/H7Jks5J78UXqfzYsYMs1f98ERHYCWh2L4Jerta4gY6vTaaXLoXUBwygHNO/0iUY/PKXLBSHhPD32LGcd/ToG5NDcnORXYqKCLRNTVz/hAkQ+o0e34ULF+CeJHaPB9I9eZIFz/XrO658KStDX9Ysd9QoSgZjY9GWt2whQ58wwdSpDxrEJp2kJLL1554zLeA6Ql0dC4O/+pVZuBw5kpI+52zCXx5RDd2/UYj+W4PW/v38efHFrpP6oUPINuXlEOy0aRC6szlId1BTwyYoLZsUIVuPj+dPR7MbFy5cdB33nBTT1coX3ZXq9UKOa9YYM6rMTALEd79Lpnn2LJnxwoXUVH/ta0gzq1d37uPt8aB379tnyhd792az0uTJ7Y/xe98T+fGPAzcKKShgRnLlCmsBf/mXXEdXsl7bZrbx4YcQsGUReBYvvnF92+MhWBw8yL9F2PykTUW6q8+7cHG/Ilgp5p4idmfly5o1HVe+NDTQcCI/38ggSUlk9g0NxiJg6FAWEbduxXkxK0vk//7ftsdrr+WeCO/5+GPGpbLLwoUEnmC0ZP+yzcpKs8FIOxHNm8fYg/04PR6u7+BBxiXCjGPz5puzHT89naCjbfdGjOB6nTXzLly46BruO409L48yPI+HXZztNXZ+8UU29Hz8Ma+NiaFSRuWA7Gxsfd97r30/FCXPzlrulZdTvnjxopFdxo4lw++K/KBB4zvfQQY6dsw3OOhsob2GIk40Npqm0Jqhh4WZGvEbgdajp6QYzX/cOMokXe9zFy5uH+4JYndWvjz3XGADLRGqWX74Q0jWstCTly7l/1pbKWM8coQdma+9ZjTqzgjcH01NZMJHjpj39e4Nofv7mQeDv/s7MvvISIh55kzG7i+VtDdjEGEj05EjEG9Tk3nvwIFk6Teiczc0UOmSmmoy9LFjCRbu7lAXLm4/bojYLct6QkS2ishkEYm3bfu2tkWybaxr9+zpuPKlsZEsMi2Nn/v0Efnyl43Lo5YxFhcbU61gGmz4Z8i2bbbd19Yat0Q19+rujkm1Kxg+nNLLrixmFhVBuGfOML4JE1gQLixES1+9uvvjKi6mnv/UKbPrdehQqnHcZtAuXNw53JDGblnWZBHxisgvReTbwRL7zdDY/StfHnusLUGpJcB3vkOtuD9+8AMWL/fsQc5Yvx4rAH905L2iuH7d9C4NDWV8kycTJIKtIQ903o58b9obp20jKaWmYj3QowckPnQo0lBzM12HnI27g4UutqalsWirs5k+fZC4nD73Lly4uLm4rYunlmXtk9tE7Fu34ly4bRvktXgxi57+8kZeHnXi+flkuqtXI9X06QMx/sVfsMsxO5sqmLVru7doWFtLYDh1yhB6TAzli9pA42YgGDnIspgxpKaSkUdHUx8+Zw6LmSkpjG3z5uA1bw0WjY1cY1oaZaHh4chXoaF8BvPnux4uLlzcatx1i6eWZb0gIi+IiIwIpuC7Hbz0EvXe5eVk2DNnmv/bulXk2982RNurF7LA9Om+xP/SS+jVHo9v44auwONBhlDTL612Wb6cCpvb6Qve3GxsfbdvZ41h7VpMvrTH66VLLI6uWdO1MsOXXqLW/PRpjjVgAAGjro4dqA8/7G77d+HibkOnxG5Z1m4RCeAULn9n2/Yfgj2RbduviMgrImTsQY/QAW0yUVtLX89Ro8z/aTu4iAgyyYULyST9SezZZ0VefdXszOzOouGlS0gaZWXG92XaNKo/+vTpzpV1jvYqXvzlGmf1zqBB1PTX1DCDmDs3+ABWXY17pIhpVNLQwO7RAQOwTQim25QLFy5uP74QUkxnWrNts5Pz61+nNn3VqraLd13VqwOhrAxCv3QJ3bqlBUlj9WrfIHMnUFFBkFIPmrQ0dnv27o13uvYHDQbt3avkZKwNEhLcTkUuXNwJ3JMaeyBnxO4QdnfKFw8coFxQfc979EDbnzfv7iE5yzJNts+eNU22Azk6dgbbxn99yhTu4/TpzEjc8kUXLu4cbguxW5b1uIj8VERiRaRSRE7Ztr2ys/fdyOJpR6QcLGEH+zrbRlvevRtNOSwMmUc92Xv16trYbzW+/W1mDuXlZNcLF3Zvl2dZGQvPly9D6tnZd35G4sKFi9u0eGrb9rsi8u6NHKOrCGZ35c04Rl6eyDe/SUWJbvuPjUV2GT78xsdws3HqFD4sTU3U6HeHiJubmZkcPsyMZNUqZicuqbtw8cXCF65ArSM9PFjS76gGvKaGqprTp/FlnzOHTUaPPkoteFcbM99qOO2GR40S2bix6zMJ20a62bmTRdMZM6ju6dULPd2FCxdfLHzhiL0jBLsIGggvvYSGfOAAWr7uPJ0zB1kjKuqmDPGmQvuOFhXhGZOU1PXAU1JCYMjOppHGpk1354zEhQsXweOeIvbu4tIl/t69mw0+27eb/1u7lr+7Uj1zO3D2LBusQkPpDBVox2xHaGrC5CwtDanpkUfM7MSFCxdfbNzXxN5RDfjWrV2vnrkd8HiwRzh6lBLGTZu6Zlng345v9mwWgu/GGYkLFy66h3vKj727aGkha/W/FXcbsVdVibz9Ngu78fHs+uxKqWVREYZiubk4Vz7ySNfq2124cHFncddZCtzNaM/J8WZU4NwsZGUhEXk8ZOlTpwb/XnW3PHYMs7Pu2ii4cOHiiwGX2D9HIBK/GzR1rxc/mgMH2OX6xBPt+837Q90td+/GDkAXgruzYcmFCxdfHLjE/jnuBhL3R12dyDvvULEyYwYll8H4xIvgarljB7LNsGHILnFxt3a8Lly4uDvgEvtditxc9PSGhq5JJw0N1OGnp+PCuH49QcGVXVy4uH/gEvtdBttm5+fu3VS7fPWr1Jd3Bq+XTUp79qCpJyTQuUn7obpw4eL+gUvsdxEaG/FOv3CB7kvr1gVHzM6mIiNHYnvQlfZ5Lly4uLfgEvtdgoICukJVV4usXEnG3Zl8UldHZq9NRTZswBfelV1cuLi/4RL7HYZto4d//DGa+HPPdb6l3+sVOX6cEsbmZpEFC0SWLOlaZyQXLlzcu3CJ/Q6iuZmG3BkZ9EfdsKHzHaC5uWwyKioSGT0a2SU29vaM14ULF18MuMR+h1BSgoFXSQmLnIsXd+zTUluLDUBGBu33nngCHd6VXVy4cOGPGyJ2y7L+WUTWikiziFwWkedt2668GQO7l5GZiSVwjx70bu2od6jHg1HXvn38e9EigoB6xLtw4cKFP240Y98lIt+1bbvVsqx/FJHvish3bnxY9yZaW+mZevw4OvqmTR03v756FdmlpERk3LjAvVxduHDhwh832kFpp+PHIyKy6caGc++iogLppaBAZP58HBXbM/CqrkZ2OXOGWvYnnxSZONGVXVy4cBEcbqbG/hURefMmHu+ewYUL1KfbNiQ9aVLg13k8NMzev5/Kl6VL6VsarI2ACxcuXIgEQeyWZe0WkUB7H//Otu0/fP6avxORVhF5rYPjvCAiL4iIjBgxoluD/aLhxRfRxFNT2T26eTN9Sf2xdSta+44ddEWaOJFa9kCvdeHChYvOcMN+7JZlPSciXxeRh2zbrg/mPXebH/utQE0N+vnWrbgqrlolEhYgjFZVIbds3SoSE8PrutoNyYULF/cHbosfu2VZq0Tkb0RkabCkfj8gP1/k9df59+OPizz4YPuv+6//4t/JyWjvgcjfhQsXLrqCG+1w+TMR6S0iuyzLOmVZ1i9uwpi+0Ni6la5Ef/3X/KzOiv62wPq6732Pn5csQUu/G+2DXbhw8cWC2xrvFiLY1np3Wws+Fy5c3J0IVopxe9K7cOHCxT0Gl9hvIYLtmXo39VZ14cLFFx+uFOPChQsXXxC4UowLFy5c3Kdwid2FCxcu7jG4xO7ChQsX9xhcYnfhwoWLewwusbtw4cLFPYY7UhVjWVaJiOTc9hPfvRggIqV3ehB3Kdx7ExjufQmMe/2+jLRtu9NmmHeE2F34wrKs48GUMN2PcO9NYLj3JTDc+wJcKcaFCxcu7jG4xO7ChQsX9xhcYr878MqdHsBdDPfeBIZ7XwLDvS/iauwuXLhwcc/BzdhduHDh4h6DS+wuXLhwcY/BJfY7DMuyVlmWdcGyrCzLsv72To/nboBlWcMty0qxLOusZVmfWZb1rTs9prsJlmWFWpZ10rKsD+70WO4WWJb1gGVZb1uWdd6yrHOWZc2/02O6k3A19jsIy7JCReSiiKwQkTwROSYiT9n2/9+u3bNGEYZRGL4PRgs/65AISSHWEbEJWGhpUEsLLewVxELQHyF2NqsiGLBQCwvBxsYqSIIgmkaCkBVFG1FsgngsdgLxFzwvM+eqdqe6WZbD7DvrD6VhxSRNA9O21yQdAFaB80P/XLZJug4cBw7aXqruaYGkh8Br2yNJe4C9tn9Ud1XJHXutE8BH2xu2t4DHwLnipnK2v9he617/AtaBmdqqNkiaBc4Ao+qWVkg6BJwE7gHY3hryqEOGvdoMsLnj/ZgM2H8kzQELwEptSTPuADeAv9UhDZkHvgMPuiOqkaR91VGVMuzRLEn7gafANds/q3uqSVoCvtlerW5pzBRwDLhrewH4DQz6eVWGvdZn4PCO97PdtcGTtJvJqC/bflbd04hF4KykT0yO7U5JelSb1IQxMLa9/avuCZOhH6wMe603wBFJ890DnwvA8+KmcpLE5Lx03fbt6p5W2L5pe9b2HJPvyivbF4uzytn+CmxKOtpdOg0M+kH7VHXAkNn+I+kK8BLYBdy3/b44qwWLwCXgnaS33bVbtl8UNkXbrgLL3Q3SBnC5uKdU/u4YEdEzOYqJiOiZDHtERM9k2CMieibDHhHRMxn2iIieybBHRPRMhj0iomf+AZcIQl0P8QqCAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% EMD\n",
- "\n",
- "G0 = ot.emd(a, b, M)\n",
- "\n",
- "pl.figure(3)\n",
- "pl.imshow(G0, interpolation='nearest')\n",
- "pl.title('OT matrix G0')\n",
- "\n",
- "pl.figure(4)\n",
- "ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('OT matrix with samples')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute Sinkhorn\n",
- "----------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% sinkhorn\n",
- "\n",
- "# reg term\n",
- "lambd = 1e-3\n",
- "\n",
- "Gs = ot.sinkhorn(a, b, M, lambd)\n",
- "\n",
- "pl.figure(5)\n",
- "pl.imshow(Gs, interpolation='nearest')\n",
- "pl.title('OT matrix sinkhorn')\n",
- "\n",
- "pl.figure(6)\n",
- "ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('OT matrix Sinkhorn with samples')\n",
- "\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Emprirical Sinkhorn\n",
- "----------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Warning: numerical errors at iteration 0\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/rflamary/PYTHON/POT/ot/bregman.py:374: RuntimeWarning: divide by zero encountered in true_divide\n",
- " v = np.divide(b, KtransposeU)\n",
- "/home/rflamary/PYTHON/POT/ot/plot.py:83: RuntimeWarning: invalid value encountered in double_scalars\n",
- " if G[i, j] / mx > thr:\n"
- ]
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
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- "output_type": "display_data"
- },
- {
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DDzzA9u3befvtt1m5cuVOAXzMmDHceOONiZQ1r732GgArV67kkEMO4V/+5V8YP348r7/+es7rzkSBXUqrkBSOFM3UqdGUs2HDBs466ywOP/xwampqWLJkCdOmTaNbt278+te/5rTTTmPw4MF06tSJSZMmheueyjnnnEN9fT2dO3dus+zvf//79OnTh5qaGmpra7n33nvZddddefDBB/nJT35CbW0tdXV1LSdBk1144YUMHjyYQYMG8dWvfpXa2lp++MMfcuedd1JbW8tbb73V7tFBOn369GHEiBGccMIJ3HLLLa1a/QBTpkxh69at1NTUMHDgQKZMmQLA/fffz6BBg6irq+PNN9/kO9/5Ts7rziSWOU/r6+tdE21ILGbMCLpWJu9c5s4NjhiqYOeydOlSBgwYEHc1OoQJEyYwbtw4Tj311KKUn+67NLP57t5u2lstdulYdCWrdAAa3VE6luR+9JMnB71ydCWr5OGOO+6IuwptUotdOh5dySpVToFdOh5dySpVToFdOpZ8h0IQqSAK7NKxqB+9dAA6eSodi65kLao1a9Zw3HHHAfDBBx/QuXNnEqO5vvLKK+y6666Rr3PBggV8+OGHjB07NvKyc/H555/Ts2dP1q2LfzoKtdhFOqoizE61zz77sHDhQhYuXMikSZP413/915bH2QT1fK7+XLBgAU899VQ+1a1aCuwiban2aflK3Kf/m9/8JsOGDWPgwIHcfvvtQNDK3WuvvTj33HOpqanhlVde4Xe/+x39+/dn2LBh/PjHP+bkk08GgqtaJ0yY0DIE7u9//3s2b97M9OnTueeee6irq+PBBx9stc433niD4cOHtwyPu3Llynbrct555zFw4EDGjBnDvHnzGDlyJIcccghPPPEEALfffjunnHIKI0eOpF+/flxxxRVpP+8111zDiBEjqKmpYfr06QCsX7+eE044gdraWgYNGrRTfSOTzRCQUd80bK9EotChg9tTgUMG5zxsb+IzTZkS+WebOnWqX3fddS2P16xZ4+7BcLkDBgzwtWvX+tatWx3whx56qOW1Aw44wFetWuXbt2/3U0891cePH+/u7hdeeKHfd9997u6+du1a79evn2/evNlnzpzZMpRuqkmTJvmsWbPc3X3Lli0tw/Rmqsvs2bPd3X3cuHE+duxY37p1qzc2NrYM6ztz5kzff//9fe3atb5hwwYfMGCAv/baa75161bv0aOHu7s//vjjPnnyZN++fbtv27bNx4wZ4y+++KLPmjXLJ02a1FK/devWtbn9ymHYXpHWStHaLXaLsyNMy1fCPv0/+9nPqK2t5cgjj6Spqallwopdd92VU045BYAlS5bQv39/DjroIMyMM844o+X9s2fP5sorr6Suro7Ro0e3DIGbyVe/+lWuuOIKZsyYwerVq1vGcmmrLt27d+f4448HgiF6R40aRZcuXXYarnfMmDF88YtfZPfdd+fkk0/mhRdeaLXe2bNn8+STTzJkyBCGDh3KihUrWL58OTU1NTz11FNcfPHFvPjii/To0aOwjdoGBXYpjlIc5pci8EYZ+MoxtVOiPv3PPPMMzz33HH/6059YtGgRNTU1LUP0du/efafhbtNxdx555JGWnP27777bMr57WxoaGnj44Yfp2rUrY8eO5bnnnstYl+TzAIUO1/vTn/60pa4rVqxgwoQJDBgwgMbGRgYOHMjFF1/MVVcVZ+qKSAK7ma0yszfMbKGZaXQvKV1rt9gtzigDX7mNU1PCPv0ff/wxe++9N927d2fx4sW82kb30sMPP5xly5axevVq3J3f/OY3La8lhsBNSAyBu+eee7J+/fq05a1cuZIvfelLnHPOOYwbN47XX38967pkMnv2bNatW8emTZt49NFHOeqoo1q9PmbMGH71q1+xceNGAJqamvjoo49477332GOPPWhoaOD8889nwYIFOa87G1G22Ee7e51nMfKYdBClOMwvZouzvcCXawu83FI7JezTf9JJJ7Fp0yYOP/xwfvrTn3LEEUekXW633Xbjpptu4utf/zr19fXstddeLemKqVOnsnHjRgYPHszAgQOZFs4Scuyxx7Jo0SKGDBmy08nIe++9l4EDB1JXV8fy5cs588wzs65LJsOHD2f8+PHU1tZyxhlnUFdX1+r1E088kVNPPZWvfOUrDB48mNNPP50NGzawaNGilpO5V111FZdeemnO685KNon49m7AKqBntsvr5GkHUcQTc63KL9bJzfZOzua7/mzne81DvnOelpP169e7u/v27dv97LPP9v/8z/+MuUatZTpZG6VCTp5GFdj/AiwA5gMT21hmItAINPbp06cY20HKSSl6lBS7V0w2ct15FXlnVw2BfcaMGV5bW+tf/vKX/cwzz2zpyVIuOlJgPyD8+3fAIuCYTMurxd4BlEPQLZVsW+Al2NlVQ2CXQOzdHd39vfDvh8DDwIgoypUK1lGmwMslx1+inHbw/y+VrNDvsOCxYsxsd6CTu68P738DmF5ouSJlL/nkamK8mUwnREswTk23bt1Ys2YN++yzT1ZdCKX8uDtr1qzZaf7UXEQxCNi+wMPhj6gLcK+7a+AGqX6ZWuAx9XTp3bs3TU1NNDc3x7J+iUa3bt3o3bt33u/XZNYimZRq8usqn2RboqHJrEWiUKqLisrt4iWpaBqPXSSTUk1+rUm2JUJqsYu0J/kK2hNO2DldEtVYL5pkWyKiwC7SnkSXxoYGuPtuuOGGHc9HmS7RJNsSEaViRDJJ7dJYVwcXXAALF8KTT0aXLsm166RIBmqxi6RKHtwr0aUx8fx558GZZ8Jdd0WbLtEk2xIhdXcUSZXaek5+DDrBKbFRd0eRfLU1vC5EP355OUy+UQ51kEgpsIukk66HShTpktQgOnw4nHwy/OAHweM4+q+rD331yWaksKhvGt1Ryl6xhtdNN8Jjjx7uX/hC8catT5Vu5M3rr3ffbbfS1UHyQimH7c31psAuZa3Yw+um22kUcfKNNtef+vkaGkpXB8lLtoFdqRjpuNrKLV93XXF7qKSmeaC0/dfTnUO45JKg+6b60FeHbKJ/1De12KUslGKWp0zrnTJlRxqm1HVw33GU0NAQz3aQnKEWu0g74phcOnWC7G9/G5LHTS9V//Xkq1x/+9ugxa4+9NUjm+gf9U0tdikrpcxvRz1lYD7lxXWkIgWj1C12M+tsZq+Z2WNRlSlSdMUenyU1j58YWz25j3jylIG59inPp6uirnKtftlE/2xuwHnAvcBj7S2rFruUhVK0XHNdRz51KlbXTCk7lLK7I9Ab+CNwrAK7VIyo0yJtyTXwzpkT9ClPd1KzrbqlppNy+Wyl2g5SsFIH9geBYcAoBXaRNHLN4yf6lDc0BI8ztdzT7Thyafkr514xShbYgXHAL8P7bQZ2YCLQCDT26dOnBJtApEzk02JPXDBktnPLPd2y6YLyxIlBd8rk9U6cGNxSy5g4UemcClDKwH410ASsAj4ANgF3Z3qPWuzSYRSaY09tuafKlEaZM8e9e/cdRwpz5gR95nv0SF+fUvYOkryUNBXTUphSMSKt5Zq/Tl5+4sQgEDc0uO+++44US7a578Q4NN27Bzn7xIVQmVI3arGXNQV2kUqW2rpO19pu7/2pLfHddtvx3uTWuXLsFSPbwB7p1Hju/izwbJRlilStGTOC/uapk2Mn+pM/8kjwNzGxh1lwpWo2V8Ymz/yU6Kf/i1/ArFmtn7v5Znj66Z2vPL3kkmDMHE0iUpmyif5R39RiF/H2W8qJtExq6zo1FdNWuidxQjS5/HQ59tTxahIpoOQy1f2xLKBhe0UqQKbcdnIgTjdgWGoZqTuIiRN3XjabXjGZTrBKrLIN7JGmYkQkR8lD+E6ZsnPqwww8nJf400+hc+fWrydSN4nBzNqbi/XWW9PXYfRo2Hff1vVoq7xMKaTE0AgSr2yif9Q3tdhFQpla7KmpmIaGoDWdaHGntqQL6a6Yrh5tlaeTrbFBqRiRMpdNgEwNuNdfn35HUEh3xXT1SE4BpStP3SNjocAuUu7a6+PeVuBPncKu0BZ0aj0SJ1TbOjJI0AVNJafALlLpsp10upBBvNK9t60TrMnlqcUeCwV2kWpTjNx2PmXOmRPsXK6/vvV7rr9eXSKLLNvArqnxRCpFMSbIyGd6wFdfDXrPXH110BsmcUHTZZdlnuAjVa6Tikj2son+Ud/UYhcpsfbSNfnkywtNx6h3Tc5Qi11EWmSaQi/f6QGT++BPnpz78ANxTCbeUWQT/aO+qcUuEoNMozrm02qO6gSqetdkDbXYRaSVdC3sfPP2iRb//ffD9Ok7Wt65TgZe7MnEO6pson/UN7XYRWIQZRfFKOZJVY49Z2TZYrdg2dKqr6/3xsbGkq9XpMNKbmGPHr3z4zhozJmcmdl8d69vdzkFdpEOQEG0KmQb2AvOsZtZNzN7xcwWmdliM7u80DJFJE9t9Q2HnVvmo0crqFepKE6efgoc6+61QB0w1sy+EkG5IpKLGTOgS5fWJzFvuAHGjcvtwiGpeAUH9jCnvyF8uEt4K31+R6ScxHFV5fDhwdWgl1wSBPfvfAcuuCDoBaO+4R1KJN0dzayzmS0EPgT+4O7z0iwz0cwazayxubk5itWKlK9MFwQVS6Kr4tVXw4ABcNddcOaZcN552Zehy/yrQiSB3d23uXsd0BsYYWaD0ixzm7vXu3t9r169olitSPmK66rK0aPhhBPg+efh6KPhySdz6xsexw4pE+1o8hLpBUruvg6YC4yNslyRilToJff5uOEGuPtuaGiApUt3pGVyGSagnC7zL7cdTaXIprN7phvQC9grvN8deB4Yl+k9ukBJOoRSj1k+Z4777rtHM5xuOV3mr7HfW1Cq8diBGuA14HXgTeCy9t6jwC5VL46rKqO4GjTxnnILpOW0o4lRyQJ7PjcFdql6UQXZUivHy/zLcUcTk2wDuwYBEymGiy6qzAuCijGZR7JcT4ZGNdhYB6PALiKBUgw7kOvJ0GLvaKqUxooRkUCpBgpLlDt5cjBUrybXyFrJxooRkSpRqq6OcXQD7WAU2EVkh1IEXU2uUXQK7CKyQ7GDrk6GloQCu4gEShF0dTK0JBTYRSQQVdDN1KWxvW6gGhsmEgrsIhKIqu99IeO7aGyYSHSJuwIiUmWSe9fk2qWxkPdKC7XYRSR6hfSuUXfIgimwi0j0CuldU8yeOR0kh6/ALiLRKqR3TbF75nSQHL4Cu4hEq5DeNcXuDlluE4kUicaKESm2UgyuJbm57LIghz9lSnBkUCFKNlaMmR1oZnPNbImZLTazcwotU6SqdJDD/4oRx5AGpc7tZzNoe6YbsB8wNLy/J7AcODzTezTRhnQ4miyiPMQ1kUhE66VUE224+/vuviC8vx5YChxQaLkiVUVd+MpDXEMalDi3H2mO3cz6As8Bg9z9k5TXJgITAfr06TPsnXfeiWy9ImVPY5ALFJzbL/l47Ga2B/AQcG5qUAdw99vcvd7d63v16hXVakXKn0Y0FChpbj+SwG5muxAE9Xvc/bdRlClSNTSioZR45x5FrxgDfgUsdfcbCq9S9Zs2Le4aSElV6sTWEp0S79wLzrGb2deA54E3gO3h05e6+xNtvaej92M3gxguHxCRCpdtjr3g0R3d/QXACi1HRESioSEFSmTatKClbuEuMHFfaRkRiZoCezuiCrzTpgXpl0QKJnFfgV1EoqbA3o7LL4+7BiIiuVFgj8HUqXHXQESqWVUF9tS0RjZpjnTLFDsfrvSLiBRTVQ3bm9qNMJtuhe0to66JIlIuSj6kgIiIlIeKD+xtpU0ypVFySbUoHy4ilUapGKVaRKRCKBUjkdCJXpHKU1WBPTVtkk0aRamWzNSPX6TyVFUqRqKnVJVI+VAqRvKmcW1EKpta7JKRWuwi5UMt9gqT2h1TRCRfUU2N999m9qGZvRlFeR1R8knKUp6wbG8nopPLIpUnklSMmR0DbAD+x90Htbe8UjE7S055lDL9oVSLSOUoaSrG3Z8D1kZRVkfS1knK5PtKy4hIrpRjj1Fbk28k3y9GYFevF5HqVrLAbmYTzazRzBqbm5tLtdrYlHOQ1GxOItWtZIHd3W9z93p3r+/Vq1epVhubXE+AJp+k1AlLESmEUjFlIq7ujtoE6U/XAAAI0UlEQVSJiFSfqLo73ge8DPQ3syYz++coyq00lZi7Lue6iUh+dOVpkZRTN8Jp0xTARaqBrjyVFhqhUaRjUWAvEuWuRSQuCuxFEkXqo5AyKjHfLyLRUGAvokKDaCEpFPVVLx/a5lJqCuxFlEtg1j9/9dI5Dik1BfYsZRt48w3QiX/+YqRQlO8X6VjU3TFL2XZfTATkVFOnZg7O6covpy6Tkptp09K31Nv7HYhkou6OMco2t51N67zag0C1fj6d45A4KbBnkG1aJNPwu+2Vn+mff+rU6s/PVvvnE4mDUjFZyiUVk1gulys+2yq/2tMx1f75QFf+SnSUiikDufwzJ5/gLHUf9FIHnY7Wx75aP5eUL7XYs5Rtq6uQ1lm695aiRRtnq7kjtNhFopJti12BvYzE1TNGgV2kMigVUyWK1Qe9XNIh6mMvEj212GNWDv2d1WoWqQxqsScp55NX6u8sIlGLagalsWa2zMxWmNnFUZQZJfWVzkzpEJHqUnBgN7POwH8BJwCHA2eY2eGFltsRpXZ5LBUdHYhUlyha7COAFe6+0t0/A2YB4yMotyDlcnIwF8l1i/Moo5y3kYi0r+CTp2Z2KjDW3b8fPm4AjnD3H6UsNxGYCNCnT59h77zzTkHrza2OlXdyUF0QRSRV2Z08dffb3L3e3et79epVqtVWlEo8yhCR8hNFYH8PODDpce/wubJRKScH4+who52KSPWIIhXTBVgOHEcQ0F8F/tHdF7f1HvVjb59SMSKSqmSpGHf/HPgR8DSwFLg/U1CX7FTSUYaIlJdIcuzu/oS7H+buh7r7lVGU2dEVGjDbmtgjG7nsVHSNgEj50ZACVapUA4opbSNSOmXXKyYOShMEot4OOtEqUt6qusXe0VqTbQ0olk5Ug4x1tG0sEie12MtYMVrQib9Tp+7cXTJxP/k5ta5FqlfVBfZKSBNEfcIxubzk+6nbAdrfDrlup0rpvSPSkSgVE4Nc6pXNVHvJ5SXuJ78v3XNR1E1ESkupmDKT75FEW637tspL3L/88p3LLqejFhEpnqoO7OWUJihkuIBcA3Jq2e1th0pIX4lI9qo6FVOu2kt35DpdXqK8RGAu5CtVKkakfCkVUwL5tmizaUEnt+4h+9Z9OR2liEg8FNiz0FZAzbd3S7bpl0QLHNpOjySfIE3UqZA0inYMIpVPqZgstJWeKEXaIpGWyWY9SqOIVDelYoqk1CcadQJTRHLV4QJ7toEyU3fCUk+Gke3l/0qjiAh0wFRMPumKOFMxcaxLRMpTSVIxZnaamS02s+1m1u7Kqo1ayCJSjgpNxbwJ/D3wXAR1KZpC8+JtBfBi579zrbfy8SICEaVizOxZ4AJ3zyq/UkgqJpvxTjIxi27I2lLKJhVTjHRNodtbRKJTdr1izGyimTWaWWNzc3Pe5WTqO55tANJ0btnTthKpPO0GdjN7xszeTHMbn8uK3P02d6939/pevXrlX+MMsglClZoXz5QO0jgvIpKs3cDu7l9390Fpbo+WooIQTfBKlJEI/pUWADPl1aPufqmdhUhlq7gce2oeOdcBs9KVUS00WbVIdStVd8dTzKwJOBJ43MyeLqS8fBSjxVqpKjXNJCLRKiiwu/vD7t7b3bu6+77uPiaqirUliuCVbRmVtnMoRn21sxCpPFV15WkhXfPSvVdpCBEpJ9mmYqoqsBciXRBXYBeRclJ2/dgrhXqEiEil6xJ3BeKU2qMmEcwTPWrUYheRSqRUTEipGBEpd0rFREA9QkSkEimwh9IFceXVRaQSKbCHFMRFpFoosIuIVBkFdhGRKqPALiJSZRTYRUSqjAK7iEiVUWAXEakyCuwiIlWm0Ik2rjOzt8zsdTN72Mz2iqpiIiKSn0Jb7H8ABrl7DbAcuKTwKomISCEKnUFptrt/Hj78E9C78CqJiEghosyxfw94MsLyREQkD+0GdjN7xszeTHMbn7TMvwGfA/dkKGeimTWaWWNzc3M0tS9z2Y4/o3FqRCRKBY/HbmYTgB8Ax7n7pmzeU47jsRdDtuO5a9x3EclGtuOxFzSDkpmNBS4CRmYb1EVEpLgKzbHfBOwJ/MHMFprZLRHUqaJlO2eq5lYVkWLR1HhFpFSMiERJU+OJiHRQCuxFlO2cqZpbVUSipFSMiEiFUCpGRKSDUmAXEakyCuwiIlVGgV1EpMoosIuIVJlYesWYWTPwTslXXL56Ah/FXYkypW2TnrZLetW+XQ5y917tLRRLYJfWzKwxmy5MHZG2TXraLulpuwSUihERqTIK7CIiVUaBvTzcFncFypi2TXraLulpu6Acu4hI1VGLXUSkyiiwi4hUGQX2mJnZWDNbZmYrzOziuOtTDszsQDOba2ZLzGyxmZ0Td53KiZl1NrPXzOyxuOtSLsxsLzN70MzeMrOlZnZk3HWKk3LsMTKzzsBy4HigCXgVOMPdl8RasZiZ2X7Afu6+wMz2BOYDJ3f07ZJgZucB9cAX3H1c3PUpB2Z2J/C8u99uZrsCu7n7urjrFRe12OM1Aljh7ivd/TNgFjA+5jrFzt3fd/cF4f31wFLggHhrVR7MrDdwEnB73HUpF2bWAzgG+BWAu3/WkYM6KLDH7QBgddLjJhTAWjGzvsAQYF68NSkbPwcuArbHXZEycjDQDPw6TFHdbma7x12pOCmwS9kysz2Ah4Bz3f2TuOsTNzMbB3zo7vPjrkuZ6QIMBW529yHARqBDn69SYI/Xe8CBSY97h891eGa2C0FQv8fdfxt3fcrEUcC3zGwVQdruWDO7O94qlYUmoMndE0d1DxIE+g5LgT1erwL9zOzg8ITP/wV+F3OdYmdmRpAvXeruN8Rdn3Lh7pe4e29370vwW5nj7mfGXK3YufsHwGoz6x8+dRzQoU+0d4m7Ah2Zu39uZj8CngY6A//t7otjrlY5OApoAN4ws4Xhc5e6+xMx1knK24+Be8IG0krguzHXJ1bq7igiUmWUihERqTIK7CIiVUaBXUSkyiiwi4hUGQV2EZEqo8AuIlJlFNhFRKrM/we0W8V0yxv+TgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% sinkhorn\n",
- "\n",
- "# reg term\n",
- "lambd = 1e-3\n",
- "\n",
- "Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n",
- "\n",
- "pl.figure(7)\n",
- "pl.imshow(Ges, interpolation='nearest')\n",
- "pl.title('OT matrix empirical sinkhorn')\n",
- "\n",
- "pl.figure(8)\n",
- "ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('OT matrix Sinkhorn from samples')\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_OT_L1_vs_L2.ipynb b/notebooks/plot_OT_L1_vs_L2.ipynb
deleted file mode 100644
index 8cc4e5d..0000000
--- a/notebooks/plot_OT_L1_vs_L2.ipynb
+++ /dev/null
@@ -1,374 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# 2D Optimal transport for different metrics\n",
- "\n",
- "\n",
- "2D OT on empirical distributio with different gound metric.\n",
- "\n",
- "Stole the figure idea from Fig. 1 and 2 in\n",
- "https://arxiv.org/pdf/1706.07650.pdf\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "import ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dataset 1 : uniform sampling\n",
- "----------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 504x216 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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\n",
- "text/plain": [
- "<Figure size 504x216 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "n = 20 # nb samples\n",
- "xs = np.zeros((n, 2))\n",
- "xs[:, 0] = np.arange(n) + 1\n",
- "xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n",
- "\n",
- "xt = np.zeros((n, 2))\n",
- "xt[:, 1] = np.arange(n) + 1\n",
- "\n",
- "a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n",
- "\n",
- "# loss matrix\n",
- "M1 = ot.dist(xs, xt, metric='euclidean')\n",
- "M1 /= M1.max()\n",
- "\n",
- "# loss matrix\n",
- "M2 = ot.dist(xs, xt, metric='sqeuclidean')\n",
- "M2 /= M2.max()\n",
- "\n",
- "# loss matrix\n",
- "Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\n",
- "Mp /= Mp.max()\n",
- "\n",
- "# Data\n",
- "pl.figure(1, figsize=(7, 3))\n",
- "pl.clf()\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.axis('equal')\n",
- "pl.title('Source and target distributions')\n",
- "\n",
- "\n",
- "# Cost matrices\n",
- "pl.figure(2, figsize=(7, 3))\n",
- "\n",
- "pl.subplot(1, 3, 1)\n",
- "pl.imshow(M1, interpolation='nearest')\n",
- "pl.title('Euclidean cost')\n",
- "\n",
- "pl.subplot(1, 3, 2)\n",
- "pl.imshow(M2, interpolation='nearest')\n",
- "pl.title('Squared Euclidean cost')\n",
- "\n",
- "pl.subplot(1, 3, 3)\n",
- "pl.imshow(Mp, interpolation='nearest')\n",
- "pl.title('Sqrt Euclidean cost')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dataset 1 : Plot OT Matrices\n",
- "----------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 504x216 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% EMD\n",
- "G1 = ot.emd(a, b, M1)\n",
- "G2 = ot.emd(a, b, M2)\n",
- "Gp = ot.emd(a, b, Mp)\n",
- "\n",
- "# OT matrices\n",
- "pl.figure(3, figsize=(7, 3))\n",
- "\n",
- "pl.subplot(1, 3, 1)\n",
- "ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.axis('equal')\n",
- "# pl.legend(loc=0)\n",
- "pl.title('OT Euclidean')\n",
- "\n",
- "pl.subplot(1, 3, 2)\n",
- "ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.axis('equal')\n",
- "# pl.legend(loc=0)\n",
- "pl.title('OT squared Euclidean')\n",
- "\n",
- "pl.subplot(1, 3, 3)\n",
- "ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.axis('equal')\n",
- "# pl.legend(loc=0)\n",
- "pl.title('OT sqrt Euclidean')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dataset 2 : Partial circle\n",
- "--------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 504x216 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 504x216 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "n = 50 # nb samples\n",
- "xtot = np.zeros((n + 1, 2))\n",
- "xtot[:, 0] = np.cos(\n",
- " (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n",
- "xtot[:, 1] = np.sin(\n",
- " (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n",
- "\n",
- "xs = xtot[:n, :]\n",
- "xt = xtot[1:, :]\n",
- "\n",
- "a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n",
- "\n",
- "# loss matrix\n",
- "M1 = ot.dist(xs, xt, metric='euclidean')\n",
- "M1 /= M1.max()\n",
- "\n",
- "# loss matrix\n",
- "M2 = ot.dist(xs, xt, metric='sqeuclidean')\n",
- "M2 /= M2.max()\n",
- "\n",
- "# loss matrix\n",
- "Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\n",
- "Mp /= Mp.max()\n",
- "\n",
- "\n",
- "# Data\n",
- "pl.figure(4, figsize=(7, 3))\n",
- "pl.clf()\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.axis('equal')\n",
- "pl.title('Source and traget distributions')\n",
- "\n",
- "\n",
- "# Cost matrices\n",
- "pl.figure(5, figsize=(7, 3))\n",
- "\n",
- "pl.subplot(1, 3, 1)\n",
- "pl.imshow(M1, interpolation='nearest')\n",
- "pl.title('Euclidean cost')\n",
- "\n",
- "pl.subplot(1, 3, 2)\n",
- "pl.imshow(M2, interpolation='nearest')\n",
- "pl.title('Squared Euclidean cost')\n",
- "\n",
- "pl.subplot(1, 3, 3)\n",
- "pl.imshow(Mp, interpolation='nearest')\n",
- "pl.title('Sqrt Euclidean cost')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dataset 2 : Plot OT Matrices\n",
- "-----------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 504x216 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% EMD\n",
- "G1 = ot.emd(a, b, M1)\n",
- "G2 = ot.emd(a, b, M2)\n",
- "Gp = ot.emd(a, b, Mp)\n",
- "\n",
- "# OT matrices\n",
- "pl.figure(6, figsize=(7, 3))\n",
- "\n",
- "pl.subplot(1, 3, 1)\n",
- "ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.axis('equal')\n",
- "# pl.legend(loc=0)\n",
- "pl.title('OT Euclidean')\n",
- "\n",
- "pl.subplot(1, 3, 2)\n",
- "ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.axis('equal')\n",
- "# pl.legend(loc=0)\n",
- "pl.title('OT squared Euclidean')\n",
- "\n",
- "pl.subplot(1, 3, 3)\n",
- "ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n",
- "pl.axis('equal')\n",
- "# pl.legend(loc=0)\n",
- "pl.title('OT sqrt Euclidean')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_UOT_1D.ipynb b/notebooks/plot_UOT_1D.ipynb
deleted file mode 100644
index 2354d4f..0000000
--- a/notebooks/plot_UOT_1D.ipynb
+++ /dev/null
@@ -1,210 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# 1D Unbalanced optimal transport\n",
- "\n",
- "\n",
- "This example illustrates the computation of Unbalanced Optimal transport\n",
- "using a Kullback-Leibler relaxation.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Hicham Janati <hicham.janati@inria.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "import ot.plot\n",
- "from ot.datasets import make_1D_gauss as gauss"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n",
- "\n",
- "n = 100 # nb bins\n",
- "\n",
- "# bin positions\n",
- "x = np.arange(n, dtype=np.float64)\n",
- "\n",
- "# Gaussian distributions\n",
- "a = gauss(n, m=20, s=5) # m= mean, s= std\n",
- "b = gauss(n, m=60, s=10)\n",
- "\n",
- "# make distributions unbalanced\n",
- "b *= 5.\n",
- "\n",
- "# loss matrix\n",
- "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n",
- "M /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot distributions and loss matrix\n",
- "----------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x216 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot the distributions\n",
- "\n",
- "pl.figure(1, figsize=(6.4, 3))\n",
- "pl.plot(x, a, 'b', label='Source distribution')\n",
- "pl.plot(x, b, 'r', label='Target distribution')\n",
- "pl.legend()\n",
- "\n",
- "# plot distributions and loss matrix\n",
- "\n",
- "pl.figure(2, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve Unbalanced Sinkhorn\n",
- "--------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Err \n",
- "-------------------\n",
- " 0|1.838786e+00|\n",
- " 10|1.242379e-01|\n",
- " 20|2.581314e-03|\n",
- " 30|5.674552e-05|\n",
- " 40|1.252959e-06|\n",
- " 50|2.768136e-08|\n",
- " 60|6.116090e-10|\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Sinkhorn\n",
- "\n",
- "epsilon = 0.1 # entropy parameter\n",
- "alpha = 1. # Unbalanced KL relaxation parameter\n",
- "Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)\n",
- "\n",
- "pl.figure(4, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_UOT_barycenter_1D.ipynb b/notebooks/plot_UOT_barycenter_1D.ipynb
deleted file mode 100644
index 43c8105..0000000
--- a/notebooks/plot_UOT_barycenter_1D.ipynb
+++ /dev/null
@@ -1,336 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# 1D Wasserstein barycenter demo for Unbalanced distributions\n",
- "\n",
- "\n",
- "This example illustrates the computation of regularized Wassersyein Barycenter\n",
- "as proposed in [10] for Unbalanced inputs.\n",
- "\n",
- "\n",
- "[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Hicham Janati <hicham.janati@inria.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "# necessary for 3d plot even if not used\n",
- "from mpl_toolkits.mplot3d import Axes3D # noqa\n",
- "from matplotlib.collections import PolyCollection"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# parameters\n",
- "\n",
- "n = 100 # nb bins\n",
- "\n",
- "# bin positions\n",
- "x = np.arange(n, dtype=np.float64)\n",
- "\n",
- "# Gaussian distributions\n",
- "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n",
- "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n",
- "\n",
- "# make unbalanced dists\n",
- "a2 *= 3.\n",
- "\n",
- "# creating matrix A containing all distributions\n",
- "A = np.vstack((a1, a2)).T\n",
- "n_distributions = A.shape[1]\n",
- "\n",
- "# loss matrix + normalization\n",
- "M = ot.utils.dist0(n)\n",
- "M /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x216 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# plot the distributions\n",
- "\n",
- "pl.figure(1, figsize=(6.4, 3))\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycenter computation\n",
- "----------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: overflow encountered in square\n",
- " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n",
- "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: invalid value encountered in double_scalars\n",
- " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# non weighted barycenter computation\n",
- "\n",
- "weight = 0.5 # 0<=weight<=1\n",
- "weights = np.array([1 - weight, weight])\n",
- "\n",
- "# l2bary\n",
- "bary_l2 = A.dot(weights)\n",
- "\n",
- "# wasserstein\n",
- "reg = 1e-3\n",
- "alpha = 1.\n",
- "\n",
- "bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n",
- "\n",
- "pl.figure(2)\n",
- "pl.clf()\n",
- "pl.subplot(2, 1, 1)\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "\n",
- "pl.subplot(2, 1, 2)\n",
- "pl.plot(x, bary_l2, 'r', label='l2')\n",
- "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n",
- "pl.legend()\n",
- "pl.title('Barycenters')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycentric interpolation\n",
- "-------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/rflamary/PYTHON/POT/ot/unbalanced.py:500: RuntimeWarning: overflow encountered in square\n",
- " err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \\\n",
- "/home/rflamary/PYTHON/POT/ot/unbalanced.py:500: RuntimeWarning: invalid value encountered in double_scalars\n",
- " err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \\\n",
- "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: overflow encountered in square\n",
- " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n",
- "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: invalid value encountered in double_scalars\n",
- " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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D9u3bI57/5je/iePHj6OiogIXX3xxzPvvuOMOnHHGGdi0aRM2btyI008/PaZuKl2Ki4tx+umnY/369bDZbADCNy8tLS2ora2N+5545zJTTjvtNCxbtgz19fUR4rFt2za43W7s3LlTe+3dd9+N999/H+Xl5bjwwgvxla98RXsuFArh1ltvRU1NDerr6zE2NoZ7770XAPD8889j/fr1KCkpwY033ognn3wShYWFaG5uxr59+/DTn/4Uy5YtQ3NzM+6///6EgquqKn7+85+jsbERVVVVeP311/HrX/8aAPCFL3wB69evR319PWpqagAA//Ef/4GOjg5s374dZWVlOPvss83ap3kAFz2tlAKz3cYc8+ijj+K3v/2tNlVokpyHH34Yv/vd78zzZWIyv0hrznluK+pMMsLv9+OBBx7AP//zP8/1ocwbiAiiKEJVVVgsFvA8D57nDV0LMzExmRvMKb4FwgsvvIBly5ahrq4OV1555Vwfzpyjqip8Ph+CwSBEUUQwGITP54PH48GxY8fgdrvh8/kgSRKICIqixCShmJiYzG/MKT6TBQOrjZAkCaqq4sCBA9i5cydkWYaqqlrUdOjQIWzdujWinoLjuHBdBc9DEISIaItFXGbUZWIya5hTfCaLA5rxVmNCBEATlHiikug5djOmKEqMTx/HcXHFyxQuE5O5wxQok3kLm5pTFEWLkOIJBs/zaaVPs/fFExwmgoqiaOnHDEEQIv6ZwmViMjuYAmUy72DCJMuyNj2XTBCMWFtKtH29cLFjYfA8r0VbTLjMBA0TE+MwBcpk3kBEkGU5QgxYceVckUy4WAZhPOGKjrhM4TIxyRxToEzmHCZMbF0oG2GKFol8k0y4gHAvKkmSAAAulws8z6OystIULhOTDDAFymTOYFNnemFa6IN1vHWuYDCoCS4TrniZhfGEa6GfDxOTXDAFymTWYRl5iqIAWBzClAozs9DEJHNMgTKZFaJrmID8CtNsT/lli5lZaGKSGFOgTPJKshomo7bP/jHY1NlCH6yzzSw017lMFgumQJnkBX2q+NGjR7FhwwbD7/A5jkNvby8GBgZARBAEAUVFRRBFEePj4ygtLYXdbl90g3OqzEJJkiCKoilcJgseU6BMDCVeDZPH4zF0IJQkCX19ffB6vZBlOcLWyO/3w+12Y3p6GiMjI1qCQlFREYqKilBcXIzi4mIUFhYuusE5k8xCr9eLYDCI2tpaU7hM5i2mQJkYwmzUMIVCIfT29sLpdKK5uRllZWVoaWkBz/OQJAmCIKC0tBQFBQVobW2F1WoFEE5ACAQCmpnsyMgIAoEAOI6LEK2ioqKI5nmLhXjrXKIowu/3AzAzC03mL6ZAmeSEETVMqQgEAujp6YHL5UJLSws6OjrA8zxGR0fjukiwQZYhCAJKSkpQUlIS8TpVVeH3++H3++HxeDA6OopAIAAAKCws1ISLRVyLSbiSOXSYmYUm8wVToEyyYjZqmHw+H7q7u+Hz+dDa2oo1a9bErKukI1CJ4HleEy59J1pVVbWIy+fzYWxsDIFAAEQUI1xFRUULUriSJZEYkVnIRMwULpNcMAXKJG1mK1Xc7Xaju7sboiiira0N1dXVCV3LEwlULvA8rwmQHlVVtb5TPp8P4+Pj8Pv9mnBFTxcKgpDTceSTbLMczcxCk9nEFCiTlOQ7VZzhcrnQ3d0NAGhra0NlZWXS13McF9fFPNHjuaJPtli2bJn2OBFFCNfk5CT8fj9UVYXdboeiKBGR13wQLqPT8M3MQpN8YAqUSULSbXeRimR1SUSE8fFxOBwO2Gw2rFq1CmVlZYZsd7bgOA6FhYUoLCxETU1NxDEEg0H09fVBkiQMDg7C5/NBVVUUFBTETBVaLLN3Oc5WnVgmmYVA+CaFRaIWi0WbJmT/NVlamAJlEkOm7S5Swfo16SMHIsLo6CgcDgdKS0uxfv36mCm1VORris8omHCxqKmhoQFA+LOHQiEt4hocHITf74eiKLDZbBHCxQZqo5nrQuZE61yTk5MQBAF2uz3i98dea2YWLi1MgTLRyFequF6gVFXF8PAwent7UVlZic2bN6OwsDCr7SYTqNmMoDKF4zjY7XbY7XZUV1drj7P2HUy4hoeH4fP5oCgKrFZrjHCxNPpsYKnk8w32O4k+NjOzcGliCpQJiAg+n0+70I1OFec4DpIkYWhoCAMDA1i2bBnOOOMM2Gy2nLabqJPufBeoRHAch4KCAhQUFKCqqkp7nK3hMOEaHR2Fz+eDLMuacOkTNNI5r3MdQSVCVdW4vz2jMgvNda6FhSlQSxh9qvjx48excuXKtNd/0kWWZQQCAbz33ntoamrCtm3bcrrz17NQI6hM4TgONpsNNpstJnGEFdz6fD44nU709PRAkiRYLJa4ERcbmBeaQCXDzCxcvJgCtQSJ1+5CEARDB3VRFNHb24uxsTHwPI+NGzcaLn5LRaCSwYSroqIi4nFJkjThGh8fR29vL0RRhCAIKC4uRigUQklJCUKhEGw227wZmFkyjhGYmYULH1OglgipapgEQdAEKxeCwSAcDgdcLhdWrFiBHTt24OjRo3lZ7zAFKjFWqxXl5eUoLy+PeFyWZfh8Ps3L8MSJEwiFQprRrj7iKigomPWBmZn+5pNMMwvZc6FQCBUVFZpomZmF+ccUqEVOujVMidZz0sXn88HhcMDj8WDlypURrg+5bjsReicJ/ecxBSoxFosF5eXlmoMGq+eSZVmLuFwuFwYGBhAKhbTaL71w5dMhXlGUORv0E61zMRNih8OBtWvXxrzHzCzMH6ZALVIyTRXPNoLyeDzo6uqCKIpYuXIl1q9fb7j4JYIV5DIRZnfepkClJnpNxmKxoKysLGYaVlEUTbimp6cxNDQU4xBfUlKiGe3mOihnswaVb9jvjIkQw8wszD+mQC0y4glTOhd8piKid31YuXJlRNZZNPlydgDCdTOdnZ2aUNntdoiiCJ7nYbVaF6xXXr5JN0mCOcSXlpZGPM4c4r1eL9xuN4aHh+M6xLOIK+F3QCo46gOvDkMRzpi36e+yLMfUo5mZhfnHFKhFQq41TOlEUESEiYkJdHd3Z+T6YHQERUQYGRnB4OAgysrK8JnPfEa7wIPBIDo7OyGKIvr6+uDz+RaVyatR5JrFl8ohnrU2SeoQb+dhl38NjvrC21ReQaHtM/NyoM40sjMzC43BFKgFjlHtLpKJCBFhbGwMDocDxcXFWLduXczAlO22M0Ff5FtVVYXGxkaUl5ejsLBQy8Zig2BZWZlmO0REEe7kzOQVWPxtNRKRrzRzvUO8nliH+BHUFv8JpYU9EHgegsUCQRjA8qoRqMoXwQvZFW/nC0VRDEneMDMLM8MUqAWK0e0uBEFAKBSK2QcThIqKCnzmM5/JyvUhV4FSVRWDg4Po6+uLKPJ1OBxpZfGxaadok1e9O7nX69XaagCnhKukpGRRduCd7TqoaId4QX4OFnkKoHIoM79lRZZh5b0Y7P5vDLt2ziuHeEVR8uqVmE1mIUvQ0K9xsX+LBVOgFhD5bHehFxFFUTA4OIj+/n4sW7YMW7ZsQUFBQU7bziZpQVEUDAwMYGBgAHV1dTFFvonWttJNkkjkTq6/2/d6vRHTVNHrKwtVuOa0UJc8sMivhv9flwUHmw0hUURHcy+a2y5DQKxK6BAfPV2bb+FSFGVOBv5kmYXsuILBIE6cOIENGzZor73nnntw//33z+7B5gFToBYALPHB7XZrEYzR2UCCIECSJDgcDgwODqKhocEw14dMkyRkWUZ/fz+GhobQ0NCAM888M+7da77qoJL1g9Kvr4yMjCAYDCZMDJjPwjWXAmWRXwQgJnkFwaL+HYWFX03oEK9PiZ8Nh3hZllFUVGTItoxAL1zsu9QX27/88stzeXiGYQrUPEbf7kJRFBw+fBg7duwwfGARRRHDw8NwOp1ob2/Hjh07DL0jTXeKT5Zl9Pb2YmRkBI2NjQmFiTHbhbrJ1lf8fj+8Xm/cVGxZllFYWIhgMDgnxa/xmDOBogkIylspXyYo70K2XARw9ojH2TpjYWFhjNFuPh3i52P6O0OfYchxHAKBQE4zHvMJU6DmIclSxY0cVILBIHp6ejA5OYmamhrU1dWhtbXVsO0zeJ5PmiEoSRJ6e3sxOjqK5cuXY/v27WkJ5HxxkkgkXKyGqL+/H36/H59++qkmXPrBsqSkZNbthuZKoCzy6wAS/BYivjIRgvI+FMvOtLabrUM8q+FK5RBvVJJEPohOgZ+eno5xEFmomAI1j8hXu4to/H4/uru74fF40NraitWrV8Pj8aC3t9fwfQHhATx6gRcIR249PT1wOp2aLVKmqbzxhG++FOqyGqKysrKIflCKomgDpt61gfnk6f/lS7jmRKBIgqC8k/hpRB6ToLyVtkAlIheHeL1L/EITqGhvxoWKKVDzAKNSxVPh8XjQ3d2NYDCItra2CNcHo7z44hE9xaeP3FpaWtDR0ZF1anwiIZoPApUIQRDiujYwnzyfz4eJiQn09fVBFMW4zuS5tiqZC4Hi1Q8BBBK/gAj6I+JoAJzqBPHLEr4lWzJ1iPd4PPB4PCgrK0voED9XMPd6hhlBmRhCNqni2QwsU1NT6O7uhqqqaGtrQ2Vl5azZEbFts1okh8OBqakprFy5EqtXr87p4p4vU3xGwXzyEhm8shquRC01SkpK0k5qmQuBEpS3kz5PABD9u1SPQeH35uuQ4hLPIf6jjz5CW1sbFEWB1+uN6xA/G5FvPKJT4KempswIyiR74rW7SOfHHK91eiKICJOTk+ju7obFYkF7e3vSu6p8RlCiKGJ0dBROpxNtbW1Yu3atIRfvYhOoRCQSLv0Ulb4XVLrdd2dToDh1DLzalfxFUREUMCNQ2Juvw0obRVFQUFCgucTriY58+/v7Z9UhXpKkiIxTc4rPJGOMqGFiIpJMoPSuD0VFRVi7dm1arg/5iKB8Ph+6urrgdrtRVFSEz372s4ZenImm+BabQCXCarWioqIiZjDSJwVEr62wwmNRFPN2QxKPZGtPjPgRVBdAAYCbW2eJZNddssg3kUN89BpXLmUJ0RGUKVAmaZNuu4t0SBblqKqKkZER9PT0oKKiAps2bcqobsPICIqtdYVCIbS1tWH58uUYGRkx/M5xqURQmZJsbYUJVzAYxMcffwxVVSPSsFlWm6GuCUQz60+pXhZv2lEFr56AKnzWuOPJgmxMbLN1iNeLVzqF4PHWoFavXp3Rsc5XTIHKE5m2u0iHeCKid32oqanJ2vXBCPFwu93o6uqCLMtob2/Xsqamp6fz2m4j3uNLWaASoRcup9OJDRs2wGKxRAjX0NCQloYdXfhaXFycVSYbR8PgyJn6hXGm+ACAV4/PuUAZSSYO8cFgEECsg4neId6MoEzSJtt2F+mgFyjmtmC060M2TE1NoasrvL7Q3t4ec3Hksx8Ui1BdLhcKCwu1qRJToFLDbpgSpWGLogiv16sVvjLHhkythnj1cFrHE2+KL/z+kwBR3OcWE9k6xLvdbkxNTSEUCqGoqMgUKJNYZqOGSRAEBINBOJ1OjI6OoqmpCdu3b8+riWUyWBKGIAjo6OhImISRrRdfKogIbrcbBw4cQFlZGURRRDAYjGgupy+ENTlFqiw+vXAlc2xI5JHHpgp5noegpCdQiSIojqYATAKojvPs4ieVQ/zhw4cRDAbxxhtv4P7779ci4e3bt2PdunXYtWsXGhsbE27/+eefx4033ghFUXD99dfj1ltvjXh+//79uOmmm/DRRx/hySefxKWXXhrxvNvtxrp163DxxRfjV7/6lXEfHKZA5QwTJr/fj08++QSbNm3KSw1TMBjE1NQUxsbG0NbWlnFRq1Gw7MCuri7YbDasXr06ZqoiGqMbFqqqioGBAfT09MBqtWLbtm0Rz4+OjmJychKCIGBiYgK9vb3aPD1LEmD/nStxn2uyvWFI5tjAnOH1wmXlJ7B2+acz7TQEWJgxbDzn7vAO4u6XV7ugCnMjUPM1GmfJFhaLBW1tbWhra8Pll1+Of/zHf8Sdd94Jp9OJ48ePo6GhIaFAKYqC733ve3jppZewfPlybN26FRdddBHWrVunvWbFihV4+OGH8bOf/SzuNu68806cddZZefmMS/PqNIDoGiaLxaKryshlAAAgAElEQVR1FDUSv98Ph8OhGcW2tLQkvRvKF0SE8fFxdHd3o7CwMKOeUEZN8endzevr67F+/XqMjIzAarVGOFVYLBbYbLaY8yRJkjZlNTIyAq/XG7PWwu7856trgFEYXQel98jTm7sK0ovgxGKtnYZflz3IolyLIECwWJIeU1igtsV9Lt/MZxeJeHi9XmzZsgV2ux0XX3xx0tceOnQIHR0daGtrAwBcccUV2LdvX4RAMfuzeDfE7733HkZHR3H++efj3XffNe5DzGAKVAawVPF4NUxGRwkejwcOhwOBQAArV67EunXr0NfXl7diWkb0IKFPWy8pKcHGjRszdnXOVaCihYmZyLrd7oySJKxWKyorKyOy26LXWgYGBrS1Fn0zw5KSkkXVzHC2CnV5+hi8rp2G7gC0PlCyLCMkipAlGUD4+mJ9jgRBgMALqWuo8shCEyhJktJOlBocHERzc7P29/Lly3Hw4MG03quqKm6++WY89thjeXNPNwUqDdJJFTfqYmeuD4qioK2tDVVVVRF2RCxiywf6QmDWVr2npwfl5eVZNyvUbzdTmDD19/fHbbuhFyL9gJtJkkSytRZ9B1in0xnTE4pNE8731hqJyPsxkw+82pNo51oExdYHg8EgQIDVZoUyY/0VCoVmbgan0Os6CHth3az34prPAhXdp2o2pyMfeOABXHDBBVi+fHne9mEKVBL07S5UVTUkVTzRftJxfbBYLDFdb42E9YQaGRnR2qp/9rOfhd1uT/3mJGQqUIqioL+/HwMDA2hsbEyYCJLPOqhkXXhZRpXb7Y6oYdFHW7NtdzMf4dVPEGVRnhxdXyNBEGCLeo4vkTHlK9R6cQUCgZjaoXzcMMx3gUp0baRDU1MT+vv7tb8HBgbQ1NSU1nsPHDiAN954Aw888AC8Xi9EUURJSQnuu+++9A4+DUyBikMuNUyZTJ0QEZxOJxwOBwoLC7FmzZqkCQf5tCNSVRWhUAjvvvsuamtrtbbqRpCuYKQrTIm2q/+u8nUnmay1hj5BQG/0SkTa+kyylg6LDV49ntHrCUkGVo5DccEYCko+F/Fwql5c7GYhF5uh+SxQ0UW6rOdYumzduhUnT56Ew+FAU1MTnnzySTz++ONpvfcPf/iD9v8PP/ww3n33XUPFCTAFKoJcU8XT9cpjrg+9vb0oKytLe10nHwKlL/QlImzcuNHwGopUg0KmwsRg6evRojQXdVCJHMolSdI88sbGxuD1eiHLcox7Q7ZFsPMWIgjKiQzfkiL1nfpiHkvVi8vn82FqagqDg4Nxe3GlI1zzWaBy7QVlsVjwq1/9Cueddx4URcF1112H9evX46677sIZZ5yBiy66CO+88w4uueQSuFwuPPPMM7j77rtx7NixfHyc2OOblb3Mc4xqd2GxWLQF3nioqorBwUH09fWhpqYm4+kzIwWKicLg4CDq6+uxbds2fPzxx7N6ISqKgr6+PgwODmZV07UQnCSsVisKCwtRUlKi9YOKbqIXrwiWiRarJVpocNQPwJvZmxLUQTF4dQAgGeBS/0aSuTVk2otroQlUpjeYF1xwAS644IKIx3784x9r/79161YMDAwk3cbXv/51fP3rX89ov+mwpAUqm3YXyWACFR1iy7KMgYEBDA4Ooq6uDlu3bs1q+swIgZJlGX19fRgeHo5pq57PKcToY2DimEux8UL14kvm3sBqiVhLB7/fDwDa9CATrtlKEMgWXv044/fMxL9JXiGBoxEQl/2ifLJeXGyqUN+LSxAEcBwHq9UKl8tlSC8uI1nM3XSBJSpQ2ba7SAUTKAZrZT4yMoKmpqaYLLRMyUVA0mmrns+eUIBxwsRYqAKViES1RMwxgFndRCcIGLHOYjS8mtn0HgAgDTcjXu2DwhufNZbI2FWWZTgcDkiSlLQX11wJV7RALaZeUMASEigj2l2kgglUKBRCT08PxsfHsWLFCuzcudOQaZpoAUyHTNqq8zyflwiKnZO3334bTU1N2LFjhyFTJgu1o26m6NdNamtrtcf16yzxpqv0wjWrgycFwKu9mb8tDYXiqBdAbm3gM4EVfZeVlaGurk57PNdeXEYRPWOzmHz4gCUgUKyGaXJyUltIzUeqOBC+03U4HBBFEa2trVi1apWh6weZRFChUAgOhyOjtuqCIBgaQbHpxKGhIQAwTJgYiy2CypRE6yysgZ7X640ZPJlosantfFg9hYtqM/8dUYo1qPC2+1O8wnjirUEl6sWldyuJ7sUVfdNgxLmXZXnRNisEFrlAKYoCSZJARDh69Ch27NiRF2Hyer3o7u6Gy+VCbW0ttmzZkpf9pGO6GgwG4XA44HK50NramlFbdaPWoPTrXCxiOnjwoOELzcmSJJYyiRroscQMr9cLSZJw+PBhzepJP3AWFxfndGPFq59mf/ApI6hhgEIAl3lLmWzJJEkinlsJENmLa2RkRBMufTZnNsLFxI+xmHpBAYtcoBjsYjN64JqenkZ3dzdkWUZbW5t25zIXA6Tes2/lypVYs2ZNxseR6xpUtDBFr3Plw/9tKUdQmaLvBzUyMoItW7ZEuJN7vd4Id3KWfah3bkhHuMIFupkTjqBS/T4IHA2BuJVZ7SMbjMjii9dEMjqbc3h4OONeXNFZw2YEtYDgeT7C/kZV1Zyn3IgILpcL3d3d4Hk+QphEUdTscGYLn8+H7u5u+Hw+tLW1Yd26dVmLQLYRlCzLEckgyRIwjJ7iA07dKLB+OjabbVbbmS9kkrmTs8QMr9eLsbEx+P1+zWFD7wofkZhBLnA0mt3BEJIn8c3Aq4NQ+IUlUPHIphdXtLExm7plmAK1QGGO19l0mwUiXR/sdnvcNhPZJDFki9frRVdXF4LBINrb21FdXZ1zdMLzfIQreCr0wpQoM1C/baMFyu12w+/3o7OzEy0tLVrSwMTEBDweDw4dOqRd0PpIYCHWFc02qayeop0bWGLGsvJOLCuWIFiEjM8zIb0IO1xjNXvMdh1Uur24+vv7MT09jcOHD6O/vx9vvvkmBgYGMDo6ilAolHKsy7YP1IcffogbbrgBbrcbgiDg9ttvx+WXX278icAiFyj9jz1bgdKbpqZyfZgNgVIUBR988EFMW3UjSHeKLxNh0m/bqGk3j8eDzs5ObSpky5YtWnZmZWUlamtrEQwGsXnz5ojpq4mJiYi6In0UsFANX7Mhl+8hkXMDqyOyiK9AlEQoARnqzJSuRbBAsJxqq5HwPKeRJAGEI6jZZL4U6saLdg8dOoQzzjgDzc3NkGUZhw8fxh/+8Afce++9kCQJ9913H84999yYbeXSB6qoqAiPPvooVq1ahaGhIWzZsgXnnXdeXiK3RS1QwKn1iEzFQ1VVDA0Noa+vL23T1HwK1PT0NLq6uiCKIpqbmyPqZIwi1RSfJEno6+vLSJgYRtRYeb1edHZ2QpIkdHR0oLKyEm+99VbM69h3nmj6itUVeb1eeDweDA8PR0QBTLRKSkoWpW9ePlptWCwWlJWWwiaOgKNTWWWkqpAVBYqsIBQKQfb7QUThTru6XlCCIKRRqBsmnCiRnqOEEcwXgUoEz/NoaGjA5Zdfjt/+9rf4/e9/D7vdHmFCEE0ufaBOO+007f8bGxtRW1sLp9NpClQuRDe1S4Te9SFT09R8CJTL5UJXVxd4nkd7eztOnjyZsoNttiSqg9IX+TY3N2fVzTcXgWLTmaFQCB0dHSmjxlRJEvq6Ij0sPZvVtjgcjohMKyZaC72hYb56QXHkBEfuyMd4HlaejxR6AlRSwy01FAVSMAhlpnDe43Gf6r5rsUDg+TiZfUrOjhKZwAR1IaCfJWKu8PHIpQ+UnkOHDkEURbS3t2d3wClY9ALFBiur1ZpUPFh0wDLQsnF9MEqgkrVVz6cdUXQdlBHCxMimoSNbXwoEApowpbVGkWUWX7z0bH2mldfrRX9/f0yW20KxH2LkS6B49WR6L+QAnuPB22zQx6dTU1MoKk7cfZdFWxZBAKcOgPLgKLGQiNdcdDYZHh7G1VdfjUceeSRvAr7oBYphsVjiRlB61wc2CGd7d5yreKTTVj3fAsVqx4wSJkYmEZTf70d3dze8Xi86OjoyTgAxMs08WaaVfppQbz8UPU0438hFoGTVjfHQswgqvRC4ElQXnI8iSweADAQqCdpdf3T3XUWBPDNlFQwG4Rz6O0amLRHneqn14EpkTJ3O58+lDxQQTlC68MIL8ZOf/ATbt29P+32ZsmQEymq1wufzaX8HAgE4HA5MTU0Z5vqQbSJAJm3V87nOpaoqpqencejQIcOEiZGOQAUCAXR3d8PtdqO9vR3r16/ParCZjQFKn+UWbT+kT8ro7e2F3++HIAjweDwREddcTRNmK1ABpQeD/l9DJXHmkVEM+v8L1QVfQpXtC4YIVFw4Ljztp5vRKKmwokH4bMIeXNHCtRjXEqOLdDPpBZVLHyhRFHHJJZfgmmuu0TL78sWiFyh2IbKB3ev1wuFwwOfzYeXKlVi7du2c3XFl01Y9HxEU61k0MjICjuMMFSZGMoEKBoPo7u7G9PR01rVc0a+fq0LdeG7ZAwMDICKUlJTA6/XGtNeInibM93pHNgIlqRMY8j+oE6eZbQEYD/0VBZwFBfDFf3Me4NUhCFY+YQ8uJlzxenDpSw5S3STM54LvXJzMc+kD9b//+7/Yv38/JiYm8PDDDwMINyzcvHmz4Z9x0QsUIxQKYWRkRHNaMKJuKFtYw8Kenh5UVlZm1BfKSIESRRG9vb0YGxvDihUrsHXrVhw5ciQvA2S86DIUCmkWUW1tbXN6s5BPWBZptAUOa6/h9Xq1YlhW6K0fSFnxsVHnJlOBUknGkP93UChxf6ex4B9QZrXBkm1mXcY6EAQwCaA65pl4PnnZ9uCazxl8ufaCyrYP1FVXXYWrrroqiyPOnEUvUB6PB8ePH9d+iFu3bs37PhMNACx1vbe3FzU1NdiyZUvGdVlGCJRemFpaWrSISZblvLXb0EdQoiiiu7sbk5OTWdsypWIhCJ2+vYa+GJYVHHu9XrhcLvT392tTV3rRytZwNFOBmhJfQ0gdSfoaRZ3AqMyjyZr+OkbUUaVVA6WHV4egCrECFY9se3CxdG2/3z/vkmAWey8oYAkIFMdxWL16NQoKCnD48OG8749NJernhvVt1Wtra7NuWAjkJlD61ht6YTJi26ngeR6iKOLTTz+F0+nEypUrMzKyTQaLzPQD73yemklFIpdyvVO23rct0y68mQiUrE5jUnwpxasIgA/TiooaoQYFfOZuLQSkbgYVBUeDADZmvK+IbaTowTU5OQlFUdDV1TXvenAt9l5QwBIQqLKyMs3RfDZsiPQCFa+teq6LtYIgZGRHBKQWJka+LjJJkjQXh46ODkPXuObTHW2+ieeUnSwCKCoqisgmZANpJgI1HvprzLpTNByCAJTwepQyjiY+8ygqPaPYSHh1CPlyXGTZmKqqwuv1Yu3atQCS9+DSi9ZsFHkb0e59vrPoBYoxWw7XFosFwWAQw8PDcduqG7H9dA1p0xWmfKFPVy8uLkZjYyOWLze2dmWpt9xIFgHoPfMGBwe1gbSgoADBYBBTU1NJM9xE1QmP9G7qg6BTyRHTyjSWCctg4zOcIUjTKFYPR0OZvSELov0jc+nBZWQfKLZP/dq1KVAmSWHZQ0eOHMGKFSsysgJKl3Sm4URRhMPhwPj4OFpbW9NqVmgkeq8+lq7OMtmMJtGNx0Ke4jOCZJ55Y2NjGBoaimioF89U1yW+mlbuAqcTKALBpbhQx9cleUcs6RrFRu53HKAgwKWXYJQNiWqNokmnB1d0O41ce3DFi6AWUy8oYAkIVLz0Y6PvrvVJBzabDW1tbRHtoY0kmUBFC5PRHX1TIcuyNqXZ3NwcIdBGePHFgwmUx+OBKIooLS1dlDUvRsESLUpKSrTBTN/egfWE8gXGgGUvghfCGYhhJwcLeIFHZKhDAPwR+5hSplBrqc3sOkvTKDYajobz2hsq1xY9ifpApduDq6ioKOF5NKf4FhlscDcqxGYuFBMTE1ixYgV27NgBh8OR17v3eAKlP46WlpZZFya21saq0eO5cbAswXzsmyW/FBQUoKenB7IsIxAIoLOzM8I/b6H4qc0G+kEvXnuH8ZADk6GSGcPRsINDKBiCoirgOA6CYIHFIsAiSLDxSoRmyZDhVb0oFdL3jMwmSQLIf2+oaBEwgmx6cOkjLbaeaArUIiBey41cf3DRbdX1gpDvlht6gQqFQnA4HJicnDQ0Yko3ylQUBQMDA+jv70djYyO2b9+e8NwaHUH5fD50dnbC7/djw4YNqKmpgSRJWr3VwYMHUVlZGdNmg2VgsX9LyRqHker7VUnCtPgWgLAQCYIFQIHu/eqMX54CVZ6GzJ/6vXM8Bw4cJjCBEr4k7XObTZIEkP91KCOanKZLpj24mBuO3+/HyMhIRll82faCAoBHHnkE99xzDwDgjjvuwLXXXmvAp4/PohcoIH3D2FTorXgS1e8k8vwzCpbFd+LECU2YjErXBtJrLKiqqiZM9fX1SYWJkY1ZbDxYZMQyAhVFiXES4DgOPM+juro6ps1GvPoi/UL2XNsQzQaqqib9vfjkI1DIn/B5juNhsfCwWKzgVQXaMEJhoSEiuGU3XMFJ8BAg8Lxm8ipYLBB4ITYhIoskCSD/AqUoStYlIUaRaD3x0KFDaGxsxNGjR/Hss8/igw8+wGWXXYba2lps2LABN9xwg5Z9qCeXXlCTk5P40Y9+hHfffRccx2HLli246KKLIqYwjWRJCBQjW/HIpK16Jll2mcKcF6amptDU1GSoMDFYhBZvgFZVFYODg+jr60NdXV1GafO5RlChUAhdXV2Ynp5Ge3s7li1bBo7j0NfXF3dKNd55SXSh6xey9Q4D0Wnai6WpYaoIalo6kOaWVESsP3Hh884iIc7Oo0KogKIqWmuNUCgERVXBAafaaggWqDNTh5nCq0MAUVbTg+kgy3JK+7G5pKKiArt27cKuXbvwxS9+Efv374fP58OxY8cSFu3m0gvqhRdewDnnnKMVO59zzjl4/vnn8bWvfS0Pn26JCVS6PaEYXq8X3d3dCAQCaGtrQ01NTcqLKB9TfHpLoNbWVrhcLjQ2Nhq6D0Y8IVFVFcPDw+jp6UFtbW1W9VzZChRL/JiYmIhrh5Qsiy/dqcpEC9mJmhrqo62SkhLD1yjygUwS+gInIFIQnGyFwMV3WReVMfjlzrS2ySGAsEjFx626UWmp1BzK9XEIzTiUK7IMURIhiSJUIsiSFJ5WtAhackby71AER+MgblmS12RPqtmE+QTrBWW323HWWWclfF0uvaDivXdwMH8djuf/lWUAmXbV9Xg86OrqgiRJaGtrS7sPEWCsQOnXuvRTij09PYZsPx76NS4i0oSpuro6JweMTAVKlmU4HA6MjY2htbUVp512WsKoKN0IKhMSuZXr611GR0fR1dWluTnoTV+TZV/NNiOhXhya/n8IqUEAgCSJKOPqsEJthi3K9WFaejv9DSfx5gMAn+qDTHJcfz6O42CxWGCxWFAAIGSxQFVVFBQUaMIVDAahKIrWMNASMU14qpEhR0Mg5EegjEyqMpLo3/xiLauYf2c+j6SKoFhbdVVV0d7entW8qhEClUiYZgPWVXd4eBgOhwNVVVVZeQbG2246F5GiKOjt7cXw8HBaLT8SRVCZOiakS6Kmhno3B5Z9xdwIJElCUVERRFGc9fWMkVAv3praB4VOZX4SARMYxOuup/GFqisgzAgIkZJeYe4MXJJ1KiBc2+RVvagQUi/csyQJnufBx+vAqyozreNlhGYaGXII31B5lY+gWpfnJelFUZR5mf2ZKLLLdy+opqYmvPbaaxHv3bt3b1rvzYYlIVDsS7NarVo2l57otuq5GC5aLJas/ezmUpiAU/UZhw8fRk1NDU4//fS0XdZTkSqCUlVVS1VnGYHpTK2w5Ivo8zRbziFsX/HcHJgtTl9fH7xeL44dOwZJkmCz2WKSMvIxCLrlSbw19f9FiFMYAscBU5IThz2v4/SyLwIA/MpJyORJc+tR60+JjkFxpyVQSZMkOIAXBNjiNDKUFQWKOIGByVO2Q6wflD6izTYKmq9u5tEp5rPVC+q8887DbbfdBpfLBQB48cUXce+992b+AdJkSQgUQx/dsLbq3d3dsFqtOO2002KywbJBEISMIyh9P6S5Eian06lFj6tWrUJ9fb2h+0iUxad3eK+vr8/YFipVBDWXMFucsrIyWCwWNDQ0RBTF+nw+9Pf3a400E3nnZQORinenX4BCyX+LXf6PUGdrQZO9I8PoyYd0emT4VB9UUsFzqQQ4izTzmWnCMqsbHVUd2sPM0cXr9WJkZARerzfGvSHd2riFIlCz1QuqqqoKd955p9YV4q677opwhzeaJSFQ+ghKFEU4nU6trfratWsNbcudiSN4tDCl2w+JDfa53nWzFvNdXV0oLS3F5s2b0d/fn5cLMjqC0q9v1dTUZG2kO58FKh7ximKBxN55eueHTFLgT/o/wISUoEVGuCpW+/Ow53XU2RrhlT9K/3Ok2ZxQhQqf6ktZtMvWmbKBo0mAAgAXzrZL1A8qFAppbhnRtXGJbgwWkkDNRi8oALjuuutw3XXXZXjE2bEkBAoI/0Cnp6fhdDrB8zw2bNiA4uJiw/eTjsBkK0wMJoLZXtBEhImJCXR1daGoqAibNm3SWszny5KIbZe1t+/u7kZFRUXO61sLTaASkSgFXt9iY2hoCF6vN8ISR9+Jl/2Ggqofx7xvJdxX9FnxKW587P1/KEAGJRiUfvdcj+pJLVBATqniHA2BuPbEz+vcG+KZ6vp8vogiWJatGQwG4Xa7Z8WdPBOWQi8oYIkIlNvtxuHDh1FaWoqioiJs3JhbD5lsYZXf2QoTgwlUNhfMxMQEOjs7UVhYGFek89UTiuM4BINBHDx4EKWlpRl1EU5GIkFdaAKViEQtNvSWOCMjIwgEAhAEAcXFxRgu+hgBLgCLRQAXd2qNYrTgY9/b2FhEENL6PcoA0q/186re1AkrWXrxMcKWR4kFKuH7dDcGev9Mlq05Pj4Op9MJh8MRYaqb7/XDVCyFXlDAEhGowsJCfPazn0VBQQEOHEi3CDE39Bek3oHCiNbm2YiIy+VCZ2cnbDYb1q9fn3BaMx8RlMvlwqeffopQKITt27dr0ZoRLJYIKhmSKkPgeG0dJ5EljizLmPCM4b3pTyEpIvx+fYp2uCBWsAiIPS0y/MoUxqQCNKSRZchlED0BgEQSQhSCPYnreO4RlLG1OCxb02az4bTTTgOAuOuHfr8fRITCwsJZLepeCj58wBIRqNn2W2ODPGttzoQpmQNFJmQiUEyYrFYr1qxZE9PHJpdtp2J6ehonT56EIAhYu3Ytjh8/bqg4AfNboE66xzEW8sLjc2FTWebu9l45gDddR3DU44BKhEZ7Nf5h2ZmotsVP5rFYLBgUPobNboNNK4slqKoKWVagKDLEgKiVWoRbSVjAWTwARxgRJdRbrSl/oxyS1z/F/SyqF3Y+ScSccwQ1nMO70yPZ+iGLaPVF3azMQD8Va1SZwVLoBQUsEYGa7YJJjuNw7NixtKyRsiGdWqvp6Wl0dnaC5/m0hInB83zOXoIejwednZ1aRmB5eTmIKK/tNtgNAVvgnkuBCiky/tR/DAfGewEAgUAQ73hH8e2qMtTZ00vI8ch+/GHwZUxKbu2xvsAoHhp4DhfX7UJHcWzdSlD1oydwLOpRDjwvwGYTgBnRCgbD03NWixWyIkNUpqBChptkDCkSKixWWCzCjFFstJMDpSzQjYdX9aIGNQmfN2INCqQAnHEJDen+fpgQFRcXRxR1K4oS0ek4uomhvq1GpokYS6EXFLBEBEqPURlw8QgEAujq6oLX60VDQwM2btyYF3FMFuW43W50dnaCiNDR0ZHxwmkuERRzGA+FQli1alXEukm+bhI4jtPW1ZjXotVqRSgUwtjYGKqrq2d1nYCI8D9d7+Jj91jE4/1BD35z8iD+be1uFFmS30UHlBAeG3wJLim2JklUJfx55E1c1/wPMZGUw38kTs1TfDiOg2CxgLcQZFkCj/B6plsQUGsL1/IFg4Hwb4EAXphxcrAoKOBnbmAy+Er9qj9punnuRdUyOHKCOOPKI3IdJwRBQFlZWUz5in6aUO/9qO8FVVJSEpH4Eo05xbdIYdGHkRX9fr8f3d3d8Hq9aGtrAxGhvLw8b4NyPBFhUYuiKOjo6Mj6x5rNGpRemDs6OlBdXZ33qJXVbvX396OkpARbtmzRIidRFHH06FFIkhRTZ8TuXEtLS/Pi6vDySGeMODEmQj480v0+vrPqTPBJzs+L4+/GFSeGRBL+Mvomrmk6F1Y+fAmrpKArcDitYwx7q86kUZM74jmXokAV7LBbI5zztL5QpExBJllLBWTnnP1LJFoESp5unuMUHwBwNACCcQKVrxRzm82GqqqqiPqh6MSX0dFRBIPBiF5Q+hYxpkAtIuL1hDJicIoWpvXr12t39PnIhGPoBcrr9aKzsxOSJKGjoyNn2/tMIii9u3p7e7v2+fPN5OQkTp48ieLiYjQ1NaGoqAg2mw2iKILjONhsNtjtdtTX12tTmyyd2OPxwOVyoa+vL8LVwYjGhn2+KTw7eCLpaz52j+Hvzl7srm2N+/wn3j4c8zhS7ms0NIm/u45ib/VmAEB/8FMElHSTF06JgaxOxzw7LsloKtBfH6f6QvGqhHjtNVgJwczLwy1POO5UFiGHpAKV6xQfEM7kU4UzctqGntmsgUqU+MLcSFjtVl9fH0RR1Na4JEnC5OSkob2gQqEQrrnmGrz33nuorq7GU089hdbWVkiShOuvvx7vv/8+ZFnGNddcgx/84AeGnodoloRAAcb1hAIihSnewDwbTQv9fj8OHz6MUCiEjo4Ow6q504mg9A7js+l84Xa7cfLkSfA8r2Ui9vb2atND0S7nehLVGSUq3tTfsbK71lTsGzgONQ13heeHPjSHcrMAACAASURBVMG26uUoECIvP1GV8LzznZTvZxyaOoEt5aeh1FKELv+Hab8PAMABBAkKxaaLOyUJjbZ4yRIqoC/QjWqvAUCLrIgIKqmAqmjC5ZJdqJDLtd5QnP5GwKAIykjmQ5EucyOJXkN+5513UFtbiyNHjuChhx7CO++8g69+9atob2/Hxo0b8Y1vfENrp6EnnV5QDz74ICorK9HZ2Yknn3wSt9xyC5566ik8/fTTCIVCOHLkCPx+P9atW4evfe1rWmuOfLBkBIqRS0NBv9+Prq4u+Hy+pBFDPgXK7/drxYQbNmzIyGk9HZJFULIso6enB6Ojo7PaWt7n8+HkyZOQZVlLumDkmsUXLytLURQcHR7CielpVHk8EEQxZbR1wu3ESc94Wp/HI4fw6mg3zm88LeLxd6c/gU9Jv75IJhl/dx3F9oqViV0jkr7fHfdxv6rCr6oojhqcw+nlKaZ/Z36KHMdBgO79BChQIEGGIqoIyDJUIvAcD8EiQFHDXXotHJd1JMWrA4b2hpoPApUIIkJVVRX27t2LvXv34otf/CJef/11jI6O4siRIwlrJNPpBbVv3z788Ic/BABceuml+Jd/+RftJtDn80GWZQQCAdhsNkPs4ZKxZARKH0FlKlDRwpSqL1Q+BIpFbT6fD9XV1VBVNWJQNYp4EZSiKOjr68PQ0FBaDuNGEQwG0dXVBY/Hg1WrVsX9vEYX6rpDITz40YfonnZpj128ajW+sKJVW9z2eDxatMWmZp7yOCCp4V5GPJ96gHx5pBO7a1tRPJMwEVREHHAdz/h4P5zuRLEwmdF7aMbqKN70HsMpybEClUV6ue7N4MBBtsqoFGamoQlQSQ33hAqFEJhprwGEb5T07TV4jk8jKSMAYBKAMdfFfBYoIHKWQJIk2O12tLa2Jo1o0ukFpX8NqwebmJjApZdein379qGhoQF+vx//+Z//mVcfPmAJCRQjE/FgnXT9fn/aDQvZPozqqsuKfD0ejyaOk5OTcDqdhmw/Gn0EpW/tnonDeK7opxDb29uTpunrTWj1mWDZCFRIlvHfH76HPk9kZPGXk5/AJ0q4aNVpcaOtD0b7MDzphyzLUBQ/SCXwAg9BCPc7itcaQVRlvOXsxTkNqwAAB6c+RkgVMzpeAFBIwdtTR9FalMn3QgAnQY0zvccYlyW0UFT9YNpO54nxKb5TAsUBPMeDt9nA8fypaayZZoayokCUJCjBoOZYb0nRzJBXB6AKS0OgGLNVTnHo0CEIgoChoSG4XC7s3r0bZ599dtypRKNYMgKlN4xNJR4+nw9dXV0IBAJob2/POCvNqJ5QzK8vupYqX3ZEwKl+UAMDA+jt7UVdXV3GDuPJSJZOLMsyent7MTIygtYkTQr16IUonU67yY7r98eOxIgT46XebnRUVmJdTWRjPEEQ8J5vDHZ7AYAZT0HCzJSVDFlWIIkiQjM9o1hEYLEI2D/qwBfq26GQgnenP0n7WPUEVB/GpACW24thSSNyYyhILjaiSvAoKsos4QGaQwhA5gIajU/1pU4pn0mBFyyWCJ9GUlWtJ1QwGIQiyyAAAs/PnFMLFLUbXOEmQ6a956tAxWsvAxjXC4q9Zvny5ZBlGdPT06iursbjjz+O888/H1arFbW1tfjc5z6Hd9991xQoI7FarXC74w9CuQoTIxeB0rd3T2SLlC+BYqnbzHgylw668WBNC6M/D+sFxS6KTKYQjXKSODbuxGHnaNLXPPHxMdy+YxfsOrF2Bn04Ph2VVs4BgsBDEGyw2U6lYhcUFECRZciKglBIRJ9/FP/71quwFEuY5KdgEcLCxfNC2ssoAcUDhYDRkISmwvS+KyKAUggUAEzIkiZQ2RTnxkOGnNL2KBEcz8Map5mhooZFS5ZluN1HcHKoSfMl1K8ZZnqTNV8FKrrLr9G9oC666CI88sgj2LFjB/74xz/iC1/4AjiOw4oVK/DKK6/g6quvhs/nw9tvv42bbrrJ0M8WzZITqHji4fV60d3dnbMwJdtHKjLJjDNaoPT9oMrLy1FYWJiXqnS2XsTEh7XccDgcqKurw/bt2zMeRIwQKFlV8eeTqSOYqVAQf+06iUtXr9Uee8PpAKWRuRc+JsBitcBitYBFW84SOzjrNCzBcHFsKBSCoirgwGluDhZLeForunZKJhkhCrdxHwxKaLSntikCAIIIIAgOyW8CJmQZrSxD0oDpPYZP9SW3PcoELnw9CIIAG4CiIhGVTVsh61wcRkdH0dXVBUVRYLfbI4SrsLAw4c2Qoiiz3gE5HfLdC+qb3/wmrr76ai07+MknnwQAfO9738M3vvENrF+/HkSEb3zjG9i0aVNePqN2vHnd+jxCP8XHkiS8Xi+6uroQDAa1L2O2vfJEUURPTw/Gx8fTntYyMgmDuTAUFxdj8+bNKCwsxFtvJW7VkAtsvUgviJWVlTlFaolayWciUG8O9GPMn14N0ZsD/TindSXKC+yQVAUHx/tTvykJh6cG0VgRRGFB5OdXiaDM+OeFQiHIfhlEBIEXwmswggVBzgvWijagEqZkBZXW1Jc08d60UrpFleBVVZQKANLs/5QOXtWLaoMSGeJsHcAULJZKlJeXRwzc0T2hnE4nAoGAlugSXVYwXyOofPeCstvtePrpp2PeV1JSEvfxfLJkBIphtVoRDAa1GqL29nbDU7XTERBJktDT04OxsTG0tLRg+/btaU9rGRFB6d3N89UbKxqe5zE5OYne3t4IQcyFRJ160xUoWVXxt97UhbEMhVT8rbcHXzltDT6aGkFAyc23cEr2wuonrCiLnKLhOQ681QKrTnCIMGP8KkNWZHjUKSiQwSH8eQe8AZSW2sMGsEl+z8SlJ1AAMC5JKOODSKd7brr4Vb8B1kaJ4dVeqEJswXqinlDRxbC9vb2QJAmyLKO0tBSyLM9pa41oJElaEr2ggCUkUBzHaa4LLG3ZaGFiJBMofS3RihUrskrZzqUlhr7YNRMT2Vxxu92Ynp4GESVt95EpuU7xfTQ2iqlQMKN9/n2gH+etbMOhHKMnSVXgV0IYC/BoLk3tuM/p1ragquAlLuyhN+PmMCmr8AaC4FQFROHXsmw3i8US/t2QCOJEAOlFBpOygjarOyPfvVSoUOEnP4q5mZsig5PQeLUPqrA57dcnKoY9fvw4SktLNcssfWsNfbSl78A7G0SvQS3WXlDAEhKo6elpHD9+HO3t7fD7/XmpIWLEm3bSZ6jlWkuUzcXg9Xpx8uRJKIoSU+yaT/RFtmVlZVizZo1h4gTECpFKhM6xSRwbGseGpjrUJnkvALza15vxPkVVwXPdnTjhyS3V3y2Hp80CsgqPpKLMlv50UkDRrQnpXDQC1gI02K0I++edyiQMhUJQFBWcZQokEFSo4HmdFVECQqoCvxpAscEzXT7Vh2I+LFAEY6MpjjL/ThNRWVkZ8XtlrTW8Xi+mp6cxODiIUCgEi8USYfRaXFxsWOZrNPEiKFOgFjjl5eXYtm3brO9XX+S6fPnyWaslYvj9fnR2dkass80GwWAQnZ2d8Pl8moHskSNHDK/ZYDcDRISxKTceevsInB4fAoEAXukaxkVbRJyzLn6n1Z7pKfS4p7La7/O9nSiszH5gJQLcsl/7e8wvpS1QRISgGn9NaDQkzQgUdyp5QFveIvjlMUgzwb2iqgApmnms9o/X2xcpmFQEFAu5TWVG41N8p0YfA2yO9PBqvyGtN+KtQelba+g78EqSpCVlDA8Pw+fzQVGUmEaGyRzKMzkuU6AWGdE/inzOgbPt9/T0YHBwEE1NTbMuTHoXhmwcxrM9P9FFtno7qHx06+W4cCv5Nw4cxJ9O9GMyEDpVL8YL+OuHJwAinLO+I+a9fx/M3rtt0OdBnd2OsqL4ljKp8CkByLrWGOMBGW3llNTlnBFUfQk9/6ZlFSFFRYEQGxmpFABBQrhHFB/x/TKRJyKosjrz/QMCL2FcsmK5NRAWLYMumQAFoJACgRMMMYqNRARHoyCuMaetZJIkYbVaUVFRESEUNFP7xpIyRkdHEQgEYhoZlpSUJLQmigdzjWAs1l5QwBISKD0sySAfITir6WE9Xowsco0mnojoHcazbZaYzflJp8jWaIHy+Xz49NNP4fP58FHIArWgEBUFhSCi8HqBGh4gnvr7+5geHsDahmWn1g2KivDhaOb+dUC4IWFQkeHyhrIWKH30BACSSnCLCioKUp9zv5q8JmlMlNEcpyZKpsTWRtFmu2EIoCB8qgUBCbBxsu61AMfpRC5DfWHtN8qEMsMjKADgqA+E2ROouMfAcSgsLERhYWGMQzmLtpxOJxwOB2RZRkFBQcQ0YSJnfTOCWoTEcxs3Ujj0tkD19fUoLy9Hc3Nz3sSJDfbsApIkCQ6HQ0tXz8VhPBMhYYI8MDCQssg2UcZdprDo0Ov1orGxEe9196F78tRUHceFp7cgQLvT/CTE4+zmZgT9frhcLrxz/BhGxsfB87yWts0SClKNltNiOKnC45cgySqslszWEmVVhV+JTcwYD8gpBUohBSE1uROKMxRPoCjSey+tn4Y8E9hwmIIdjdaQltCgkgqVVJBKp3pDzUwNalOEKfbBBMr4CArg1R6owvactpGvxqbxGhkSkeb1mMhZn/1XFEVToBYj0Yax+jA5W1RVxdDQkGYLtG3bNlitVkxPT0NRlIxC90xgUQ4RaZFLpunqqbadDCLC0NAQenp6UF9fn1akmGsEJcsyHA4HnE6n5tHnmp7Ga44RwBb5XXLgIgpoJ30BvN03hvM3hKf6nne7UFFREU7bVmQosgy///9n702DJDuv88znu0vutfa+oNFoNPYdzQYb5iLT1BgmOUOFZFmkNAxK4aFD84MKBhkO0b8UDM84RCv0Y0IjTUjhEMMUZRNUiLIoStzAFaQgAI0GQTSAXqqqq7t6qX3L9W7f982Pu+TNysyqzFpAmMUDFLpRefNuefO+95zznvf1oiFZIksIKwEvEcsIaVj1nfivrNQ89g31dx1VZL1jgW6xEXDn0Pql1YbcWNGhIhUNqcinynxS19D0N5ogaDJRl2SGw7gJ6LQ546a9oWTTG6rN0DBcMRDOQ8HOlNsN1fvowHrxZrHzYqWRtVqPsY9ZtVpleXmZ69evs7q6mhA1zp8/z8LCQs/3ss16QQG8+uqr/PZv/zblchnDMDh79uy23EPXi10FUHFshyeUUorp6WmuXbvGvn37EmCKY6c9oQzD4Nq1a8zOzm67wvh6QKK1Zm5ujomJCUZHR/sast0sQCmlmJqa4ubNmxw7dqwFhM9em2bV8RjMdPiirEGB712c5Mk7j6IFXF5aDMVKTYOMmSHFJEBHQ7JBEA7J1oOQXmyaJoEBTuAnzLnVfgFKt5f34vCiMt9QlyxKa2io3hQd5t2AY4XmMQU6TQbppaSmIQVQZWkRaIElupBcunpDaVTkDaVl873CEDg41I06NvYOlPhmQddA7Px8305GJx+zl19+mXvvvZdr167x3HPPMTExwcc+9jG01pw8eZLf+73f48EHH2xb11a8oIIg4CMf+Qhf+MIXeOSRR1hcXNyxh+907CqAijOorXhCxfI8V69eZc+ePbztbW/reIPeKYCKS4nLy8sUi8VNyQNtFN0yqLTqxOOPP97301M31YdukZZCOnjwYBvRRCnNP47f6DxHI9pVngOl+OHYFMWhzLqjN0KIlCRRvDOh5tt0vYzSGq0kaKj4Psurgnw2k1LY7r5uR/l4qvu1t9AIugJUoD183dt1O+elAUoiVWf9ye7Reu1qBCvSZq/Vh2BsVB5MiB/xR5fKtlacZfJBIcy8KpVWi40tPnAZahJltt+o/2ePmB143333cd999/Gd73yHZ599Ftu2GR8fZ//+zsMVW/GC+ta3vsXDDz/MI488ArCjYzrp2FUAFcdmPKG01szMzDA5Ocno6CinTp1aV6DRNM1tBah0SW3//v3s37+fQ4cO7UiPa22ms7q6ytjYGJZlbUl1otcMSmvNwsIC4+PjDA8Pd83Szt+YYbne35DtP01cZ/DgJtQrRMgKrOu4cR6LqGrqviCXCedj2vyMLLOlhFXpkj3FsegEnOhS8qr3mD0B1KSiLhUF0yDQqz3rBcYhaP9+LAZ9AlT3lSfZlrQVpWwRz/fJZbNNi41Go8XQMAEu0+y5X2WoK5sGqDfLwmIzsbYk6vt+Miy8HptvK15Qly9fRgjBU089xfz8PB/+8If53d/93W0+svbYlQDVT3ajtWZ2dpbJyUmGh4c3BKbNbKOX7V+5coU9e/YkN+sLFy7sWAkxzqBi5Y1OTrabiU4A9frkDF/7xwsYhsGdR/bwroeOcGVinGw2yyOPPEKhUOi6vu9fugrQ8eYrQk/ztqg4HlevrzI62n293aIhfTy1JrMUgporObynmNw0tA6fcsMSoYfve2gNrueyTBktoDNrDlypqfqKgTUzUVr31n9Kx4IXcCyfwVedZr3Wu8nL6Kc1loMMStfow9Vjw6ipGkqEN9xOFhtKqUQBvu55yQNAYlsS9Qk7ZVtCb74PtVMEie2ONwtIgyDgRz/6EWfPnqVQKPDe976XU6dO8d73vndHt7urACotGOs46z95r1X4fuyxx/oqaW0VoNJZxNDQUFtJzbKsHfOEUkoxOTmZqE5s13DvWhbft164xHfOjgMgZcD41Zu88sYY/+e/fjcH969fQrixtMqV+aXwPtvHd3TVc1lxHEZG+h+YjMkRa8P1FY4vyWfCr5MQYFkmlmUCWRqN0FjPMwK0G362sWguhISDZEBWCJacoA2gwtmn/vp3C27A0Zxe15iwU3TKngAkgrK0GLa278FIImnoBlk69zENIzQ0bOl2aJ34Qnm+h2wE7dmWZWHoKdA+iP57JW9VodhuhJKd9oI6evQo7373uxMNw/e///28/PLLOw5Qb/1HhB2I9Up8MTC98MILzM/P8+ijj3L//ff33W/ZCkAtLi7y4osvMjMz03X7O+EJ5XkeFy9eZHZ2loGBAZ544oltVZ5IZ1Bj1+f57kvjKCWpVCuUKxVy+TwNmeHrL0xs+GT44uRNoJ2tl0SX72vZdQikolbrs1Sloey7XV8u1zYuGVeVE9Lao/KfbdvYth09qYcusr7vc2u1TqVSpV5v4Hk+Sum+yntxVKSiGnSwg1/31LaSI9bGktx++4maqvZHMxcCy7LIRtYZg0NDDA8NUxookbHthPm2urrIG699jQsXLnD9+nWWl5d7Lu2/VQFKStmS2W3WC8rzPJ5++mk++MEPtiwTe0EBLV5QTz31FOfPn6deD52jf/CDH7T0rnYqdm0GtRY8tNYsLi4yMTFBsVjk4YcfXre8tFFsxvZ9ZWWFsbExbNvesNeznQCVFrC94447yGQy5HK57af+RgBVdzy++M0fU61W8TyPQqHAQKlEjCoXr83z48s3efyeo533VyrOXQ0Bar0Mai1w+VJRjz738qpLqdTbFxu6lPdSsVr3ODDSvbel6Dz7BPH8kJk8LQaAsLMYQoUSOo0qNbOcWraZba1fqtPMuQ0O9fVsFbAegi0FNndktndsqUadPezdeMH1ImUfn86XHhi1WHWOdB2KjX8KhULL9f5WBaiflhfUyMgIn/rUpzh9+jRCCN7//vfzgQ98YEeOsWWfd3wLb8FYy+KLgSmXy22b9UQ/GVS5XGZ8PCx19aowvh0AFdO3b9y40UJVn5qa2pHyYWwn/zffPsv16VnyuTzDIyOt1OQo/u6Hb3DX0b0MFNvvrhem56m6zQyoaw9qTZS9ZgZUq3kEgcLqcch2vewJojKfJ8l10dOrSqcvmsJqoDlSykIWVOBhSQt0KIabnjWKrTZaZo2SXpjPkm9zKNf7g5LYwNbd1SZ1ZVI0t+/6cLWLL3xybP9MjcUYg4MfaBuKXesLVa/XWySIuvUIf9rx0/KCAvjIRz7CRz7ykT73eGuxqwBqrWlh2hPp/vvv31aV7V4AKk1COHnyZF8XmmVZuO76N81usXbIdi1VPQaS7QytNSsrK1y5OsW5y6uMDA+vq6TdcHy+d26CD777gbbXzk42NfQ6AVH0QlsiUE6dLw2Uy05vZAndvf/Usv66Ry7TKYvS1LTTlxzQohNwpJRBa6jLCkTKDG1afWkNvaivpYkwSnhUtImrBFmjF3gMoIc+15K0txWgABo0GGD7rV8MNQW6AaL5uaznCxVLEC0sLFCpVHjxxRfJ5XIt2dZ2CL5uNoIgaJk/+ln2goJdBlBxVCoVyuUy165d47777ttWYIpjvQynXq8zMTFBvV7fNAlhMxlUesg2zQjstG7P2wY6cRQx2SObzTJdM8l0vIm3x4tvTPEvTp+klG+W4hqez+s355oL9VjiC5SiHrT2H8qrbk9kiY3Ke8n66j77h9uPzdMBngowOgi4dl2XK/GVRtJArtMTigeG10gho3WA1Ao0LDgm++1Gop+n6dxsF/T2wLMYZLgt0x+9f/3Q1LbRsbc1FIaa6IlunpYgsm2bgYEBjh8/3lXwNQ1apVJpx2TN0hEEQUvp8WfZCwp2GUA5jsMrr7yCYRjkcjkefbR3U7N+o1MG5TgOV65cYXV1lZMnT7J3795NP4n1C1CLi4uMjY0xMDCw4ZDtevNKUiqU0liWseG+r66ucvnyZTKZDA8//DCVao2/+O44mWxvvT0/UPzwlUne9+S9ye/O35gl6LJvvu+HTC7TbMusKp7XhmOeL3FdSS63/tdgo/JeHA1P4vmSjN1a5quq/m/mGlh2Aky73wFbAIHGT/TwyjLPkULYWwozrTBbCH02ACEwhMIQskcNPQtHGeSM7RH+1UBd11FatUsobUMY6nLf81BxD2o9wdc0aE1MTCCl3PFsq1MG9XOA+hmJTCbDXXfdxeDgIM8999yObisNUJ7nceXKFZaWljhx4gT33Xffli/aXgEqPWT70EMP9dRf67buhYUqT//VC8zPVygUMnzo3zzB8ePtze16vc7Y2Bi+73PPPfck9f+zb0zhepJM79wE/un8VX7hsRMUcmGm98rUdNsyWmuWl5fDJ8toBkkTipg6poNlWax2GSuoVNz1AarH8l4c5YbP3hRAKa2pq82VYucbHiNmf0QbIPJ4an5+5cDGVwLbiAdkW9WwtVYInLDHRUr8NZHOa1csXwoyHN7GLCpWNx8wd6LMd7Hv92xEkjBNk6GhoZbyWjd7DdM0W0BrK2aGazOonwPUz1DEKTw0ZY92qpYcK0mMjY0xNzfHHXfcwT333LNt29sIoGIHXaUUd999d0uTeKPolEHNzq7yuf/6IxwnLJPV6x5/8ZfP8Su/fIoHHwhnKTzPY2JioiVDTMdPxmf6VjRwPclLF2/w7kdPUHd9Ls0sJK/JQFKtVVFKMTI80nIj9X0fpxHeQOsNh5VGrOAgIifZ8Kdacdm7t9D1c+m1vBdHue6zd7CZndakE80v9f+5LzouQ0X6HoxVHYgOy36G/dnOQCmERKDDciEkABV/Vkonv4jVi1gIbA7b/fXVuoYO11lW5R0BKKHnEGoebezbeOEolFJ9g0i3bCsIAmq1GpVKpc3MMA1cvTBngyBoYRf/LHtBwS4DqLWWG77v9yx02k8EQcDU1BS1Wo1cLretQq5xdAOoRqPBxMQEtVpt2/pbWmu++g8/ScApDikVX/m7H3Po0CDLS7PMzs5y4sSJjlYf88tVrs2uhmWlPuPF16d41yN3cP5mWN5TSoVf8kBSLBWpymqo86d0c34qconNZXM4OFiWnRxLWObSaB2qPSzMr1AsZrGsUJ3ANJqWG/1kTwB1JyCQCivqN60Gm++teCqg7hmUsn2U0rRE6/ae1VJXgNLtvac1mVPyUeomaJUDi7ovyUQqEK0swt53N73NqqyirZ15aDTU60jjn/e8fBAE26bUHUsGrc22YkXyGLgcx2nLtkqlUkvG9PMM6mc80oKxQRBsK0BJKVu8kYrFYov21XbGWiWJuIy4vLzMnXfeyb59+7bND+rcy9e4fr3DwCewulrhv/yXr/PhDz2xLhC/dPFGd8bdBjG/XOPq9BIvX71JrVbDcz0KxQLZUhYd/VMpVxJrjCAIcD2XQqGARrPqusRpQSt9OPyiSxkKw8bSRFLKSHrHZMmrJdJEvYQGKg2fkVIWTwU05ObKe1L74XF5FqVs74SVTtkThIASKIG1hs0XglOPDw2itdy3KgocssL3K7VGsTwl55T0ldY5hQIICKjrOsUdUCA31GtI/nnPy++01JEQgkKhQKFQaBF3DYIgKRFOT09TrYYVgkKhQKlUolqtMjQ0lFR/ftYBalcqScDmBGO7RWza9/zzzyOl5MyZMxw/fnxHqahxCTEIAsbHxzl79iyDg4OcOXOG/fv3b2nb6QzK8wK+89032pZxXZfl5eXQvbZhU6vbXb/QWmteuXwTITaVQKHR/MOzr/DChTEMw2B4ZJhMJpP0mYaHh8kX8iilqFQrOG6Y9biOS73eoOK6MTy1rRnCmSjLtCjk8wwMlBgeHmZwcBBpGgRahbp6vh/+BAFKSrRSXQ+mXA+vq61kT0GUBVVcs/dz1iV7AtBasOyvlfwJoIusUS+xGGSaJAszUsiwrUjY1QqBSetElzDwA2QgwxmulNFhGOFnU5abIYVsHIa6Etpv9Bg75bi9UViWxfDwMEePHuXee+/lbW97G6dPn+bEiRMUCgUcx+H69et84Qtf4F3vehfnz5/n29/+NmfPnt1QGOAb3/gG99xzDydPnuSzn/1s2+uu6/KhD32IkydP8va3v52rV6+2vD41NUWpVOIP//APt/OQ141dDVBbFVuN54mef/55HMfhiSee4M4770wu7O22OF8bjuPw/PPPk8lkePLJJzl8+PC2gGJ6v189f4N6vflU7vs+Kysr+L7P0NBQMoH/ve9f7CpPNDW7wkrFAdFFlqhraFzPZWV5mZcnZigODJLP5+OXwhDNp06A0ZFRRkdHGRkZoVQq4RI+2Yf+Tj5BQ7WLxwAAIABJREFU4COljGaGwlVIqWk0ArQm+lFoNFXlY8Sq5LaNZduJmrZSKrrphqAlU6BVbfgEUlHx11cu7xZSB8l58pXACXr5mmrUBjTxJT9dLdAhMWILsSptPNXheosELoQhMEwzzGwj4IofYprnLwgFYZVEK01ZlkPw2vZQGKr9QatbBEHwlhGLjbOt/fv3J2ICH/3oR/mbv/mb5Pv3p3/6p7znPe/hRz/6Ucd1xF5QX//613njjTf44he/yBtvtJ6PtBfUJz/5ST796U+3vP6pT32K973vfTt2nJ1iV5f4tuIJlTbt28gTajvLiGkHX2BH/aC01rz44hWg2egVQjAwMNDGcJqfr3D58iz33HOwbX2vjqeYdz2mA37gU6tWMSK21I2VCmrVZTCWExLNwUqg4z4ZpkFdKUzTxGy6Y4QAtEawdWmpgmkWm7bvQDlw2/ZXAMIwIHXzSvpasU9UAFOLC3g5P7ze6K8tI9dkQRXXJG+v/6CjddDC3OsUq4FNEAGKoEFfKrtdYlFmOGT0WMbsYmoYBAECgdIKJ3CYcWYoikKoVr4Jm41uYcpXUObpnpZV0XXzVot0D2rfvn00Gg3+/b//9xv2y7biBSWE4G//9m+54447tkVlp5/YdQAVx2Y9oRYWFpiYmOhpniguw20HQHUasn3ppZd21A/q6tUFbk0vU6/XkUpSKq4/jPij58baAEprnQBUKOy6foSgU0VrTak0gGWZSKWpOh7eimJwJI/WmlqtRhAEFIvFrs6eWmsqXuvNMx5WbVkO8LxwzxzXQQaSmvJxlY+xVkIoTrPS6yTqURkGcV+rFrhkoochpVRieBgql4cg1ynblTpoUy2veCb71y3FKZTeGCTiMt8eq0ovihG9xLyf4ZC9uT4bkCC3YRjJ33VWUzIHwqxXBonNhoDIYsNMwKufioGhLoCugth4MP+tqsW3FjhjL6iNYiteULlcjv/8n/8zzzzzzJta3oNdCFDxBd2vVNDS0hLj4+PkcrmehWS3yxOqnyHb7QjDMAiCgK99/UXK5TLFYrEnkJ2aWmTq+iLHbmtaZVydXqZcDUtJQtA1g1JaUa/V8H2fYilUpdaR9lzVdVFa49Q9yuUqSgXkC3lKxdK6qUnF85oU6XVCAGF1zmKgFGZoq7VlTC9oZkYxISVp/Ivk70ALcEmt8BxFdtCged8N+zQh01AlGWq4yub6gg5A5AQGXiDIWN3OXa9EB82ib7DXlmwPPxwqysZVBtltGtqFsA910DqInbGxU9KvOtXP8lyPer2O1hrTMFqyLSPFwmwNhSlfQVrv3HAf3qoAlY43ywvqM5/5DJ/85Cd3RHFno9h1ABWHbdtJ32K9SA+69qvXt1WASisxdBuy3WiWq7xS55tfehGAkw8e4bF33LXuNoMg4Nq1a5TLVW5N1xkZGelrn198cbIFoF67MpN6tX0/Y7qt4zoUCgWKpRKJJXj0jnLDC/sUUtKo+hw4PNLT/XW1T63CatWlULAJtKLiewloAGCaIQS0lPOaRIk4IxJC4GuJQhD4GsvWkVcRCfVdGGZL81drjVYaqX2UVsmpah6ioOyZ7O3gw6S135UYsWZJQFL2M/jK6FGbr7dYCDIc2cahXYmkqqoMmq2zeyKy2WjJ4jUoJRPCkOM4KKXCZSMzwxi4EAJDnUPyswFQcey0F9QLL7zAX//1X/O7v/u7rKysJEo8H//4x7f9WNbGrgOo9Sw30lGpVBgbG0Nr3fegaxybBaj0kG1aiWFtxL2ibmW3metL/Lf/99s0Iu+jS6/eoF51ecdT7bIvSilu3LiRXKArqzqZHeonLlycpl73KBQyaK15vQWg0qFxXJd6vU4um4vEY0ULMAkErueyVAkfJGzLwmvonsCpU3lvo6hWXfbtK7Lqde7PhEOqUQbVciSgo35WoGTCwHNqilxRYphG61N9y6pDRXIMUFqmMrLmghrNSl0wbPvNeSMjLJgqvREw6OhHJf+35Oc5ZG2OwNEp5oIsh21nay2iNe9dkSttANXtfYZpkjHNFstDrVRiaug0GgSJE++rzDuvkCseo1QqrVsZeKupma+lvm/WC+rIkSM8/fTT/Pf//t9blom9oJ588skWL6gf/vCHyTKf+cxnKJVKbwo4wS4EqDi6kSRqtRrj4+N4nsfJkyf7ziDWbqMfgGo0GoyPj/csIrseQEmp+Pu//KcEnOL43t+9QqGU47F3nARosZTft28fb3/727Esiy8+/QJ0cTldL4JA8trrN3ji9AmmF8osl9upr57vUatWsW2b4eFhDCGScl5EACOICBBOIDEiTTQAzwnwnIDMBvp5vZb3Wvdd4TgBS0G/DrQRcQLwlUSoEGh9z6AwENKp/UioNl3OM4SRZGiB8lPVzzU9LgGustDCxjQUSmuUlCBiIBVrMq44VNu6ABb9Aofy2wdQdWVSUyalbVQ4r6oqvvaxN+GGC+HnYRtGa38yKhEOip8wvVjk2rVrSQ+nVCoxMDCQ6Oe9FeOn5QX104xdC1BrSRJpBYaTJ0+yZ8/6luO9RK8AFUsEraysJEO2tyZm+dZXf8DhOw/w4Dvv7fi+9eSOXvrBJWZuLHd87Tv/4xz3PHIUx6tz+fJlBgYGOHXqVPI0Vq97zMzWGRraHLnj5R9P8cTpE2vKeyR0bKfRYHBwCNM0EmCCuBekqdVDKZhSsUit1kCI1nNYXXUYza1fal1xN1dyWirXcbNB38w7CHtPvgz3VQgBWoCGMBE1ExXx+CeQEZ1caKSIr8UYolMRZVNlV7CnIDGFQmqXJtFBr/lz/ajKLI40yW0joMwFWUrm9oGeRrMiV9hn9S5PtGEIgWlZ7CldorTn10FkWryhKpVKop/XaDS4dOlSAlzFYvGnXvL7aXpBxRGz/N6s2HUAtdYTynXdRD9uqwoMa2MjV921TraxRNDEK1f5y//ry8lyY+eu8MGP/yvMNXYN3QDKqXv84B9+0nW71UqDL/x/f8/b/+XJjr2tS5dnUFuYRZmeXmF6ZoXXr8wCYY8g1h8zDIPBwcEWYAJAa+qNBm7ksJvNZCJVhnZlhFrZZWR/sevnJJWm4m7OLmSp3IC9OprZao9OW9SEPQtXNUtwcXiuwLKbAJw2FCTUtsVVjVb9uzVirfGWK16G0UKQYuzF10Ncxus1NAtegaO5ynZxJZj3MxzP1PvWDVwvluUye83NK/53jzqGehllnunoDaW15uzZsxw4cIBqtcqtW7faFB3ibKvXEtt2xFYyqP9ZY9cBVBxBENBoNDh37ty2KYyvjbVyRHHEkkgxrTMtEVSvNPjKH3+jZflXn73AoRMHOPO/nepp/a++eAXPac/clJRUazWUUsxcgSMHOs81XLzUrhjeb/zon8aZXlilXq+Htu4RE3B5aRk/muWIz7bjODQaDXL5PMPDwwnrre55Ha01PFfiuZJslzJf2XP7FqUFUBo8P8AOTIwOlaVOMKBUqIyAIdBGSgoo3lcHCl2SPQ342ouyqPioo/+kelAQ3jRrHrhBHcvUpJZOv6HL3rafiwWvwKFMmUQ6L62/t4mvQYDBYpBhn719PmK+9qmoSm+9qD7DCp7FM97ecbZKax0qlgwPt2QoSqlEP295eZnr16/jeR6ZTCbRzRsYGCCfz+/IkO9agPpZ94KCXQhQUkrGx8eZnZ3FMAzOnDmzYxPja0t86SHbQ4cOJf2edHz7L56lstwuyfK9L/4j9z15N0N7m2rP8ZxVOrTWvPzs5ZbfKaWo1+shhbtYiBrDgh9+/VV+5d++q2VZ35dMTMxtWe39h/94kfqgopAvMDwcse60Jp/PU6/VkDLsj6hoaLo0MBDOtaTujuVGd5JDbdUh26XMt9LFWmOj8LUM6QQNjWG3H3f6NzGrUBgGlm3hyPDGrNcsLwOBDDRmh2+a1EHbUG7rxpqsChH1kypelpG8E/WrVHPhNay/9j1u7p3WCkdZ1HSWQcNrgnnKbmMzoDXjZ7cVoAAW5eKOAJTQtzDUeZT5cNtr3Rh8sSV8sVjkwIEDye/T9vGLi4vU63WEEBSLxSTT2g5Dw62W+P5njF0HUADZbJYnn3yS559/fkflTGKAShMR9uzZwxNPPNFxuLReaXD+2Qsd1+W5Pt/48+/yoU//UvK7TiW+a5dnWZgN9cxiCrfrOOQLBUqlIuk7zcUfT1FerjE40syirlyZx/flpks/rudSr9VZrPrsGR0ln89FPZdwlblcDssyQ1UKw6SQySCjJ9N4GDNUc7BYqXcvj1a7lPl8qaj5/d8kNeBF51I1NHS5J4a9o4gRFg2KhrRy3XLKwvwlvPG7jiZfijMaAxAoLQn0evsZz1W15m1lN8do3m0DsLC/Ff9dt4i6prOtcJnw5jvvjTBQKBMCnUQIFQKhpjfQSu8CUFY2NWn2Zwe/QaJbV3Xqqk7B6M3ksp+wgq/jGQ+1ZVH9Usyz2SzZbLalbx2rnMR9rdjQMG2xMTAwQDab7fkh8OcAtQvCsqxkWtowjB2ddzDN8Eb8wgsv9DRk++r33yAIun+5L744zvSVWQ6dOJCsfy1AvfyPYxAZpzUaDXK5XMhE7PAlUEpz7odjvOeDTWfhuLwn6C+D8n2fWq0W2QUMMLO6RG3ZITuQjdZHYpOhlIpM29pBWmtFEEjKjQaeH7QMswohMIxQicHvUuZbcjZh8EfIvkv8jwKN8luzqLjPpKNJ/kRTDo2n2tmgaVDwXYNCKV67ROoAP3lPpzt097u241v40sA206VPkQKPGDribCmGONVcMjqsRS/LsbyNJRRgN7cqQsACFXlFqdYeGWs8olKgNe1nObmNZAmAhWCBY5lj27pOAKGnMdQrKPOxlt9vxz0hbR8fRzeLDcuyWjKtYrHY8cF5rSrNz7oXFOxCgErfcGOixE4A1OrqKhcvXqRer3PmzJkNlSe01rz0re7Ehjhe/Icf80u/86+AdoDyXJ/Xzk6wulomk8kwPDLcJuuzNn78j2O8830PYtsWWmsuj4XMu7jEt1HET4pa60QPb6US0p9ryw1Gjw6CgFq9ge97FApFMhmbbimaEAa2beDWG6mnRZ1o6Ckpk5vj4swKI/uLoRCpaYEQLG+ivJfOnuJQTghQmhBYlQzp7qZtt+y5pzZmaQa+QEqNYYLUmkBLEGsmqZKe08bnvOxm2VPYCIgjiBQhY1AgEiCJsy2pNXMNgwPZ1HyViOWaTFJ5VARa4Y+Isq14v3UChpo5L8MRo0LGILXOzfW14qioCg3VIG9sP/3bCr6KZzwAonnj36mH1m4WG77vJ6B1/fr1RF+yWCy2EDI6mRX+rGdQbw253jc50nJH2yFFlI5qtcqPf/xjxsfHuffee8nn8z3JIl174waLtzrTwtNx/ocXqK2GT6hpgFpeXuarf/1tarUGQ0NDFIulDcEJoF51eePcNQBu3lqhWg37PhsBlNY6+VLl83mGhoZCKR+tqdTDdSipWFmssLKyimkaDA0PJ/2v9UKjKbew9+LsKdRgs207HLR2wyzYcz1Wy6vcWlyg4bptauUbha/CEl06VCPU0At8PxQXtm1Mw2jZc19L5AYCrXG4DfCVG5Ei1kbE7BMGCBOEFf1pEpcE01F2e2GO6WZpNWEWxuPPTTBaDIoIEd6MQ4XxlK1ISkw3BCwbyKDJoymGPyIHIosQNsIw0YbBvC51VC3varXRA3jNBXM9HHP/IfQSVvDNlt+92SoStm0zMjLCsWPHuP/++zl9+jSnTp3itttuw7ZtFhYWOH/+PDdv3uTatWucO3eOz3/+88zPz/csILBZq41nnnmGU6dO8dBDD3Hq1Cm++93vbuehbxi7LoNKx3Z6QsVDto1Gg7vuuisZ8O1VL+vSi+M9LSel4qVv/oRf+LUnMU2T1dVVzp07h2EY+OUMAwP9W2a/8tw4j5y5k8uXm3NL3QBKo2nUG7ium1BuNU0nW6U0NSclTbTicOjkvp7AMo6q4yF7sCnxfYXApFgKb9irq6tYSqYccwPiQVZhiFbh1+R42rMnAOkp8MDOdhYklVp1LO2tjTBbUTQakkKhn7mjuG7WTnRwAwNXZslaPuj286Sj3lUTlLpHXVrUlUXJat23tc7DaaBrzbYMWjItNLf8PIcyQ1iGg4EDxKrwISmmxdgwerdWqnmNdNjlqqpSUzWKxvaraZvye0jzFNo4DLw1ZI4Mw2BgYKDl+/z6669z8OBB5ubmuHDhAi+//DIf+9jH2LNnD4888gi/8zu/w733ts9MxlYbzzzzDEePHuX06dN88IMfbFEyT1ttPP3003z605/mS1/6Env37uWrX/0qhw8f5rXXXuOpp57i5s2bb8o5gF2eQW2HJ5Truly4cIFXXnmFgwcPcvr06b7VJ7TWXH7pSs/Lv/TNn1CtVLl+/Tpzc3PceeedPPjAQ1y7vLmnzOsT8yzNV7g8Nrvuco7jsLK8AgKGR4bJZrPhzTC+1whYrdbxPB+lQqmkoK7CgdU+YqXee5muVg6zNU9KKp6b9Kksy8S2w2zLsqxIrUITBBLf9/H9gCCQuIHfkj3FungCMPxQ6VWl/9EKqRVOF3CKyRFKq+gn7G3JwEAF2zHGEGZaZaeIKYqYRgnTKGCILGCFOCCIbva9bW/Gbe+LxufRNE0sy47Oo4lhiES01fdjfy2VylgNAixmggGUOIwUJ5DcixR3oMQRhLEX0xzAsjOJtUlotREOLrdlW6nra8af2SGBVIXtfx6i+bK3AkB1CiklxWKR++67j89+9rMcPnyY733ve3zta1/j13/917uW+9JWG5lMJrHaSMdXvvIVfvM3fxMIrTa+853voLXmscce4/DhELgfeOCBkHTVp8blVmJXZ1Bb8YQKgoDJyUnm5+c5fvx4MmS7mVi4scTSzEpPy2qlmL0xyze//B0ee/dDFAoFhoeHufST6/je5pUBXnr2MtPTzX1IZ1Ce51Gr1RJpIhEZD6ZvFjEBYmm1gWWZydOwkppGxaUw1JsCu1SKSh9fgJjNt1Dv3phPW2ykfaGUVniBTI0cRdlWOFGLbIA10OpfpFA4ymuKutKaP6wXvmuS7SD4uplYdSz2l0I7ELSBDBRC2FhmLsIlFRkvhmQHjeza3lryM7iq0YMieZg5rb13x/5aUslwmEzAlJSMKEXGDhmZQhTQutDCDkS4KFUnUFXytgYcIIgIjFEGJ5tlxpqoMSNn2Gvv7dtqY6MQehYr+CsC6yNvWYBay+Irl8sMDw+Ty+V45zu7C+BuxWojHl4G+PKXv8zjjz/+pg4n70qAWqsm0U9IKZmamuLWrVvcdtttPc1RbcSGu3R2YuMNa029Xsd1XfL5Aiyb7N27NzEuHDt/o6/jWBv/9N0L6H1N2rYQgkAGNFYbCCEYHBzENM02YNLRfvm+T75QINBO27HWlxs9A9Rqw+25dwShNl+j4fVNjhAC3Li0l846dLPhr12N11AYtkAYBhqNG80tGamSZfSO5Lx0A6zAMckUgq2Jqsbr0oKqa1C0PbTSYSbSch2aCGEiUnYVCBWZGoaApQnV2LUWzDg5bi9sjn0nIo8rI9wsELL8ZqVkv5IEQQ10KOpq22HWZJkWgTSp10xyuQNIkYlOZIAQDkLE5UEH8BIyxpJaIu/mEPWw3xmuK1QsN1OOvZsJU55DiwNIed9Pxe59o1gLnL16QW1HvP7663z605/mW9/61puyvTjeep/Cmxj9eEKtHbI9c+ZMT09ZGymOA1x+aR2A6kIZv/TiBP/io+9MfIXGXttaXXj21jIDOZPcYB6lFK7rorVmcHAQy7LagAnCvpvjRDYZxSLVemdwqa86YdlsQx0czfI6s09d932hgsrpHgtaYXhS4qsQoFr2a42ig/ANjCx4OoiW16lFm32imHjQPJLovwnLTaOiMp9pb0OZSmsWayalYYFp9/o1NpJMMtnTiJ234Ge4HQtDNFB6631ZIQRzGo4Wi5SMEPilDA0IPc+j4lZBayzbQiqF7/tYpoUwbDQ2mtQQtlYgHAQOmA5LRsAd9gEEoVV8EAR4vo9sOCitMERY4o09osyu/lDtYQVfI2+WUea7t3wOtjvSD7r9lDq3YrURL//Lv/zL/MVf/AV33nnnNhxJ77GrAaqXDCo9ZLt3796uQ7bdImYKdgMop+Zw4/KtThtOSmuZTCYsraWeDn0vYOLHVxEjkumpJWqVzfvxhA61HsxWkKbC87yEum1aZtuXwXVdGo0G2Ww2Aszw96v1zmCvpMapuOQ3yKLqXoDj91cC01qzuuJgHLQ75i1tFAMNvgxoyCAp5a0XsiHxCwotNIYRkg50vCIiIFLNLtZa0Ar/bU4maTdPJiNRUQlOoXouEcYHoKPZo7q08XVAtp/3t0VIdNBYLPgDHC9k0QQo7UQ/DaR20NrteytSa6Zcl5P5PKFQqxn1/3wGBkpk7EzI8ot6T27EwDQMo8XLyTBMhChGJUKoariu3sle+3EM8xamdYscNzH1LdAuSofMQRkE1F0XmfhDpUArpZC/Nkay38QzAP2vN7w+ftrRS5lzK1YbKysrfOADH+Czn/0s73jHO3bqMLrGrgSoXkgSWmsWFxcZHx9ncHCwRe27n9iIyn7tjZttWYfv+dRqVUzTjOjbnTO1C8+NcfdTx7Zc3qvVXIIgYGV6idKRAYZHhnEdNwSq2CBOhPtVr9cxrXC/0pmHkppqF4ACqK04GwLUZrInLxqexVcIu93KXaXKbkqFT9yeXi+ba6oyaDT4QKBpqZTBmkxrfdAKwSpc2m8IjAETU4RP9fE7lFboiITREbRilYiITRcj73LD4uDA9jBRbzR8juQy2IaFKUqYLdboCqUdpG40wQtnwyf5Wc9nn21TJBxLCPuYI8l3MPFyyjS/W0opZBCEJeZGAxmEyiah8WAIXCs8S94+QdF8e1OvUWvQC1hiGtO6RUaHwCV0OSLIhKDlOA4yCBXr06BlRWVSraFofQ8jcAisXwWxsw7WvcTaNkE/XlBbsdr44z/+Y8bHx/mP//E/Jsrn3/rWt1rmuHYyRJ+smJ2g0LzpEdtG1+t1Ll26xGOPtU6Sr6ysMDY2Rjab5eTJkz3NMXWL119/nSNHjnRl2Hzjc9/jhX94OdyvIKAaDemVikXMDerghhC887cfZ+KlWldrjfVD47oet24t4TgKwzQ4+MBh8sOFpMwXO5UqpRCGIJfNkclkEtCKo1x1uLVQTq27FQAMU3DbQwe7AoNUikuzC331n5TWiayRMWhiDHU+XzHlHWHgIZFaRyXL8BxsuMmihsH+L/020Ir+XhySZHIRUy5SN28nk4NConUIqkpH8lNrnphNoblrTwNzm/i4x/IZThR7fRBTKO2FWRZOAlx6zWyYpSR3CxgsDWx4TXeLkDkYJNdjEASgDXKNX2W48GAy1GpZVkSPV8n7DKoY+iammMbUtxD6JgYLoJumhkEQlh9DFqfCzmTIZDIY1n5U9iNo8+Sm9nu7IggCXn31VR5//HEAZmZm+PjHP843v/nNDd75lo2eUtNdn0GlS3xpF9177713UzNFa2OjDGry1SmUbNpRFIsl7ExvJUSlNVdevsHMJhKoUJqoimlaKGVimiEzrzpfJT9cSGyda/VaKJJZKmIIAz/wE928+Pgsy2K5EjfYO91qozJf1SU/mO243FK90Rc4AbiyeV51XaEHW58yY1o5AgzToqH8lPdUrKyQ7hs1e0Ytu9IABjoc2gbRLdPyXUE2H/YOg2gYVkSzWi1zRhE7zzRsMlb4FN9SGoyo7KuOxWhhe9iBNx2PIzmbbE+IZ2CIHIbIpW4kGq09pHZw/QqeX0XbguWcxcgWiAeh3bvdKo+lNWrwuwhvH8vLI0xNTeF5HrlcLlFfGBgYwLSH0Azh6/vwEokmF6FvYRm3MDPT2OomBtOgJeVKOSRxBAGBM4Uq/99UnLupyf+FfPE2SqUShUJhR3U818Zu1OGDXQpQccTg0Wg0GBsbw3GcliHb7dxGp1iZX2XywlU8z6MY2VH0W/Mee2GK0qHedcpkECRSKgOlAfxAEchq8np9sYa+U+O4TsQYzFMqlpKbs2VbECvO6PCL47gelZqLjqjXIatLYBhpBQOoLTvkB3OkJX3iMtxitb/ynq9kixWHDjT4GjKhO29IHlGYpoVEU5f+hrnS2p5RuH8alABPo7LN/d5sCCDwBCiD9P1aA1rpKGOK1BYgAa24xGNiQGqgVQNVN8/xAYHEw9cuvvJ6yQs7htQwUXe5f2CzskICpUxqVY1pDjNcPIwQBmXlY5iPs8cGR17HlTfx1OImtxFvSmCYErfwVxza+1Hutk+hI1JRpVKhUqkwMzNDo9HAtu0W0Mrl8ghxkkCdwNc6UnYKMJjj1vJz3H7UIGvNUdC3gAaDagYZfJ6ycxezNx9gqTyIEEaL8GupVNoxevpu9IKCXQpQiX245+E4Dq+88gonT55k797tN0frBFAxVf35b7yEaZpdxVx7iZmxBe7Ye3hDJpdSinqtRiADisUitm2jNVSWW6nFge8zOzXL0IGh8Altvd0SIWC5dW+Nbl6oQCClagGtylKVwcMFbNum2ZWBlXojUo5IqwvEm2jfAa01bgfQV3UFZljeMQwTDBNHBQQd1BZ6jRi0TMfALpiolBpCOLjblm/1FE5DUBxIMQIh9JSS4fFZVtwPCUtOMiU7ZEQqDjF4BVJQ83IcLIayN1pDoD187UXySiFoKXo7D3NuwKFswEimv9tDc+TAi8ptzWxHYPNa7RK/uPc32JP7lwBI3cCVNxPAcuQNPDnT9/lUOuBW7XPsyT3FnuxT5PN58vl8S5/E87wEtK5du0atFlYG0uCSyWQYn6zjOPdy2LofGbEPBcuYxi0M6xbD2ZuMDv0IyOOLxyg3RilXBdPT0y2mhmkwTAu8bjZ2oxcU7FKACoKAsbEx5ufnMU2TM2fObDswxZEeBtZac+vWLa5evcqhQ4co6kFy+S0IYCqNU/OpLqwydKh6nFMiAAAgAElEQVSzRX2ooFzHdVwKxSKlTKlJAtA60d4LAUViCIHhQr7Q435pWG1hEHYa5ox04QJNZamKmRUtDer5cpVkGClZS+p9zbWET8iBjOdBk7doDaoaoIsG2jBwVdCmr7eVUE6oMGEYcd8oPQcVgpbUKgKwjZl5bj00MowvOxn1yUzDiAA8Og9CgGnG40XRZxfui5IqGRi+sigZIEiUM2wjg00GzFJyfqT220BL0nm4e7zm8rhtYvb0vYgZp3Xy+RzF4jCdnmx85fGDxS/z3r2/TsEcwBR5CtZJClazv6O0jytvJYDlyhu46iaqm29W6rwsON+kFlzmYP5DZM1DLa9nMhn27NnTZolRrVYpl8tcuXKFlZUVMpkMQ0NDzM3NJX0t09qH0nsJ1IPNw9I1DH2DwfwVhvISvX8QYYwQcIyGo6lUKiwvLzM1NZXMK7VmcLm+7jk/L/HtojAMg0KhwJkzZ3j++ed3dFux7fvc3BwTExOMjo5y+vRpMpkMX7vw/S2tu1EPZ5Wqs8sdACqan6qn56fiPn0IBp4X4HohsyneVyGgsVQPsxBhbNh3qTse3joWIWFEPRUTcA2GDwyjCQFxbrWK5wfN3lBiq5HqxaTW05BxHyk0kNAqBQUSfFehs9vP5dE6FJA1i+0nRCAwhcBcM7wbyx3JDqCltcBzNJlc01/KtuwNE+mQJyHAjBc0w76W1ix5mhHtUq/XUNEQqx3Tqi0Ly7CxsMmbxeYxEeCrZmnQ0y5SB9SkYrLucrK4PoNNqfAmL4RgeGhozbBwe9RlhWeX/oZ/PvpvyJnt5CND2OSt28lbt6fOlcJTcwlgxX9K3V4WbgSTXK38ASPZdzOa/UUso3sf2TRNMpkMCwsL5HI53vWud2FZFvV6vQVgOvW1bHsAuA9f39tkMkoPgxnyGUF+b5YD+4YRxgG0LuJ6XiKuPDs7S6MRqvWnM7huNhvwc4DaVWGaZjKoFpfg+plt6iccx+HGjRvs2bOHRx99lHyUMVWWqj2pl68X8exTdbGMCiSGFVokJPNTdmy5ISKtzmbhTCnFwsIqMrZfT1PGlaKx3KA4WmxNbJpvT2K52t/8VW3FYfRoSPPWCFZdH9OymhlCSuJmLWi5SiKVDm/iWgCRMZ9o7qBwBEHOiABhe0PWNEahN48sAZjCwBRGwlBPQIsw23KqIEwPyzbDrGyTEYPWLUdzeKiIaTT7cPEQa1BvoLTCNMyE2BLOGFnkTIscTbCQWuFrl4rvkhEHyJo1qsHymvMZlvPC/mmpr+/Pqr/Adxe/yC+M/ipFa+M+ihAGWfMgWfMg8LZw61oT6OU1oHUTX62gUSy532fFe47hzD9jOPMuMubelnVqrZmammJ6epq7776b0dHR5LW4r3To0KFk2fX6WjHAhFnR8SaLUANSAmUs02RkuMDoSCksP2PhB0FHm421fa1Os5S7wQsKdilAAUnjOWbybTdAVatVxsbG8DyPoaEhHnrooZbXr72xtdklgFrZQQBaKqoLZQp7S1SrYW19aDC2v0hPnYuo5FfDdT1cV4akhw5RX6hS3LNGOTrEhASo/EBSqbmsLc+tFypQEZsvx2y51jS+izcRq40bRgJaUikaQZQ5pY5HpCna0X9MV5CJvKF0VHaTWiO13jJoKV+jfdLWQX2FIJJIUgokGGQ4mt1LNidwlYer/PDPHggdncKTiptVj2ODWYQAyzKxLBMImZNap+wv4nkgJUPlhWgwOx5izRl5MPJM1gX/9tivUTQzrATzLPuz3FyZZGppAqNgRk/x/YNrJVjhO4tf5MzwB9ifvW3jN6wJIQS2GMU2Rhmwm7btgaq09LWq/ussuz+gYN3FYOY0JfshahWfixcvJtWMjYgNQohN97WKxSJChAPJ8fUrpSaUcBIMDpQYGhyIQCu81mu1GtVqtcWJV2sdrUvged6u6UHtyjkoCC8urTXnz5/n9ttv79lXZaNwHIfx8XFqtRp33XUXmUyGiYkJHnnkkZbl/v5Pn+HcM69ueju+63P18ixBEGAaBtnREqN3HYqeuMxUKQ9iZAklkxzyuRxgMnVjqev6DdPgtrcfX5dKO7tYZTkyJ0xHvOm1NO44SqN5MgfzXF8qt73Wsh6t8ZVMNPOSLLBlvbp18wKMUQujaHUsl6kIrKRWSd+on4vaLAjskc0xtZTWyCAIe3SWhQAKOYs7D7Vee1qDp/wEsBzlR6C1McnBEIK3HSz2SBMPQ8aDsUGAHwQoqSKAC/tZB/Oj/B93/K+IQHP58mW01txzzz3YWYtVf4Flf46VYC78059HbtAvSocAHhj4Z9xXegJD7AwDTmkXV96k7k9xc/Y1GnXFsSP3M1p6kIxxYFv7z3Ffq1KpUC6XW4gTg4ODbfNa8Q8QqoRAi8qFERFlLl68SCaTYW5ujt/7vd9L5Ire85738Oijj/Le976Xffv2ddynb3zjG3ziE59ASsnHPvYx/sN/+A8tr7uuy0c/+lHOnTvHnj17+NKXvsTx48cB+P3f/33+/M//HNM0+aM/+iOeeuqp7TpVP5+DWi/WZlBbDd/3mZycZGFhgTvvvJMHHngAIQSO43SkmV99/XqHtfQe1bIDhM3yQEmMcoOhwQEQRkufCUIwrtdr2HaG4eEhhDCYm1sfHJRUNJbrFPeUOr4upWIloYa3XmshfqwdhNXJHlWWGzjZLnYVEYAEShEomRA6YmBqd2cVbVilahKVbZ6DZj/LCEkgQmCnnGZUKsOKs61uGYxqaPRQL7qCqWMCpAxCUdfI+iOOuhNQdXxKuRTjTUDWtMmaNkSlN63B1wGujAArAi+1hqGotGZ82eH+Pfmeb7ymYWBGg6nJepROMq2p1Rn+nxf/klONY+wd2cP+/ftxXRfbthnNHGQ0czC1fUUlWGLZn2PZn2XFn2M5mMNXXqdNo4HXKs9x3bnMqcH3si97tKd97icMkaW2Msj4eI4jR36Jh+47CkhcNUM9uIghsgiRxSCDbYwitgCUsfpLmgKulOqpr5XJZJol7miEQiYPZ5o9e/Zwxx138Mwzz/Bbv/VbfOITn6BWq/HKK69w8+bNjgC1FS+oN954g6effprXX3+dW7du8Yu/+Itcvnz5TVV637UAFcdWPaGUUkxNTXHjxg1uv/32NnXzTjTz8mJla/0nrVlZquB7QUj1Nk200tSWqhT3xBOlgiCaeTIMg8HBwaSMoLWm0oN2X22h1hWgllbrGwzWNi3Gm4TykO1Wc1z8skLljYSm3VHVQTeHZxGiY0a0ZpPhtjwwhYmwROoL32pg2Am06AhazWxLRfspaxprYOObv2atXbzR8bFxdqlB8dD69hFCQEZYZAyLuO0f0sklTlwelOGfS07AfCNgf2HzZWvDEGQydtj781zKeZ+ZI4rHB2+nUa1z48YNqtVwfq5YLDI4OJj0Y4bsvQzZeznO/dF+aqpyhRV/vgla/iyOapIcwr7UlziUu4MHSmfYkzm86X1Ph+u6XL58GaUUjz76KLlcTPqwyJlHE/X1eD+lLoM2ENG8mcBAkNlSlhWX/TbT18pms9y4cYNarUY2m0VKied5XLhwgePHj3Ps2DHe9773dd122gsKSLyg0gD1la98hc985jNA6AX18Y9/HK01X/nKV/jwhz9MNpvljjvu4OTJk7z44os8+eSTmz4X/cauB6jNekJprZmenmZycpKDBw/y5JNPdnyySNuyx3H1tU1mT1rjui7VShW34WNn7FBTTGuE1lTnVyjuGUSpUJlCKUWxWGydvifU3uvFsbaxWEPJUAIpHb4vWar0r5sH4PgBWoPtaMSAFR3WmgxGhX9PgGkTA/u6JhFDVooVmHotBi2t0bKT624atCC+iykdZgi6AcURG0dJAtWZwZiU8wwDK0Ub7xR1N6Bc9xkq9tfcEgJsYWIbeQai6emQTi7xXc1jB45TUxWm3SUqQa2vdSulo2tIMlAawLJMptxZ/r5yll89/AuJd5BSKilpzczMMDY21lLSim+2A5kRBqwRbsvfHe2nxlE1lv3ZKNsKQWvamWTamWRv5hB3Fh7haO4uLKP/pl880jE1NcXJkye7lr/SIYTAEq2kjXDMIYgS+HjoPPzZCmj10teamJhgeXk5cV34kz/5E44cOcLnPvc5HnrooRZiR7fYihfUzZs3OXPmTMt730w3XdjFAJWWO+rHIVJrzcLCAuPj44yMjCSU8Y22k47JV6f63l8/YuZZloUpsphmmAEZhoGUCikDlm7MkzlQQmsoFPLR02L79ldWe/P9UUrRWKpT3NeaRc0tV/uWJdJa0/B9ZKSQoOsByAzCFNGNIcxglFJILRGGBYbRAlr9EAdUTSEGOzPuEtCC5Ak6JJOodUBLRKAVIt2ILDA0lCPQiob0aQQ+jgyoBx6O74W9hDXlvPViZrnBYMHecj9ECLCiEtVri1U+df+7sQ2TWuAw6y4x7S4x7Swy4y6x4lfa3q81OG5o71IsFMhkSi2Z6w1njj+f+hq/eugXOJLfm2Tng4ODCTM2VMevUalUWFhYYHJyMvQLy+dbQCuXLXI4dyeHc00LB1c1ol7WHNPuJGO1HzNgjXAkd5ID2dvJGBsLt9ZqNS5evEipVOL06dNb8nYKxxzaM9HYpLGtvL3Fzy+TyTAyMsLKygq+73P69GkKhQLj4+N88Ytf5K/+6q+wbZuxsTH+3b/7d/yn//SfkuzoZzF2LUDFYVlWQu/cKFZXV7l8+TLZbLaFMt5PaK2ZPH+t5+VlREUVQoS6YqbJjfl5YmaAMAwsQ6CkQno+ygnID5XwPJ9Gw0nsz+OGdxAo6vXO/YBOUV2otABUreFR6eP9oAmkwglkik0Y7X49gIHwyx+LgYLAtqwUdTw1W9Qh0+oKWlKjHY3I93bDCAmBRkfQCvctVroIwWphoUqxGLLeBqwsJSsT2mFrTXZgEGkKHBlEwOXjdcm04vB8yULZZV+Pxo69xPX6Ck9f/QkfueMxilaOE9ZhThSbpbOGdJl1lxPAul6ZZWp1GsuyGB4ajuxF2qMS1Piv17/OO0Yf4p2jD2EZrZUDIUTHklaj0aBSqbCyssL169dxXZdsNpuA1uDgILlcjoPZ2zmYbc5B+cpjJZhnqnEJU5hkjBw5o8CwvR9TNG9hSikmJydZXFzknnvu2VEpINEhre9EONvIrHRtrK6ucvHiRQ4cOMDb3vY2DMPg1Vdf5ROf+ARPPfUUn//858lmswRBwOXLlzfMDLfiBdXLe3c6di1A9eOqW6vVGBsbQ0q5ZRHZpekVVherGy6npKJWqzYFZCM6uO8HNGoe8ZNbaE0gMUyBbduoqk/hUHOmRSuFH8R+Ow6Liw0CP2SSCcOIVBG67ISG+mIdz/URpglac2t+fXJF8taI6OBLmWRNbctUA0TJQkqJ0npDG+90ppXeTjfQ0hUJ+c0LesagBa1W8VorPC9gYaFMNhuePyUVdibsH1hm+FkNWNmY4Y3UioYMcKQfZVwBnmrtTc5GWVTW3r4m9AsLUwzYWX7p6P1t5zZvZjleOMjRzF4mJiY4Ws7xv9/zXuoZPwGtGWeJeW+l7UFAo/nR0qucr1zhF/ee4t7SsQ0/u0KhQKFQ4MCBA+E6opJ1zHibnp5O+jDpTKtYLLIvc4R9mebNUeqAarCCEAYmJuXVChNjVzh68FhyY3+zY73jXwtea5eVUnLlyhVWV1d58MFQnd11Xf7gD/6A73//+/zZn/0Zjz76aLK8ZVktfaRusRUvqA9+8IP8xm/8Bp/61Ke4desWY2NjPPHEE72cim2LXQtQcawHUK7rMj4+TqVS4a677mqRSeknhBCRPpzB5Pn1y3taKer1Bp7nUigUEs+X+AKvlZ1kuUDK8KZtN2/s1bkV9p48nPy/MAwyEUNLSsXsnINlWWEpSysCGd7Qm2raKdASABp3pcHA/kGuzizjBTIhNaTnq6K9T/o0Sm1ckNOOxG94mDkbe5M3lHVBK1BksfANnbjnbjXSoFWva/L5kBJcyOeRSlGv1ZFSIgwRSg5ZFpZlY5omJStDyWqWg6VWYZYl/eTPW4t1jh8obSv1+dvTY9jC5P1H7mlZb2zGOTk5ybFjx7j77rsRQrAHuC3f7Iv4KmDeW2HGCUuEM84Ss95SqKTuV/ny9A/YnxnhydEHuH/g9tDrqocQQpDL5cjlci2ZQNyHKZfLzM/Phx5kppkAVkzXHrL34vt+IvT82MOnsHMmigCtY+dgsWP09V4i+R6u83kuLy9z6dIlDh8+zKlTpxBCcO7cOT75yU/yK7/yKzz77LObntPcihfUAw88wK/92q9x//33Y1kWf/Inf/KmMvhgF89Bqchm2nEcXn/9dU6dOpW8FgQBk5OTzM/Pc+LECQ4c2NqsxNmzZ3nkkUfIZDJ88ff/B5dfutK+kG61do/Lh+nPRwjB1PhspCARUpY77dfxM/eRLbWXH+fmyt37T5oEtLRqBa38cAEODVKpu7SmWxEgqTh7iXo4GyJTNPshBMawjRjp3wiy1xgYybH/yCCBUjSCgIYfld2CYNOgFdLGQ6PEgwcH2dOB6Ri7usY/Mn6YSKk4xJlWy/vQvOv22zg8XOR6fZWp2gozjcqmBnfXxjv2HefXjj+MKQxqtRqXLl0il8tx8uTJvgVNpZYseKtMO0tJpjXjLpExLB4ePMEDA3dwMDu6bUAbBEFCHiiXy9RqtcSdd9//396bh0dZnv3fn3u2JDOTnYSQhRDIyiKQBNGqFeujVmtpX1d89MFWbdXWgrWty89W7VO1bj9tK09dqlVrq23f2rcoD2Lr3lqFgBtKyEIIZN9ny6z38v4xuW9mkgmZhGzA/TmOOSDJwFwzmbnO+7zO8/x+s7LIz8/XjsAjURT1vawydsCYLkRR1GYmFy9eTFJSEj6fj3vvvZcdO3bwxBNPxJUlHaXoc1DxEJlBRbaMz58/f0TL+ERRW80FDCMbJJQY1u5DH57I4KQoCo5+Jx63D6PReNh1ebodIwJUMCjidB2m805gqKBvJFKZVFZknD0eRIuAYAorN6hNA1q3m9EY5Qcka514I4NWWN1c0DTbFI8EaeM7px8PHoefjGwbJrORZIuF5IiNWA1afjEctHyhsYOWan5oMBgwms04HAHS020j6jUGwYDFbMFiPvR4iiIjihIhMexMrAUtoyms5DCk4vBBSxsb557Empxw80BQEmnzuWgZdNIy6KDF66Dd645bnVzlvZ5munxuTjdmEnJ6KC0tnbAagVEwMjchg7kJhzrJZEWmL+iiM9DPZ+797HLUk2K2kp+YRUFSNmbDxLcbk8lEeno66enp+Hw+9u7di81mY968efh8Ptrb27WhWHWuSL0Nzz6ihmOZmWDV19dHQ0MDBQUFlJWFM9t///vf3HzzzVxxxRW89dZbR9Tccaxw3GdQAO+99x5FRUVay3hhYeGkvjlUtYruxj7+cM9fte+LoRAeT9jaPVIocvjvxOfz4Q8E8LtFBl2Ha1AIF/ET7EksOKki6icdHQ7c49TNU2TwB8Odd8Y5NgwZSVGNA7I8pHs0LGgN/8BrdtuyAgYhPFMUcQQoZCUg2Kbuw5iamcScefHVDSVZCQerYUEr3DYuIQhgNEarVKSnJ5GVFXtebCzU1yZ8C4WPbRFIS0rku8tWUDAnK6aIaEiWaPe6tCyrZdBBu8+FdBhrkUAgiNfrJd1q58ry1VTPyZ/yzVlRFAZCbvqCLoyCgQSDBYvBRJrZPu6AJcsyLS0tMfXzIu+jdhC6XC7cbjeiGLaYiTwinAwLjImgHkkGAgEqKirCpqCDg/z0pz/l888/54knnqC0tHRG1jbNxPXGO24DlDKUufT29vLRRx+Rn5/PokWLpuSNW1tby9y5c9n+14/Y8erHSJLEoMeDoihRJmfDfxeBQBCvz0tiQgIJCYk013UOBYXDPjNQoPALS7AkWVAU8HgCdHU7416vLId19kRJ0jIfIcGEcX5ajA1NORS0hrImULRWbvVnh7K+yHpVONMSEo2Y5iXhi1A1n0wEg8D8kgxME2g+UGQF96CHwWAQwWIhqMgxM63CwnQSEiYnyKpBK91k4qJ5eUhD3Zh2u11r6bbb7SOClijLdKiZltfBwUEHbV4XATHEoMcDgoDdZtPm2kqS53DB/KUU2KZX001RFAYlf1hxXc3CEbAYRm+zd7lcmn5eUVHRuGohqk9VZNBSlRwiOwgTEhKmNGD39PTQ2NjIggULyMkJq2+8++673HbbbXzrW9/iuuuum/YazwyiB6jDEQqF2L59OxaLBbfbzamnnjplj9XQ0EBKSgov/ORvtDd3EAqJ2Ow2LOZDLdYqgiAQDIXwDs08qdbSzv5ButsdcT9mVnEuGQtyEEWJ5uY+xKE2abW5YfgER3hwNazLNloQNM1PQ0iMp1gbbsuWJSmiXVyJyrA0UdghciuysCSZCIgS/pCILxTCFxTxT1LQSstMIjPOLEpFrQlak6wkJEbXySIzLb8oIpgFsubZJn2DK0xN5TuVq0gyGqM2WFXFITIrGO7oKssy+w80U9t+EEvOHBxGmVavg1avM6rtfUlaDl/KWURp8uQbdo6H0FBXoxChQiKLMk1NTbjdbsrLy7HbJ5apDidSyUF9Tf1+PxaLJaqD0Gq1HvFrEgwGqaurQ1EUysvLsVgsuFwufvzjH3Pw4EGefPJJTfvuOEIPUGPR39+PzWbj/fffZ/Xq1VPWmrpv3z6aa1t45f/+g6QkK4mJ0Z15EA5MqjQRMKQAMaS0ICs0N3QhhuIv6icmWylYVUpr6wD+QIwuRSW8yaoNDrKsjJmdGdKSMGYffoNQ6yxh00JjxIf7cJmWAfucJLIXZIyYL1FQCIoSvpCIPxg+cptI0BIMAgXFGZgtY1+hiiERz6AHs8kc3qDi1N07rbSQxYXZtLicHHS5aHE56fHGNxR9OLJtNr5buYo51mj/pEjDPVWYFNACVV9fHzk5ORQVFUW9t2VFodvv0Y4GD3odtAw6SbMksnrOfKoz88lMGOnVNN309PTQ0NhAfn4B+Xl5UYFiqgJpZNu72+3G6/ViMpmiMi31onEsFEWhu7ubpqYmFi1aRHZ2Noqi8Prrr/OTn/yEDRs2cNVVV81IS/wsQA9QY6Eqmu/cuZNly5ZpLd2ThSqHVF9fT+O7B2n7uDvqZ5F/93q9iKKI1XYos1JxDXjpahundp+ikFCUR3AcvzFFYShQyUjy0ExRZBA1GjAWZcTcsMPDrOFhXJPJGHOQMcYjagVrRVFIW2jHYBAwmowRLdqmwwYtXzCEP86gZU9NYG7B6MObiqzgGRwqtNvsGE3jO24RELh2TTVlOYe8h7yhEC0uFwddzqHANbGgZbdYuGLpCSzLyj7s/Xw+H7W1tfj9fpKTk/H5fFrjgHo8OFq3W7d/kBZvOGgBpFqSWGBLp9CeFmXGONUEAgHq6uoAKCsrG/G5HG3PmqqgFQqFooKWqm8Z2YgxPHsNBALs3bsXo9EYVn43mxkYGOC2225jYGCAxx57jPz8yRfGPYrQA9RYqAHqk08+YdGiRZN2fADhLp36+nrS0tKw2Ww8d8v/ixxQRny4vF4fgYBfm3ka0WAgKxxo7CIUjD97kmWFQCCEKTMVS1b6ET0PNWipR3+Gucko9sg63dBxnizFqDONj5RsGxn5KZrRnjpgrChhd9j4glb4aNAXEgnECFq5RWkkDde8U8Dn9xHwh2fPLAkTr0PaLBY2/MdJZKfYRr2PGrRa3M6hwOWiO041k9PnF3J+cSnWGJ1pra2ttLa2alfrKqpenrrBut3uqG43NWgNbwxSFIXewCC9AS8JBiMWowmzYCQjIQmzYfJrJYqi0NbWRktLS9z6eZH/NpKpPqoUhxRehh+52mw2FEXB6XRSUlLC3LlzURSF//3f/+VnP/sZN998M5dffvmkZ01XXXUVW7ZsITs7m88++2zEzxVFYePGjWzduhWr1cqzzz5LZWXlpK5hnOgBaizUAPX555+Tl5c3KQZgbrdbk6QvLS0lKSmJj9/bzXM/+RMJCQmYzGZMRhPBYACvz0diQkKUZNKhduzw8Zej101f90jNtFgoskJIlAiFpHBzXYIZ66K8Sf2wJiQnkr0kF38ghMfrxz3oQ1KEoVrTEerIGQTyl87FaBr+4Q3boouiiBgSEaVw0DIZVXdYc8ysTUEJ17SGjgZ9IRHFoJC7MF3LAkOhEIOecIt/kjV+i4rDkWmzsvGsk0hOjD8j94ZCtLrDmdZYQSslIYGvFpdyYm4eJoMBp9NJXV0d6enpLFy4MK5Cu9rtFhm0wqoltqigFatF2xUKa1eaDAYMhDs4LQbjEb12Ho+HvXv3kpyczKJFiya9xXo62soHBwfZs2ePJgT72muvadJEBoOBO+64gzPPPJP09CO7aIzFu+++i91uZ/369TED1NatW3n00UfZunUr27dvZ+PGjSNEY6cZfQ5qLCbTE8rv99PQ0IDP56O0tJTU1FTNvbT5o1aSk5MJiSI+r5dQKIQghKWJ1NqTKvNz6LMj4PMF6e1yERb2PpR9CREeSGo9R5bCx3KRlxBKIIQcCGIcx0Y5FgG3n5DHj0QIe6KR7IxsDAYDoiTjC4TwB0X8Q3+OJnE0Goqs4Or2kJ473DxyaFbIaNKkgxQULdMKBAIMDoZQYChohXUHTWYTiabwTb30UFA4IXse+XkpfFS/jy4piCUtFWUSN62+QS+Pv13Dt0+vJjUpPm09q9lMaUYmpRmH1Ep8YohWl4uDrkOBq8c7iCsQ4A+f7+bVfQ2UmywstCSwbPHicZ0ARB5Rqai+RS6Xi+7ubs3NNVKZPCUlhVRL9HMK26+H1ecjGxwMcSh+R+rnlZeXT5px6HCGzxZGujKPVy9vOGr22tbWprW/qyaDSUlJXHnllWRlZfHvf/+bTZs28dxzz1FYWDj2fzwOvvjFL9Lc3Dzqzzdv3sz69esRBIGTTjoJh8NBR0eHppU4WzmuA1RxieEAACAASURBVJTKkXhCiaJIU1MTvb29FBcXM2dOuP6g1mN8bj97/lUPQDAQQBAE0tPTw5t6hPV2+PGFsLCr2YzRYKSnzREOWkZQI5KiKMiSeqwWDkyHS4JF5+DkBSgl/Lx6D/YyryI3yi7eZDKQbEog2aZFEEKipAUsX1DEHxTHbMRwdQ+SPMeGaYxmBuFwQSsU1h0cHBSjg5Y5PAi745MmrKEMrji1iszMTERZptPpobXfycF+J20DLtodbsQ4LElGo93h5tE3tnPd6dXMSR79uO9wJJnMlGRkUjIsaLU4XXx6sJnPW1vZnWDhI7+Xfc1NrJqXR0lGRtwK6sOJ9C3KzQ2Lyo6mTK4GLTVwxRrPkIdqi8NXowYDVeInJydn2vTz4pEeGg9er5fa2lpNOd1oNNLZ2clNN92EzWbjzTff1PaE9evXT8pjToRYthttbW16gJrNqG/SiXhCybJMa2srLS0tFBQUcNJJJyEIghaYwh5EBna8+iED/QPa8UnkkYnZbI76WlGUcN0lGKLtYD8+T1BTeFDFXcN24QLGSO25iBqRLIX/rmZSotODJTv9iD+Q4QwtPM8kecQhb5zDIIDZbMRsNkYFraAoaRmWPxBucogMsIqs4OhwMadw/McgUUGLxKGHVDQ7c+/gUPZqEPh3/QBlixyYTCbsdjv56Snkp6dw0qLwh1iUZTodbg72O2kdcNHS76TT6RlX0OrzeHnkH+/zn6tPYEne4Zsb4kXyB3A3N7PUZuNrXz4Ps9mMXxS148HPertJTUggM8lKaXoGtiOc6xtNmVzNtPr6+qKClppljTYMqyhK1LDq8uXLJ+QKMJlM5LOhKAoHDx6ks7OTsrIy0tLSkGWZP/zhD/zqV7/i7rvvZu3atbNCUulo5rgOUCpmszluyw21dXTfvn1kZWWxevVqjEajJoEDaIGqqbGJ1//4jibWOtabVRAEjAYjfX0uxKCC2WIGRdGuRGXVskILWoawwKtBGGF5oHbjybKCORRCTkzUbKTHgyyHsxKjwaApqiuyjLPNQcaCcYrnCmAxG7GYjWgHOUrYasIXDOEPhFvJvf0+gll2LEfgCHvoIcPBPRAIYjAayEgOdyFKosjm9xo5t9qH3xv+3Q/vdMvPSCU/41DXnyhJtDs8tA44ael30TLgpNPhOax6gzcY4ql/7uLUkkLOW1ZCkmViz0nVh3Q4HJSVlUUdhSWaTBSnZ1CcfkhZISCKtHncKIMeEkwmzAYDNrMF+yQMoguCgM1m06SGAM1Ow+VyMTAwwIEDBwgGgyQlJUXVtBwOB/v376eoqEjTuJzuBocjxePxUFtbq/nBGQwG2tra2LhxIzk5Obz77rtTUmc6EmaDdcZEOK4DVGQGFc8R38DAAPX19dhsNiorK0lISNDqTGrWJEkyXV2dtLS00F/nItEUf+HdNxigu91BMBCxFiHC2VWzfFBbs2VkMTxfJAhoauTh7E3QLN4TxBB5i+YTDIoE/EOBwB8iEAiN2pqtDAUmQVD9maJ/7u5wkpqfNu5W7BEIYLEYsViMpNrV5wdpiVa+cHop7b0u2npcdPS5kKRx1rSUcE0lFAphs9sxRzgLm0xmnD6Zj1v8XH5ONQJoXVmRduaRGYHdbmd+ZirzMw8FrZAk0eHw0NLvpGXASWu/iw6ne8Tr+q+GA+xu7eLcZSVUL8jFGOdxlqIo9PT0sG/fPgoKCiguLo7r/ZRgMrEwLXqTDIgizkAAk/r+QMBoMGCahKO1SDsNVSVBHYZ1uVz09vby+eefoygKKSkpDA4O0tPTMy4FhyOtFR0psixz4MABenp6tHqZLMs888wzPPnkk9x///2cc845szLArl27lk2bNrFu3Tq2b99OamrqrD/eg+O8i08tsjudTlpaWli6dGnM+w0ODlJfX48sy5SVlWGz2Yayk/CVs/qG7O3tZd++faSmpJGRPIff3vIiPo9/aDbIgMEYrVOnKApiSMI7GMDj8OEdjN/ZdziKooRVyIdcYcMSeUOOsAYDi05bijlx5DxJMCji94sEAiH8/hD+QAhpKFMLq6WP/pjp8zNImz+27fREOeP0cr54WliXTJQkOvs8tPU4aet20tbjpLPfM0pNS8EfCODz+khKShzVWVildP4crvhyJeZhwTbWIKx65JWSkkJqampsnTxJom3ATduAa+iIMHw8qAatrGQba8oWULUgl4TDdKt5vV7q6uqwWCyUlJRMiQyXKMuHlOtRHWQnL4tRBZgjj8IiFRxcLleUcaGaaSUmJsbUdJypzd/tdlNbW8ucOXNYsGABBoOB5uZmvve971FaWsoDDzxwRD5xR8pll13G22+/TW9vL3PnzuWnP/2pVra47rrrUBSFG264gW3btmG1WnnmmWeorq6esfWit5mPjRqg1I1g5cqVUT8PBoM0NjbicrkoKSkhIyMDWZa1DEaV63G5XDQ0NJCQkEBxcTGJiYm88uu/88lbnwPhbCQQCBH0i4SGspigP4QYkqb0BQ0HrfB6kwvmkJybPjQEa9YsHyK7m3w+HwG/H5MpAUkWhoKWSDAoxrR7MJiM5FfNxziJBnuRGA0GrrnqNHJyYg/XhkSJjj43bd1OWnvCQau924Hb48FkNGGzWeMcGIaCuWlcfs5KUu2H77qTJClqc/V4PFpHnLrBHi5oRWZaLl+AEwrmUlWYy8KsQ3VCWZZpbm6mp6eH0tLSaT8uirUnTCQwqPp5mZmZI9Qshj9eIBDQWt5dLhd+v5+EhISo1zVW0Iq17skMYrIclloaGBigoqICu92OJEk89dRTPPfcczzyyCOsWbNmVmZNsxw9QI2FqmgeDAb55JNPWLVqFRDehJqbm+ns7KSoqEhLhSMbIARBwO/309jYSCAQoKSkRKsLfPpOLS9v2jbGYysEfEH8vtDQn8FxDeOOF3OShaIvLEGWJUKhQz5FKEq4JiNJWCwJ2Gwj9eQUWcEfCB8J+vxDATYUPoZMyU0jc+GcWA85KaSlWrnmqtOw2Q7fiahdTLgHScmcx4BXpK3HSWu3k17H4NgeVYAtycKl/7GckoLxPZ/hXkVq0FI31tHkcQKiSPuAm5YBJy5fgJSkBIxiEHGgh8K8PObPn39UyuCIosi+ffuOWD9veNDy+XxYLJaooJWUNDmza7FwOBzs3buXefPmMX9+2DG4oaGBDRs2UFlZyd13343NNrEOTR09QI2JGqAUReGDDz7gpJNOoq2tjQMHDpCbm0thYSGqG27kcZ4oijQ3N9Pf38/ChQuZM+eQyGZrXTvP3/UXJHH8wUaS5HCw8obw+4IEfEFEceKtzsPJXVZE8txDV+PqNLzBIAy12ktIktruHmGsZzIy/P0kSzL+QDi7WnxGKQ5PEKfryHXnYlGQn8H6K04eWkc0sizT1tZGa2trVOE9En8wRHuvWzsabO120jeacSOwsiyXc08uJ9k68fZ8NWipmdbg4KDmChuZaalr9fv91NfXExQlUnJysVgSSDAZMRsN2BIsE26umG5Uxe6CggLy8iZ3SBzCFyLqa6pq5UVaxKsXA0fyuJIk0djYiMfjoaKiAqvViiiK/M///A9/+ctf+NWvfsUpp5wyic/quEQPUGOhWm4Amq1yenq6Nsk+PDCpUiytra0UFBSQm5sbdYW759/1vPLrvxOKJc46QcSQNBSsQtqfkjSxoJWYYmX+qjJttkWW5ShRWhVFCbdmh4Y8iiQxrEpuNqnGeuYotYJFi+dxyXVr8HqDtLc7aO9w0t4+QHuHA88R1NUiKS3J4aILqjBHHCeqTSvqEdJ4rAq8/hDtveFg1dbjoq3byYD7kKGj2WzklGWFnLJ8AfakyZkji9R0c7lcmpU5hANUUVFRzE09IIqExLBJomGoacZoEOJutJgOxtLPm0oiLeLV11UVeFUDV6yTgVj09/dTX19PXl4e+flhv6w9e/awceNGTjvtNO66666hmqbOEaIHqLFQFIXe3l7q6+txOBx84QtfICkpSWsZj7SE6OnpoampiaysrBGGhn3t/bz3/9Xw6dt7pmXNoaAUdTwY8Ifi8IkCFIWsJfMxJJmw2qxYLPFvIsqQhbl6PKi6yqpZ1lf+czWrTq8Y0QTicvtpb3fQ0eGgrd1BR4cTn/9wpoujs7Aoi0svXoUsizQ0NCBJEqWlpVitk6O8PegLhpsweg4FLo8vwAnF86guz6cod/IszOHQEZLdbsdqteLxeLTNNTLTipURRM5jTUVzQ7wciX7eVDLaxUBky3tkrVAURU0JpqKigqSkJEKhEI888ghbt27l17/+9Uw3FRxr6AFqLERRZOfOnSxcuJDPP/+cE088MapALAZFejp72bunDkICGSmZ4ZZYg4DP48fR5eRgbRut9e0z+sooikIwIEZlWkFfKGpJshQOuvasFBasKudIdfMgPA+lZlmgcPrFpWRmh7vb1M01lhL1wICX9g5H+DYUtNSa1uEfEBISFCpXpFNZuVib0J9K3N6AdjTo8QVJsSWQn51GUW46pgmaywWDQW1Qtby8fESADYVC2saqbq7qMdbhai/TrfI91fp5k40atNTApaqSm81m3G43eXl55OXlkZiYyCeffMLGjRs599xzuf3226fMgXfbtm1s3LgRSZK45ppruPXWW6N+fvDgQa688kocDgeSJHHfffdx3nnnTclaphk9QMWD3x+2Qd+1axcmk4m0tDRSU1MxGAw0NTUhSRIlJSXY7XbEkEj3gV7a93XR3thJe0Mnve39s/JVkWWFoD/EoMeH2zmIGJJRJEAQKKguxZo2ecrtKvPmZ3DRtafi83txOp24XC6CwWCULE5KSsoIAVJZVujr82gBq73DQWenC1E6VMcLDtmVh9uR7Zz5pQqqqxZgNE7vMZeiKLgGA3QPeLCYjNrgcao9CdMYa4nMNhYuXEh2dnbcwSNW7UU11ztcl9tUBC1Jkti/fz/9/f1Tqp831YRCIWprawkEAmRkZNDT08P111+PLMu4XC6uu+46vv71r7NkyZIpCVDqCcA//vEP8vPzWbVqFS+++CKLFy/W7vPtb3+blStXcv3117Nnzx7OO++8w2ruHUXoYrFj0dnZyZYtW6isrGTJkiX4/X5aW1u1wJSYmEhGRgZut1sbRMwtziG3OAfOWQ5AwBugo6mLtoYuOvZ10tbYibvPM8PPLHwkF5ICGC0K+UXZmEwmJEkm6A+RJAcpX1FAZ0s/jr74FDTioeNgP6/96UMuuPpUze5BbV93Op309vZqr63VatUyreTkZLKywrflJ4SlhiRJpqfHTdP+Lj79tIH+AQMWSyoIAiFRYtvfP+Pjj1s444xySorj3+iPFEEQSLUnRrWjK4qCxxdEEMAgGDAawoOwkXNVbrebvXv3kpqayqpVq8adbVgsFubMmROVNUYGrY6ODq3LbaygFblu9TnFO2Ok1mjmzZs3bfp5U4GqBhN5oTAwMIDdbudrX/saa9as4ZNPPuGXv/wlp5xyCt/61rcmfQ07duyguLiYhQsXArBu3To2b94cFaDUMRYAp9OpaSQeLxzXGVRXVxdPP/00u3btoq6uDkmS8Hq9XHfddVx88cVkZWVpxwFOp1O7ao08wopVMHX3ew5lWY3hPwPeyWkWGIuwTpqPYHDI22gUiaX/58bzWHJKGV6Pn46D/XQc6KP9QB8dB/sZdPuPaA0rTynmy5euOuzGqFo9OJ1OzZ9oeN2lubmZgYEBSkpKSE9PJxSS6Opy0dE5lGm1O+jp9TB3bgrVVQtYuiSPhITZcc2lKArikO39vqZ9eNweysvLpjzbUFuz1Zs6TxQZtGL6jo1hRxEKhcJdhsEg5eXlM66fN1GCwSB79+5FEATKysqwWCz4fD7uuecedu7cyeOPPx4VIKaSv/zlL2zbto2nnnoKgOeff57t27ezadMm7T4dHR2cffbZDAwMMDg4yOuvv05VVdW0rG+K0Y/44uWdd95h48aNnHfeeaxYsYKPP/6YmpoaOjs7WbhwIVVVVVRVVWnyRm63WzvCCgQCYx5hKYpCf6eD9oZOOvZ10dbYSWdT94Ra0UcjPOwYxOvzah5Th7siTpmTzLX/979IsI6sEbkdXi1YdRzso/NgP37f+DoTF1fO5/wrTsYU5xCvaqrndDrp6urC6XRisVjIzMzULghiDcAGgyLtHU46Ohz09nqw2SzMnZvKooVZJCbOXGu2oih0dXWxf/9+CgsLDysrM9XZnyo3FDkEm5iYOGIINhaKotDZ2Ulzc/OobfxHA5HPQ23mUBSF999/n5tvvpkrrriCDRs2TGsdLZ4A9fDDD6MoCj/4wQ94//33ufrqq/nss8+O2sw1Aj1AxUtzczNWqzXKhRTCm2ZDQwM7duxgx44d7Nq1C5/Px+LFi6mqqqK6upqlS5ciy7IWsFwuV7gZIUIOJzk5ecQbShIlelr6aG/spK2hk/bGTnpa+yb0CodCIW3OJtYmPhonrFnM2u+eM+b9FEWhv9tNx8Fw0Go/0EdX6wBi6PABNn/hHNau/wJpmfHVu1SzR6vVyqJFizAajSNUG9ROLDVoxepw8/mCdHW5MBgEEhLMWCxG7PYETex2qhkcHGTv3r1YrVaKi4tHXLBEMlmqDeMhUrkhUm4oMTEx6kJLlmVqa2tJTEykpKRk1Ocx0xp5Y+H3+6mtrSUhIUF7Hh6Ph5/+9KfU1tbyxBNPUFJSMu3rev/997nrrrt47bXXAPj5z38OwG233abdZ8mSJWzbtk2zyli4cCEffPDBiL3qKEQPUFNBMBjk008/Zfv27ezYsYPdu3djNptZuXIllZWVVFdXs2jRIvx+vxa01BpW5MYaay4j6A/Ssa+b9n2HjgadPa5R1yJJEoODXhQl9jxTPFz0w69Svrp43P9OEiV6Op10HOjXAldPu2NEu7slwcQZX1vBii8Uj9rQEAqF2LdvHx6Ph9LS0sMeg0W2D6vHrmazecSxa6ygpShKeJbIEB4fMJkMk7qxRjYPlJWVkZoaW6JpPEyHE6z6OGqm5XQ66enpwe/3k5KSQmZmpvbeHc1CIxYzHbQi5xZLSkrIzMxEURTeeecdbrvtNq699lquu+66GctGRFGktLSUN954g7y8PFatWsULL7zAkiVLtPuce+65XHrppXzjG9+gtraWM888k7a2thl/bScBPUBNB4qi4Ha72bVrF9u3b6empoaGhgbmzJmjHQ1WV1eTnZ0dtbEODg5GbaypqakxawODTm+4lrWvi/ahTMvr9sVVZ4qHBGsC37jnUrLyx2mdEYNQSKSrdeBQ0DrQp9nVZ2Ync+p5yyhfXqApoEd2tS1YsICcnJwJPQ+1WUC9IIisu6ivb6x2dzWYRjoZT/R1VBXHIwc8JxM1S5lqawrVPj4zM5MFCxZENWKoXZlJSUlRmdZoQWu4i+1UrHc0fD4ftbW1WhZrMplwuVz8+Mc/pqWlhSeeeIIFCxZMy1oOx9atW7nxxhuRJImrrrqK22+/nTvuuIPq6mrWrl3Lnj17+Na3vqUJFT/wwAOcffbZM73syUAPUDOFet69Y8cOLWipun5qwKqsrCQxMTGqnuX3+7UPv7qxDjc07OjoYM/HtRj8ZkSXTMe+LjqauhGDE3MEBkjOtPPNu9eRMmfy1Zj93iCdLYeyLLfDS2FpDgWl6fQ5O0lPT6eoqGhSz/6HH2E5nU5tY43MtGLVClUig8HhNlWfz0ddXR1Go5HS0tJpVVCYzKO1SP28ioqKUTXmIn2f1Ntwh91Yr636b2HqM8GWlhba29spKysjPT0dRVH4xz/+wR133MHGjRv55je/eSzUcI529AA1m5BlmcbGRu1oMLKepR4NqnYfasByOp2aE6/FYqG/v5+0tDQWLVoUddUqSTK9rX1RXYPdB3tR4lGXGCIzN53Lbr+AtOyp7zL79OPP6WoZYF5OHpnZaSRaLWRmp2C2TF2NaLSN1WazRdUKY8k+qURmBIqicODAAbq6uigtLSUjY+psR6YaVT9v/vz55ObmjjuARDrsqqcE6msb2YhxuFrcZDA4OEhtbS2pqaksXLgQo9FIf38/t912G06nk8cee+yoMOk7TtAD1Gwnsp5VU1PD7t27MZlMUfUsg8HAK6+8wsknn4zNZtMGi9UPvepJNHxTCQVCdO7vCTdhNHbS0djJQJfzsOuxpiRxyc1ryS+b/FmLSF+gRYsWRQnsKoqCxxXWwTOZjBiMBoxGQ9wdgBMlst1dvcmyHNXgYrfbR2j89fX1UV9fT05ODoWFhUft1XggEGDv3r0YDIZJz/4ig5Z6E0VRuyBQ59/iaSAZK2Cq762uri7Ky8tJTU1FURS2bNnC3Xffza233spll1121P6ejlH0AHW0EVnP+uc//8kf//hHenp6WLFiBcuXL6eqqopVq1aRnZ2ttWSrki2qOKZ6hBWrUcDr9oXb3IdqWe2NnXhdvqj7CAaBk9dWc9rFJ01aRtPX10dDQwPZ2dkUFhbGJeqqKAqyJA+5BIefx3TUL9R290jVBgg761qtVvr7+xEEYcxZoOk4zpooiqLQ2tqqNQ/MmTNn2o7fIi8I3G63dkIQqUY+nuNe1Ugw0nOqp6eHH/3oRyiKwqZNm5g7d+6UPSedCaMHqKMVSZI4/fTTufTSS7n22mvp6+vTWt1ramro6OiIqmetXLmSpKSkqCYMddYlMmgNL2YrioKr132oCWPoiDAUCJGalcIpF5zI8jWLJ2zr7vP5qK+vRxAESktLJ0UFeqqbBGKh1mc6Ozux2WyIohglPJqamhqz3X02tl97PB7tGExt5VeZifXKsjwi01JV9iMzreFBS5Zl9u/fT19fHxUVFSQnJ6MoCi+99BIPPvggd9xxBxdddNGse/11NPQAdTQjiuKoV5Kx6ller3fEfBYQ1SggiqImMaTWXIZnM7Is09c+oHUMOnvd5BRlseSUcubkx6fmrRo+9vb2ak7EU8lUBi21qy0jIyPK0kMUxahNVe3KHCuLHW39U72RHol+3nRfFMiyPCLTijx6NRgMtLa2kpOTo5k6dnZ2ctNNN2G32/nFL34xpULCYwm8Avz5z3/mrrvuQhAEli9fzgsvvDBl6zlK0QPU8UQwGGT37t1R81kmk4kVK1Zo9aySkpKoWRe32x22g4/IBGLalQdFupp7CPqC2FKtJFgTsKVZRxwBKopCd3c3TU1NWrv1TJz7T8bwaygUorGxEa/XS3l5eVzOqcNbsn0+X5TMkDpKMNp6p2rjj9TPKygoOKLfSaxgNR0BVhVwbWpqwu12Y7FY+PDDD3nnnXdIT0/n/fff5+c///mUZ03xCLw2NDRwySWX8Oabb5Kenk53d/exMFg72egB6nhm+HzWzp07NXM/dT5LrWcNDg5q9SxVrSEyE4glm6SaMhpMRgwGAbfbTWNjI4mJiRQXF0+ZPcFEiTdoqa38Bw4cOKLZLBX1giBSsSGeOaLJQLX1mGr9vOkY1B0YGKCuro7c3FwKCgoQBIF9+/Zxyy23IIoiubm51NbWIssyjz/++JTp1cWj/nDzzTdTWlrKNddcMyVrOEbQ1cyPZwRBICUlhTPOOIMzzjgDOKQPp85nPfPMM7S3t4+Yz7JarVo9q7OzU8sEIoeKLQnhTTWcaTThcrlYtHARycnho6NQUMRgECZcv5psRgtGkXg8Hurq6rDb7VRXV09KW3RiYiKJiYkj1N1dLhf9/f00NzdHtburt7EaBQ6XdUXqzo3X1mMijLaG4Wrpo933cIiiSGNjI4ODgyxfvlwzFH3mmWd48sknefDBBzn77LO1/zcQmFpR5ra2Nk12CCA/P5/t27dH3ae+vh6AU045BUmSuOuuu/jyl788pes6VjmuAlQ8Z8fHMoIgkJOTw9q1a1m7di0QXc/6+9//zr333htVz6qqqmLlypVAuJ7lcDg4ePAgwWAQg8GA3+8nNzeXFStWxPB5krVMS8VgMk67h9NoqJuaKIo0NTXhdDqjpJam4uhKtW2xWq3k5ORoj6PWXLq7u2lsbIyquaiNApH1QnXTHx5kvV4vdXV1JCYmTlqQnQjDX7fh643ndVW7PwsKCigrK0MQBJqbm/ne975HWVkZ7733HsnJ0cPl0zkoPRqqO+/bb79Na2srX/ziF9m9ezdpaWkzvbSjjuMmQEmSxHe/+92os+O1a9dOm7T+bEWdgSktLeW//uu/gOh61rPPPsunn34aNZ9lMpn461//yh133EFubi6Dg4N89NFHKIqC3W7XMi273Y45IXqDlEQpOmgJAiazcUa6rSJrZgUFBZSUlMSVaU1F0LLb7djtds3vJ7Ldvb29PardXQ1adrtdqyfJssyBAwfo7OykvLyc9PT0qPXPhm62eNcQCoU0x+EVK1aQmJiIJEn85je/4fnnn+fhhx9mzZo1M/Kc8vLyaGlp0b5ubW0dMfybn5/P6tWrMZvNFBUVUVpaSkNDA6tWrZru5R71HDc1qHjOjnVio9az3njjDe699146Ozs1a+zIelZOTo62qTqdzqh6lno0GKueJYZEhr8Np1JVAsKZxt69ezWF6/HUgWZCgRzCF1nD1d0NBgMJCQm4XC6ysrIoKSkZdc5sNs9mqaiqFpH1v/r6ejZu3EhlZSX33HMPVqt1xtYXj8Drtm3bePHFF3nuuefo7e1l5cqVfPzxx2RmHrne5TGEXoOKJJ6zY53YqPWszZs3c+utt3LBBRcARNWznn32WTo6OliwYEFUPctms2l6g93d3RG27aMLuUK4hhXJZNWzIlvgy8rKJnTsMpZD7Vj3myhGo5G0tDRtzaIoUl9fj8vlYu7cufj9fnbs2KG1u6s39aJgrPXMZKYVDAapq6tDURSqqqqwWCyIosimTZt46aWX+NWvfsUpp5wyI2uLxGQysWnTJs455xxN4HXJkiVRAq/nnHMOf//731m8eDFGo5EHH3xQD04T5LjJoOIxB9M5MmRZZt++fVqr+86dOxkcHIyazzrhhBOA6Pms8eDNJgAAEpdJREFUYDAYZQEfq0lAUZQR/lOqJFK89Pb20tjYOCnt1vEwlQFLtSyPpZ83Vrv7aAaFM6E6HnnMumjRIq2ZZM+ePWzYsIHTTz+dO++8c1KGvHVmFXoGFUk8Z8c6R4bBYKCkpISSkhKuuOIKILqe9dxzz/Hpp59iNBpZuXIlK1eupLq6muXLl2vq45FNApH1luTk5BHHfpIkj8i0YtWz/H4/dXV1CIKg1TSmg3gyrfEGAvW5GAwGLdMYjsViYc6cOVHDqpHt7q2trXG3u0/lcaaqBWgymbSGjmAwyCOPPMKrr77Kr3/9a6qrqyflsXSOTo6bDCqes2OdqUdRFDweT5R/Vn19PRkZGSPqWZHzWW63G4PBEFXPiiUvJImStqnKskxrSyvdvd2aYd1sJJ5MS9XPa2tro7i4+IiVElSDwkgn6Hja3ScjYEXOmqlagACffPIJGzdu5LzzzuP//J//M+tm6XQmFX1QdzixzMF0Zp7I+SxVb7C9vZ3CwsKoepbdbh/hpmuxWEbIC0F4sLO+vp6srCzy8/IRBEOUKeFsmc+Kh8Pp500mY6m7x2p3H/7vI4kVuHw+H3v37tVs5E0mE4FAgPvvv593332Xxx9/XDsG1jmm0QPUbKOlpYX169fT1dWFIAh8+9vfZuPGjfT393PppZfS3NzMggUL+POf/xzVJnw8MryetWvXLgYHB6moqIiqZwmCMMJNV5IkDAYDRUVFZGVlxTQmlEQpyu5DMIyvnjUdROrnVVRUYLfbo9c8TeruwzNZGL3dfTQiM8BI/6yamhpuuukmLr74Yn7wgx/M2NyWzrSjB6jZRkdHBx0dHVRWVuJ2u6mqquJvf/sbzz77LBkZGdx6663cd999DAwMcP/998/0cmcdoVAoSm/w008/xWAwsHLlSlasWEFTUxO9vb3cdtttmsW3y+VCkqQoj6fk5OQRG6pq7xG58RuMhhnralOHVCOlfWIxE513o7W7Rx4NRnqUeb1eamtrsdvtFBcXYzQa8Xq93HvvvezatYvHH3+cioqKaX0OOjOOHqBmO1/72te44YYbuOGGG3j77beZN28eHR0drFmzhrq6uple3qxHrWe9+OKL/PznPyczMxNJkkhNTaWqqorKykqtnuXz+aKyAEEQSE5O1o4GY5k+SpLM8AGtqT4aDAaD1NfXEwqFjlg/bzqDVyx1d5PJhCAI+P1+ioqKmDdvHoIg8O9//5ubb76Z9evXs2HDhik7slSJV0HmpZde4qKLLqKmpkZvzph69AA1m2lubuaLX/win332GfPnz8fhcADhTSU9PV37Wufw+Hw+Lr/8cv77v/+bpUuXam3L6nxWTU0NbW1tI+azkpOTo+pZql1GpN5gQkLC2EFLECblaDCycWCy9fNmon3c4/Hw+eefk5SUhM1mo6amhnvuuQeLxYLP5+OWW25h7dq1U95JG4/6OISND7/yla8QDAbZtGnTpAQop9NJV1cXpaWlR/x/HYPobeazFY/Hw4UXXsgvfvGLEb488QxU6hwiKSmJv/71r9rXgiAwd+5cvvrVr/LVr34ViK5nvf7669x33314PJ6oetaKFSswGo1altXe3o7f79dasdXAFatGIkly1NcGw/h+h6qqRVJS0pTo503n+0mVXOrp6aGiooKUlBQURaGlpQW73c5ll11GRUUFu3bt4pprrmH9+vVcdtllU7aeHTt2UFxczMKFCwFYt24dmzdvHhGgfvKTn3DLLbfw4IMPTsrj7ty5kxdeeIHy8nJKS0tnjdzU0YYeoKaZUCjEhRdeyOWXX64pMsydO5eOjg7tiE/3jplcYs1nRdazfve730XVs1T/rGXLlhEKhXA6nfT19dHU1KRZlKsBK1ZXm6IocQUtdTPv7u6esKrFbEK1X8/KyqK6uhqDwYDT6eQnP/kJra2tvPzyyxQWFgLh4+3pIB4FmQ8//JCWlha+8pWvHHGA6u3t5etf/zpz5sxh165drFu3Dpjd8lKzGT1ATSOKonD11VdTUVHBTTfdpH1/7dq1PPfcc9x6660899xz0/bhPZ4xm81UVlZSWVnJ9ddfHzWftWPHDu6//37q6upIT08fMZ+l2mUMF3FVnYptNtuIYz9FUZDlQ0HL4XDQ0NBAVlYWq1atmhFjx8lClmWampoYGBhg8eLF2O12FEXhtdde48477+TGG2/kG9/4xqx8jrIsc9NNN/Hss89Oyv+3b98+zjrrLO68806+853vsG/fPu1xZuPzn+3oNahp5F//+hennXYay5Yt096s9957L6tXr+aSSy7h4MGDFBYW8uc//3nKbdJ1xmasepbaiJGSkoLH49GOB9UGgVj275H+RmVlZSOET4+2TczhcFBXV6fZrwuCQH9/P7feeisul4vHHntsRhVbxhKJdjqdLFq0CLvdDkBnZycZGRm8/PLLE6pD3XvvvezevZsXX3wRn89HZWUlL730knakqB/1aehNEjrxI0kS1dXV5OXlsWXLFvbv38+6devo6+ujqqqK559/Xp/s51C2EKk3OLyedcIJJ2A0GqP0Bv1+P4IgEAgEyMnJYcGCBYe1f49kNm5okiTR2NioPXer1YqiKGzZsoW7776bW2+9lcsuu2zGA+54FWTWrFnDQw89NOEmiY8++ognn3ySDRs2UFFRwQknnEBiYiKnnnoq99xzz5S5Gh+F6E0SOvHzy1/+koqKClwuFwC33HIL3//+91m3bh3XXXcdTz/9NNdff/0Mr3LmMRgMFBcXU1xczOWXXw6MrGft3r1b0/2rqqoiLy+Pxx9/nJtuuoni4mK8Xi+7d+/WpIUiRXJjtVzPtqDV399PfX09eXl5lJaWIggCPT09/PCHP0QQBF5//XXmzp07Y+uLJB718cnEarWSlJTEP//5TwDOOussKisr+dKXvqQHpwmgZ1A6tLa2cuWVV3L77bfz8MMP88orr5CVlUVnZycmk2nEMYnO4VHrWTt27ODRRx/lX//6F2VlZZhMJu1osLq6mtzcXK2e5XQ6cbvdKIqiqTSo9axYWchUW3vEQnWK9fl8VFRUaPbrL730Eg899BB33nknF1544azM+KaTP/3pT7z22mu88sorPPTQQ1x55ZWAfrw3DD2D0omPG2+8kQceeEAr+Pf19ZGWlqYJhebn59PW1jaTSzyqUIeAP/zwQyoqKnjhhRdISkqiu7ubmpoatm/fzvPPP09rayuFhYVUV1dH1bNUaaEDBw5EmT6qmVYs00eI3gAnezPs7e2loaGBwsJCysvLEQSBzs5Ovv/975OSksJbb711xAK2xwqXXnopX/3qV3nggQe010QPThNDD1DHOVu2bCE7O5uqqirefvvtmV7OMYV65KUyd+5czj//fM4//3wgup71xhtvcP/99+PxeCgvL9eyrOXLl2M0GrWh4s7OTs3fKXKo2GKxRD2WIAiTMqAbCoWoq6tDkiQqKytJSEhAlmX+8Ic/sGnTJu655x7OP/98ffMdhtVqxWq1IkkSRuNICxid+NAD1HHOe++9x8svv8zWrVs1z6CNGzficDgQRRGTyaR7Z02QsTal0epZn332Gdu3b+cPf/gDP/rRjzAYDFo9q7q6mqVLl2rSQg6Hg4MHDxIMBjWrDFVvcLhVBhy6ko/H/l01RYxUtmhtbWXDhg3k5+fz7rvvHvWzW1PNVMs4HevoNSgdjbfffpuHHnqILVu2cPHFF3PhhRdqTRInnHAC3/nOd2Z6iccdw+ezampqqKurIy0tTQtYaj1ruL+TavqoZlrxqI5DWA9w7969CIJAWVkZFosFWZZ59tln+c1vfsMDDzzA2WefrWcFOkeC3mauMz4iA1RTUxPr1q2jv7+flStX8vvf/z5mW7TO9KMoCj09PVHzWZH1rMrKSqqqqkhNTcXj8WhNGJH1LPUWafqoKAqdnZ00NzdTXFxMVlYWAPv37+d73/seFRUV3HfffSQnJ8/k09c5NtADlM7RjcPh4JprruGzzz5DEAR++9vfUlZWpntnxSCynlVTU8POnTtxu90j6lkmkwm3261lWl6vl4SEBKxWKw6HA5vNRnl5OWazGUmSePLJJ/n973/PI488wumnnz7lWdNYyuMPP/wwTz31FCaTiaysLH77299q8kk6RxV6gNI5urnyyis57bTTuOaaawgGg5qHkO6dFR+R9ayamho+/vhjDAYDy5cv14JWSUkJTz/9NMXFxeTk5BAMBrnzzjsJhUL09fWxdOlSHn300WmZa4pHefytt95i9erVWK1WHnvsMd5++23+9Kc/TfnadCYdPUDpHL04nU7NhDDyqr2srEz3zpogkfWsmpoa3nrrLT744ANKSkpYvXo1q1evZvny5WzevJmtW7dy5pln4nQ62bVrF2azmTfffHNKM6ixZImG89FHH3HDDTfw3nvvTdmadKYMfQ5K5+hl//79ZGVl8c1vfpNPPvmEqqoqfvnLX9LV1cW8efMAyMnJoaura4ZXevSgzmetWbMGURT54x//yObNmykrK9PqWffffz8VFRW88cYbJCYmav9WkqQpP96LR3k8kqeffppzzz13StekM7PoAUpnViKKIh9++CGPPvooq1evZuPGjdx3331R99G9sybOiSeeyL/+9S9Nfkedz/rZz34W8/6zrV3697//PTt37uSdd96Z6aXoTCFHl3SyznFDfn4++fn5rF69GoCLLrqIDz/8UPPOAnTvrCNAVaSYTeTl5dHS0qJ9Pdr83euvv84999zDyy+/rHeWHuPoAUpnVpKTk0NBQYFWX3rjjTdYvHix5p0F6N5ZxxirVq2ioaGB/fv3EwwG+eMf/zhCzPWjjz7i2muv5eWXX9YvTo4D9CYJnVnLxx9/rHXwLVy4kGeeeQZZlnXvrGOYrVu3cuONN2rK47fffnuU8vh//Md/sHv3bq0OOX/+fF5++eUZXrXOBNC7+HSmhpqaGq6++mp27NiBJEmceOKJ/OlPf2Lp0qUzvbRp4ZFHHuGpp55CEASWLVvGM888Q0dHh+6fpaMTP3qA0pk6fvzjH+P3+/H5fOTn54/aCnys0dbWxqmnnsqePXtISkrikksu4bzzzmPr1q1ccMEFmjTU8uXLdf8sHZ3RiStA6TUonQlxxx138I9//IOdO3dy8803z/RyphVRFPH5fIiiiNfrZd68ebz55ptcdNFFQHjA+G9/+9sMr1JH5+hHD1A6E6Kvrw+Px4Pb7cbv98/0cqaNvLw8fvjDHzJ//nzmzZtHamoqVVVVun+Wjs4UoAconQlx7bXX8rOf/YzLL7+cW265ZaaXM20MDAywefNm9u/fT3t7O4ODg2zbtm2ml6Wjc0yiD+rqjJvf/e53mM1m/vM//xNJkvjCF77Am2++yZe+9KWZXtqU8/rrr1NUVKQpfV9wwQW89957un+Wjs4UoGdQOuNm/fr1vPTSS0BYYWD79u3HRXCCcFvzBx98gNfrRVEUbT7rjDPO4C9/+Qtw/Mxnbdu2jbKyMoqLi0eofAAEAgEuvfRSiouLWb16Nc3NzdO/SJ2jGj1A6eiMg9WrV3PRRRdRWVnJsmXLkGWZb3/729x///08/PDDFBcX09fXx9VXXz3TS51SJEniu9/9Lq+++ip79uzhxRdfZM+ePVH3efrpp0lPT6exsZHvf//7x9VRsM7koLeZ6+jojJt4lMfPOecc7rrrLk4++WREUSQnJ4eenh5dP1EH9DZzHZ3jg6uuuors7OyoQen+/n7OOussSkpKOOussxgYGADClhsbNmyguLiYE044gQ8//HBCjxlLeXx452LkfUwmE6mpqfT19U3o8XSOT/QApaNzlPONb3xjRCfhfffdx5lnnklDQwNnnnmmViN69dVXaWhooKGhgSeffFIfJtaZ1Yz3iE9HR2cWIgjCAmCLoihLh76uA9YoitIhCMI84G1FUcoEQXhi6O8vDr/fOB/vZOAuRVHOGfr6NgBFUX4ecZ/Xhu7zviAIJqATyFL0TUcnTvQMSkfn2GRuRNDpBFTP9jygJeJ+rUPfGy81QIkgCEWCIFiAdcBw1daXgSuH/n4R8KYenHTGgz4HpaNzjKMoiiIIwqQGBkVRREEQbgBeA4zAbxVF+VwQhP8GdiqK8jLwNPC8IAiNQD/hIKajEzd6gNLROTbpEgRhXsQRX/fQ99uAgoj75Q99b9woirIV2Drse3dE/N0PXDyR/1tHB/QjPh2dY5XI47Urgc0R318vhDkJcI63/qSjM13oTRI6Okc5giC8CKwB5gBdwJ3A34A/A/OBA8AliqL0C+EhpE3AlwEv8E1FUXbOxLp1dMZCD1A6Ojo6OrMS/YhPR0dHR2dWogcoHR0dHZ1ZiR6gdHR0dHRmJXqA0tHR0dGZlfz/ACopcEzbTgUAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# barycenter interpolation\n",
- "\n",
- "n_weight = 11\n",
- "weight_list = np.linspace(0, 1, n_weight)\n",
- "\n",
- "\n",
- "B_l2 = np.zeros((n, n_weight))\n",
- "\n",
- "B_wass = np.copy(B_l2)\n",
- "\n",
- "for i in range(0, n_weight):\n",
- " weight = weight_list[i]\n",
- " weights = np.array([1 - weight, weight])\n",
- " B_l2[:, i] = A.dot(weights)\n",
- " B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n",
- "\n",
- "\n",
- "# plot interpolation\n",
- "\n",
- "pl.figure(3)\n",
- "\n",
- "cmap = pl.cm.get_cmap('viridis')\n",
- "verts = []\n",
- "zs = weight_list\n",
- "for i, z in enumerate(zs):\n",
- " ys = B_l2[:, i]\n",
- " verts.append(list(zip(x, ys)))\n",
- "\n",
- "ax = pl.gcf().gca(projection='3d')\n",
- "\n",
- "poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\n",
- "poly.set_alpha(0.7)\n",
- "ax.add_collection3d(poly, zs=zs, zdir='y')\n",
- "ax.set_xlabel('x')\n",
- "ax.set_xlim3d(0, n)\n",
- "ax.set_ylabel(r'$\\alpha$')\n",
- "ax.set_ylim3d(0, 1)\n",
- "ax.set_zlabel('')\n",
- "ax.set_zlim3d(0, B_l2.max() * 1.01)\n",
- "pl.title('Barycenter interpolation with l2')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.figure(4)\n",
- "cmap = pl.cm.get_cmap('viridis')\n",
- "verts = []\n",
- "zs = weight_list\n",
- "for i, z in enumerate(zs):\n",
- " ys = B_wass[:, i]\n",
- " verts.append(list(zip(x, ys)))\n",
- "\n",
- "ax = pl.gcf().gca(projection='3d')\n",
- "\n",
- "poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\n",
- "poly.set_alpha(0.7)\n",
- "ax.add_collection3d(poly, zs=zs, zdir='y')\n",
- "ax.set_xlabel('x')\n",
- "ax.set_xlim3d(0, n)\n",
- "ax.set_ylabel(r'$\\alpha$')\n",
- "ax.set_ylim3d(0, 1)\n",
- "ax.set_zlabel('')\n",
- "ax.set_zlim3d(0, B_l2.max() * 1.01)\n",
- "pl.title('Barycenter interpolation with Wasserstein')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_WDA.ipynb b/notebooks/plot_WDA.ipynb
deleted file mode 100644
index df46812..0000000
--- a/notebooks/plot_WDA.ipynb
+++ /dev/null
@@ -1,316 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Wasserstein Discriminant Analysis\n",
- "\n",
- "\n",
- "This example illustrate the use of WDA as proposed in [11].\n",
- "\n",
- "\n",
- "[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).\n",
- "Wasserstein Discriminant Analysis.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/rflamary/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
- " from ._conv import register_converters as _register_converters\n"
- ]
- }
- ],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "\n",
- "from ot.dr import wda, fda"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n",
- "\n",
- "n = 1000 # nb samples in source and target datasets\n",
- "nz = 0.2\n",
- "\n",
- "# generate circle dataset\n",
- "t = np.random.rand(n) * 2 * np.pi\n",
- "ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\n",
- "xs = np.concatenate(\n",
- " (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\n",
- "xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)\n",
- "\n",
- "t = np.random.rand(n) * 2 * np.pi\n",
- "yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\n",
- "xt = np.concatenate(\n",
- " (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\n",
- "xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)\n",
- "\n",
- "nbnoise = 8\n",
- "\n",
- "xs = np.hstack((xs, np.random.randn(n, nbnoise)))\n",
- "xt = np.hstack((xt, np.random.randn(n, nbnoise)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x252 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot samples\n",
- "pl.figure(1, figsize=(6.4, 3.5))\n",
- "\n",
- "pl.subplot(1, 2, 1)\n",
- "pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('Discriminant dimensions')\n",
- "\n",
- "pl.subplot(1, 2, 2)\n",
- "pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('Other dimensions')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute Fisher Discriminant Analysis\n",
- "------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Compute FDA\n",
- "p = 2\n",
- "\n",
- "Pfda, projfda = fda(xs, ys, p)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute Wasserstein Discriminant Analysis\n",
- "-----------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Compiling cost function...\n",
- "Computing gradient of cost function...\n",
- " iter\t\t cost val\t grad. norm\n",
- " 1\t+8.1744675052927340e-01\t5.18943290e-01\n",
- " 2\t+4.6574082332790778e-01\t1.98300827e-01\n",
- " 3\t+4.4912307637701449e-01\t1.38157890e-01\n",
- " 4\t+4.4030490938831912e-01\t7.00834944e-02\n",
- " 5\t+4.3780885707533013e-01\t4.35903168e-02\n",
- " 6\t+4.3753767094086027e-01\t5.81158060e-02\n",
- " 7\t+4.3658534767678403e-01\t4.44189462e-02\n",
- " 8\t+4.3516262357849916e-01\t4.15971448e-02\n",
- " 9\t+4.3332622549435446e-01\t7.82365182e-02\n",
- " 10\t+4.2847338855201011e-01\t5.28789263e-02\n",
- " 11\t+4.1510883118208680e-01\t6.83664317e-02\n",
- " 12\t+4.1332168544999542e-01\t1.01013343e-01\n",
- " 13\t+4.0818672134323475e-01\t5.07935089e-02\n",
- " 14\t+4.0502824759368472e-01\t9.56110665e-02\n",
- " 15\t+3.9786250825732905e-01\t6.68177888e-02\n",
- " 16\t+3.5518514892526853e-01\t2.01958719e-01\n",
- " 17\t+2.5048183658126072e-01\t1.82260477e-01\n",
- " 18\t+2.4055179583813954e-01\t1.48301002e-01\n",
- " 19\t+2.2127351995549377e-01\t1.77690944e-02\n",
- " 20\t+2.2106865089529223e-01\t6.44501056e-03\n",
- " 21\t+2.2105965534255226e-01\t5.35361879e-03\n",
- " 22\t+2.2104056962319110e-01\t3.63028924e-04\n",
- " 23\t+2.2104051220659457e-01\t2.27022248e-04\n",
- " 24\t+2.2104049071158929e-01\t1.43369832e-04\n",
- " 25\t+2.2104048094656506e-01\t7.87652127e-05\n",
- " 26\t+2.2104047690862835e-01\t1.39217014e-05\n",
- " 27\t+2.2104047689800282e-01\t1.33400288e-05\n",
- " 28\t+2.2104047685931880e-01\t1.09433285e-05\n",
- " 29\t+2.2104047677977950e-01\t3.27906935e-07\n",
- "Terminated - min grad norm reached after 29 iterations, 8.50 seconds.\n",
- "\n"
- ]
- }
- ],
- "source": [
- "#%% Compute WDA\n",
- "p = 2\n",
- "reg = 1e0\n",
- "k = 10\n",
- "maxiter = 100\n",
- "\n",
- "Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot 2D projections\n",
- "-------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot samples\n",
- "\n",
- "xsp = projfda(xs)\n",
- "xtp = projfda(xt)\n",
- "\n",
- "xspw = projwda(xs)\n",
- "xtpw = projwda(xt)\n",
- "\n",
- "pl.figure(2)\n",
- "\n",
- "pl.subplot(2, 2, 1)\n",
- "pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('Projected training samples FDA')\n",
- "\n",
- "pl.subplot(2, 2, 2)\n",
- "pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('Projected test samples FDA')\n",
- "\n",
- "pl.subplot(2, 2, 3)\n",
- "pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('Projected training samples WDA')\n",
- "\n",
- "pl.subplot(2, 2, 4)\n",
- "pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('Projected test samples WDA')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb
deleted file mode 100644
index bd73a99..0000000
--- a/notebooks/plot_barycenter_1D.ipynb
+++ /dev/null
@@ -1,308 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# 1D Wasserstein barycenter demo\n",
- "\n",
- "\n",
- "This example illustrates the computation of regularized Wassersyein Barycenter\n",
- "as proposed in [3].\n",
- "\n",
- "\n",
- "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).\n",
- "Iterative Bregman projections for regularized transportation problems\n",
- "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "# necessary for 3d plot even if not used\n",
- "from mpl_toolkits.mplot3d import Axes3D # noqa\n",
- "from matplotlib.collections import PolyCollection"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n",
- "\n",
- "n = 100 # nb bins\n",
- "\n",
- "# bin positions\n",
- "x = np.arange(n, dtype=np.float64)\n",
- "\n",
- "# Gaussian distributions\n",
- "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n",
- "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n",
- "\n",
- "# creating matrix A containing all distributions\n",
- "A = np.vstack((a1, a2)).T\n",
- "n_distributions = A.shape[1]\n",
- "\n",
- "# loss matrix + normalization\n",
- "M = ot.utils.dist0(n)\n",
- "M /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x216 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot the distributions\n",
- "\n",
- "pl.figure(1, figsize=(6.4, 3))\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycenter computation\n",
- "----------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% barycenter computation\n",
- "\n",
- "alpha = 0.2 # 0<=alpha<=1\n",
- "weights = np.array([1 - alpha, alpha])\n",
- "\n",
- "# l2bary\n",
- "bary_l2 = A.dot(weights)\n",
- "\n",
- "# wasserstein\n",
- "reg = 1e-3\n",
- "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n",
- "\n",
- "pl.figure(2)\n",
- "pl.clf()\n",
- "pl.subplot(2, 1, 1)\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "\n",
- "pl.subplot(2, 1, 2)\n",
- "pl.plot(x, bary_l2, 'r', label='l2')\n",
- "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n",
- "pl.legend()\n",
- "pl.title('Barycenters')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycentric interpolation\n",
- "-------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% barycenter interpolation\n",
- "\n",
- "n_alpha = 11\n",
- "alpha_list = np.linspace(0, 1, n_alpha)\n",
- "\n",
- "\n",
- "B_l2 = np.zeros((n, n_alpha))\n",
- "\n",
- "B_wass = np.copy(B_l2)\n",
- "\n",
- "for i in range(0, n_alpha):\n",
- " alpha = alpha_list[i]\n",
- " weights = np.array([1 - alpha, alpha])\n",
- " B_l2[:, i] = A.dot(weights)\n",
- " B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n",
- "\n",
- "#%% plot interpolation\n",
- "\n",
- "pl.figure(3)\n",
- "\n",
- "cmap = pl.cm.get_cmap('viridis')\n",
- "verts = []\n",
- "zs = alpha_list\n",
- "for i, z in enumerate(zs):\n",
- " ys = B_l2[:, i]\n",
- " verts.append(list(zip(x, ys)))\n",
- "\n",
- "ax = pl.gcf().gca(projection='3d')\n",
- "\n",
- "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n",
- "poly.set_alpha(0.7)\n",
- "ax.add_collection3d(poly, zs=zs, zdir='y')\n",
- "ax.set_xlabel('x')\n",
- "ax.set_xlim3d(0, n)\n",
- "ax.set_ylabel('$\\\\alpha$')\n",
- "ax.set_ylim3d(0, 1)\n",
- "ax.set_zlabel('')\n",
- "ax.set_zlim3d(0, B_l2.max() * 1.01)\n",
- "pl.title('Barycenter interpolation with l2')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.figure(4)\n",
- "cmap = pl.cm.get_cmap('viridis')\n",
- "verts = []\n",
- "zs = alpha_list\n",
- "for i, z in enumerate(zs):\n",
- " ys = B_wass[:, i]\n",
- " verts.append(list(zip(x, ys)))\n",
- "\n",
- "ax = pl.gcf().gca(projection='3d')\n",
- "\n",
- "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n",
- "poly.set_alpha(0.7)\n",
- "ax.add_collection3d(poly, zs=zs, zdir='y')\n",
- "ax.set_xlabel('x')\n",
- "ax.set_xlim3d(0, n)\n",
- "ax.set_ylabel('$\\\\alpha$')\n",
- "ax.set_ylim3d(0, 1)\n",
- "ax.set_zlabel('')\n",
- "ax.set_zlim3d(0, B_l2.max() * 1.01)\n",
- "pl.title('Barycenter interpolation with Wasserstein')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_barycenter_fgw.ipynb b/notebooks/plot_barycenter_fgw.ipynb
deleted file mode 100644
index 8da80a6..0000000
--- a/notebooks/plot_barycenter_fgw.ipynb
+++ /dev/null
@@ -1,312 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "=================================\n",
- "Plot graphs' barycenter using FGW\n",
- "=================================\n",
- "\n",
- "This example illustrates the computation barycenter of labeled graphs using FGW\n",
- "\n",
- "Requires networkx >=2\n",
- "\n",
- ".. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n",
- " and Courty Nicolas\n",
- " \"Optimal Transport for structured data with application on graphs\"\n",
- " International Conference on Machine Learning (ICML). 2019.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Titouan Vayer <titouan.vayer@irisa.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "#%% load libraries\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import networkx as nx\n",
- "import math\n",
- "from scipy.sparse.csgraph import shortest_path\n",
- "import matplotlib.colors as mcol\n",
- "from matplotlib import cm\n",
- "from ot.gromov import fgw_barycenters\n",
- "#%% Graph functions\n",
- "\n",
- "\n",
- "def find_thresh(C, inf=0.5, sup=3, step=10):\n",
- " \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n",
- " Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n",
- " The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n",
- " and the original matrix.\n",
- " Parameters\n",
- " ----------\n",
- " C : ndarray, shape (n_nodes,n_nodes)\n",
- " The structure matrix to threshold\n",
- " inf : float\n",
- " The beginning of the linesearch\n",
- " sup : float\n",
- " The end of the linesearch\n",
- " step : integer\n",
- " Number of thresholds tested\n",
- " \"\"\"\n",
- " dist = []\n",
- " search = np.linspace(inf, sup, step)\n",
- " for thresh in search:\n",
- " Cprime = sp_to_adjency(C, 0, thresh)\n",
- " SC = shortest_path(Cprime, method='D')\n",
- " SC[SC == float('inf')] = 100\n",
- " dist.append(np.linalg.norm(SC - C))\n",
- " return search[np.argmin(dist)], dist\n",
- "\n",
- "\n",
- "def sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n",
- " \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n",
- " All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n",
- " Parameters\n",
- " ----------\n",
- " C : ndarray, shape (n_nodes,n_nodes)\n",
- " The structure matrix to threshold\n",
- " threshinf : float\n",
- " The minimum value of distance from which the new value is set to 1\n",
- " threshsup : float\n",
- " The maximum value of distance from which the new value is set to 1\n",
- " Returns\n",
- " -------\n",
- " C : ndarray, shape (n_nodes,n_nodes)\n",
- " The threshold matrix. Each element is in {0,1}\n",
- " \"\"\"\n",
- " H = np.zeros_like(C)\n",
- " np.fill_diagonal(H, np.diagonal(C))\n",
- " C = C - H\n",
- " C = np.minimum(np.maximum(C, threshinf), threshsup)\n",
- " C[C == threshsup] = 0\n",
- " C[C != 0] = 1\n",
- "\n",
- " return C\n",
- "\n",
- "\n",
- "def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n",
- " \"\"\" Create a noisy circular graph\n",
- " \"\"\"\n",
- " g = nx.Graph()\n",
- " g.add_nodes_from(list(range(N)))\n",
- " for i in range(N):\n",
- " noise = float(np.random.normal(mu, sigma, 1))\n",
- " if with_noise:\n",
- " g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n",
- " else:\n",
- " g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n",
- " g.add_edge(i, i + 1)\n",
- " if structure_noise:\n",
- " randomint = np.random.randint(0, p)\n",
- " if randomint == 0:\n",
- " if i <= N - 3:\n",
- " g.add_edge(i, i + 2)\n",
- " if i == N - 2:\n",
- " g.add_edge(i, 0)\n",
- " if i == N - 1:\n",
- " g.add_edge(i, 1)\n",
- " g.add_edge(N, 0)\n",
- " noise = float(np.random.normal(mu, sigma, 1))\n",
- " if with_noise:\n",
- " g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n",
- " else:\n",
- " g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n",
- " return g\n",
- "\n",
- "\n",
- "def graph_colors(nx_graph, vmin=0, vmax=7):\n",
- " cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n",
- " cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n",
- " cpick.set_array([])\n",
- " val_map = {}\n",
- " for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n",
- " val_map[k] = cpick.to_rgba(v)\n",
- " colors = []\n",
- " for node in nx_graph.nodes():\n",
- " colors.append(val_map[node])\n",
- " return colors"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% circular dataset\n",
- "# We build a dataset of noisy circular graphs.\n",
- "# Noise is added on the structures by random connections and on the features by gaussian noise.\n",
- "\n",
- "\n",
- "np.random.seed(30)\n",
- "X0 = []\n",
- "for k in range(9):\n",
- " X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 576x720 with 9 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% Plot graphs\n",
- "\n",
- "plt.figure(figsize=(8, 10))\n",
- "for i in range(len(X0)):\n",
- " plt.subplot(3, 3, i + 1)\n",
- " g = X0[i]\n",
- " pos = nx.kamada_kawai_layout(g)\n",
- " nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\n",
- "plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycenter computation\n",
- "----------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph\n",
- "# Features distances are the euclidean distances\n",
- "Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\n",
- "ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\n",
- "Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\n",
- "lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\n",
- "sizebary = 15 # we choose a barycenter with 15 nodes\n",
- "\n",
- "A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot Barycenter\n",
- "-------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% Create the barycenter\n",
- "bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\n",
- "for i, v in enumerate(A.ravel()):\n",
- " bary.add_node(i, attr_name=v)\n",
- "\n",
- "#%%\n",
- "pos = nx.kamada_kawai_layout(bary)\n",
- "nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\n",
- "plt.suptitle('Barycenter', fontsize=20)\n",
- "plt.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_barycenter_lp_vs_entropic.ipynb b/notebooks/plot_barycenter_lp_vs_entropic.ipynb
deleted file mode 100644
index 9c8e83e..0000000
--- a/notebooks/plot_barycenter_lp_vs_entropic.ipynb
+++ /dev/null
@@ -1,497 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n",
- "\n",
- "\n",
- "This example illustrates the computation of regularized Wasserstein Barycenter\n",
- "as proposed in [3] and exact LP barycenters using standard LP solver.\n",
- "\n",
- "It reproduces approximately Figure 3.1 and 3.2 from the following paper:\n",
- "Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational\n",
- "Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n",
- "\n",
- "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).\n",
- "Iterative Bregman projections for regularized transportation problems\n",
- "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "# necessary for 3d plot even if not used\n",
- "from mpl_toolkits.mplot3d import Axes3D # noqa\n",
- "from matplotlib.collections import PolyCollection # noqa\n",
- "\n",
- "#import ot.lp.cvx as cvx"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Gaussian Data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Elapsed time : 0.010422945022583008 s\n",
- "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n",
- "1.0 1.0 1.0 - 1.0 1700.336700337 \n",
- "0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 \n",
- "0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093 \n",
- "0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897 \n",
- "0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358 \n",
- "0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432 \n",
- "7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717 \n",
- "5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203 \n",
- "1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519 \n",
- "6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383 \n",
- "2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321 \n",
- "1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827 \n",
- "3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923 \n",
- "1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379 \n",
- "5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414 \n",
- "1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752 \n",
- "5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713 \n",
- "3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261 \n",
- "1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485 \n",
- "5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135 \n",
- "1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958 \n",
- "6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 \n",
- "1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 \n",
- "Optimization terminated successfully.\n",
- "Elapsed time : 2.927520990371704 s\n"
- ]
- },
- {
- "data": {
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1RpNiiFpTUkG028XoAUlOhwLAMYOTEbHGTWpSjCCNtbD4j7D4cfDWQVQc9BsHx1wMdRVWgtz0DmAgYySc9WsYfiY4NK5Wqc5oUgxRq4srGDMwiWhPcNSAJ8ZGkZeZoDPbRAqfD9a+Ah89ANV7YeyFcNJt0Hc0uNxfPbahBrZ9BB/eD3+/1CoxnvUb6DfGkdCVOpLg+ERVXeJt9rGupNLxQfttjbc72xijK2aENW8DvD4b/vUDSBwA310Alz4H/cd9PSECxCTAmFlw01I4+0HYvQKePAU2/LPXQ1eqM5oUQ9DWAzXUNTUHXVKckJXCwdpGSsrrnA5F9ZSGavj7t2DDGzDjPrj+I8ie5t9rPdFw/E3wo1UwcBL84zpY/lTPxqtUF2lSDEH/XRkj+JIi6IoZYau2DOacBzs+hVl/hpNuBddRfITEp8PV/4QRZ8M7P4OFD4LWLqggoUkxBK0priC5TxS56XFOh/IVI/snEuNxabtiOKophWfOhgMb4fKXYOKV3TtfdBxc9hJMuBL+/RAsuCswcSrVTdrRJgStLq5gfFaKYytjdCTK7WLcoGQtKYYbbyO89h2oLLFKeDknBOa8bg/MegJiEuE/T0D6MJgyOzDnVuooaUkxxNQ2eNmyv5oJg51ZVLgz4wensH5PJU3NPqdDUYFgDMy/DYqWWAksUAmxhQic/VsYfha8e7tVNauUgzQphpj1uyvxGYJmJpu2JmSnUN/kY8t+XTEjLCz7P1g5B076GRxzSc9cw+WGi5+CtKFWibR8Z89cRyk/aFIMMWtKgmsmm7Za5mLVKtQwsH0RvHcHjDwXTru7Z68VmwxXvALGBy9fYfVyVcoBmhRDzOriCrLS+pCeEON0KO3KSutDWny0drYJdTUHYO53IWMEXPTk0fUy7ar0YdZ4x9LN8M5tPX89pdqhSTHErCmuDNpSIlgrZowfrJ1tQpox8NZPrJloLn3O6gjTW4adBiffZs2Ws/Ht3ruuUjZNiiHkQHU9uyvqgm7Qflvjs1LYeqCGmgav06Goo7H2Vdj8Dsy4B/qO6v3rn3SbNYn42z+xxkYq1Ys0KYaQNcXWIr7BnhQnZKVgDKzTRYdDT+VumH87ZB8P025yJgZPNFz4V6ivhLd/qgP7Va/SpBhC1hRX4HYJYwcG53CMFuO1s01oMgbm3Qy+Jrjgz+3PY9pb+o2FU++EjfNg/evOxaEijl9JUURmishmESkUkTva2R8jIq/a+5eKSG6b/dkiUiMi2nreDWtKKhjZL5E+0Q5+WPkhNT6anPQ47WwTalY8Z62JeNavrOERTjvhxzB4ijUVXPV+p6NREaLTpCgibuAJ4BxgDHCFiLRd82U2UG6MyQMeAx5us/9R4N3uhxu5fD7D6uKKoB2f2NYEe8UMFSJqy+DD+yD3JMgPklll3B644C/QdBg+uMfpaFSE8KekOBUoNMZsN8Y0Aq8As9ocMwuYYz+eC8wQew4yEbkA2AFsCEzIkWnHwVqq671fjgMMduMHp7Cvqp59lfVOh6L88eF91oLB3/hDcC0AnDEcpt9idf7ZudjpaFQE8CcpDgKKWz0vsbe1e4wxxgtUAukikgD8HPjlkS4gIjeISIGIFJSWlvobe0RZXRScK2N0pCXOlskGVBArXgarXoTjfwiZI52O5utOvBWSs63p5pqbnI5Ghbme7mhzP/CYMabmSAcZY540xuQbY/IzMzN7OKTQtKakgvhoN3l9E5wOxS9jBybhcYlWoQY7X7PVZpc4EE6+3elo2hcdB+c8BAe+gGVPOh2NCnP+rJKxG8hq9Xywva29Y0pExAMkAweB44BLROR3QArgE5F6Y8yfuh15hFlZVM6xg1Nwu4KoausIYqPcjBmYxMpd5U6Hoo6k4BnYt9YepB/EX7hGnmtNGr7wQRh3MST2dzoiFab8KSkuB4aLyBARiQYuB+a1OWYecI39+BLgY2M5yRiTa4zJBf4X+K0mxK6rbfCycW81+bmpTofSJZNzUllTUqErZgSrmlL4+Fcw9FQYc4HT0RyZCMx8CJob4P0enodVRbROk6LdRngzsADYCLxmjNkgIg+IyPn2YU9jtSEWArcCXxu2oY7emuIKmn2GSTmhlxTrm3x8safK6VBUexb91upcc84jwdW5piPpw6xON+v+AcXLnY5GhSm/Fhk2xswH5rfZdm+rx/XApZ2c4/6jiE8BK+wqyEnZoZcUwYo/VDoIRYwDm6xxiVO+B5kjnI7Gf9N/AiufhwW/gNnvh0YyVyFFZ7QJAQW7yhnRL4HkPlFOh9IlA5L7MCilz5dJXQWRD+6B6EQ45edOR9I1MQlw+t1Qsgy++JfT0agwpEkxyPl8hpVF5UzOSXM6lKMyKSeVgl2HMDp/ZfDYthC2vm+tRhGf7nQ0XTfhSug3Dj64D7wNTkejwowmxSC39UAN1fXeL6siQ01+Tir7qxrYXVHndCgKrCEY798NKTlw3I1OR3N0XG5rKrqKXTpEQwWcJsUg11L1mB+iSbF1u6IKAqtfgv3r4Yz7wROcC1X7ZdjpkHcm/PsRqD3odDQqjGhSDHIrdpWTbk+wHYpG9U8kLtqt4xWDQWMtfPwbGDwVxl7odDTdd9avoLEaPvmd05GoMKJJMcit2HWIyTmpSIj2svO4XUzISqFAk6LzPv8z1OyDs34dHr02+46GiVfD8qfh0A6no1FhQpNiECuraWDnwcMh257YIj8nlY17q6ht8DodSuSqLYPFj8Oob0L2cU5HEzin3gkuD3z8a6cjUWFCk2IQ+7I9McRmsmlrUk4qPqOLDjvqk99DUy3MuM/pSAIraQAcfxOsnwt7VjsdjQoDmhSD2Mpd5US7XYwdmOx0KN0yMTsVEe1s45jynbD8KauqMZQG6vtr+i3QJw0+vN/pSFQY0KQYxAp2lXPM4GRio9xOh9ItyX2iGNE3UdsVnfLxr60qxlPvdDqSnhGbDCf/D2xfCNs+djoaFeI0KQapBm8z60oqQ749scWknFRW7SrH59NB/L1q7xprrtDjb7KqGsPVlNmQkm2VFn06Ab06epoUg9T63ZU0NvtCbr7TjuTnpFLd4GXLgWqnQ4ksH94PfVKtKsZw5omB0++xvgRseMPpaFQI06QYpJbtsKoaw6Wk2NJZaPmOQw5HEkG22dWJJ91mVTGGu3GXQL9jrOWwvI1OR6NClCbFILVkWxkj+iWQmRjCs460kp0Wx8DkWBYX6uwjvcLns0qJydkw9XtOR9M7XC44836rY9GKZ52ORoUoTYpBqMHbzPKdhzhhWIbToQSMiHBCXgafbz9Is7Yr9rwNb8De1XD6XaE9nVtXDZsBQ06Gfz8M9bqOp+o6TYpBaOWuCuqbfEzPC5+kCDA9L53KuiZddLineRutKsR+4+CYIy5zGn5ErHldDx+Ez//kdDQqBGlSDEJLtpXhEjhuaGguF9WRlpLvkm1lDkcS5lY8Z1UhnnG/taJEpBk02ZrbdcmfoHq/09GoEKNJMQgtLizj2MEpJMWG1qLCnemXFEte3wQWb9N2xR7TUG1VHeaeBHlnOB2Nc06/B5obrPdCqS7QpBhkquubWFNSyfS8EFz81Q/Th6WzfMchGr06lqxHLPl/cLgMzvxleEz6fbTSh8Hk66xSc9lWp6NRIUSTYpBZtuMQzT7D9DDqZNPaCXkZ1DU1s6pIZ7cJuKo9sPiPVtXhoMlOR+O8U26HqD46/ZvqEk2KQWZx4UFiPC4mhcn4xLamDU3HJWgVak9Y+BswzVZbooKEvnDiT2DT27BzsdPRqBChSTHILNlWRn5uasjPd9qR5D5RHDMomSWF2tkmoPatg1UvwdQbIDXX6WiCx7QfQtIgeP8unf5N+UWTYhApq2lg077qsBqf2J4T8jJYXVyh6ysGijHw/t3QJwVOvs3paIJLdJzV6WbPKlj/utPRqBCgSTGILLGrFMNtfGJb04dl4PUZlumUb4FR+BFsXwSn/Nya51R91bGXQf9j4KNfQlO909GoIKdJMYgsKSwjMdbDMYPCe57K/NxUoj0uFmsVavc1e61SYuoQyJ/tdDTByeWCs34DlcWw9K9OR6OCnCbFILJ4WxnThqbjdoV3V/rYKDeTs1O1s00grJwDpRutIRieaKejCV5DT4HhZ8Mnv9cB/eqINCkGiV0Hayk+VMf0YeE5PrGt6XnpbNxbRWl1g9OhhK7Dh6zp3HJPgtHnOx1N8Dv7N+Cth48ecDoSFcT8SooiMlNENotIoYjc0c7+GBF51d6/VERy7e1nisgKEVln/z49sOGHjw++sL69zhjdz+FIesfpo6y/86ON+q39qC38jTXp9TkPR/ZAfX9lDIdpP4DVL0JJgdPRqCDVaVIUETfwBHAOMAa4QkTGtDlsNlBujMkDHgNa5lYqA84zxhwDXAO8EKjAw8176/cxekASWWlxTofSK0YPSCQrrQ/vbdjndCihad86KHgGplwP/cY6HU3oOOV2SOgH8/9Hh2iodvlTUpwKFBpjthtjGoFXgFltjpkFzLEfzwVmiIgYY1YZY/bY2zcAfUQkgtax8U9pdQMrisqZOba/06H0GhFh5tj+LCk8SHV9k9PhhBZjYP7tEJsCp93pdDShJSYRznwA9qyE1S85HY0KQv4kxUFAcavnJfa2do8xxniBSqBt49jFwEpjjDYitfHBF/sxBs4eFxlVpy3OHtufxmYfCzeXOh1KaFn/OhQtgRn36hCMo3HsZTB4qjX9W12F09GoINMrHW1EZCxWleqNHey/QUQKRKSgtDTyPiAXbNhHTnocI/slOh1Kr5qUnUpGQgwLtArVfw3V8P49MGA8TPqO09GEJhE49xFrzcVFDzodjQoy/iTF3UBWq+eD7W3tHiMiHiAZOGg/Hwz8E/iOMWZbexcwxjxpjMk3xuRnZmZ27S8IcVX1TSzZVsbZY/sjEdZZwuUSzhzTj0WbDlDf1Ox0OKHhowegei+c+4fIXCsxUAZOgCmzYenfoGSF09GoIOJPUlwODBeRISISDVwOzGtzzDysjjQAlwAfG2OMiKQA7wB3GGN0Rt52LNx0gKZmw9kR1J7Y2sxx/altbNaB/P4oXgbL/g+mfg+ypjgdTeibcS8k9od5P4JmbddWlk6Tot1GeDOwANgIvGaM2SAiD4hIy+Cop4F0ESkEbgVahm3cDOQB94rIavunb8D/ihC2YMM++ibGMDErxelQHHH80HQSYz1ahdoZbyPM+zEkDbQ+zFX3xSbDub+HAxtgyR+djkYFCY8/Bxlj5gPz22y7t9XjeuDSdl73a+DX3YwxbNU3NbNwUykXTRqEK8xnselItMfF6aP68sEX+/E2+/C4dT6Jdi1+3Jq55opXrR6UKjBGf9Oa+GDRwzDmAmtxYhXR9BPIQZ9uLaOuqZmZ4yKz6rTFzLH9KT/cxPKduvBwu8q2wie/sxYPHjnT6WjCz7mPgCcW3rrFGu6iIpomRQct2LCPpFgP04ZGxtRuHTllZCYxHpdWobbH1wxv3mytID/z4c6PV12X2N+aO3bnp7DiWaejUQ7TpOiQusZm3t+wjzNG9yMqwqsM46I9nDwik3fW7aWpWWcZ+YrPHoXi/8A5j0BiZI1j7VWTroGhp8GCu6Cs0OlolIMi+9PYQW+v3UNVvZdvTcnq/OAIcFl+FqXVDToXamu7V8Cih2DcxXDst5yOJry5XHDBX8ATA29cr71RI5gmRYe8tLSIvL4JHDckzelQgsJpo/oyMDmWl5YWOR1KcGiogde/Bwn94RuP6oTfvSFpAJz3R9izSgf1RzBNig5Yv7uS1cUVXHlcdsQN2O+I2yVcMTWbT7eWsbOs1ulwnLfgF3BoO1z0N+gTmcN1HDHmfJh4FXz6KOxa4nQ0ygGaFB3w92VFxEa5uGjiYKdDCSqXTcnC7RJeXhbhpcUv5lmLB0+/BXJPdDqayDPzYUjNhTdutNasVBFFk2Ivq2nw8uaq3Zx37ECS46KcDieo9E2K5awx/XitoJgGb4RO+3ZgE/zrBzBwEpx2l9PRRKaYBLj4aWs6vddnWz2AVcTQpNjL/rlqN7WNzVw5LcfpUILSlcflUH64iXfXReDwjLoKeOXb1vCLy14ET7TTEUWuwZPhG3+AbR/DR790OhrVizQp9iJjDC/9ZxfnNdk3AAAPLUlEQVRjByYxfnCy0+EEpROGpZObHsdLS3c5HUrv8vngjRugYhd863lIbrs6m+p1k6+B/O9aswmtf93paFQv0aTYi1YWVbBpXzVXHpejHWw64HIJ3z4um+U7y9m8r9rpcHrPot/C1gUw8yHIOcHpaFSLmQ9D1jRrAoV965yORvUCTYq96JnPdhAf7eb8CQOdDiWoXTI5i2iPi6c/2+50KL1j9cvwySMw8WqYcr3T0ajWPNFWyT02GV6+AipLnI5I9TBNir1kVVE576zby+yThpIQ49c87BErLT6aq47LYe6KErbsD/PS4qb58OYPYcgpVhuW1iAEn8R+8O1XrTbfFy6E2oNOR6R6kCbFXmCM4cH5m8hIiOaGk4c6HU5I+NHpecTHeHjo3U1Oh9JzdnwK/7jWWvD28pes2VRUcBowHr79ClQUwUsXQ0OYf1mLYJoUe8GHGw+wbOchbjljhJYS/ZQaH80PT8vj400HWLItDBcg3r3Sqo5LGwJXztXloEJB7olw6RzYu9b6t2uqdzoi1QM0KfYwb7OPh97dyNDMeC7XeU675NoTchmYHMtD727C5wujJX1KCuDFiyAuFa7+J8TpVH8hY+RMuPCv1ooar1xhTcenwoomxR72akEx20pr+fnMURG/GkZXxUa5ue3skawtqeSttXucDicwtn4Ac86D2BT4zjxI0k5XIefYb8GsJ2D7Inj+fG1jDDP6Kd2Dahu8PPbBVvJzUjlrjC77czQumDCI0QOSeGTB5tCf5Wbta/Dy5ZCeB7Pft6pOVWiaeBVc9hLs3wDPnA0VxU5HpAJEk2IPeuCtLyiraeDOc0fruMSj5HIJd507mpLyOh5+d7PT4RwdY+Czx+CN70H28XDtO5DQ1+moVHeNOteq/q45AE+fZa2uoUKeJsUe8uryIl4tKObm0/KYnJPqdDgh7cThGVx7Qi7PLN7B26FWjXr4kNUp48P7YeyFVqea2CSno1KBknMCXDcfxGUlxmX/Z30JUiFLk2IPWFdSyT1vbuCk4Rn89MwRTocTFn5x7mgmZadw+9y1FB4Ike7wJSvgb6dA4YfWzCiXPAtRsU5HpQKt/zi48RNrrOn822Dud6G+yumo1FHSpBhgFYcb+cFLK8iIj+bxyyfidmm1aSBEe1z8+crJ9Ilyc+MLK6hp8DodUse8DbDoYautCeC7C2Da93VgfjiLT4dvvwYz7oMv3oS/nWx1xFEhR5NiADU1+/jJq6vZX1XPE1dOIi1eVzkIpP7Jsfy/Kyayo6yW2+euoTkYh2lsXwR/OcGay3T0eXDjv60VF1T4c7ngpFvh2ret58/Pgtevh+r9zsalukSTYoBU1Tdx3bPLWbS5lPvPH8vEbG1H7Akn5GVwxzmjmL9uHze+UMDhxiApMR7cBnNnWx+Evma46g249FkdgxiJck6Am/4Dp9xhlRr/NAU+fwKa6pyOTPlBTJA1Cufn55uCggKnw+iS3RV1XPfsMraX1vLQxcdyyeTBTocU9l74fCf3zdvA2IHJPH1tPn0THWqrK9sKn/we1r0G7miYfguceKu2HSpLWSG8+z/WuozxfWH6j63lqKLjnY4s4ojICmNMfqfHaVLsnnUllcyes5y6pmb+etVkpudlOB1SxPho435u/vsq0uKjefrafEb176Venb5m2L4QVsyBjW9ZiwJPmQ3H/8iaPFqptnYuhk9+Z1Wvx6XD5GutsY5pOhdyb9Gk2MNKqxv43w+38MryYvonxfLsdVMY0U/nr+xt60oq+e6c5ZTXNnL18TncMmM4KXE90JZrDJRtgXVzYfXfoaoE+qRZC9EefzPE65ch5YfiZfDpH2Dr+2B8kHMiTLwSRp4DfbTJpSdpUuwhtQ1enluyk78s2kZ9UzNXTbM+iFO1U41jSqsbePSDLby6vIiEGA8/njGcq6blEBvl7t6JG2qgeKn1AbblPSjfCQjkzbC+5Y88V1e2UEenao/15WrVi1C+A8QN2dNgxNmQdwZkjrY67qiACWhSFJGZwOOAG3jKGPNQm/0xwPPAZOAgcJkxZqe9705gNtAM/NgYs+BI1wrGpFjf1My/t5Ty1po9fLTxAHVNzZw5ph93njOKoZkJToenbJv3VfPb+Rv595ZS4qPdnDmmH+eNH8hJwzOJ9nTyAdN42CoJHtgIuwusb/T7N4BpBk8sDD0Vhp9lfaPX+UpVoBhjTRC/5T3YsgD2r7O2xyRbvZYHT7WWFsscBSk5mii7IWBJUUTcwBbgTKAEWA5cYYz5otUxNwHHGmO+LyKXAxcaYy4TkTHAy8BUYCDwITDCGNPhJJZOJ8X6pmZ2V9SxcW8V63ZXsq6kkrUlldQ0eEmLj+bcY/pz0aTBTNLepUFr6faD/Gv1buav20dlXRNJsS6mD/QwOdPLMckNDImtIbVpP1HVJdb6eAcLoXwXYP9fiE6AQZMhaypkTbN6E0bHOfo3qQhRWQI7PrG+lJUshwNfWNWsAJ4+kDkCUnMhOQtSsiF5MCT0s6rv4zO1A88RBDIpHg/cb4w5235+J4Ax5sFWxyywj/lcRDzAPiATuKP1sa2P6+h6gUqKyz/8B95mLz4fNBtDs8/gbfbR1Gxo9PpobPZR1+jlcKOPw41eKuuaOHS4kZr6/3bxd7uErLQ4hmTEMSk7jTEDEnFHwje1LlWptzn2K681bbaZdh4b6z99y2Ofz37us0ppvmbwea3nzU3ga7J+NzdBcwN4G63fTfXQVGuV+JoOQ0MVpq4S7+EK3I1VuPB9LfIKEilz9+VgzGAO9hlKRcJQDqcMpy5xCNFRUcR4XER73HjcgscluO0flwguARGhZTh+y+OW8flf/uYIA/Z1LL/qhLupmvjKQuIrtxJfWUhcZSF9akuIObwHd3PD145vdsfgjUrCG52INyqJZk+c/dMHnycOnysan9v+cUVjXFEY8eBzeTAuD0bcIG6MuOzHgsEF4sKIAGJv++/jlhvZIK3u6bY391efmy5MZDFs8pkkJHW/EOJvUvRnxdtBQOsp4EuA4zo6xhjjFZFKIN3e/p82rx3UTrA3ADcAZGdn+xFS50Z/+iMS5CjGBbVtGqyyf7YHICgVWK4oq03PHW31AI2Ks0p0UfGQOADJGElUbDLEJkN8Jo2xaRQ3xLP1cDw7mlIpqhH2VNRTfriRqromqiq8VBc20dS8zem/TKk2htg/Z9rPDRlUMUAOki6VZEgV6VSR5q0iseEwSVJHErXESylxNNCHBuKkgWiaiKGJaJpwS3D1J+nIzv4fkZDUaS4LmKBYBt4Y8yTwJFglxUCcs/zSuVQY35ff7t0iRLldRHsEj9uFJxJKfN3ShWLM1w5tteHLb4Sti1CtHouLr3zjdLmtbSJW5wOXx97mBrfHSoTuKOt3F/8No4Fh9s+ReJutmoSGJh8NXh9en8+qafBZNQ7GgM+ufWhhDBi7ZNxSGD7SjRxsHdxU+Kq3f8rb7vB5EZ8X8TVZv41VI2M9tmprxPgAH+LzYd3Rxt5u/vucll+taoFaka/d612797OzR3bp+O7yJynuBlovGT/Y3tbeMSV29WkyVocbf17bI7LGndgbl1FhyON24XG76ImRHUqp4ObPV+3lwHARGSIi0cDlwLw2x8wDrrEfXwJ8bKyvwvOAy0UkRkSGAMOBZYEJXSmllAqsTkuKdhvhzcACrCEZzxhjNojIA0CBMWYe8DTwgogUAoewEif2ca8BXwBe4IdH6nmqlFJKOUkH7yullAp7/vY+1d4mSimllE2TolJKKWULuupTESkFdgXodBlAWYDOFc70ffKfvlf+0/fKP/o++a8771WOMSazs4OCLikGkogU+FOHHOn0ffKfvlf+0/fKP/o++a833iutPlVKKaVsmhSVUkopW7gnxSedDiBE6PvkP32v/KfvlX/0ffJfj79XYd2mqJRSSnVFuJcUlVJKKb9pUlRKKaVsYZkURWSmiGwWkUIRucPpeIKJiGSJyEIR+UJENojILfb2NBH5QES22r+7v6pnGBARt4isEpG37edDRGSpfW+9ak+SH/FEJEVE5orIJhHZKCLH6z3VPhH5qf1/b72IvCwisXpfWUTkGRE5ICLrW21r9z4Syx/t92ytiEwKRAxhlxRFxA08AZwDjAGuEJExzkYVVLzAz4wxY4BpwA/t9+cO4CNjzHDgI/u5gluAja2ePww8ZozJw1qibrYjUQWfx4H3jDGjgPFY75neU22IyCDgx0C+MWYc1iILl6P3VYvngJlttnV0H52DtfLScKxF6v8SiADCLikCU4FCY8x2Y0wj8Aowy+GYgoYxZq8xZqX9uBrrw2sQ1ns0xz5sDnCBMxEGDxEZDHwDeMp+LsDpwFz7EH2fABFJBk7GWi0HY0yjMaYCvac64gH62GvPxgF70fsKAGPMJ1grLbXW0X00C3jeWP4DpIjIgO7GEI5JcRBQ3Op5ib1NtSEiucBEYCnQzxiz1961D+jnUFjB5H+B2wGf/TwdqDDGeO3nem9ZhgClwLN2VfNTIhKP3lNfY4zZDfweKMJKhpXACvS+OpKO7qMe+awPx6So/CAiCcDrwE+MMVWt99kLREf0WB0R+SZwwBizwulYQoAHmAT8xRgzEailTVWp3lMWuz1sFtYXiYFAPF+vLlQd6I37KByT4m4gq9XzwfY2ZRORKKyE+JIx5g178/6Wqgf79wGn4gsS04HzRWQnVhX86VjtZil2tRfovdWiBCgxxiy1n8/FSpJ6T33dGcAOY0ypMaYJeAPrXtP7qmMd3Uc98lkfjklxOTDc7s0VjdWIPc/hmIKG3S72NLDRGPNoq13zgGvsx9cAb/Z2bMHEGHOnMWawMSYX6x762BhzJbAQuMQ+LOLfJwBjzD6gWERG2ptmAF+g91R7ioBpIhJn/19sea/0vupYR/fRPOA7di/UaUBlq2rWoxaWM9qIyLlY7UFu4BljzG8cDiloiMiJwKfAOv7bVvYLrHbF14BsrKW7vmWMadvgHZFE5FTgNmPMN0VkKFbJMQ1YBVxljGlwMr5gICITsDokRQPbgeuwvnTrPdWGiPwSuAyrJ/gq4HqstrCIv69E5GXgVKwlovYD9wH/op37yP5S8Ses6ufDwHXGmIJuxxCOSVEppZQ6GuFYfaqUUkodFU2KSimllE2TolJKKWXTpKiUUkrZNCkqpZRSNk2KSimllE2TolJKKWX7/0J1xNhklKqsAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 460.8x216 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% parameters\n",
- "\n",
- "problems = []\n",
- "\n",
- "n = 100 # nb bins\n",
- "\n",
- "# bin positions\n",
- "x = np.arange(n, dtype=np.float64)\n",
- "\n",
- "# Gaussian distributions\n",
- "# Gaussian distributions\n",
- "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n",
- "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n",
- "\n",
- "# creating matrix A containing all distributions\n",
- "A = np.vstack((a1, a2)).T\n",
- "n_distributions = A.shape[1]\n",
- "\n",
- "# loss matrix + normalization\n",
- "M = ot.utils.dist0(n)\n",
- "M /= M.max()\n",
- "\n",
- "\n",
- "#%% plot the distributions\n",
- "\n",
- "pl.figure(1, figsize=(6.4, 3))\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "pl.tight_layout()\n",
- "\n",
- "#%% barycenter computation\n",
- "\n",
- "alpha = 0.5 # 0<=alpha<=1\n",
- "weights = np.array([1 - alpha, alpha])\n",
- "\n",
- "# l2bary\n",
- "bary_l2 = A.dot(weights)\n",
- "\n",
- "# wasserstein\n",
- "reg = 1e-3\n",
- "ot.tic()\n",
- "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n",
- "ot.toc()\n",
- "\n",
- "\n",
- "ot.tic()\n",
- "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n",
- "ot.toc()\n",
- "\n",
- "pl.figure(2)\n",
- "pl.clf()\n",
- "pl.subplot(2, 1, 1)\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "\n",
- "pl.subplot(2, 1, 2)\n",
- "pl.plot(x, bary_l2, 'r', label='l2')\n",
- "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n",
- "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n",
- "pl.legend()\n",
- "pl.title('Barycenters')\n",
- "pl.tight_layout()\n",
- "\n",
- "problems.append([A, [bary_l2, bary_wass, bary_wass2]])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dirac Data\n",
- "----------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Elapsed time : 0.014856815338134766 s\n",
- "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n",
- "1.0 1.0 1.0 - 1.0 1700.336700337 \n",
- "0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 \n",
- "0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 \n",
- "0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 \n",
- "0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 \n",
- "0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 \n",
- "0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 \n",
- "4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 \n",
- "8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 \n",
- "3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 \n",
- "7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 \n",
- "8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 \n",
- "3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 \n",
- "1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 \n",
- "Optimization terminated successfully.\n",
- "Elapsed time : 2.703077793121338 s\n",
- "Elapsed time : 0.0029761791229248047 s\n",
- "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n",
- "1.0 1.0 1.0 - 1.0 1700.336700337 \n",
- "0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 \n",
- "0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 \n",
- "0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 \n",
- "6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 \n",
- "2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 \n",
- "1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 \n",
- "3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 \n",
- "7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 \n",
- "1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 \n",
- "4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 \n",
- "2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 \n",
- "Optimization terminated successfully.\n",
- "Elapsed time : 2.6085386276245117 s\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x216 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% parameters\n",
- "\n",
- "a1 = 1.0 * (x > 10) * (x < 50)\n",
- "a2 = 1.0 * (x > 60) * (x < 80)\n",
- "\n",
- "a1 /= a1.sum()\n",
- "a2 /= a2.sum()\n",
- "\n",
- "# creating matrix A containing all distributions\n",
- "A = np.vstack((a1, a2)).T\n",
- "n_distributions = A.shape[1]\n",
- "\n",
- "# loss matrix + normalization\n",
- "M = ot.utils.dist0(n)\n",
- "M /= M.max()\n",
- "\n",
- "\n",
- "#%% plot the distributions\n",
- "\n",
- "pl.figure(1, figsize=(6.4, 3))\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "pl.tight_layout()\n",
- "\n",
- "\n",
- "#%% barycenter computation\n",
- "\n",
- "alpha = 0.5 # 0<=alpha<=1\n",
- "weights = np.array([1 - alpha, alpha])\n",
- "\n",
- "# l2bary\n",
- "bary_l2 = A.dot(weights)\n",
- "\n",
- "# wasserstein\n",
- "reg = 1e-3\n",
- "ot.tic()\n",
- "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n",
- "ot.toc()\n",
- "\n",
- "\n",
- "ot.tic()\n",
- "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n",
- "ot.toc()\n",
- "\n",
- "\n",
- "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n",
- "\n",
- "pl.figure(2)\n",
- "pl.clf()\n",
- "pl.subplot(2, 1, 1)\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "\n",
- "pl.subplot(2, 1, 2)\n",
- "pl.plot(x, bary_l2, 'r', label='l2')\n",
- "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n",
- "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n",
- "pl.legend()\n",
- "pl.title('Barycenters')\n",
- "pl.tight_layout()\n",
- "\n",
- "#%% parameters\n",
- "\n",
- "a1 = np.zeros(n)\n",
- "a2 = np.zeros(n)\n",
- "\n",
- "a1[10] = .25\n",
- "a1[20] = .5\n",
- "a1[30] = .25\n",
- "a2[80] = 1\n",
- "\n",
- "\n",
- "a1 /= a1.sum()\n",
- "a2 /= a2.sum()\n",
- "\n",
- "# creating matrix A containing all distributions\n",
- "A = np.vstack((a1, a2)).T\n",
- "n_distributions = A.shape[1]\n",
- "\n",
- "# loss matrix + normalization\n",
- "M = ot.utils.dist0(n)\n",
- "M /= M.max()\n",
- "\n",
- "\n",
- "#%% plot the distributions\n",
- "\n",
- "pl.figure(1, figsize=(6.4, 3))\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "pl.tight_layout()\n",
- "\n",
- "\n",
- "#%% barycenter computation\n",
- "\n",
- "alpha = 0.5 # 0<=alpha<=1\n",
- "weights = np.array([1 - alpha, alpha])\n",
- "\n",
- "# l2bary\n",
- "bary_l2 = A.dot(weights)\n",
- "\n",
- "# wasserstein\n",
- "reg = 1e-3\n",
- "ot.tic()\n",
- "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n",
- "ot.toc()\n",
- "\n",
- "\n",
- "ot.tic()\n",
- "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n",
- "ot.toc()\n",
- "\n",
- "\n",
- "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n",
- "\n",
- "pl.figure(2)\n",
- "pl.clf()\n",
- "pl.subplot(2, 1, 1)\n",
- "for i in range(n_distributions):\n",
- " pl.plot(x, A[:, i])\n",
- "pl.title('Distributions')\n",
- "\n",
- "pl.subplot(2, 1, 2)\n",
- "pl.plot(x, bary_l2, 'r', label='l2')\n",
- "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n",
- "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n",
- "pl.legend()\n",
- "pl.title('Barycenters')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Final figure\n",
- "------------\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1440x432 with 6 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot\n",
- "\n",
- "nbm = len(problems)\n",
- "nbm2 = (nbm // 2)\n",
- "\n",
- "\n",
- "pl.figure(2, (20, 6))\n",
- "pl.clf()\n",
- "\n",
- "for i in range(nbm):\n",
- "\n",
- " A = problems[i][0]\n",
- " bary_l2 = problems[i][1][0]\n",
- " bary_wass = problems[i][1][1]\n",
- " bary_wass2 = problems[i][1][2]\n",
- "\n",
- " pl.subplot(2, nbm, 1 + i)\n",
- " for j in range(n_distributions):\n",
- " pl.plot(x, A[:, j])\n",
- " if i == nbm2:\n",
- " pl.title('Distributions')\n",
- " pl.xticks(())\n",
- " pl.yticks(())\n",
- "\n",
- " pl.subplot(2, nbm, 1 + i + nbm)\n",
- "\n",
- " pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n",
- " pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n",
- " pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n",
- " if i == nbm - 1:\n",
- " pl.legend()\n",
- " if i == nbm2:\n",
- " pl.title('Barycenters')\n",
- "\n",
- " pl.xticks(())\n",
- " pl.yticks(())"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_compute_emd.ipynb b/notebooks/plot_compute_emd.ipynb
deleted file mode 100644
index 26c125e..0000000
--- a/notebooks/plot_compute_emd.ipynb
+++ /dev/null
@@ -1,248 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Plot multiple EMD\n",
- "\n",
- "\n",
- "Shows how to compute multiple EMD and Sinkhorn with two differnt\n",
- "ground metrics and plot their values for diffeent distributions.\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "from ot.datasets import make_1D_gauss as gauss"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n",
- "\n",
- "n = 100 # nb bins\n",
- "n_target = 50 # nb target distributions\n",
- "\n",
- "\n",
- "# bin positions\n",
- "x = np.arange(n, dtype=np.float64)\n",
- "\n",
- "lst_m = np.linspace(20, 90, n_target)\n",
- "\n",
- "# Gaussian distributions\n",
- "a = gauss(n, m=20, s=5) # m= mean, s= std\n",
- "\n",
- "B = np.zeros((n, n_target))\n",
- "\n",
- "for i, m in enumerate(lst_m):\n",
- " B[:, i] = gauss(n, m=m, s=5)\n",
- "\n",
- "# loss matrix and normalization\n",
- "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\n",
- "M /= M.max()\n",
- "M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\n",
- "M2 /= M2.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot the distributions\n",
- "\n",
- "pl.figure(1)\n",
- "pl.subplot(2, 1, 1)\n",
- "pl.plot(x, a, 'b', label='Source distribution')\n",
- "pl.title('Source distribution')\n",
- "pl.subplot(2, 1, 2)\n",
- "pl.plot(x, B, label='Target distributions')\n",
- "pl.title('Target distributions')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute EMD for the different losses\n",
- "------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f60fcdd67f0>"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% Compute and plot distributions and loss matrix\n",
- "\n",
- "d_emd = ot.emd2(a, B, M) # direct computation of EMD\n",
- "d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n",
- "\n",
- "\n",
- "pl.figure(2)\n",
- "pl.plot(d_emd, label='Euclidean EMD')\n",
- "pl.plot(d_emd2, label='Squared Euclidean EMD')\n",
- "pl.title('EMD distances')\n",
- "pl.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute Sinkhorn for the different losses\n",
- "-----------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%%\n",
- "reg = 1e-2\n",
- "d_sinkhorn = ot.sinkhorn2(a, B, M, reg)\n",
- "d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n",
- "\n",
- "pl.figure(2)\n",
- "pl.clf()\n",
- "pl.plot(d_emd, label='Euclidean EMD')\n",
- "pl.plot(d_emd2, label='Squared Euclidean EMD')\n",
- "pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\n",
- "pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\n",
- "pl.title('EMD distances')\n",
- "pl.legend()\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_convolutional_barycenter.ipynb b/notebooks/plot_convolutional_barycenter.ipynb
deleted file mode 100644
index d0df486..0000000
--- a/notebooks/plot_convolutional_barycenter.ipynb
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Convolutional Wasserstein Barycenter example\n",
- "\n",
- "\n",
- "This example is designed to illustrate how the Convolutional Wasserstein Barycenter\n",
- "function of POT works.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Nicolas Courty <ncourty@irisa.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "\n",
- "import numpy as np\n",
- "import pylab as pl\n",
- "import ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Data preparation\n",
- "----------------\n",
- "\n",
- "The four distributions are constructed from 4 simple images\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\n",
- "f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\n",
- "f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\n",
- "f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n",
- "\n",
- "A = []\n",
- "f1 = f1 / np.sum(f1)\n",
- "f2 = f2 / np.sum(f2)\n",
- "f3 = f3 / np.sum(f3)\n",
- "f4 = f4 / np.sum(f4)\n",
- "A.append(f1)\n",
- "A.append(f2)\n",
- "A.append(f3)\n",
- "A.append(f4)\n",
- "A = np.array(A)\n",
- "\n",
- "nb_images = 5\n",
- "\n",
- "# those are the four corners coordinates that will be interpolated by bilinear\n",
- "# interpolation\n",
- "v1 = np.array((1, 0, 0, 0))\n",
- "v2 = np.array((0, 1, 0, 0))\n",
- "v3 = np.array((0, 0, 1, 0))\n",
- "v4 = np.array((0, 0, 0, 1))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycenter computation and visualization\n",
- "----------------------------------------\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 25 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(figsize=(10, 10))\n",
- "pl.title('Convolutional Wasserstein Barycenters in POT')\n",
- "cm = 'Blues'\n",
- "# regularization parameter\n",
- "reg = 0.004\n",
- "for i in range(nb_images):\n",
- " for j in range(nb_images):\n",
- " pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n",
- " tx = float(i) / (nb_images - 1)\n",
- " ty = float(j) / (nb_images - 1)\n",
- "\n",
- " # weights are constructed by bilinear interpolation\n",
- " tmp1 = (1 - tx) * v1 + tx * v2\n",
- " tmp2 = (1 - tx) * v3 + tx * v4\n",
- " weights = (1 - ty) * tmp1 + ty * tmp2\n",
- "\n",
- " if i == 0 and j == 0:\n",
- " pl.imshow(f1, cmap=cm)\n",
- " pl.axis('off')\n",
- " elif i == 0 and j == (nb_images - 1):\n",
- " pl.imshow(f3, cmap=cm)\n",
- " pl.axis('off')\n",
- " elif i == (nb_images - 1) and j == 0:\n",
- " pl.imshow(f2, cmap=cm)\n",
- " pl.axis('off')\n",
- " elif i == (nb_images - 1) and j == (nb_images - 1):\n",
- " pl.imshow(f4, cmap=cm)\n",
- " pl.axis('off')\n",
- " else:\n",
- " # call to barycenter computation\n",
- " pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n",
- " pl.axis('off')\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_fgw.ipynb b/notebooks/plot_fgw.ipynb
deleted file mode 100644
index b41f280..0000000
--- a/notebooks/plot_fgw.ipynb
+++ /dev/null
@@ -1,359 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Plot Fused-gromov-Wasserstein\n",
- "\n",
- "\n",
- "This example illustrates the computation of FGW for 1D measures[18].\n",
- "\n",
- ".. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n",
- " and Courty Nicolas\n",
- " \"Optimal Transport for structured data with application on graphs\"\n",
- " International Conference on Machine Learning (ICML). 2019.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Titouan Vayer <titouan.vayer@irisa.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import matplotlib.pyplot as pl\n",
- "import numpy as np\n",
- "import ot\n",
- "from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n",
- "# We create two 1D random measures\n",
- "n = 20 # number of points in the first distribution\n",
- "n2 = 30 # number of points in the second distribution\n",
- "sig = 1 # std of first distribution\n",
- "sig2 = 0.1 # std of second distribution\n",
- "\n",
- "np.random.seed(0)\n",
- "\n",
- "phi = np.arange(n)[:, None]\n",
- "xs = phi + sig * np.random.randn(n, 1)\n",
- "ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n",
- "\n",
- "phi2 = np.arange(n2)[:, None]\n",
- "xt = phi2 + sig * np.random.randn(n2, 1)\n",
- "yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\n",
- "yt = yt[::-1, :]\n",
- "\n",
- "p = ot.unif(n)\n",
- "q = ot.unif(n2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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KPELs0BNJQ1VJJd899Hr+5+3fsqO1FoiVQY1YhDkjT+LSAy8scIQiItIbZbQPvMubOXcHcLiZHdlVW+0D78jMqGncwIbGjZT6SjhyyCEMDFQUOiwREcmzdPeBd2sbWQrb0BB6tzjnmDxwvIq2iIhIWrK2jcw7zORUQMVcREREciyTbWTHpbjHWOCzwFHArVmIS0RERFLIZAj9GWJbxJJxxA47ub5HEfXAqtq13L5+Ka/vXA/A4VUT+PSEuRw5ZGKhQhIREcmJTBL4D0icwKNAHfCCmT2blai64e/vL+O3ax+kNbr3ILSVdWt5fdcGvjTlXD42JulR5iIiIkUnk21kN+YykJ7Y2lzHb9Y+QCi6bzGU1mgbv3znfmYPn8Z+ZVUFiE5ERCT7+kQt9Hs2Pkuq7XBmcP+m53Iex65QM/e+9zJ/rnmeZz+o0SliIiKSM9neRlYQa3Zvos0iSa+3WZg1uzfl7Plmxs/ffJzb1i4n4HyELULA56fCX8Ivjr2YI4aOzdmzRUSkf0qawJ1za7p5TzOzg7r52W6pDJan0WZAzp7/m7ee5I6a5whFw7QfDBqKRmgKh/jcsjtYfOrVjB84PGfPFxGR/ifVEPoAoLwbv3KXKZM4e/9jKPeXJL1e7i/ho6OPzsmzG8Ot3Lp2GS2RtoTXW6Nhfvf20zl5dlfC0SiPb1zL7W+v4IENb9IcThyjiIgUn6Q9cDMbk89AeqJ66BTGDxjJ2oYttHU61SvoAkwcOJoZQybl5NnLP1iH3yX/HhQx49HNb/LDmTl5fFJPba7hy8/cT1s0SjgaJeDzEcX4+lGncdlBM/IbjIiIZF2fWMTmcz5+OuMqjhk2lRJfgAH+Usr9pZT4AswefjD/e+SVOLfvaV/Z0Bxpo6ty8m0JVsfn0qrtG/nCU39nZ6iFxnCI1miYxnCI5nAbN616nLtrXstrPCIikn19YhEbwIBAGTcd8Rm2tezk1Z3vAnBk1URGlA3O6XMPqhxJNGV9GxhbMTSnMXS24KUnaYkk/tLQHGnjhy89wQUTD8WXoy81IiKSexkncOdcEJgJHACUJmpjZn/pYVzdNrKsirmjjsrb86YOHsn4gcNYs2tbwkRe7g/y+Skn5C2exrYQq7anXnHfHG7j9dqtHD5sdJ6iEhGRbMsogTvnLgd+DCRbUu2IVWsrWAIvhJ8dcxGfeOoWmsIhQtG929nK/UFOGDmZ88cdkbdYmiNt+J0jnGJQwOccTVrQJiJS1NKeA3fOnQH8EfgQ+BqxZP0P4FvAE97rxcBV2Q+zdxtXMZT7T7uWKybPZkTZICoCpUwfPJrvHnUePz16Pr4Ui9yybUhJOaX+1N/LWiNhJlUOy1NEIiKSC5n0wL9KrOb5LDOrd84tAFaZ2feA7znnrgZ+CfwsB3H2esPLBvLl6XP48vQ5BY3D7/Nx+UEz+f2bLyScBw84HyeMnsCI8ooCRCciItmSSddwJnC/mcWf973n82a2EHgO6LU10/uLLx12PNOGjKS8U0+81Odnv/KBLJh9VoEiExGRbMmkB14BbIl73QoM6tTmBeAzPQ1KeqbUH+DOuZewqOYVbn3zRbY172ZwSRmXTJ3BZVNnUFlSVugQRUSkhzJJ4FuBEXGvNwOdS6ZWZnhPyZESv59Lps7gkqkq2iIi0hdlMoS+mo4JexlwunNuNoBzbhpwkddOREREciiTBP4wcLxzrn3z8I+IbRl7xjm3BXiNWA/8+9kNUURERDrLJIEvBA4EagHM7HVgLrAEaCC2lewcM3sg20GKiIhIR2nPV5tZCNjU6b1lwJnZDkpERERSy6SQS2UuAxEREZH0ZTKEvsU593/OuTNcro72EhERkbRkksA3AZ8ktpjtfefcTd7KcxEREcmztBO4mU0Fjgd+DwwAbgBed84975y7xjk3JEcxioiISCcZnbJhZsvN7GpgNLHe+CPADOBXwGbn3GLn3DnZD1NERETideuYLDNrNbO7zOwsYAxwPbAGuBC4N4vxiYiISAI9PufSzLYBK4GXgDZix4qKiIhIDnW7brlzbjLwaeAyYCyxxP0ucEd2QhMREZFkMkrgzrnBwMXEEvcsYkm7AbgduM3Mns56hJJSU1uIe9a8yd/ffoPmSJijRx3AZw6fwfjBWlMoItKXpZ3AnXN3AecCpd5bTwK3AXebWVPWI5MuvV+/k4/f81caQiGawm0AvFO7g7vefI3/PvF0Lp52WIEjFBGRXMmkBz4fqCHW277DzN7LTUiSDjPj0w/ezY7mJqJme95vi0ZpI8q3nnmMQ0eM5JDh+xUwShERyZVMFrGdaGZTzOx7St6F9+KWTWxtaOiQvOOFIhEWvvRCnqMSEZF8yaSQy7JcBiKZeXHrJloj4aTXo2Y8t/n9PEYkIiL51ONtZFIYAefwdVGSPuDTX6+ISF+lf+GL1CnjJuB3yf/6gj4fH5kwJY8RiYhIPnV7H7gU1kHDRnDkyNGs2rqZUDSyz/Wgz89nD59ZgMhERArnvZ07uX/1W2xvbGTysKF8bPo0KsvKCh1WTiiBF7GFZ36MTz9wN2tqd9AcbsOAAcEgDsetZ13A2MrBhQ5RRCQvomZ849Gl3PvGaiJmhKNRygMBfvjUv/ju3NO58NBDCh1i1imBF7HBpWXcc+GnWLl1Mw/WvE1TWxszR+3PuZMPpjwYLHR4IiJ589N/LeP+1W/SGtk7Itkcji30/eaSxxhdOYjZ48YVKrycUAIvcs45qkcfQPXoAwodiohIQTS3tXHbqlV7EnZnLeEwP3vmWWZ/qp8ncOdcADgFmAYMNLObvPdLgIFAnVmSzckiIiJZtmrT5pSLegFe2ryFUCRCid+fp6hyL6NV6M65OcA6YueA/xz4XtzlmcB2YrXSRURE8iIcjXbZxgHRNNoVk7QTuHNuBvAAsV77fwB3xl83s+XAeuCCLMYnIiKS0iEj9yMU2Xc3Trz9Kysp62NrgzLpgX8TaAaqzewnwNsJ2rwIHJmNwERERNIxvKKC0yZNTDo8Xh4Mcs2sY/McVe5lksBPAO4xs80p2rwHjO5ZSCIiIpm56cwzmDBkCAPietkOKA8GOOfgg5h3WP/eRjaQ2Bx3KuWoupuIiOTZoNJS7rn8Eh5Z8w7/99Ir1DY3MWnoUK6onsExY8bguig9XYwySeCbgK6+whwJvNv9cERERLqnxO/n3GkHc+60gwsdSl5k0lt+BDjTOTc70UXn3BnA8cQWuomIiEgOZZLAfwDsApY6574PHAzgnPuI9/puYBvwk6xHKSIiIh2kPYRuZhudcx8B/gZ8HTBiawQe8n5fD1xoZl3Nk4uIiEgPZVSJzcxWOOemAh8DZgHDiPXKnyO2Qj2U/RBFRESks7QTuHNuf6DN62Hf7f0SERHpVULhCEtWv8N9r7xJc6iNGeP255PHHMGowYMKHVpWZdIDfx+4A/hMjmIRERHpkQ/qG/jU7++irqmZplAbAK9u3Mrty1/i++fP5ezD+84K9UwWse0EPshVICIiIj1hZlz1p3vYWr97T/IGCEUitIbD3HjfEtZs21HACLMrkwT+PHBUrgIRERHpiVc3buW92p1EookPxGyLRPjDMyvyHFXuZJLA/xs42Tl3RY5iERER6bYX129MeahJJGo8W/NeHiPKrUzmwE8HHgdudc59gdjBJVuJbSeLZ+1nhIuIiOSLzzkcjn3T0l59qaJqJgk8/uzvY7xfiRigBC4iInk1e9I4fvnE8qTngwd8jlMOmpjnqHInkwQ+N2dRiIiI9NC00ftx8KgRvL55G+HIvkk86PdzxXEzCxBZbmRSie2xXAYiIiLSU7+55GNc8cdFbKyr37MSvSwYwAE/vfgcJgwfUtgAsyijSmwiItJ/hSNRnntjPVs+rGfIoAGccNgEykqDXX8wj4YMKOeeL17GszUbeOC1t2gKtTFj7P5cMOMQBpeXFTq8rFICFxGRDtZs3s7L6zfj9/k47qADGT2kkuWvr+fG3z9EWzhCJGL4/T7MjOvmncS8U48odMgd+HyOE6aM54Qp4wsdSk5lUkq1jVRL+/YyMyvtfkgiIlII2+sbuO7W+1m79UMMw+GImnHEuNG8tXozraG4LVpenZSfLXqKstIA5xx3SGGC7scy6YE/T+IEXgVMBkqB14D6LMQlIiJ51BIKc9nP72Lbrt37FEJZsW4j+I1Eg+UtoTC/WPwvzpo1HZ+vD+3RKgKZLGI7Idk151wl8AugGjg3C3GJiEge/fPlt6lrbE5YxcwAAhD1gS/BDq2W1jbeem8b08ePynmcslcmldiSMrN64HPE/p6/n417iohI/vz9uddpjqsfnkg0yXo1n8/R3Jr6s5J9WUngAGYWAZ4ALsjWPUVEJD8aW0OpGzgHSUbIQ20RDhw1NPtBSUpZS+CeEqDvbLITEeknDh03En+qOqMGLkGZcb/PcfS0cQwfXJG74CShrCVw59wUYD5Qk617iohIflx60gwCAX/S6z7nGOA6Xi8J+hk+eCDfvOKMXIcnCWSyjezmFPcYC5zk/fcNWYhLRETyaMro4Vx75mx+88/ltLSF97zvc46SgJ8fXXYWO7bv5i9LX2L7zgYGDSjl4ycfzkWnHcmgAX2rQEqxcGbpbO0G51zi6vB7rQV+bGa3pHO/6upqW7Gi75zLKiLSF6xYu5HfP/YCr6zfjM/n46TpE/jc6UczedTwQofWbzjnVppZdVftMtkHPiXJ+1Ggzsx2ZnAvERHphaonj6F68phCh9HrtLS08dgTq/nHQy/T2NjKxAkjmHfh0Rx2SOH+rDLZB665bRER6XfqdjZy7XV/om5nEy0tse1ymzbX8fyL6zjv7KO45urTChJX2ovYnHM3O+fO6aLNWSnmykVERIrOf3//Pj7YvntP8gYwg9bWMP946GX+tWxNQeLKZBX654EZXbQ5ilhBFxERkaK3aVMdb761hUiC88UhNrT+p788m+eoYnKxDzzBTkEREZHi8+bbW/D7U6fKde9+kKdoOso0gSddsu6cCwInAtt6FJGIiEgvEQj4SFXfBsDny3ZfOD0pF7E55zoP7F/nnLssQVM/sB8wANAcuIhIgYRawzz9+GqW/+ttMDj2hKmcfPp0SsuSFDKXlGYceSDhcPKBZefgmOqJeYxor65WoQ9gb6/bgCBQnqBdBFgDPAb8d9aiExGRtG14dztf/eLthEJhmptitc1XPF/Dzb9Ywo9/fRkTJo8scITFp7KynDPnHsYjS1+ntTW8z/WSkgCXX3JcASLrIoGb2Z4Nbl4hl/81s+/kPCoREclIa0sbX/3i7eza2dTh/eamEM2E+I9r7+BP915HeXlJgSIsXv/vmjk0NLbyzLPvEIlEiUSilHkjGjd+7VymTinMMaqZFHKZC6zLVSAiItJ9Ty19I2EPsV0oFOHJJW/w0fOOymNUfUMg4OcbXz+P9zfW8sRTb1K/u5kJ40dw2inTKC8r3BeiTAq5PJbLQEREpPuWPf02Lc3JjwRtaQ6x7Mk3lcB7YOyYoVx+yfGFDmOPTHrgwJ7V5jOBA4DSRG3M7C89jEtERDJg0a7PtYim0UaKR0YJ3Dl3OfBjIFlVe0dssZsSuIhIHh17whReXvkuLc1tCa+XlQeZdeLUPEcluZRJKdUzgD8CHwJfI5as/wF8C3jCe70YuCr7YYqISCqnfeSwlOd5+/0+5px5eB4jklzLZPf5V4E6YJaZ/dh7b5WZfc/M5gBfBM4H3sxyjCIi0oXy8hIW/PIyBg4s27NCGqCsLEjFwFIW/PIyBlQknPWUIpXJEPpM4D4zq497b88XADNb6Jy7BLgROCtL8YmISJqmHDyaP993HUsffpVnn34LM5h94kHMPfsIKpS8+5xMEngFsCXudSswqFObF4DP9DQoERHpngEVpZw372jOm3d0oUORHMtkCH0rMCLu9WbgoE5tKunGynYRERHJTCbJdjUdE/YyYL5zbraZLXfOTQMu8tqJiEga1r2/g7sfeZn1Gz9k+JCBfGzO4Rw1fQyuqxM0pN/LJIE/DPzUOTfazLYAPwLmAc845z4g1jv3Ad/PfpgiIn2LmfHbv/yLRQ+/RDgcIRI1HPDMihoOPWh/fnzD+ZQENaApyWUyhL4QOBCoBTCz14mVV10CNBDbSnaOmT0VD/VUAAAgAElEQVSQ7SBFRPqaJcveYvE/X6I1FCbiFVgxoLm1jVff2sTPb3+yoPFJ75d2AjezkJltMrPWuPeWmdmZZjbFzOaa2UO5CVNEpG/5/aJnaUlSu7w1FOahJ9+gsak14XURyKwHLiIiWdDYHGLLtvqUbQIBH2+u25aniKQYdacW+iHAJ4FpQIWZnem9Pw6oBh43s51ZjVJEpA9xjljtylSs6ybSv2XUA3fOfRN4BfhP4AJic+DtgsAi4NKsRSci0gcNKCth7KiqlG0ikSjTJ4/OU0RSjDKphX4R8G3gcWI97QXx182sBlgJnJfF+ERE+qQrLz6estLEg6ClJQE+NvdwyuNKoop0lkkP/DqgBjjXzFYBLQnarAamZCMwEZG+7NRZU7n8/GMpCfoJ+GP/FPuco6w0wLFHjOfaS04qcITS22UyB344cFv8KvQEtgAjexaSiEj/cMXHZ3HGidO4d8krewq5nHPaYUyfPKrQoUkRyCSBOyDaRZsRxGqki4hIGvbfbzDXqLcNwPs1H7Bq2TuYGYcdPZFJ0/cvdEi9WiYJfC0wO9lF55wPOAGVUhURkQw01Dfz/f/3Z1avWo/Fatrg8zkOnDKSb/3uCoaO6HxulkBmc+B/A2Y6565Lcv16YvPff+1xVCIi0i+YGf95xS28vuJdQq1h2kKxX60tbdS8uZmvfvK3tIUSF7zp7zJJ4D8DXgN+4pxbBnwEwDn3Q+/194EXiZVcFRER6dIrz9Xw/rrthNsi+1yLhKPU7djNs0veKEBkvV8mpVSbgFOI9bCPBWYRmxe/3vvvO4EzzKwt+2GKiEhf9MT9L9HSFEp6vaUpxJK/r8hjRMUjo0psXoW1S51zXwGOAYYBu4DnzWxrDuITEZE+rKkh0Y7kjppTJPj+rFtn1ZnZduDBLMciIiL9zLSjDuTFp96mtSXx4G0g6GfakePyHFVxSDmE7py73Dl3eL6CERGR/mXux6tTFn33+RznXnpc/gIqIl3Ngd8GnB//hnPu0865x3MWkYiI9BuDBg/gaz/5FKVlQXz+vSnJ+RylZUGu/fYFjDxgSAEj7L26M4Q+Hjg5y3GIiEg/Nev06fz87i+x6PdPseKpt7Gocfixk5h/1SlMPWxMocPrtbo1By4iIpJNB04ZxVcXXFzoMIpKRseJioiISO+gHriI9HtrVtaw/P4VhENhph93EMecdRR+v7/QYYmklE4Ct5xHISJSAA07G7nxnJtY+/J6Qi0hLGqUDyqjfGA5P3zkRiYcqu1L0ns5s+T52TkXJfMEbmbW5ReD6upqW7FC1XVEpDDMjOuOv5G1q9YlrLU9cEgFt6/5JZXDdJCG5JdzbqWZVXfVLp05cJfhL82ri0iv9/aLa3n3tQ1JD8oItbTx0O8fy3NUIulL2VM2MyVjEemTnr3vRVpTlOgMNYd4/K//4hM3nJ+0jWTu9eXv8Jf/fYg3nnsHgENmTeZT/342h86eUuDIio8WsYlIvxRqCZFqChGgrUXHWGbTQ7c/zc3fWExr894vTi899RarX1jHld+Zx9lXnFTA6IqPetgi0i8dctzBlA8qS3rdH/Bx+MnT8xhR37Z9Uy0L/2tRh+TdrrU5xM03LmL7ptoCRFa8lMBFpF+afV41JWUlSa8HggEu/PLZeYyob3vwtqdTjniYGQ/+8ek8RlT8lMBFpF8KBAP88J83UjF4ACVlwT3v+/w+SgeU8KVffY4Dp6mMZ7asffW9pAsGAdpCYd559b08RlT8NAcuIv3W5KMm8Me3f8GDCx/liTuXEW6LcPhJ05j3/53LgdPHFjq8PmVQVUUabQbkIZK+QwlcRPq1IfsN5tJvzOfSb8wvdCh92pxPzOa5R16lpbE14fWyilLmflLHhmZCQ+giIpJzR518MAceNJpg6b79xmBpgHFTR3HUyQcXILLipQQuIiI55/P5+MHdX2bmqdMJlgYoqyilrKKUYGmAmadO56a/fwWfTykpExpCFxGRvBgwsIxv/ekaPthYy+vLY4VcDp09hf3GDC1wZMVJCVxERPJqvzFDOW3+sYUOo+hpvEJERKQIKYGLiIgUoZTHieb0wc5tBzYU5OEiIiK914FmNqKrRgVL4CIiItJ9GkIXEREpQkrgIiIiRUgJXEREpAgpgYuIiBQhJXAREZEipAQuIiJShJTARUREipASuIiISBFSAhcRESlCSuAiIiJFSAlcRESkCCmBixQx59wC55xl+VddoX8uEemaDjMRKWLOuYlAjffyBjP7UQafrQKqgRnAXGBO3OW5ZrY0a4HufeYM7zk7gXXACjPbme3niPQHSuAiRc45Nw9Y5L2caWarunmfKmABcBWw2MzmZynE9vtf5f1nLXAxMA/YaWZDsvkckf5CCVykD3DOLSKWENeZ2aQe3msesMjMXFaC23vfhWZ2ddzrKmBid79wiPR3SuAifYRzrgaYSBZ6z86564l9GVicleBi91xpZjOzdT+R/k6L2ET6jvakPc/rRXebN5d+dM9D6mCdc25Blu8p0m+pBy7Sh3g95/YkOcnM1hUynnjOuTnAEnK0QE6kv1ECF+ljnHNLiK30XtXbhqy9Yf6hwAStPhfpGQ2hi3icc1XOuZXOuTpvP/RVSdot8BaN9VbziW3TmtGbhqy9HvgCoAq4pcDhiBQ9JXARj5nt9HqsN3lv7TPM662cvp7YHuZeyevZts+HX+8lzoJxzs1wzi0ys6VmdjOxP9d53p5wEekmJXCRfR1NbH9yoiTd3iu/K4/xZMybY77Ze7nI++KRd16Sfgy4Mu7thd7vF+fyud5oiuYIpc/SHLhIJ14p0aWJtmJ5c7jrzGxuGvdZSMfqZplYmElVtRQxtG8tW5pOzNnmPb/Dz+J9magjB8ViOj37KmB+IX5ukXwIFDoAkd7EK01aRWy1dOdr84glw7QSQnzRkgKaS6zU6hzn3Ix8Fk3xEuhE9o4EALEhfuccxCqy5dJcEvw9ivQVSuAiHbX3mDvMf3u9xluAq3vT1qyumNk659xSYqMG+a54Np9Yzz/ZavOaJO9nyxz2rmcQ6XM0By7S0VwSz38/RiwZ3ZzgM72Wty98aIFGA6qBfb40eKMcxF9zzs1xzl3VedW8c25Rdxa7xT1jonNunrdzYGLKD4kUGfXARTqaA6yIf8PbMrYu0/naQs+Be6vPvw5M6Ml9Utz/emJFWVJNKXyY4L32mu1LvftUAVVmdrO36OyGTm2vTHCPruwZSfGG7NcRO/ClV+2LF+kJJXART+f5by+xPAbc1Z1kWsg5cO9nWQScnsOCKTfTaX67kxVAooNVrmZvtTiAajNb7H3h6NArJzYasjPuvSrgljS+TM0ldrxq+2d3Ejs2dY8M7iXSK2kVuojHW3S1kNg//jOIbSe7oZjmvNt5q78XFHLI3/sSsZK4qmvtQ+RmdkOC9guBmvYvS14P/+juJFhvJ8HM9r87bwHiLTq6VPoS9cBF9ppJrEDLXLrZ6+4NvCH/Hs/XO+cmJvry4iXDocQSZNJRBm8B3enALc65F4n1xmtS/LleRMch7rnsPec8k7jbh+TjY59LgsI8IsVMPXCRPsTr4c7paQ10r/d8Q+cE7Y1SLPWScx1ZqmnuPa8m/gxybz58Zvvq+bhtfItTjYp491oSfy66F+vpmd5LpDfTKnSRPsKbM74KOD0Lt1vE3opp8Wq95F1FbL1AttQSm6cG9nxRIC7hziHWg040lz0xvja9l5Dj77UAuDmde4kUE/XARfqAuPnmPb3Mbt6niljyrk41X+wlwYXxvdye8ua8IZZY5xIbBp/bqc1C77mdF7stIlZ1rX1l+zxiaxg+JLYQbp/phET3EikmSuAiRc5LuiuJDXkv7sE9riK27awK+FGihWZx7RcQS7A5WWnvHYm6pPN8uXNuZbLpAefcvEx+/lT3EikGWsQmUvzah4+HJjsCtZMqYJj3+1Biw8idi5x0dVhLVqucxc9Re18mqtl7olq8hOVXvRGITOeyc13KVSSnlMBFipiXsNuLliSas+6OdMquziBLq7q9hH1z3DNvAa7svDiu8z7xTuZlsmugi3uJFAUlcJEi5s3t5nWvt1fadGfnBNtdXqW0Gu/LyCRi89KJvhwkPJzE+wKQaTLWQSdS9DQHLiIZ6UmBlW4+b55Xqa3Hc9bZvJdIoakHLiKZupj8nvJ1sXNuKN2riZ7Le4kUlHrgIpKSN0T9LrEqaVXAomxuHxOR7lEPXETSsZTYYrlJ6EQvkV5BPXAREZEipFKqIiIiRahgQ+jDhw+38ePHF+rxIiIivdLKlSt3mNmIrtoVLIGPHz+eFStWFOrxIiIivZJzbkM67TSELiIiUoS0Cl1ERIqeRbZCy6NgjRCYCqUn41zfTnF9+6cTEZE+zSyM1X8bmu8FHNAGrhwogSG/xZX03SPfNYQuIiJFy+q/D833AyGgFYjGeuFWh9V9FgunNZ1clJTARUSkKFm0FpoXAy1JGrRijXk96yevlMBFRKQ4tf4L8KdoEIGWf+YrmrxTAhcRkeJkLUC0izZteQmlEJTARUSkOAUPAedStwlMyU8sBaAELiIiRckFDwXfGJKmMleOG3h1XmPKJyVwEREpWm7Ib8ENBko7XSiHsgugdG5B4soH7QMXEZGi5QLjYMQ/saa/QvPdYE0QmIqruBJKjselGGJvDr3Bh7v/SGu4hqB/FEMHXkpF6XEpP9ObKIGLiEhRc74huIHXwMBr0mpvZmzZ+S1qG/+KWQiIAFDf8hgVJUdz4Ig/4nMlOYw4OzSELiIieReNfEjj7l+w88NL2VX7BVpbHsUskpdn1zXe5SXvZtqTN4BZE42tz7N153fzEkdPZdwDd87NAj4DHAUMBnYBq4DbzOy57IYnIiJ9TUvzQ+yu+zfAaC/C0tb6OD7//lQNuxuff1jOnm1mfFD/My95J7hOC7WNdzJy8Nfw+ypyFkc2ZNQDd879FFgGXAlUA1O8368CljnnfpL1CEVEpM8It73N7p3/BjQTX0HNrJFIeD27aq/I6fMj0Z2EI9tStnEEaGl7PadxZEPaCdw5dw1wHbCBWAKfAgzyfr8KeA+4zjn3xRzEKSIifUBTw2/BQkmuthEOv0k4h8nTOR+WVsveP8OcSYTXAFuBajO71cxqzKzR+/33wNHANuDaXAQqIiLFL9T6FPHzzvuwCKHWZ3L2fL9vMCWBsV20ilBecljOYsiWTBL4JGCRmdUmumhmO4C7vXYiIiLdkF7/uCdGDv4PnCtPeM25coYO/Cw+V5bzOHoqkwReS+ystlRagA+7H46IiPRlJaUnkfIAEhcgWHpCTmOoGnAuIwZdi6MUaN8u5se5MirLP8Kowf+R0+dnSyar0O8DznPO/aeZhTtfdM4FgfO8diIiIvsYMPCLtDY/QOJh9ACBwMEEg4fmPI6Rg7/MkIqPU9vwf7SGawj4RzG04pOUl0zP+bOzxZmlN1zhnBsMPE5s29jXzOyFuGvHAj8ktqjtNDOr7+p+1dXVtmLFim4FLSIixaul6QF277yO2HC5N7DrKvD7RlI1/B58/uGFDK/gnHMrzay6q3ZJe+DOuTUJ3i4FjgSWO+dage3ACPYWod0IvAgclHHEIiLSL5QNOIeS0mNobvo/2lpfwPkqKCufR0nZHJxTgdB0pfqTGkDi1QSb4/7bR8c5b5/3ORERkaR8/v2oGPSV2LitdEvSBG5mY/IZiIiIiKSv9+9UFxERkX0ogYuIiBQhrRYQEZFexyzKhobHeaPuz+xu20jQV8GUyvM4qGoepf7BhQ6vV1ACFxHpA5raNrCxfjFN4fWUBUYzZtA8BpZMLXRY3WIW5aktX2dL0/OEzTutLNrA63W38/auuzlr7B+oCI4qcJSFpwQuIlLEzIy1dT/lvfo7MItghAE/m3YvYlTFWUwf/l2cS3+2tC60idW7HqMxXMvQkrEcMngu5YHK3P0ACbyz6342Nz1PxFo6vB+xENHITp7eeiMfHfv7vMbUGymBi4gUsc0N9/Be/Z+JWnyl6whRi7C18SHKA2OZOOQLXd7HLMqjW3/B6l2PYRYhSoSAK+Ff22/jtJFf5IghZ+Xuh+jkjbo/7ZO898RJlLrWd6gPvUdlybi8xdQbaRGbiEiRMjPW1f2aqDUnvB61FjbU/4GotXV5r2Xb/8Sbux6P9XK9MqdhCxGxEE9s+x3vNryY1diTMYvSEN6cso3DT13rO3mJpzdTAhcRKVKtke2EoqnPjzKL0hBKnezaoq2srP07YUt8XlXYWnlm+x3djjMzDl+qw04A5xwBX+LTxPqTjBO4c+4Q59z3nHN3O+f+Gff+OOfchc65quyGKCIiiUXTbJf6zIstzW91OU/+Qcta2qJdHUjZc845xgw8EXBJ25hFGVk+I+ex9HYZJXDn3DeBV4D/BC4A5sZdDgKLgEuzFp2IiCRV6t+PgK+rWqRGRXByFy0iuBQJE8DhMEt0glj2HT708/hdacJrflfGIUMuJ+Dr/ed151raCdw5dxHwbWInklUDC+Kvm1kNsJLYkaIiIpJjzvmYMPgL+Fzi4WSfK2NM5afw+xInw3b7lU4mbKGUbQYGhxPM07D1kNJJnL7/Tyj1VRFwA/ARJODK8bsSplV9gsOGXpGXOHq7TFahXwfUAOeaWatz7twEbVYDJ2clMhER6dLYyk/REHqLLY0PELUQsWF1h9+VMaRsFpOHXNflPcoDlUwddAJrdj9DJMGCt4Ar5dhhn8C51L30bBo5YAbzJj7I5qbnqA+9R4lvEGMHnqgiLnEySeCHA7eZJVnlELMFGNmzkEREJF3OOaaP+C4HVF7M+/V/obltA6WBUYyt/CRVpTPTTrpzR19HbWgjta0baduzqt0RdKUcXHkyh1d9NHc/RBI+52dMxfFQcXzen10MMkngjq5XTIxgz+nsIiKSL4NLD2XwiB90+/MlvnIuGf9z1u5+lpfq/kFTZCdDS8Yyc+gFHFB+SF5735KeTBL4WmB2sosutoTxBGLD6CIiUmR8zs/UyhOZWnlioUORNGSyCv1vwEznXLIJleuBKcBfexyViIiIpJRJD/xnwEXAT7wV6Q7AOfdD4ERgFvAisDDbQYqIiEhHaSdwM2tyzp0C/Ar4BHt779cTqxJwJ/BFszRq9omIiEiPZHSYiZntBC51zn0FOAYYBuwCnjezrTmIT0RERBLo1mlkZrYdeDDLsYiIiEiadJyoiEg/VBvaxit1y2iI7GJk6RiOqDqBUv++ldbMjJZoCwHnJ+grKUCkkkzaCdw5d3OaTc3Mru5mPCIikkNRi3DvpltYVfcUUTOihClxpfxj8x+ZN+ZajhhyvNcuymMfLOGfWx+mIbwbM2PiwElceMA8pg46qMA/hQA4s9Sn1Oxp6FxXRVyM2Mp0M7PUZ8EB1dXVtmLFirSeLSIi2fHwlj/z7I6HaUtQVDPoSvjcxG8wbsBB/KbmV6ze9TqhTjXSg66Ez034PNVDj8lXyP2Oc26lmVV31S6TfeBTkvw6GriGWBnVu4CpGUcrIiI51xpp5tkdDyVM3gBtFuLRrXfxys6XeLP+jX2Sd3ubP66/ldbI3ntELUpzpIWopXu8qWRDJtvIalJcXumcexh4FXiE2KEnIiLSi6xveguf86c8HvzdxjeoC5XQ2sXZ36t2rmDaoMO48/2HeOKD54hYBL/zc8qIY/nkuLOpKqnMcvTSWUbngadiZhuA+4AvZ+ueIiKSPZE0z/PeHtqW8nprtJX3mt7nKy/fxNKtzxKKthGxKKFoG49tW85XXv4Bta07sxGypJC1BO7ZhobQRUR6pQPKJxCxcMo2w0pGMTCQuvccdEFerltDfVsDETp+KYgQob6tkVvW/a3H8UpqWUvg3mEmpwL12bqniIhkz+DgMCYPPBx/ktnTElfKqft9nFNGnEpJii1jhrGuYTvRJAdURomysu4NGsNNWYlbEks7gTvnjkvy6yTn3CXAEuAo4P6cRSsiIj1y0dgvMbR0JCW+0rh3HSW+Uo6oOoEZQ05m9rDjGRocit/tu6GoxFfCsUOPx+8LpnxOwBdgh4bRcyqTQi7PkHLpAw54llhtdBER6YUGBAbxb1N+zKu7nuW5HY/QFGlgROkBnDjiHCZWxM79LvWX8vVp3+C29bfy2q7XCPoCmIFzcPbo8zh6yGwe3vJyyueEo2EGBSvy9FP1T5kk8B+QOIFHgTrgBTN7NitRiYhIzgR9Jcwccgozh5yStE1FoIJrJ/8bu9p2sbHpfYK+IBMrJhHwxdLGhIoxrGlYn/Tz4ysOYGjJ4CxHLvEy2UZ2Yy4DERGR3mdwcDCDB++biD83cT7ffP3ntEb33Ste4gvyuYnz93k/HI3w9PbX+Pv7z1Ab2s3+5cOYP/ZEjhl2MM65nMTfl6kWuoiIZGzqoPF885Br+eU7f6YutAu/8xGxKENKKvl/ky/joEETOrRvjrTylZW/Y0PTNpojsaS/uflDXt+1nuohU/nvwy/H77K9MapvUwIXEZFumV45md/M+BbrGzfyYWgXw0oGM75iTMLe9C/evpd1jVsIRTtuY2uJhHix9m0WbXiKT4w/NV+h9wlJE7hzbk0372lmpkr3IiL9gHOOCQPHMoGxSds0hJt5bNtL+yTvdq3RNu5870kuOvBkfOqFpy1VD3wAqVedi4hIL2JmPLjpZf5Q8xTvNX6I3+fjpP0O5qrJpzKlclTB4lrfsI2gCxAieRGZpkgrdaEGhpWqBGu6kiZwMxuTz0BERKT7zIwbX1nME9tW0xJpAyAajfDE1tUs276Gn828lGOGTypIbAGfH+uiPxg1I+jTrG4mNFYhItIHPP3BWzwZl7zbRTFaIm1c/9KdtEXTq4WebZMH7p+wKEy8cQP2ozI4IE8R9Q1K4CIifcAd656huVPyjheORnj6g7fyGNFeAZ+fyyecTlmS6m2lviCfn/zRPEdV/DIer3DOBYGZwAFAaaI2ZvaXHsYlIiIZWN+4I+X11kgbGxpSt8mleWNPoi7UwKL3/wVmtFmEUl8Qw7hmyrkcN3x6wWIrVhklcOfc5cCPgeHJmhBb+KYELiKSRwMDpdSFGpNeD/oCDAyW5TGijpxzXDX5bC4cewKPbX2JHa31HDBgGKePPIpBGjrvlrQTuHPuDOCPwNvA/wALiB1csgI4BTgNWAw8kvUoRUQkpfPHzuTmd56gNclWrSjG6aMOyXNU+xpeOpiLDzyl0GH0CZn0wL9KrOb5LDOrd84tAFaZ2feA7znnrgZ+CfwsB3GKiEgK88Ydw53rn6M21EjEOh7zWeYPcuHYoxlWOrBA0fVMbUsTi9a9wmu1WxlcUsYFEw5l5vDEBWPamRmPbH6T3771L9bWbyfg83HyqClce/BJHDR4ZB6jzx1nlt5Wb+fch8B9ZvZZ73UU+I6ZfTuuzdNAg5md1dX9qqurbcWKFd0KWkRE9rWteRc3vHQnb9dvIeCLrfoOR6NcMmE210ydU5RFUv6x/g2uf/5BHNASCeOA8kCQw4aO5tZTLmJAYN9zy82M77zyMPdseKXDwj4fjlJ/gF/PuojjRxZmS106nHMrzay6q3aZ9MArgC1xr1uBQZ3avAB8JoN7iohIlowsH8xtx13N+obtvF2/hVJ/kGOGTWRAIOF6417vlQ83c8PzD9Ia2TstYEBTuI2Xd2ziq8v/wW9O/Pg+n3tu+7v7JG+ITSM0R9r4t+cXs/zsf6fEX9z7zjOJfiswIu71ZqBzydTKDO8pIiJZNn7gCMYPHNF1w17u168v65C847VGIzy+aS1bmuoZPaBj9bY/vPNcyi11hvHo5rc4Z+yhWY033zIZT1lNx4S9DDjdOTcbwDk3DbjIayciItIjy7auT1m/LeDzsXzrhn3ef6f+g5T3bQyHqKnf3sPoCi+TBP4wcLxzbrT3+kfERjOecc5tAV4j1gP/fnZDFBGR/qir8qtYbFi8s4HB1FMGJT5/l22KQSYJfCFwIFALYGavA3OBJUAD8ARwjpk9kO0gRUSk/5kxPPWRHBEzqkfs22b++BmU+RNXfYtxnDmm+AvHpD1fbWYhYFOn95YBZ2Y7KBERiXltx1Zufu0FXtmxlYpgCfOnHMb8KYcyqKT4e5Bd+dKhx/PSjk0J57ODPj8zR4xh/KCh+1z7+IFHcuuaZ2mLRhJuqTtrzCEcMKAqZ3HnSybbyCrNrD5bD9Y2MhGR1H758rP86pXnCEUjRL1/q8v9AQaWlHLPOZcydtDgAkeYe7e++QL/8+qTRKJRwl4yrgiUsP+ASu6aexlVpeUJP7etuZ5/e34xb+3aig8fzsW21F00fgZfO/wMAr7eu6Uu3W1kmSTwRuBe4HZgiaX7wSSUwEVEklu+5T0+s2QxzeF9V2H7nGNK1TAeveCzBYgs/9bVf8gda1bsKeQyf+IRzBkzhaAv9QlnADW7d/Ba3WZKfQGO228Cg0sSJ/zeJBf7wDcBnwQ+AWxxzv0JuMPM3uxmjCIiksSvX1meMHlD7Ozs93bv4pXtWzhixOiEbfqSiZXD+Hb1R7r12UmDhjNpULLjOzJT19LMG9s/wO/zcdTIUZQFUs2z514mc+BTvS1jVwDzgRuA651zK4j1yv9qZnU5iVJEpJ95ZcfWlNej0Sirtm/uFwm80BpDIf7rqaU8XLOGEn+s1x8x43NHzOQrxxyHL0VJ11zKaBLAzJab2dXAaGK98UeAGcCvgM3OucXOuXOyH6aISP8S6KLsqXOOkjSGkKVnwtEon7z3bzxcs4bWSITdoRC7QyGa2tr4/csr+K8nlxQstm7N4ptZq5nd5dU8HwNcD6wBLiQ2Ty4iIj3wkQOn4E91WAfGKWMm5jGi/unRdWtZu7OW1khkn2vN4TB/f3s1G3btLEBk3Uzg8cxsG7ASeAloI3YmuIiI9MDVhx2TtFZ3qd/PnLGTOWBgZcLrkj1/fv1lmtqSl2WNmHH3W2/kMaK9up3AnXOTnXPfdc6tB5YClxXuKlAAACAASURBVAMbge9kKTYRkX5rwuCh3Db34wwKllLhLZbyO0eZP8AJ+4/nJyd1eeijZMH2pqaU18PRKNubGvMUTUcZHTzinBsMXAx8GphFrLfdQGwR221m9nTWIxQR6admjR7Hyk9dy8Pr1/Dajq0MKinlrPEHMXVIdlZVS9cmDRnC2roPkxZ1LQsEmDxkWF5japd2AnfO3QWcC7SX/3kSuA2428xSf0UREZFuKfUHOH/SdM6fVPylP4vRZ4+YyVPvbaA5nHgY3cy48ODC/N1kMoQ+n9he8G8BE8zsdDP7k5K3dLatoYEXN25kzY4d9LDej4hIQR09+gDOnXIQ5YF9+7tlgQDfOuFUhpQVpjhMJkPoJ3q1z0US2lRfz9cfeZQXN22i1O8nHI0yvKKCb512KqdO1GpZESk+zjkWnHoGR40cza9WPs+2xgbMjOnD9+Pfjz2eUw6cULjYCtVDUinVvuWDhgbOvuNP7GppIdLp/6myQICfn302cyZPKlB0IiI9Z2Y0trUR8LmcVmFLt5Rq763mLkXlV889T31r6z7JG6AlHObGJUv2HMYgIlKMnHMMLCkpeAnVdkrgkhV/X72acDSa9HpTuI2V/397dx7nRH0/fvz13iR7cSgIcgseqAioHCoqCkqp900p3kcrHvWobe23WtuirW3VCrWXZ621/hQFtYKiqBxSVFAUlYpHQRG5FHA5dzfZJO/fHzOBISTZZDfn8n4+HvNIMvP5zLwzm807M/OZz2flyqTLjTHGZMYSuGm2SDRKXYqODgAEYX0j91MaY4xJnyVw02y+sjJ2q6hIWSaqSte21muUMcZkiyVwkxXnH3rItlF6EulQXU3/Tp3yGJExxrRslsBNVow97DC6tmmTMIlX+f3cffJJSIGG3DPGmJYo4wQuIn4R+ZaIXCsiN3nml4tIe7Fv6V1Sm4oKnj3/PL7bvx9VgQCVfj+BsjKG9tyLp84dw8CuXQsdojHGtCgZ3QcuIt8CHga64fSDrqrqc5cdCcwFzlfViY2ty+4Db7lCkQg1dXW0Ki+ndXl5ocMxxpiSkvX7wEVkIPA8Tu9tNwI7JGlVfRNYBpyVUaSmxSn3+ejUurUlb2OMyaFMulL9JVAHDFbVVSLyqwRl3gYGZCUykzMNkQjzln1JTW0de7XbnUO6dbbr08YYU2IySeBDgWdVdVWKMssBG6S2iP37g8X85uXZRKIKKKrQvlUVE846mUO6dSl0eMYYY9KUSQJvDaxtpEwVu2DL9uXrN/DSok/YVB+kd6cOfLtvb6rKi6OrPa+p//2YX06bQX04vMP82g0NXPTYZCZdei7772njDBtjTCnIJIGvBPo2UuZQ4POmh9M86zfX8o/ZC/j32x+ypT5Ix7atueCYAYw56hAqApm81fSEI1F+/sx0Xv7wf0SiSjgapao8wK+nzuTu757CsAMKN0pNvEg0ym9fnr1T8o6pbwgzYfbr3Dv6jDxHZowxpikyOVqeDpzotjbfiYh8Gzgap6Fb3q2u2cTZdz/K43MXsrG2nkhUWbNhM39+6Q0u+uuT1DckTlzN8dsXZvHK4iUEw5Ft/YDXhRqoDTVww8Tn+Wj111nfZlMtWvVV0uQNoMBrS5bREInkLyhjjDFNlkkC/y2wEXhVRG4HDgQQkRPc108DXwHjsx5lGn4+cTobttbTENlxQI1gQ5ila9bz4Kvzs7q9DbV1PPPuh0l/GITCEe6bld1tNsfmYJCyNBqqBVMkeWOMMcUj7QSuqiuAE3CS9E3AaJx7wae5r9cCJ6lqY9fJs251zSY++GJ10uEqg+EIT7zxPtFo9oazfHPpcgIpug6NqjLn02VZ215z9WrfjlAjR9fV5QGq7dYvY4wpCRldGFbVBSKyP3AGMATYA+eofB5OC/VQ9kNs3Odrawj4fQTDyRNUfaiBLcEgbasqs7LNhkiUxjrBiaQYXjPferTbjb6dO/HeysQ/dCr8Pi4YfEhaR+nGGGMKL+0ELiJdgQb3CPtpdyoKrSvKkx59x0RVqcxiQ7b+3To1mqD371RcLbrvOuNERj38OJuDoR3G7q70+9m3Q3uuOPrwAkZnjDEmE5lcA/8SuDNXgTRHvx6dUyZnAY7avyfl/uwl8L07tqdv1074yxIfsVYF/FwxvLgSYo92uzF17IWcO+hgWleUUybCnq1bcc2xQ3ji4u9SFSi+W9+MMcYklnZf6CKyHnhIVf8vGxvOdl/oL7zzEeMmv5qwUVllwM9j147hgK4ds7Y9gLWbt3Lu/ROp2VpLnbtdcbf3ncP6838nDbMezowxxmQk3b7QMzkknU8Rd5N6yqA+hCIR7pzyGqpKVJ1k2qaqgrsuODnryRugY5tWTLnuIqa+9xGT3l7E5vog+3fuwCVHD2JQr25Z354xxhgTk8kR+BHAHOAKVX2kuRvO1WhkDeEIb/5vORu21tGtfVsG7t3NjoKNMcaUjFwcgY8AZgJ/F5ErcQYuWYPTB4iXqurvMlhvVgX8Po7tUzw9oBljjDG5kEkC/43n+eHulIgCBUvgxhhjzK4gkwQ+MmdRmJwKNoSZ9/EXbNxaT88923Hw3l3ssoIxxpS4tBO4qs7IZSAmN578z/vc8+//IAgaGz60TRV3XnYKfXt2LnR4xhhjmij7Q3S1QOFIlP+8v5TZC5cSjUY5om8vRh62f05GOMumyXM/YMKzc6gP7Xhr3cr1DVz+p8n8vxvPY+/O7QsUnTHGmOYo7gxUBFat28jYO55i09Z6aoMNALy2cCkTJs7mbz8ZxQF77VngCBNriES457m5OyXvmPpQmHunvcmdl52S58iMMcZkQ9o9sYlIg4iE0piCuQw4n8KRKGPvfIqva7ZsS94AtcEGNm6t58q7JrGltjjf7sIlK1N2LxtVZeb7Sxrtz90YY0xxyrQjl0Tf9rsD+wEVwCJgUxbiKgpz3/+MTVvrkybChnCEqa9/yLkjB+Y5ssZtqQ/RWDO1SDRKOBpNOaqaMcaY4pRJI7ahyZaJSFvgT8Bg4LQsxFUUXntvKbX1DUmX14fCvLrg06JM4Pt0br/T2Ojx2reutuRtjDElKpPBTJJS1U3A93CO0G/PxjqLQaSRBAg0OgpaKsFQmHCKIVCbo1en9uzbZY+kw4NWBPxccHzx/fAwxhiTnqwkcABVjQCzgLOytc5CO7J/L6orko/QVRHwMbR/Zr2+qSrTZixizJUP8u0xf2TE6Alc+dPHWPD+F80Ndye/v/RkWldV4Pft+GeuDPjp02NPLjjOErgxxpSqrCVwVznQLsvrLJgRg3pTHkh+irmsrIyzhh2c9vpUlT/c+woTHniVlWs2EI0q0ajy4aer+dntz/DCq4uyEfY2e3Xcnck3X8h3hh5Mm6oKfGVCl/Ztue6MoTx43SgCfjt9bowxpSrtwUwaXZFIb2AesFpV+zVWPleDmWTb/75cy9g7n6IhHNl2S1ZFwE9ZmTDhujMYfOBeaa/rg8Ur+PGtk6kPJr6uXl7u59m/X0nbNlVZid0YY0zpyfpgJiLyQIp19ACOdZ9nZbzwYtG7R0em3vl9pr3xEa8u+IRIVDm6/96cNaw/7dpUZ7SuSVPfIRhK3ihOgJdfW8yoUwc1M2pjjDEtXSa3kX2/keVLgLtU9aFmxFOUWldVMHrEoYwecWiz1rNsxXpSnfAIhsJ8vnx9s7ZhjDFm15BJAu+dZH4UqFHVDVmIp0Xbfbdq+DJ5gvb5ymjfrlUeIzLGGFOqMrkPfGkuA9kVnHnioXyyZA11Se4t9/nKOGH4QXmOyhhjTCnKpCvVB0Tk1EbKnJziWvkub9iQ3nTv0o5AgpbtlRV+Rh7Th+5d8teI//MV65k+9yNmvfVp0XYJa4wxJrFMr4GvAJ5PUWYATocuY5sTVEvl9/v48+1juPOv05n71pJtiTwSiXLOKQO5/Pxj8hLHmnWbuPmPU/lsxTp8ZQIiRMJRRp0wgKvHHENZmY0VbowxxS7bo5GVA7npWqyFaFVdwa03nk7Nhq18svQr/P4y+h3YjcoUHcZk06Yt9Xz/F49Ts7mWaHTHFnVPv7KQUEOYH118fF5iMcYY03SZduSStA21iASAY4CvmhXRLqLd7q0YMmgfBh/SK2/JG+DZGe+zpbZ+p+QNUB8M89zMD1i/YWve4jHGGNM0KY/AReTTuFnXi8iFCYr6gD2BasCugRexqbMWEWxIfpJERJj99v84Z2TzbpkzxhiTW42dQq9m+1G3AgEgUTdhEeBTYAZwa9aiM1mXanQ1cIZItQZtxhhT/FImcFXtHnsuIlHgblW9LedRmZzp2bU9NZtqky6vLPfTq2v7PEZkjDGmKTK5Bj4S+FeuAjH5cf6pg6lKcc3d7/Nx9MB98xiRMcaYpsikI5cZuQykJQrWNzBvxmK++XoTHbrsxhHH9aE8jw3WEjl6wD4cd0RvZs7/lPpgeNv8MhHKy3387ken7zT8qDHGmOKT8W1kbmvzQUA3oCJRGVV9vJlxlbyXn36be389BRGhIRQmUO7s6ut+fTbDT81PA7E1X21kydKvKS/3cXC/HlRWBhARbrniRA7v14t/Pjef5au/we/3ceygfbnkrCHs071DXmIzxhjTPBkNJyoiFwF3Acm+5QVQVW10oOlSGU60KV5/eRF3/eRJggkajFVUBrjlLxcy+NgDdlq2emUNy5evp1V1BX36dsPnb9qRcE3NVm6/43kWLfpye2cxUWX0qMO5+IKjraMWY4wpYrkYTvTbwD+AT4A/AHcAU4AFwHDgeGAyML0J8bYYqspDv38hYfIG57T6Q3e8sEMCX71qA7+/7d8s+fQrAgEfqorf72PsD0ZwwimHZLT9uroQV1/3L9au20wkEiXkuWXsyUnzqd0a5AdXjWjamzPGGFM0MjnE+wlQAwxR1bvcee+q6m9U9VvAVcCZwEdZjrGkrF6+npp1W1KWWbV8Peu/2gTAN+u3cM3lD/Px4lWEQmG2bg1SWxti06Y6/jz+JV54bmFG239x+iI2bNhKJBLdaVkwGOa55xeyfn3q+IwxxhS/TBL4IGCKqm5KVF9V7wfmAbdkKbaSVF8bavTUt99XRn1dCICnHn+T2q3BhD2jBYNhHvjbq4RC4Z2WJTN12ns7NE6LJyLMnvNx2uszxhhTnDJJ4K2A1Z7XQaBNXJm3gCOaG1Qp69xjD8IpejoDUIWOXXYDYPq0DwiHdz5a3l4Y3n3787S3v2VzfcrloVCYLVtSlzHGGFP8Mknga4COntergPiWWG3J/gApJaW6dQXDTjmUQHnidnzlFX5Gjhq87XayutpQyvVFVdm0qS7t7XfvnroTlqqqAN26WUctxhhT6jJJ4IvZMWG/DowQkSMBRKQPMNott0u74pbT6LLXHlRU7njPd0VVgB777sllPz5p27wOHeNPYuxIgK7ddh4jfP3azcyc9gGvTH2PVV9+s23+6HMOo7Iy+b3mgnDs0P3TfCfGGGOKVSZHyy8CE0Ski6quBu4ERgFzReRrnKPzMuD27IdZWlq1ruSep6/l5clv89y/3mDj+i2069CGMy8ZysizB+3QmcvZow/n4ftnEUxy3bpN2yr69t/Woy3B+gbG3/ocr89cjN/vQ4FoOMpBh/bg53eMZsgR+3LUkfvxxptLqI9rCV9R4efnPzuN8vJd+iSJMca0CGnfBy4i5ThJep2qBt15RwO/APYFlgETVHVaOutryfeBZyIUCvOTax9j6ZKvCHmSuAhUVAS444/nc1C/btvm33z1oyx694sdygL4Az66dm/PvU9eRZmvjGkvvc/jT85nzZoNiAiDBvbi0ouG0ufArnl7b8YYYzKX7n3gGXXkkk2WwLcLBcM89cQ8np30Flu31APCkKN7c8n3h9Frn+3NDj75cCU3fv8fSe8xr6ou50fjzuTYkX23zQuHI5SVlVnnLcYYUyKy3pGLyZ3yCj8XXDKU8y8+mvr6BsoD/oS3os184X0aUtxSVlcb4sVnFuyQwP3+RjvFM8YYU4Ka0hd6X+BcoA/QSlVPdOfvBQwGZqrqhqxGuYsQEaqqypMu37SxLuH94l6bN9ktYsYYsyvIqLNtEfkl8D5wM3AWzhCjMQFgEnBB1qIzO9jvwC47tWz38vnK6N2nSx4jMsYYUyhpJ3ARGQ2MA2biHGnf4V2uqkuBd4DTsxif8Rh5+qFOLzBJ+P1lnHnukDxGZIwxplAyOQK/HlgKnKaq7wKJztUuBnpnIzCzs7a7VfPjW8+kojKAxLVJq6gMcN7YYfTcd8/CBGeMMSavMrkGfjDwSOwWsiRWA52aF5JJZdgJ/enUrR1PPDiHhfOXElXlgL7dOO/yYQw6cr9Ch2eMMSZPMkngAqTotBtw7hNPleBNFhzYrzu33nNeocMwxhhTQJmcQl8CHJlsoYiUAUOxrlSNMcaYnMskgT8FDBKR65Ms/ynO9e8nmh2VMcYYY1LK5BT6H3EGKxnvtkgXABH5PXAMMAR4G7g/20EaY4wxZkdpJ3BVrRWR4cBfgDFsP3r/KaDAROAqVU3cz6cxxhhjsiajntjcHtYuEJEbgMOBPYCNwHxVXZOD+IwxxhiTQJP6QlfVtcALWY7FGGOMMWlK2YhNRC4SkYPzFYwxxhhj0tPYEfgjON2nfhCbISIXAxer6vG5C8uUiq2b65gx6S1em/IukXCEgcMO5NSLjqF9p90KHZoxxrRoTTmF3gsYluU4TAla9vEqfnrOPTSEwtTXhgD4bPFKnrl/Fj9/4DIOO75vI2swxhjTVBmNRmZMTLghwk3f/QubN9RuS94ADcEwwboQt499mHWrbVRZY4zJFUvgpknefOkDgnWhpMujkSjP//M/eYzIGGN2LZbA86RuSz1TH5zJNceO43sDfsbvLr2XT975rNBhNdnCuZ9QtzV5t/cNoTALZlmvusYYkyvpXANPPgC1Scvald/wwxG/YcuGrQTd082rPv+aedPeY9QPT+LCm84scISZ8/ka/+2XThljjDFNk8437DgRicQm4JcA3nlxUzi3IZeeW8/9EzVfbdyWvAE0qgTrQky+50XenflhAaNrmiNG9qOqVUXS5eWVAYaeNiCPERljzK4lnQQuGU522OXx2aLlfPnJaqKRxCOxBmtDTLz7+W2v62uDvPXiQuY+O5/Vn3+VrzAzNnDYgbTbsy1lSY6yAwEfJ45JOnidMcaYZkp5Cl1VLRk308cLGr/O/em7n6OqPHrrJCb9YQo+v7Pbw6EwfYbsz82PX0/7zu1yHWpGysrKuGPSdfzfqD9Rs3bTtuvhVa0q8Ad83P7ED2jTrlWBozTGmJarSV2pmvT5Az6kTFKW8fl93H/jozx/3ysEa3dsGPbfuR9z7ZCbeXDReKrbVOUy1Ix16LI7D8y5hXdmf8QbL75PuCHCgGMO4JhTB1BeGSh0eMYY06JZAs+xQSP6EQlHki4v85Ux8PiDmPK36TTU7zyQWyQcYeO6Tbz08EzOvv6UXIbaJD5fGYeP6MvhI6zTFmOMySc7RZ5je3Rpx9AzBic9Ig2U++nWqyNlkvwoPVgb4vn7X85ViMYYY0qQJfA8uOGvlzHoW/0orwpsu75d2aqCqtaV/GridQQCPkIJjr69tmyozUeoxhhjSoSdQs+D8ooAv3r8Or74eCVznn2b2k117Nt/L4456zAqqsrZvHYjla0qqNtSn3Qd3ffvmseIjTHGFDtL4HnU88BuXHhTt53mH3nGYcjY+5LWq2xVwXd+fFouQzPGGFNi7BR6ESivCHDLxBuoqC4n/lJ4RXUFQ04bzJBTBxUmOGOMMUXJEniROOzEAYx/7TYOP3kgZb4yRKDLPp24esIl3PTYdUiKRm7GGGN2PaJamK7OBw8erAsWLCjItoudqhKNRvH5fIUOxRhjTJ6JyDuqOrixcnYEXoRExJK3McaYlCyBG2OMMSXIErgxxhhTggp2DVxE1gJfFGTj+dcBWFfoIArM9oHtA7B9ALYPwPYBpN4HPVW1Y2MrKFgC35WIyIJ0GiS0ZLYPbB+A7QOwfQC2DyA7+8BOoRtjjDElyBK4McYYU4IsgefHA4UOoAjYPrB9ALYPwPYB2D6ALOwDuwZujDHGlCA7AjfGGGNKkCVwY4wxpgRZAs8iETlRRD4RkSUi8rMEyytE5El3+XwR6ZX/KHMrjX3wIxFZLCIfiMgMEelZiDhzqbF94Cl3joioiLS422nS2QciMtr9LHwoIo/nO8ZcSuP/YC8RmSUiC93/hZMLEWcuicjDIvK1iPw3yXIRkT+5++gDERmY7xhzLY19cL773heJyBsickhGG1BVm7IwAT5gKbAPUA68DxwUV+Zq4D73+RjgyULHXYB9cBxQ7T6/alfcB265NsAcYB4wuNBxF+Bz0BtYCLRzX+9Z6Ljz/P4fAK5ynx8ELCt03DnYD8cCA4H/Jll+MvAiIMAQYH6hYy7APjjK8z9wUqb7wI7As+dwYImqfqaqIWAicEZcmTOAf7rPJwMjpGWNE9roPlDVWapa676cB3TPc4y5ls7nAODXwB1AfT6Dy5N09sHlwF9VtQZAVb/Oc4y5lM77V6Ct+3w3YFUe48sLVZ0DfJOiyBnAo+qYB+wuIl3yE11+NLYPVPWN2P8ATfg+tASePd2ALz2vV7jzEpZR1TCwEdgjL9HlRzr7wOt7OL/AW5JG94F7qrCHqr6Qz8DyKJ3Pwf7A/iLyuojME5ET8xZd7qXz/scBF4jICmAacG1+QisqmX5ftHQZfx/6cxSIMSmJyAXAYGBYoWPJJxEpA8YDlxQ4lELz45xGH45z1DFHRPqr6oaCRpU/5wKPqOrdInIk8C8R6aeq0UIHZvJPRI7DSeBDM6lnR+DZsxLo4Xnd3Z2XsIyI+HFOna3PS3T5kc4+QES+BfwcOF1Vg3mKLV8a2wdtgH7AbBFZhnPtb0oLa8iWzudgBTBFVRtU9XPgU5yE3hKk8/6/BzwFoKpvApU4g1vsStL6vmjpRORg4CHgDFXNKB9YAs+et4HeIrK3iJTjNFKbEldmCnCx+3wUMFPd1gstRKP7QEQGAPfjJO+WdN0zJuU+UNWNqtpBVXupai+c616nq+qCwoSbE+n8L/wb5+gbEemAc0r9s3wGmUPpvP/lwAgAEemDk8DX5jXKwpsCXOS2Rh8CbFTV1YUOKp9EZC/gGeBCVf000/p2Cj1LVDUsItcA03FaoT6sqh+KyG3AAlWdAvwd51TZEpyGDWMKF3H2pbkP7gJaA5Pc9nvLVfX0ggWdZWnugxYtzX0wHfi2iCwGIsCNmR59FKs03/+PgQdF5AacBm2XtLAf84jIEzg/0jq41/p/BQQAVPU+nGv/JwNLgFrg0sJEmjtp7INf4rSD+pv7fRjWDEYos65UjTHGmBJkp9CNMcaYEmQJ3BhjjClBlsCNMcaYEmQJ3BhjjClBlsCNMcaYEmQJ3Jgi4o5OpiIyPG5+L8+yXgUJzhhTVCyBG2OaTER2F5Fx7rR7oeMpBBE51P1h9WXjpY3JHuvIxZjS0AB84nleLHbH6ZwC4BFgV+nL3Cs20thzBY3C7HIsgRtTAlR1JXBgoeMwCVkCNwVhp9CNMaaJ3L6sB+AMDTy7sNGYXY0lcFOURKSHiNwpIu+JyEYRqRORpSLynIhcJCKVCer4ROQyEZkpIutEJCgiK0VkUnyjsCTbHO6WXenWXSciM0TkUhHxJakzzr3+Odt9fY6IvCwiX4tIVETGxZVvJyJ3ue+lXkRWu9sc1EhsSRuxuXGriKj7ej8ReVhEvnTfxwoReVBEEo61LCJlIjJCRP4kztjcK0QkJCLrReQ1EblSRAIJ6s0GPvfM+twT47Z9ElenXESuFpFZ7v4Nicga9+96Uor3XyUiPxGRN0WkRkQaRGStiCwWkX+KyDmp9l+C9d3rxrghWaNAEbnKLRMWkWOTrCp29P2iqja4f99at97oRmL4tVvuM3E7wjYmI6pqk01FNQEXAnU4gzwoEATW4Vz7jc07NK7ObsAsz/IwUANEPfPuSrHN8Z5yUbdu2DNvBtAmQb1x7vLZwN2e+t+49cd5yvYClsW9r42e56d7lg2P204vz7JeccuGe5YdB2x2n2+K22crgW4J3oN33erW3xA3bw5QFVfvGZwRtGJl1gJrPNMzceV7Av+N28/x27k3QXxtgPcS/H28721Zhp+xKk8sbwD+uOX92P4ZvDXFel51y3zXM+8Rd96rKer5cIZUVeDmQv/P2VSaU8EDsMkm7wScwvakOxdngPsyd1m5+/oB4KC4epM9ifBaoNqd3xlnFLjYF/2VCbZ5jWf5/UBnd34r4IeeRDExQd1xnqSnwO+Bju6yCqCn+9yHM8yk4iT378SSBnCQmyBrPHEMj9tOL8+yXnHLhnuWfYNzLfZAzz4bjZPMFXg0wXvoDjwGnAa098xvDVyCk/gVGJ+gbtK44sq1Aj5yy80ChgEV7rLdgBs8+/D6uLq3uPPXA2d76pUBXXF+8D3QhM9aP5xRsBS43TPfm9znAr4k9Xd3PxtBoK1n/hFs/6GxT5K6p7llGmKfN5tsynQqeAA22RSbcBpVfuZ+sf0HKE+zXuwLU4GxScrEEvxaoNIzv8pNDAo8nqTutZ71D4pbNs6z7O4UMY72lBuRYHk1zrCKzU3gM3F/8CR5D7XEHW2msX8Hu3W3ePddY3HFlfsF289UBJKUOcvzN/J75k9z59+Ug8/cle66I8Bx7rz73Hk1wF4p6p7nlpueYNlCd9nvktSd6i5/Ohf/SzbtGpNdAzfF5Dhgb/f5DaoaSrPed93HFcBDScr8wn3sAIz0zB8JtHefj0tS92/Aavf5eUnKRIE7UsQYG/v9dVWdEb9QVWuBO1PUT9dvVTWaYH6shXQV0DuTFarqAuBrnKPoQ5sY1/fcx/Gqmuw2uH/jnCnoAHjbBMRuTevSxG0npc6YzM/gHM0/JiJjgSvcxZer6vIU1VO1Pr/Xfbwkvv2A2xYhdr3//iYFbgzWiM0UbAVo2gAABnNJREFUl6PcxzVu0kjXYPdxVpLkhap+hHMq2Fve+/xLVf00Sd0IzpFtfF2vJar6dRoxzkxRJtWydM1PMn+V53n7+IVu47Ir3QZ4q9zGb9sapAF7ukW7ZxqQm7B6ui//7jZa22nC+ZHU2i3X07OK593Ha0TkCRE5U0Q6ZBpHCt8HluOcjo8l1IdUdXKyCiJSjpOEFZiSoMjjOJcEOuOcLve6DOeSyufAK82K3OzSLIGbYtLZffwiw3qx5LIyZSnnCN1bvrl1vVIl73S3syLFsrSo6uYk88Oel/FHhHsCC3COGkfiHOlGcRoOfuVOsR9GrZoQVlfP8w5ApxRT7Dup2hP748A9OMlyDPAssFZE/icif22sBX9jVLUG+IFn1mfA9Y1UOx6ncd07qrrT301Vt+C0KwAYG5svImVsPxvxoKpqU+M2xhK4KSal/GUWKXQAzTAB6I/TFuAyoIuqVqlqR1XtrKqd2X4E35Tbnby34PVRVUljesS7AlX9IXAAcDPwIs5p9f2Aq4EFIvLHJsTldbnneTd33amk03lL7DT6SM+tat/GObsQBv6RWYjG7MgSuCkma9zHnilL7Sx29NvY6d3Ycu/RcnPqZiJWL+G92Gksywn3+uzZ7strVPUfqromrowP58i5qbzry/Rvu42qLlHV36nqycAewJE4180BrheR05uyXhG5BucWvgiwGOfugYkiUp2kvLjlIUUCV9VFOLeoeY+6Yz8Unovfz8ZkyhK4KSZvuI+dRSTZteZEYtfLj3NPUe5ERA5ke4J8O0Hd7iKyf5K6PpwGdvF1M7EtxhRljm/iupujIxDrFGdhkjJDPWXiedscJDw6V9VlbL90EH89uElUNaqq84BRONevYcfGiWkRkf7AXe7L24CTcY7u++CcmUjkMJzLAp+5STqV2FH4ZW5bgNj7fyDTWI2JZwncFJNZONcfASa4DYXSMdF97IbTICmR29zHdTidb8S8gnPqGJK3Qr+C7ddxn0gzpnhPuo9DJUGvcCJSBdzYxHU3R+z+cIBD4heKiB+4vZH6MalGI3vQffyeiAxIFZCItI97XZGsrNvAMHa3QsIGjCm2U4Xz2anEud/7dlX9gu3XrMcm6eEtk77PJ+F8vrriNGwLYI3XTJZYAjdFw/0yjnWqMhSYISJDY0fVbkvp4SLymIgc5Kn3FvC0+/LPInJN7PSniHQWkQdxOk4B+IWq1nvq1rE9cZ8rIveJSCe3brWIXAfErq8+qarvNPHtPQ28G3suTperPnc7fXCu63Zs4rqbzG1s9br7cryIHO/Z3/1w7sEeDGxNUn8D24+uL3UTfiJ3A4twkuUs92+0R2yhOMOSniQij+L0AeA1X5xuXoeLSCtPna4i8me2X6+elubbjpmA04nOBuB89/OHqk7C6fwH4EER6RFXL+0ErqpBnJ7ZAGLdsVrjNZMdhb4R3Sab4ifgIqCe7R2E1JNeV6qzPcsbcHola2pXqt/EbW8mjXSlmsb72gfndK/3fcW6Es1KV6qNbD/ZugfhdNLijSt2ZN6A09PZMvf1JQnWe0tc3eVu+Ylx5boCb8bt5xq2dycbm/4XV29Zgjpb4urs1EtcI/vibE/dUQmWV7O957g5uL2xAfu689aRpIe2BOvaz/M5tJ7XbMraZEfgpuio6qM4Q2f+EadRURinA5IvcBotXYjz5eqtsxEYgdNYaDbOPbitcRpQPY3Ty1bSU9Sq+iOca9BP49w21dpdxyycltkjNcktWhm8r89wOkIZj3MaVXAS3mTgKFVNdD9xzqlzVuFw4CmcxFSG896fcuP6VyOr+C3ObVcLcBJUd5zGap29hVR1Fc6ZlXNx7p1ejZMoy3GS9FScrmvjBw4ZgzPm+Ayc/VaOcyr6C5xLEyPcv19a3CPqWIc/f9cE93ur07HOuTg/rI7B+ZEC24++X1D3iL0xqroEpy93sMZrJotE1c7kGGNMOkRkDk5CP0dVn0mzTmfgS5yugk9Q1ZdzGKLZhdgRuDHGpMHt/e0onLMm0zOoeiVO8l6CNV4zWZSswYkxxpgdtQd+g9PtbsJGffHc2yF/7L4cr3bK02SRnUI3xpgsE5FlOB3CxNoBLASO0OQDuRiTMUvgxhiTZe4AMOA0onwJ+JmqflXAkEwLZAncGGOMKUHWiM0YY4wpQZbAjTHGmBJkCdwYY4wpQZbAjTHGmBJkCdwYY4wpQf8f5+iMYpa8N5cAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 504x504 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% plot the distributions\n",
- "\n",
- "pl.close(10)\n",
- "pl.figure(10, (7, 7))\n",
- "\n",
- "pl.subplot(2, 1, 1)\n",
- "\n",
- "pl.scatter(ys, xs, c=phi, s=70)\n",
- "pl.ylabel('Feature value a', fontsize=20)\n",
- "pl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\n",
- "pl.xticks(())\n",
- "pl.yticks(())\n",
- "pl.subplot(2, 1, 2)\n",
- "pl.scatter(yt, xt, c=phi2, s=70)\n",
- "pl.xlabel('coordinates x/y', fontsize=25)\n",
- "pl.ylabel('Feature value b', fontsize=20)\n",
- "pl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\n",
- "pl.yticks(())\n",
- "pl.tight_layout()\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create structure matrices and across-feature distance matrix\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% Structure matrices and across-features distance matrix\n",
- "C1 = ot.dist(xs)\n",
- "C2 = ot.dist(xt)\n",
- "M = ot.dist(ys, yt)\n",
- "w1 = ot.unif(C1.shape[0])\n",
- "w2 = ot.unif(C2.shape[0])\n",
- "Got = ot.emd([], [], M)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot matrices\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%%\n",
- "cmap = 'Reds'\n",
- "pl.close(10)\n",
- "pl.figure(10, (5, 5))\n",
- "fs = 15\n",
- "l_x = [0, 5, 10, 15]\n",
- "l_y = [0, 5, 10, 15, 20, 25]\n",
- "gs = pl.GridSpec(5, 5)\n",
- "\n",
- "ax1 = pl.subplot(gs[3:, :2])\n",
- "\n",
- "pl.imshow(C1, cmap=cmap, interpolation='nearest')\n",
- "pl.title(\"$C_1$\", fontsize=fs)\n",
- "pl.xlabel(\"$k$\", fontsize=fs)\n",
- "pl.ylabel(\"$i$\", fontsize=fs)\n",
- "pl.xticks(l_x)\n",
- "pl.yticks(l_x)\n",
- "\n",
- "ax2 = pl.subplot(gs[:3, 2:])\n",
- "\n",
- "pl.imshow(C2, cmap=cmap, interpolation='nearest')\n",
- "pl.title(\"$C_2$\", fontsize=fs)\n",
- "pl.ylabel(\"$l$\", fontsize=fs)\n",
- "#pl.ylabel(\"$l$\",fontsize=fs)\n",
- "pl.xticks(())\n",
- "pl.yticks(l_y)\n",
- "ax2.set_aspect('auto')\n",
- "\n",
- "ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\n",
- "pl.imshow(M, cmap=cmap, interpolation='nearest')\n",
- "pl.yticks(l_x)\n",
- "pl.xticks(l_y)\n",
- "pl.ylabel(\"$i$\", fontsize=fs)\n",
- "pl.title(\"$M_{AB}$\", fontsize=fs)\n",
- "pl.xlabel(\"$j$\", fontsize=fs)\n",
- "pl.tight_layout()\n",
- "ax3.set_aspect('auto')\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute FGW/GW\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Loss |Relative loss|Absolute loss\n",
- "------------------------------------------------\n",
- " 0|4.734462e+01|0.000000e+00|0.000000e+00\n",
- " 1|2.508258e+01|8.875498e-01|2.226204e+01\n",
- " 2|2.189329e+01|1.456747e-01|3.189297e+00\n",
- " 3|2.189329e+01|0.000000e+00|0.000000e+00\n",
- "Elapsed time : 0.005539894104003906 s\n",
- "It. |Loss |Relative loss|Absolute loss\n",
- "------------------------------------------------\n",
- " 0|4.683978e+04|0.000000e+00|0.000000e+00\n",
- " 1|3.860061e+04|2.134468e-01|8.239175e+03\n",
- " 2|2.182948e+04|7.682787e-01|1.677113e+04\n",
- " 3|2.182948e+04|0.000000e+00|0.000000e+00\n"
- ]
- }
- ],
- "source": [
- "#%% Computing FGW and GW\n",
- "alpha = 1e-3\n",
- "\n",
- "ot.tic()\n",
- "Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\n",
- "ot.toc()\n",
- "\n",
- "#%reload_ext WGW\n",
- "Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Visualize transport matrices\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 936x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% visu OT matrix\n",
- "cmap = 'Blues'\n",
- "fs = 15\n",
- "pl.figure(2, (13, 5))\n",
- "pl.clf()\n",
- "pl.subplot(1, 3, 1)\n",
- "pl.imshow(Got, cmap=cmap, interpolation='nearest')\n",
- "#pl.xlabel(\"$y$\",fontsize=fs)\n",
- "pl.ylabel(\"$i$\", fontsize=fs)\n",
- "pl.xticks(())\n",
- "\n",
- "pl.title('Wasserstein ($M$ only)')\n",
- "\n",
- "pl.subplot(1, 3, 2)\n",
- "pl.imshow(Gg, cmap=cmap, interpolation='nearest')\n",
- "pl.title('Gromov ($C_1,C_2$ only)')\n",
- "pl.xticks(())\n",
- "pl.subplot(1, 3, 3)\n",
- "pl.imshow(Gwg, cmap=cmap, interpolation='nearest')\n",
- "pl.title('FGW ($M+C_1,C_2$)')\n",
- "\n",
- "pl.xlabel(\"$j$\", fontsize=fs)\n",
- "pl.ylabel(\"$i$\", fontsize=fs)\n",
- "\n",
- "pl.tight_layout()\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_free_support_barycenter.ipynb b/notebooks/plot_free_support_barycenter.ipynb
deleted file mode 100644
index b8df589..0000000
--- a/notebooks/plot_free_support_barycenter.ipynb
+++ /dev/null
@@ -1,169 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# 2D free support Wasserstein barycenters of distributions\n",
- "\n",
- "\n",
- "Illustration of 2D Wasserstein barycenters if discributions that are weighted\n",
- "sum of diracs.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- " -------------\n",
- "%% parameters and data generation\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "N = 3\n",
- "d = 2\n",
- "measures_locations = []\n",
- "measures_weights = []\n",
- "\n",
- "for i in range(N):\n",
- "\n",
- " n_i = np.random.randint(low=1, high=20) # nb samples\n",
- "\n",
- " mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean\n",
- "\n",
- " A_i = np.random.rand(d, d)\n",
- " cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix\n",
- "\n",
- " x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations\n",
- " b_i = np.random.uniform(0., 1., (n_i,))\n",
- " b_i = b_i / np.sum(b_i) # Dirac weights\n",
- "\n",
- " measures_locations.append(x_i)\n",
- " measures_weights.append(b_i)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute free support barycenter\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "k = 10 # number of Diracs of the barycenter\n",
- "X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations\n",
- "b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)\n",
- "\n",
- "X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(1)\n",
- "for (x_i, b_i) in zip(measures_locations, measures_weights):\n",
- " color = np.random.randint(low=1, high=10 * N)\n",
- " pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')\n",
- "pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\n",
- "pl.title('Data measures and their barycenter')\n",
- "pl.legend(loc=0)\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb
deleted file mode 100644
index f565bfb..0000000
--- a/notebooks/plot_gromov.ipynb
+++ /dev/null
@@ -1,265 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Gromov-Wasserstein example\n",
- "\n",
- "\n",
- "This example is designed to show how to use the Gromov-Wassertsein distance\n",
- "computation in POT.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Erwan Vautier <erwan.vautier@gmail.com>\n",
- "# Nicolas Courty <ncourty@irisa.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import scipy as sp\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "from mpl_toolkits.mplot3d import Axes3D # noqa\n",
- "import ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sample two Gaussian distributions (2D and 3D)\n",
- "---------------------------------------------\n",
- "\n",
- "The Gromov-Wasserstein distance allows to compute distances with samples that\n",
- "do not belong to the same metric space. For demonstration purpose, we sample\n",
- "two Gaussian distributions in 2- and 3-dimensional spaces.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_samples = 30 # nb samples\n",
- "\n",
- "mu_s = np.array([0, 0])\n",
- "cov_s = np.array([[1, 0], [0, 1]])\n",
- "\n",
- "mu_t = np.array([4, 4, 4])\n",
- "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n",
- "\n",
- "\n",
- "xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\n",
- "P = sp.linalg.sqrtm(cov_t)\n",
- "xt = np.random.randn(n_samples, 3).dot(P) + mu_t"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plotting the distributions\n",
- "--------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = pl.figure()\n",
- "ax1 = fig.add_subplot(121)\n",
- "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
- "ax2 = fig.add_subplot(122, projection='3d')\n",
- "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute distance kernels, normalize them and then display\n",
- "---------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "C1 = sp.spatial.distance.cdist(xs, xs)\n",
- "C2 = sp.spatial.distance.cdist(xt, xt)\n",
- "\n",
- "C1 /= C1.max()\n",
- "C2 /= C2.max()\n",
- "\n",
- "pl.figure()\n",
- "pl.subplot(121)\n",
- "pl.imshow(C1)\n",
- "pl.subplot(122)\n",
- "pl.imshow(C2)\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute Gromov-Wasserstein plans and distance\n",
- "---------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 0|3.135088e-02|0.000000e+00\n",
- " 1|1.414971e-02|-1.215656e+00\n",
- " 2|9.934682e-03|-4.242736e-01\n",
- " 3|7.486162e-03|-3.270729e-01\n",
- " 4|7.415422e-03|-9.539599e-03\n",
- " 5|6.433930e-03|-1.525492e-01\n",
- " 6|6.020392e-03|-6.868966e-02\n",
- " 7|6.012738e-03|-1.272962e-03\n",
- " 8|6.012661e-03|-1.278831e-05\n",
- " 9|6.012660e-03|-1.278889e-07\n",
- " 10|6.012660e-03|-1.278889e-09\n",
- " 11|6.012660e-03|-1.278958e-11\n",
- "It. |Err \n",
- "-------------------\n",
- " 0|7.283759e-02|\n",
- " 10|4.751585e-03|\n",
- " 20|4.981526e-09|\n",
- " 30|3.401818e-14|\n",
- "Gromov-Wasserstein distances: 0.0060126599835825445\n",
- "Entropic Gromov-Wasserstein distances: 0.00591822665714488\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "p = ot.unif(n_samples)\n",
- "q = ot.unif(n_samples)\n",
- "\n",
- "gw0, log0 = ot.gromov.gromov_wasserstein(\n",
- " C1, C2, p, q, 'square_loss', verbose=True, log=True)\n",
- "\n",
- "gw, log = ot.gromov.entropic_gromov_wasserstein(\n",
- " C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n",
- "\n",
- "\n",
- "print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\n",
- "print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n",
- "\n",
- "\n",
- "pl.figure(1, (10, 5))\n",
- "\n",
- "pl.subplot(1, 2, 1)\n",
- "pl.imshow(gw0, cmap='jet')\n",
- "pl.title('Gromov Wasserstein')\n",
- "\n",
- "pl.subplot(1, 2, 2)\n",
- "pl.imshow(gw, cmap='jet')\n",
- "pl.title('Entropic Gromov Wasserstein')\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_gromov_barycenter.ipynb b/notebooks/plot_gromov_barycenter.ipynb
deleted file mode 100644
index 2271fdb..0000000
--- a/notebooks/plot_gromov_barycenter.ipynb
+++ /dev/null
@@ -1,391 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Gromov-Wasserstein Barycenter example\n",
- "\n",
- "\n",
- "This example is designed to show how to use the Gromov-Wasserstein distance\n",
- "computation in POT.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Erwan Vautier <erwan.vautier@gmail.com>\n",
- "# Nicolas Courty <ncourty@irisa.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "\n",
- "import numpy as np\n",
- "import scipy as sp\n",
- "\n",
- "import scipy.ndimage as spi\n",
- "import matplotlib.pylab as pl\n",
- "from sklearn import manifold\n",
- "from sklearn.decomposition import PCA\n",
- "\n",
- "import ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Smacof MDS\n",
- "----------\n",
- "\n",
- "This function allows to find an embedding of points given a dissimilarity matrix\n",
- "that will be given by the output of the algorithm\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\n",
- " \"\"\"\n",
- " Returns an interpolated point cloud following the dissimilarity matrix C\n",
- " using SMACOF multidimensional scaling (MDS) in specific dimensionned\n",
- " target space\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " C : ndarray, shape (ns, ns)\n",
- " dissimilarity matrix\n",
- " dim : int\n",
- " dimension of the targeted space\n",
- " max_iter : int\n",
- " Maximum number of iterations of the SMACOF algorithm for a single run\n",
- " eps : float\n",
- " relative tolerance w.r.t stress to declare converge\n",
- "\n",
- " Returns\n",
- " -------\n",
- " npos : ndarray, shape (R, dim)\n",
- " Embedded coordinates of the interpolated point cloud (defined with\n",
- " one isometry)\n",
- " \"\"\"\n",
- "\n",
- " rng = np.random.RandomState(seed=3)\n",
- "\n",
- " mds = manifold.MDS(\n",
- " dim,\n",
- " max_iter=max_iter,\n",
- " eps=1e-9,\n",
- " dissimilarity='precomputed',\n",
- " n_init=1)\n",
- " pos = mds.fit(C).embedding_\n",
- "\n",
- " nmds = manifold.MDS(\n",
- " 2,\n",
- " max_iter=max_iter,\n",
- " eps=1e-9,\n",
- " dissimilarity=\"precomputed\",\n",
- " random_state=rng,\n",
- " n_init=1)\n",
- " npos = nmds.fit_transform(C, init=pos)\n",
- "\n",
- " return npos"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Data preparation\n",
- "----------------\n",
- "\n",
- "The four distributions are constructed from 4 simple images\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `imread` is deprecated!\n",
- "`imread` is deprecated in SciPy 1.0.0.\n",
- "Use ``matplotlib.pyplot.imread`` instead.\n",
- " \n",
- "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:7: DeprecationWarning: `imread` is deprecated!\n",
- "`imread` is deprecated in SciPy 1.0.0.\n",
- "Use ``matplotlib.pyplot.imread`` instead.\n",
- " import sys\n",
- "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:8: DeprecationWarning: `imread` is deprecated!\n",
- "`imread` is deprecated in SciPy 1.0.0.\n",
- "Use ``matplotlib.pyplot.imread`` instead.\n",
- " \n",
- "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:9: DeprecationWarning: `imread` is deprecated!\n",
- "`imread` is deprecated in SciPy 1.0.0.\n",
- "Use ``matplotlib.pyplot.imread`` instead.\n",
- " if __name__ == '__main__':\n"
- ]
- }
- ],
- "source": [
- "def im2mat(I):\n",
- " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n",
- " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n",
- "\n",
- "\n",
- "square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\n",
- "cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\n",
- "triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\n",
- "star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n",
- "\n",
- "shapes = [square, cross, triangle, star]\n",
- "\n",
- "S = 4\n",
- "xs = [[] for i in range(S)]\n",
- "\n",
- "\n",
- "for nb in range(4):\n",
- " for i in range(8):\n",
- " for j in range(8):\n",
- " if shapes[nb][i, j] < 0.95:\n",
- " xs[nb].append([j, 8 - i])\n",
- "\n",
- "xs = np.array([np.array(xs[0]), np.array(xs[1]),\n",
- " np.array(xs[2]), np.array(xs[3])])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Barycenter computation\n",
- "----------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "ns = [len(xs[s]) for s in range(S)]\n",
- "n_samples = 30\n",
- "\n",
- "\"\"\"Compute all distances matrices for the four shapes\"\"\"\n",
- "Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\n",
- "Cs = [cs / cs.max() for cs in Cs]\n",
- "\n",
- "ps = [ot.unif(ns[s]) for s in range(S)]\n",
- "p = ot.unif(n_samples)\n",
- "\n",
- "\n",
- "lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\n",
- "\n",
- "Ct01 = [0 for i in range(2)]\n",
- "for i in range(2):\n",
- " Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\n",
- " [ps[0], ps[1]\n",
- " ], p, lambdast[i], 'square_loss', # 5e-4,\n",
- " max_iter=100, tol=1e-3)\n",
- "\n",
- "Ct02 = [0 for i in range(2)]\n",
- "for i in range(2):\n",
- " Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\n",
- " [ps[0], ps[2]\n",
- " ], p, lambdast[i], 'square_loss', # 5e-4,\n",
- " max_iter=100, tol=1e-3)\n",
- "\n",
- "Ct13 = [0 for i in range(2)]\n",
- "for i in range(2):\n",
- " Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\n",
- " [ps[1], ps[3]\n",
- " ], p, lambdast[i], 'square_loss', # 5e-4,\n",
- " max_iter=100, tol=1e-3)\n",
- "\n",
- "Ct23 = [0 for i in range(2)]\n",
- "for i in range(2):\n",
- " Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\n",
- " [ps[2], ps[3]\n",
- " ], p, lambdast[i], 'square_loss', # 5e-4,\n",
- " max_iter=100, tol=1e-3)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Visualization\n",
- "-------------\n",
- "\n",
- "The PCA helps in getting consistency between the rotations\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x7fa5bf8b5cc0>"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 12 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "clf = PCA(n_components=2)\n",
- "npos = [0, 0, 0, 0]\n",
- "npos = [smacof_mds(Cs[s], 2) for s in range(S)]\n",
- "\n",
- "npost01 = [0, 0]\n",
- "npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\n",
- "npost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\n",
- "\n",
- "npost02 = [0, 0]\n",
- "npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\n",
- "npost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\n",
- "\n",
- "npost13 = [0, 0]\n",
- "npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\n",
- "npost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\n",
- "\n",
- "npost23 = [0, 0]\n",
- "npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\n",
- "npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\n",
- "\n",
- "\n",
- "fig = pl.figure(figsize=(10, 10))\n",
- "\n",
- "ax1 = pl.subplot2grid((4, 4), (0, 0))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\n",
- "\n",
- "ax2 = pl.subplot2grid((4, 4), (0, 1))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\n",
- "\n",
- "ax3 = pl.subplot2grid((4, 4), (0, 2))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\n",
- "\n",
- "ax4 = pl.subplot2grid((4, 4), (0, 3))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\n",
- "\n",
- "ax5 = pl.subplot2grid((4, 4), (1, 0))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\n",
- "\n",
- "ax6 = pl.subplot2grid((4, 4), (1, 3))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\n",
- "\n",
- "ax7 = pl.subplot2grid((4, 4), (2, 0))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\n",
- "\n",
- "ax8 = pl.subplot2grid((4, 4), (2, 3))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\n",
- "\n",
- "ax9 = pl.subplot2grid((4, 4), (3, 0))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\n",
- "\n",
- "ax10 = pl.subplot2grid((4, 4), (3, 1))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\n",
- "\n",
- "ax11 = pl.subplot2grid((4, 4), (3, 2))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\n",
- "\n",
- "ax12 = pl.subplot2grid((4, 4), (3, 3))\n",
- "pl.xlim((-1, 1))\n",
- "pl.ylim((-1, 1))\n",
- "ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_optim_OTreg.ipynb b/notebooks/plot_optim_OTreg.ipynb
deleted file mode 100644
index e3784df..0000000
--- a/notebooks/plot_optim_OTreg.ipynb
+++ /dev/null
@@ -1,732 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Regularized OT with generic solver\n",
- "\n",
- "\n",
- "Illustrates the use of the generic solver for regularized OT with\n",
- "user-designed regularization term. It uses Conditional gradient as in [6] and\n",
- "generalized Conditional Gradient as proposed in [5][7].\n",
- "\n",
- "\n",
- "[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\n",
- "Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\n",
- "Intelligence , vol.PP, no.99, pp.1-1.\n",
- "\n",
- "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).\n",
- "Regularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n",
- "7(3), 1853-1882.\n",
- "\n",
- "[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\n",
- "conditional gradient: analysis of convergence and applications.\n",
- "arXiv preprint arXiv:1510.06567.\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "import ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "#%% parameters\n",
- "\n",
- "n = 100 # nb bins\n",
- "\n",
- "# bin positions\n",
- "x = np.arange(n, dtype=np.float64)\n",
- "\n",
- "# Gaussian distributions\n",
- "a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n",
- "b = ot.datasets.make_1D_gauss(n, m=60, s=10)\n",
- "\n",
- "# loss matrix\n",
- "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n",
- "M /= M.max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% EMD\n",
- "\n",
- "G0 = ot.emd(a, b, M)\n",
- "\n",
- "pl.figure(3, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD with Frobenius norm regularization\n",
- "--------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 0|1.760578e-01|0.000000e+00\n",
- " 1|1.669467e-01|-5.457501e-02\n",
- " 2|1.665639e-01|-2.298130e-03\n",
- " 3|1.664378e-01|-7.572776e-04\n",
- " 4|1.664077e-01|-1.811855e-04\n",
- " 5|1.663912e-01|-9.936787e-05\n",
- " 6|1.663852e-01|-3.555826e-05\n",
- " 7|1.663814e-01|-2.305693e-05\n",
- " 8|1.663785e-01|-1.760450e-05\n",
- " 9|1.663767e-01|-1.078011e-05\n",
- " 10|1.663751e-01|-9.525192e-06\n",
- " 11|1.663737e-01|-8.396466e-06\n",
- " 12|1.663727e-01|-6.086938e-06\n",
- " 13|1.663720e-01|-4.042609e-06\n",
- " 14|1.663713e-01|-4.160914e-06\n",
- " 15|1.663707e-01|-3.823502e-06\n",
- " 16|1.663702e-01|-3.022440e-06\n",
- " 17|1.663697e-01|-3.181249e-06\n",
- " 18|1.663692e-01|-2.698532e-06\n",
- " 19|1.663687e-01|-3.258253e-06\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 20|1.663682e-01|-2.741118e-06\n",
- " 21|1.663678e-01|-2.624135e-06\n",
- " 22|1.663673e-01|-2.645179e-06\n",
- " 23|1.663670e-01|-1.957237e-06\n",
- " 24|1.663666e-01|-2.261541e-06\n",
- " 25|1.663663e-01|-1.851305e-06\n",
- " 26|1.663660e-01|-1.942296e-06\n",
- " 27|1.663657e-01|-2.092896e-06\n",
- " 28|1.663653e-01|-1.924361e-06\n",
- " 29|1.663651e-01|-1.625455e-06\n",
- " 30|1.663648e-01|-1.641123e-06\n",
- " 31|1.663645e-01|-1.566666e-06\n",
- " 32|1.663643e-01|-1.338514e-06\n",
- " 33|1.663641e-01|-1.222711e-06\n",
- " 34|1.663639e-01|-1.221805e-06\n",
- " 35|1.663637e-01|-1.440781e-06\n",
- " 36|1.663634e-01|-1.520091e-06\n",
- " 37|1.663632e-01|-1.288193e-06\n",
- " 38|1.663630e-01|-1.123055e-06\n",
- " 39|1.663628e-01|-1.024487e-06\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 40|1.663627e-01|-1.079606e-06\n",
- " 41|1.663625e-01|-1.172093e-06\n",
- " 42|1.663623e-01|-1.047880e-06\n",
- " 43|1.663621e-01|-1.010577e-06\n",
- " 44|1.663619e-01|-1.064438e-06\n",
- " 45|1.663618e-01|-9.882375e-07\n",
- " 46|1.663616e-01|-8.532647e-07\n",
- " 47|1.663615e-01|-9.930189e-07\n",
- " 48|1.663613e-01|-8.728955e-07\n",
- " 49|1.663612e-01|-9.524214e-07\n",
- " 50|1.663610e-01|-9.088418e-07\n",
- " 51|1.663609e-01|-7.639430e-07\n",
- " 52|1.663608e-01|-6.662611e-07\n",
- " 53|1.663607e-01|-7.133700e-07\n",
- " 54|1.663605e-01|-7.648141e-07\n",
- " 55|1.663604e-01|-6.557516e-07\n",
- " 56|1.663603e-01|-7.304213e-07\n",
- " 57|1.663602e-01|-6.353809e-07\n",
- " 58|1.663601e-01|-7.968279e-07\n",
- " 59|1.663600e-01|-6.367159e-07\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 60|1.663599e-01|-5.610790e-07\n",
- " 61|1.663598e-01|-5.787466e-07\n",
- " 62|1.663596e-01|-6.937777e-07\n",
- " 63|1.663596e-01|-5.599432e-07\n",
- " 64|1.663595e-01|-5.813048e-07\n",
- " 65|1.663594e-01|-5.724600e-07\n",
- " 66|1.663593e-01|-6.081892e-07\n",
- " 67|1.663592e-01|-5.948732e-07\n",
- " 68|1.663591e-01|-4.941833e-07\n",
- " 69|1.663590e-01|-5.213739e-07\n",
- " 70|1.663589e-01|-5.127355e-07\n",
- " 71|1.663588e-01|-4.349251e-07\n",
- " 72|1.663588e-01|-5.007084e-07\n",
- " 73|1.663587e-01|-4.880265e-07\n",
- " 74|1.663586e-01|-4.931950e-07\n",
- " 75|1.663585e-01|-4.981309e-07\n",
- " 76|1.663584e-01|-3.952959e-07\n",
- " 77|1.663584e-01|-4.544857e-07\n",
- " 78|1.663583e-01|-4.237579e-07\n",
- " 79|1.663582e-01|-4.382386e-07\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 80|1.663582e-01|-3.646051e-07\n",
- " 81|1.663581e-01|-4.197994e-07\n",
- " 82|1.663580e-01|-4.072764e-07\n",
- " 83|1.663580e-01|-3.994645e-07\n",
- " 84|1.663579e-01|-4.842721e-07\n",
- " 85|1.663578e-01|-3.276486e-07\n",
- " 86|1.663578e-01|-3.737346e-07\n",
- " 87|1.663577e-01|-4.282043e-07\n",
- " 88|1.663576e-01|-4.020937e-07\n",
- " 89|1.663576e-01|-3.431951e-07\n",
- " 90|1.663575e-01|-3.052335e-07\n",
- " 91|1.663575e-01|-3.500538e-07\n",
- " 92|1.663574e-01|-3.063176e-07\n",
- " 93|1.663573e-01|-3.576367e-07\n",
- " 94|1.663573e-01|-3.224681e-07\n",
- " 95|1.663572e-01|-3.673221e-07\n",
- " 96|1.663572e-01|-3.635561e-07\n",
- " 97|1.663571e-01|-3.527236e-07\n",
- " 98|1.663571e-01|-2.788548e-07\n",
- " 99|1.663570e-01|-2.727141e-07\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 100|1.663570e-01|-3.127278e-07\n",
- " 101|1.663569e-01|-2.637504e-07\n",
- " 102|1.663569e-01|-2.922750e-07\n",
- " 103|1.663568e-01|-3.076454e-07\n",
- " 104|1.663568e-01|-2.911509e-07\n",
- " 105|1.663567e-01|-2.403398e-07\n",
- " 106|1.663567e-01|-2.439790e-07\n",
- " 107|1.663567e-01|-2.634542e-07\n",
- " 108|1.663566e-01|-2.452203e-07\n",
- " 109|1.663566e-01|-2.852991e-07\n",
- " 110|1.663565e-01|-2.165490e-07\n",
- " 111|1.663565e-01|-2.450250e-07\n",
- " 112|1.663564e-01|-2.685294e-07\n",
- " 113|1.663564e-01|-2.821800e-07\n",
- " 114|1.663564e-01|-2.237390e-07\n",
- " 115|1.663563e-01|-1.992842e-07\n",
- " 116|1.663563e-01|-2.166739e-07\n",
- " 117|1.663563e-01|-2.086064e-07\n",
- " 118|1.663562e-01|-2.435945e-07\n",
- " 119|1.663562e-01|-2.292497e-07\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 120|1.663561e-01|-2.366209e-07\n",
- " 121|1.663561e-01|-2.138746e-07\n",
- " 122|1.663561e-01|-2.009637e-07\n",
- " 123|1.663560e-01|-2.386258e-07\n",
- " 124|1.663560e-01|-1.927442e-07\n",
- " 125|1.663560e-01|-2.081681e-07\n",
- " 126|1.663559e-01|-1.759123e-07\n",
- " 127|1.663559e-01|-1.890771e-07\n",
- " 128|1.663559e-01|-1.971315e-07\n",
- " 129|1.663558e-01|-2.101983e-07\n",
- " 130|1.663558e-01|-2.035645e-07\n",
- " 131|1.663558e-01|-1.984492e-07\n",
- " 132|1.663557e-01|-1.849064e-07\n",
- " 133|1.663557e-01|-1.795703e-07\n",
- " 134|1.663557e-01|-1.624087e-07\n",
- " 135|1.663557e-01|-1.689557e-07\n",
- " 136|1.663556e-01|-1.644308e-07\n",
- " 137|1.663556e-01|-1.618007e-07\n",
- " 138|1.663556e-01|-1.483013e-07\n",
- " 139|1.663555e-01|-1.708771e-07\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 140|1.663555e-01|-2.013847e-07\n",
- " 141|1.663555e-01|-1.721217e-07\n",
- " 142|1.663554e-01|-2.027911e-07\n",
- " 143|1.663554e-01|-1.764565e-07\n",
- " 144|1.663554e-01|-1.677151e-07\n",
- " 145|1.663554e-01|-1.351982e-07\n",
- " 146|1.663553e-01|-1.423360e-07\n",
- " 147|1.663553e-01|-1.541112e-07\n",
- " 148|1.663553e-01|-1.491601e-07\n",
- " 149|1.663553e-01|-1.466407e-07\n",
- " 150|1.663552e-01|-1.801524e-07\n",
- " 151|1.663552e-01|-1.714107e-07\n",
- " 152|1.663552e-01|-1.491257e-07\n",
- " 153|1.663552e-01|-1.513799e-07\n",
- " 154|1.663551e-01|-1.354539e-07\n",
- " 155|1.663551e-01|-1.233818e-07\n",
- " 156|1.663551e-01|-1.576219e-07\n",
- " 157|1.663551e-01|-1.452791e-07\n",
- " 158|1.663550e-01|-1.262867e-07\n",
- " 159|1.663550e-01|-1.316379e-07\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 160|1.663550e-01|-1.295447e-07\n",
- " 161|1.663550e-01|-1.283286e-07\n",
- " 162|1.663550e-01|-1.569222e-07\n",
- " 163|1.663549e-01|-1.172942e-07\n",
- " 164|1.663549e-01|-1.399809e-07\n",
- " 165|1.663549e-01|-1.229432e-07\n",
- " 166|1.663549e-01|-1.326191e-07\n",
- " 167|1.663548e-01|-1.209694e-07\n",
- " 168|1.663548e-01|-1.372136e-07\n",
- " 169|1.663548e-01|-1.338395e-07\n",
- " 170|1.663548e-01|-1.416497e-07\n",
- " 171|1.663548e-01|-1.298576e-07\n",
- " 172|1.663547e-01|-1.190590e-07\n",
- " 173|1.663547e-01|-1.167083e-07\n",
- " 174|1.663547e-01|-1.069425e-07\n",
- " 175|1.663547e-01|-1.217780e-07\n",
- " 176|1.663547e-01|-1.140754e-07\n",
- " 177|1.663546e-01|-1.160707e-07\n",
- " 178|1.663546e-01|-1.101798e-07\n",
- " 179|1.663546e-01|-1.114904e-07\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 180|1.663546e-01|-1.064022e-07\n",
- " 181|1.663546e-01|-9.258231e-08\n",
- " 182|1.663546e-01|-1.213120e-07\n",
- " 183|1.663545e-01|-1.164296e-07\n",
- " 184|1.663545e-01|-1.188762e-07\n",
- " 185|1.663545e-01|-9.394153e-08\n",
- " 186|1.663545e-01|-1.028656e-07\n",
- " 187|1.663545e-01|-1.115348e-07\n",
- " 188|1.663544e-01|-9.768310e-08\n",
- " 189|1.663544e-01|-1.021806e-07\n",
- " 190|1.663544e-01|-1.086303e-07\n",
- " 191|1.663544e-01|-9.879008e-08\n",
- " 192|1.663544e-01|-1.050210e-07\n",
- " 193|1.663544e-01|-1.002463e-07\n",
- " 194|1.663543e-01|-1.062747e-07\n",
- " 195|1.663543e-01|-9.348538e-08\n",
- " 196|1.663543e-01|-7.992512e-08\n",
- " 197|1.663543e-01|-9.558020e-08\n",
- " 198|1.663543e-01|-9.993772e-08\n",
- " 199|1.663543e-01|-8.588499e-08\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 200|1.663543e-01|-8.737134e-08\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% Example with Frobenius norm regularization\n",
- "\n",
- "\n",
- "def f(G):\n",
- " return 0.5 * np.sum(G**2)\n",
- "\n",
- "\n",
- "def df(G):\n",
- " return G\n",
- "\n",
- "\n",
- "reg = 1e-1\n",
- "\n",
- "Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n",
- "\n",
- "pl.figure(3)\n",
- "ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD with entropic regularization\n",
- "--------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 0|1.692289e-01|0.000000e+00\n",
- " 1|1.617643e-01|-4.614437e-02\n",
- " 2|1.612639e-01|-3.102965e-03\n",
- " 3|1.611291e-01|-8.371098e-04\n",
- " 4|1.610468e-01|-5.110558e-04\n",
- " 5|1.610198e-01|-1.672927e-04\n",
- " 6|1.610130e-01|-4.232417e-05\n",
- " 7|1.610090e-01|-2.513455e-05\n",
- " 8|1.610002e-01|-5.443507e-05\n",
- " 9|1.609996e-01|-3.657071e-06\n",
- " 10|1.609948e-01|-2.998735e-05\n",
- " 11|1.609695e-01|-1.569217e-04\n",
- " 12|1.609533e-01|-1.010779e-04\n",
- " 13|1.609520e-01|-8.043897e-06\n",
- " 14|1.609465e-01|-3.415246e-05\n",
- " 15|1.609386e-01|-4.898605e-05\n",
- " 16|1.609324e-01|-3.837052e-05\n",
- " 17|1.609298e-01|-1.617826e-05\n",
- " 18|1.609184e-01|-7.080015e-05\n",
- " 19|1.609083e-01|-6.273206e-05\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 20|1.608988e-01|-5.940805e-05\n",
- " 21|1.608853e-01|-8.380030e-05\n",
- " 22|1.608844e-01|-5.185045e-06\n",
- " 23|1.608824e-01|-1.279113e-05\n",
- " 24|1.608819e-01|-3.156821e-06\n",
- " 25|1.608783e-01|-2.205746e-05\n",
- " 26|1.608764e-01|-1.189894e-05\n",
- " 27|1.608755e-01|-5.474607e-06\n",
- " 28|1.608737e-01|-1.144227e-05\n",
- " 29|1.608676e-01|-3.775335e-05\n",
- " 30|1.608638e-01|-2.348020e-05\n",
- " 31|1.608627e-01|-6.863136e-06\n",
- " 32|1.608529e-01|-6.110230e-05\n",
- " 33|1.608487e-01|-2.641106e-05\n",
- " 34|1.608409e-01|-4.823638e-05\n",
- " 35|1.608373e-01|-2.256641e-05\n",
- " 36|1.608338e-01|-2.132444e-05\n",
- " 37|1.608310e-01|-1.786649e-05\n",
- " 38|1.608260e-01|-3.103848e-05\n",
- " 39|1.608206e-01|-3.321265e-05\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 40|1.608201e-01|-3.054747e-06\n",
- " 41|1.608195e-01|-4.198335e-06\n",
- " 42|1.608193e-01|-8.458736e-07\n",
- " 43|1.608159e-01|-2.153759e-05\n",
- " 44|1.608115e-01|-2.738314e-05\n",
- " 45|1.608108e-01|-3.960032e-06\n",
- " 46|1.608081e-01|-1.675447e-05\n",
- " 47|1.608072e-01|-5.976340e-06\n",
- " 48|1.608046e-01|-1.604130e-05\n",
- " 49|1.608020e-01|-1.617036e-05\n",
- " 50|1.608014e-01|-3.957795e-06\n",
- " 51|1.608011e-01|-1.292411e-06\n",
- " 52|1.607998e-01|-8.431795e-06\n",
- " 53|1.607964e-01|-2.127054e-05\n",
- " 54|1.607947e-01|-1.021878e-05\n",
- " 55|1.607947e-01|-3.560621e-07\n",
- " 56|1.607900e-01|-2.929781e-05\n",
- " 57|1.607890e-01|-5.740229e-06\n",
- " 58|1.607858e-01|-2.039550e-05\n",
- " 59|1.607836e-01|-1.319545e-05\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 60|1.607826e-01|-6.378947e-06\n",
- " 61|1.607808e-01|-1.145102e-05\n",
- " 62|1.607776e-01|-1.941743e-05\n",
- " 63|1.607743e-01|-2.087422e-05\n",
- " 64|1.607741e-01|-1.310249e-06\n",
- " 65|1.607738e-01|-1.682752e-06\n",
- " 66|1.607691e-01|-2.913936e-05\n",
- " 67|1.607671e-01|-1.288855e-05\n",
- " 68|1.607654e-01|-1.002448e-05\n",
- " 69|1.607641e-01|-8.209492e-06\n",
- " 70|1.607632e-01|-5.588467e-06\n",
- " 71|1.607619e-01|-8.050388e-06\n",
- " 72|1.607618e-01|-9.417493e-07\n",
- " 73|1.607598e-01|-1.210509e-05\n",
- " 74|1.607591e-01|-4.392914e-06\n",
- " 75|1.607579e-01|-7.759587e-06\n",
- " 76|1.607574e-01|-2.760280e-06\n",
- " 77|1.607556e-01|-1.146469e-05\n",
- " 78|1.607550e-01|-3.689456e-06\n",
- " 79|1.607550e-01|-4.065631e-08\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 80|1.607539e-01|-6.555681e-06\n",
- " 81|1.607528e-01|-7.177470e-06\n",
- " 82|1.607527e-01|-5.306068e-07\n",
- " 83|1.607514e-01|-7.816045e-06\n",
- " 84|1.607511e-01|-2.301970e-06\n",
- " 85|1.607504e-01|-4.281072e-06\n",
- " 86|1.607503e-01|-7.821886e-07\n",
- " 87|1.607480e-01|-1.403013e-05\n",
- " 88|1.607480e-01|-1.169298e-08\n",
- " 89|1.607473e-01|-4.235982e-06\n",
- " 90|1.607470e-01|-1.717105e-06\n",
- " 91|1.607470e-01|-6.148402e-09\n",
- " 92|1.607462e-01|-5.396481e-06\n",
- " 93|1.607461e-01|-5.194954e-07\n",
- " 94|1.607450e-01|-6.525707e-06\n",
- " 95|1.607442e-01|-5.332060e-06\n",
- " 96|1.607439e-01|-1.682093e-06\n",
- " 97|1.607437e-01|-1.594796e-06\n",
- " 98|1.607435e-01|-7.923812e-07\n",
- " 99|1.607420e-01|-9.738552e-06\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 100|1.607419e-01|-1.022448e-07\n",
- " 101|1.607419e-01|-4.865999e-07\n",
- " 102|1.607418e-01|-7.092012e-07\n",
- " 103|1.607408e-01|-5.861815e-06\n",
- " 104|1.607402e-01|-3.953266e-06\n",
- " 105|1.607395e-01|-3.969572e-06\n",
- " 106|1.607390e-01|-3.612075e-06\n",
- " 107|1.607377e-01|-7.683735e-06\n",
- " 108|1.607365e-01|-7.777599e-06\n",
- " 109|1.607364e-01|-2.335096e-07\n",
- " 110|1.607364e-01|-4.562036e-07\n",
- " 111|1.607360e-01|-2.089538e-06\n",
- " 112|1.607356e-01|-2.755355e-06\n",
- " 113|1.607349e-01|-4.501960e-06\n",
- " 114|1.607347e-01|-1.160544e-06\n",
- " 115|1.607346e-01|-6.289450e-07\n",
- " 116|1.607345e-01|-2.092146e-07\n",
- " 117|1.607336e-01|-5.990866e-06\n",
- " 118|1.607330e-01|-3.348498e-06\n",
- " 119|1.607328e-01|-1.256222e-06\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 120|1.607320e-01|-5.418353e-06\n",
- " 121|1.607318e-01|-8.296189e-07\n",
- " 122|1.607311e-01|-4.381608e-06\n",
- " 123|1.607310e-01|-8.913901e-07\n",
- " 124|1.607309e-01|-3.808821e-07\n",
- " 125|1.607302e-01|-4.608994e-06\n",
- " 126|1.607294e-01|-5.063777e-06\n",
- " 127|1.607290e-01|-2.532835e-06\n",
- " 128|1.607285e-01|-2.870049e-06\n",
- " 129|1.607284e-01|-4.892812e-07\n",
- " 130|1.607281e-01|-1.760452e-06\n",
- " 131|1.607279e-01|-1.727139e-06\n",
- " 132|1.607275e-01|-2.220706e-06\n",
- " 133|1.607271e-01|-2.516930e-06\n",
- " 134|1.607269e-01|-1.201434e-06\n",
- " 135|1.607269e-01|-2.183459e-09\n",
- " 136|1.607262e-01|-4.223011e-06\n",
- " 137|1.607258e-01|-2.530202e-06\n",
- " 138|1.607258e-01|-1.857260e-07\n",
- " 139|1.607256e-01|-1.401957e-06\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 140|1.607250e-01|-3.242751e-06\n",
- " 141|1.607247e-01|-2.308071e-06\n",
- " 142|1.607247e-01|-4.730700e-08\n",
- " 143|1.607246e-01|-4.240229e-07\n",
- " 144|1.607242e-01|-2.484810e-06\n",
- " 145|1.607238e-01|-2.539206e-06\n",
- " 146|1.607234e-01|-2.535574e-06\n",
- " 147|1.607231e-01|-1.954802e-06\n",
- " 148|1.607228e-01|-1.765447e-06\n",
- " 149|1.607228e-01|-1.620007e-08\n",
- " 150|1.607222e-01|-3.615783e-06\n",
- " 151|1.607222e-01|-8.668516e-08\n",
- " 152|1.607215e-01|-4.000673e-06\n",
- " 153|1.607213e-01|-1.774103e-06\n",
- " 154|1.607213e-01|-6.328834e-09\n",
- " 155|1.607209e-01|-2.418783e-06\n",
- " 156|1.607208e-01|-2.848492e-07\n",
- " 157|1.607207e-01|-8.836043e-07\n",
- " 158|1.607205e-01|-1.192836e-06\n",
- " 159|1.607202e-01|-1.638022e-06\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 160|1.607202e-01|-3.670914e-08\n",
- " 161|1.607197e-01|-3.153709e-06\n",
- " 162|1.607197e-01|-2.419565e-09\n",
- " 163|1.607194e-01|-2.136882e-06\n",
- " 164|1.607194e-01|-1.173754e-09\n",
- " 165|1.607192e-01|-8.169238e-07\n",
- " 166|1.607191e-01|-9.218755e-07\n",
- " 167|1.607189e-01|-9.459255e-07\n",
- " 168|1.607187e-01|-1.294835e-06\n",
- " 169|1.607186e-01|-5.797668e-07\n",
- " 170|1.607186e-01|-4.706272e-08\n",
- " 171|1.607183e-01|-1.753383e-06\n",
- " 172|1.607183e-01|-1.681573e-07\n",
- " 173|1.607183e-01|-2.563971e-10\n"
- ]
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% Example with entropic regularization\n",
- "\n",
- "\n",
- "def f(G):\n",
- " return np.sum(G * np.log(G))\n",
- "\n",
- "\n",
- "def df(G):\n",
- " return np.log(G) + 1.\n",
- "\n",
- "\n",
- "reg = 1e-3\n",
- "\n",
- "Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n",
- "\n",
- "pl.figure(4, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Solve EMD with Frobenius norm + entropic regularization\n",
- "-------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 0|1.693084e-01|0.000000e+00\n",
- " 1|1.610121e-01|-5.152589e-02\n",
- " 2|1.609378e-01|-4.622297e-04\n",
- " 3|1.609284e-01|-5.830043e-05\n",
- " 4|1.609284e-01|-1.111407e-12\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#%% Example with Frobenius norm + entropic regularization with gcg\n",
- "\n",
- "\n",
- "def f(G):\n",
- " return 0.5 * np.sum(G**2)\n",
- "\n",
- "\n",
- "def df(G):\n",
- " return G\n",
- "\n",
- "\n",
- "reg1 = 1e-3\n",
- "reg2 = 1e-1\n",
- "\n",
- "Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n",
- "\n",
- "pl.figure(5, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_otda_classes.ipynb b/notebooks/plot_otda_classes.ipynb
deleted file mode 100644
index 2af6fb6..0000000
--- a/notebooks/plot_otda_classes.ipynb
+++ /dev/null
@@ -1,305 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# OT for domain adaptation\n",
- "\n",
- "\n",
- "This example introduces a domain adaptation in a 2D setting and the 4 OTDA\n",
- "approaches currently supported in POT.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n",
- "# Stanislas Chambon <stan.chambon@gmail.com>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import matplotlib.pylab as pl\n",
- "import ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source_samples = 150\n",
- "n_target_samples = 150\n",
- "\n",
- "Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\n",
- "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate the different transport algorithms and fit them\n",
- "-----------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 0|9.537526e+00|0.000000e+00\n",
- " 1|2.505426e+00|-2.806748e+00\n",
- " 2|2.264025e+00|-1.066249e-01\n",
- " 3|2.210620e+00|-2.415841e-02\n",
- " 4|2.191601e+00|-8.677880e-03\n",
- " 5|2.182712e+00|-4.072416e-03\n",
- " 6|2.178054e+00|-2.138572e-03\n",
- " 7|2.176320e+00|-7.971427e-04\n",
- " 8|2.174237e+00|-9.578098e-04\n",
- " 9|2.172978e+00|-5.792305e-04\n",
- " 10|2.172514e+00|-2.138295e-04\n",
- " 11|2.171279e+00|-5.689220e-04\n",
- " 12|2.169799e+00|-6.819885e-04\n",
- " 13|2.169215e+00|-2.692594e-04\n",
- " 14|2.168810e+00|-1.868050e-04\n",
- " 15|2.168289e+00|-2.401519e-04\n",
- " 16|2.168018e+00|-1.249509e-04\n",
- " 17|2.167885e+00|-6.124717e-05\n",
- " 18|2.167623e+00|-1.211692e-04\n",
- " 19|2.167335e+00|-1.327875e-04\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 20|2.166808e+00|-2.432572e-04\n"
- ]
- }
- ],
- "source": [
- "# EMD Transport\n",
- "ot_emd = ot.da.EMDTransport()\n",
- "ot_emd.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "# Sinkhorn Transport\n",
- "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n",
- "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "# Sinkhorn Transport with Group lasso regularization\n",
- "ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\n",
- "ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n",
- "\n",
- "# Sinkhorn Transport with Group lasso regularization l1l2\n",
- "ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n",
- " verbose=True)\n",
- "ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n",
- "\n",
- "# transport source samples onto target samples\n",
- "transp_Xs_emd = ot_emd.transform(Xs=Xs)\n",
- "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\n",
- "transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\n",
- "transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 1 : plots source and target samples\n",
- "---------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(1, figsize=(10, 5))\n",
- "pl.subplot(1, 2, 1)\n",
- "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.legend(loc=0)\n",
- "pl.title('Source samples')\n",
- "\n",
- "pl.subplot(1, 2, 2)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.legend(loc=0)\n",
- "pl.title('Target samples')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 2 : plot optimal couplings and transported samples\n",
- "------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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m6NXvN0ErtyylsWUYeqGoKLYYVddKVP/dMr6QmpkH8aJmRGQN9MTB/1BKfXKcpzNezCE4gVHffA/Aj0A7EVdFRJaIyC8DeC+A3zazehNhAYDr0OYxm1Gem16tD2+R1ADnGvRN55vy/KaIdIjIagD/BTofHtDytv8qujTsAug8989W+Q4XASgRuaXmb0VIHWDUB78uIivNz6ug0xwemUh7SqnPQqdDPCgit9Zw/GkADwP4oIg0GhnmL2B8k6SThTGGzCvm8H2fM+3ZP/4K5PtFl0h8OXSO/heyGhCRBhFpND/mTTs1TWhmsAB6lbRHRLqgJ0drQkReJSJbzQtFL7RE3I8P7zD/ji3QFSE+byY9Pw/gJ0Sbl+agX4r6ADxa5XIXADC2kEzmW7yoNQaYZ/93AHxIKTUVpVEZL2YxnMCYWb4qqct9v4h82d+pNN9WSlVLB7kmIgPQVUteD+CnlVJ/N4k+/Tq0YU0f9Erp56ofXsS9APaa/nwJwC+ZnDzLVwHsB/AktMfHx8z2j5jrfB/AC+bav1rpIkbW9kEAj4r2DqmpIgIhdUAf9H3yqLlPHoFWI/z6RBtUSn0cOg3jOyKytoZTfhbaR+Ic9H34XqXUgxO9/gRgjCHzjbl6338dwKD3531m+3kAPeZanwbwHrOKnMVz5twVAB4wn9dUOHYs/gw6n/wK9AvYN8Zx7nLomNILbQz4ILT5sOWT0C9wLwIIAfwaACilDkHHs/8Dreh6LYCfMPntlfhD6AmeayLya+PoI5kfzLd4UWsMeDf0i/z7/HenSfSH8WIWI8WqWUKmHjPLOgpgndKmfIQQMmUwxhBSH4jIKwF8yuTuzwlEl638qFLqYzPdF0JIfcN4cXOgAoMQQgghhBBCCCF1DycwCCGEEEIIIYQQUvcwhYQQQgghhBBCCCF1DxUYhBBCCCGEEEIIqXs4gUEIIYQQQgghhJC6Jxr7kHLy0qAa0TLVfZkRNmy7gSMHm2e6G4TMKEMYwIgaLqu7PVnGihXSkNcf4hiqEGccYP6ejky3Bea+77tRftnGBn3ZoWG3TbU1I+gf0p+TpOycMWlp0n8PDJqLCFAlhU/CACqewHUImUZmLFaYexKFQnasmEaUiRWSFStMDFPDI0XHZx1bfnKFGNBqYkV/jbEiCm/674SQWuhDz2Wl1KKpbjcvjaopaIVKEkgYAgBUnN4DbmyhADUyUna+5HN690i16pVjIICIXge2YwJpaoSy0fGGHi9IGKZ9K723AaC5EWJubzU4VPlyuQhqtOCurU8A0GzavKHblCiCKujjJAjSvgVmzToMoUYn8b0JmQbGO7aY0ARGI1pwr7x6Iqdm8sC5/fjR5XdOWXvj4ing3ikfihEyu3hUfXta2h0zVvjjioz7UCIdouzDONyyEXLlGgAgvtKj942OIFq6BABQOH/BneefEx96rrxxWz3cXlcEQYN+SUqGhsr71Jd+DDfeqvtw5Pl0465t+u9HDiLYukm38/Sz+viuhYivXC1vs1rsSYBwsR73xZcuVTmQkJvHjMUKO5coAjfa9xBz76phfWC4ZSNwXt83Sf+A2xetXAEAKJw5q08MQiDRLxfBtk1IDj5bfu3SWBGECMwLUGas6E9/DjeuBwDEzx1zu9Xu7fqUPQcQbt2o95sYFXZ2Iu7pKW+zWqyIgbCzXX+8dr3KgYTcXB5UXzw5He02Ba3Y1fRjUCMjkLyerEhueJOGZmwRdrQjCczCg4kNEEG01MSB02f0pru2IryiH/KquREAEB8+gmjNKn3ciVO6PW88URQv7P055I1bxIxB2jvdBGzhxfPFxwPAcBqDwtvW6WsfO+52++OJsnixZQPiw0d0k41eDPQnONy1zIeCQrTWfK+Tp0FIPTDescWETDzbZKGaygmM6eCBc/sBYOYmRgiZRTyqvo1edXXKp/JcrPBeEqJlSwF4D/IS5O47AABq71M1XSNobHQvEZIzq6GjIwhvuwUAEB99oezlxifcUjwg8FG7t0P2HKjeATG/toxYGq1dDSAd/BQdD7g+4vwlxL295gvp1ST7+yKknriZsSLs7gIAxJevTMk1gpYWJAN6MsO/b2+8+V4AQPOXHkXY2amvaScRbhLJy3foPn7/yXSj6aOEIfp/cicAoP3xF108cS9KcVxVoUHITPGg+uI+pdRdU91ue26R2t3xZkg+75SS0tYKAFA9190CRjI4iLC7W++3aqmBGzXd39HKFW6i0x+3+Asm9rNVNLiFCsDtS3r7nArE9itauQLKjFviy1fKFmv88+3CjD+5Ga1YrvedPeeODxelQhcJjdpCBIULeiI3NL8fhGFRPwmpB8Y7tqAHBiGEEEIIIYQQQuqeCaWQ3Gwmoqag8oKQ+kHCEMqsqtrVkrCtLVUdAAgWLNDHPqcVp7JuDQrHjfq0iioh8XJYk7s263P3HEB89AW33Sov5K6t+ufHn3b7MtNLDOHBY8Cdt+u29x8uPyAIIS/ZXNampUh5YTE5s0jiohQUXz1CyLwkCBEtX+pWPZN1epURJQoMX10FAOH6dUWS60pIUyNgFBhqt075kocPoPnLj7lj7AqnjUdJXx8ymYhSykszK2vuB+Xbwo4O16eWf9R9LCjlru3y6qm+IPONREGNjEJyOahB4/1gfXKWLkLiP1tLfGqkfQFg7nM/xav0GayGhpwyAiZlDADinmvuc+HCRQCp2hKesiEx6VyqUHDxxMYXNTQEaTWeP5evpJ4Vpg/BulVAiT+HrxpJqigo4itXITnT7zhGuH6tPufkGfN7GEbQrH19itJuCJlFUIFBCCGEEEIIIYSQumfSCoyb4TVBNQUhsxPJ5xGtXIPCyTMItmulQnzgGbff5nGq9lZnROXw1BnVVjm1M7f5XMmvwq5YeiqJoFEbdVn/jPD2DWV9SAYGAKO8kFy+XB2RxJnKi7Rznj+Gl88OAMH6tU6BIVHk2g5v36DPuXhlynL/Cal3JJdDtGQJCmfPuXsg9nxwonVrAACqqaHsPq1FfQEAyfW+9D582IsVnoLBrrj6yovSWBHdshaFF05U+S4ZsQIoV1741UVUUrzd60OwfTMSEzf9tu3vSa73F+XCEzLnCUMEbQughoYQGK+c5KpWKCQvnk8VEQASa/htlBrq8mWEXQsBlJjrZtyzgfHEKZz27q9E37NhW5vb5NSiSGNVfOZFfdzSJUiu6/GM9eNKrl2HMs/3aN0aqD7tFKxMJZH4yPOpysvEg7C9LTXpNeMIAM6Y2HpqRMuWIL54Wfe/udmNM+x3loWdFT3ICJktTHoCYyYnF2jUSUh9o0ZG3IM98SYuLG7QfdbbOE5pthoZyZRQB17qR/JSLd32DfLcxMUGU1GkdALF9sWmvtSY2hF2tLtBhpO6H3ne9dHJU0+dRbDNuIs3593LTfysHmwEnmSVkLmOGh118SDrXvRfECZ+jex72K8KMvxqPZ7IP/C42+8qjdi+VJm8qHadUqIli1ODvqxYYWXlzx135sZJPoT8QI994meOAgCCpqaarkfIdDAj6QhJAmXTwfr1y7+4cqI3AJMuEl/tcRMOQYep1HP5Sm0mlhK4CmCpEXiMsFtPBBT8iRJvwSU+rQc04SJtHppcvlIWQ8LuLrdAEZ8+i8BUBUm8RQsJ9MSFKpjxzeJuwIwtgk6dXpYMDLiJC5ci09jo+oh8Lu2b+T3AlpglZIoZa3IfmLp4wRQSQgghhBBCCCGE1D1ztowqIURTi1Jp2ksjElJHSBQ5A8RwkzFxM6vZAHD9bbvQ/ulHSk6STKVPWfnLCsf5lK1A1HDORCkqHVq0QyudouWmPOCZsxi9X5fqzD24r2J7jBWEpJx8/31Y896HZ7obN50jH9WVUTe8+/Gqx01XGVXGC1KvOMPWrVpV56c29/7cLrR95pGyc5wS2DOfzSqVOxal5XiTl+8oLs1del1Tetcqjdy1S0r4FmFV0irJHLdYs3ynJNx3CENvuAcA0PjVxzIN68PbbsGekx/H9aHzNY8tZnQCgykghNQHdfFS4vtFQAdWG1TP/tZ9AIAVf/xw9eoAJW2UMqZ0Lev8KiktQWOje1D49dsnwqX37AYALPrwHrfN5emyZjupE+ohVpTex5FXsWjktXcDAPLf3JtZdShtZIxUtTH21yyD9WJKqZfGRBl9jX4fzH0rfXmMf/glAIDw356YVNuETCXTOoERvgZBU6OboLX3V7CoG4XTZ9yx9mXMpmyEq1a4eFF4tZ60jb69D+H6dfo446kjUeSqhqXpJ5czxxfRqpW6Pe+6RftLXwiDENES/fLo+1GExnMjWbsc4Xnd36RXj3WSgYGyMYGfihK0tLjjfNRL9TuWTT0DUPZdCZlpxju2YAoJIYQQQgghhBBC6p5Jm3hOhpulvKDSg5CZQUQQNDZC2tvSuuVmRaPU6E6M6aZ68pDesLAdMAqMFX+SqhJEiidog8ZGqFg7+AdtrQAqKxYS40Ke2dcoArZv1H3Yd8g7KUN54a2+RsuMBL9GV+9Kxy/6m8dMR9IVW1mgvw+owCBzHAlDhO2dkPYFSIyDvk3zUcPDRceqjXr1ECZWKFNlANDKC0tw5BQAwN7BYUc71NCwaVvHDFVJgVHFRDhobEThJboCSPDQ/orHAUDQqu/hpK8PCMya0RhKMYsz9jz6QtF2q7zwpbgNR/XK7uR0YITMDiQKEXZ2QHI5SGND0b7C6TNptaF83plWSt6YVxbSe7vx2EW9CYAMFY9Jgo72omofAMoqijkFZn9Gmp6tHrKwEyjpY9DY4JQVRacYI1L15CHAjBXc9xsYAExMdKkCg6maK2jT6lQ1MgpVGHXbc2eMksOOWwYHIaOMFGR2QwUGIYQQQgghhBBC6p5JKTAeOLd/VqgaZkMfCZmTRBGCroVQ7a0IrEeEyS0vNQ0KBvRKQmJWFfHixXSnt1IZe+XKACBYuhiFU2dRE1VWPCWfh5zQddvt+oxEUaa3ha/kUCNVSiZmrLSqTlM7/sKl4lVe8zm0ubbXrqNw8nTltgmZS0Qh0NWBpKURgVkdtOUB4xIFRnhVxwB7Z8ZZq58ojxVYvgSJKVNsSxROBMnnkTukc+jHKvacmBKPgBcrsozPSszXAEA165VXaWhIVSie4atfznE8Rm+EzHpEILkcEIb67xICU/YU3j6xpcn9e6znuvuc9FwrvkQuB2XUGhKm671WyaFGC668qlVG2L75xyGKgBLFg0RR5tihSG1mTRAHUp+duFfHk6DJ+On4fhdGLRK0NCEZ9FQi5hz7/YMkcdsIma1MagLDnxhgmgYhpBwFJAlkaATKPtRbtYwRl0oONbLxoEVLKOMaH7Cqpcm9/Ft5uG7IM+GrZshn9qk4RuINZgCdKlL2EgQUT0gMVDbxK5KPG6RPHx+0NCMx50oucgMXMefgeu+0VaYgpO5QCjI8ComTVCbtv5h4k4HJpSvF51ZJ9yg6rCGXxgqk0nD7EqKGhzMd0su6OjKCpGRSpfLBXqyIK/cz7O4CUOz6HlzRsUd1tCO5ql+ugpYmxNd0nFImZU4uZU+0EjJ3kfKULH+vTbtIVJoyYicJR9P0Cn/ys8yQNwwBs1ihgjQFxE6OxMPDCJpaTZvp/Sd2IqEh7UNpGhy8Zz6CUFd0AKBueIsjJnZY09+gsdF9LkorKV0oaWjQcRR6oiQx39Gm2EpTU7YJOiGzCKaQEEIIIYQQQgghpO6Z0TKqZPYwW9KFyMSoh9KIZOKEm28DAMTPHM0+wKzQRCtXAMgu9eaXrS06b5zPiHDDrUhOmvbtirMEzlQsaGoCEr06lFVO0pajQ6GQrkabPtgSukCJqsWsdEkUuVU5J/ldtrjy74WMG8aKuc1Y5SBvJsnLd6Dzg9qI9frLroxxNKlHprWMKuPF9OApOlz514uXM5Vu4RZjfB7qc5Knj5YdFy5ZjPjCxfJzK5gUZ/UDAKIVy13JWCQmvcZTysnOLe75r/Y+lbZhU+7MOEn6B118s/FODQ27MYOvgnNlbfv6gDv0d5VjOiZJY2P5mIlMGJZRJYQQQgghhBBCyJyDCowxoLcHmQ/U66pqqbFduPk2yDW98h5fuuz2lZYm9c03w43rET93rIaLictZzVIG+IQb1+s+eO2q3dt1M3sOINi2SbeCnSFoAAAgAElEQVRz8Fl9fHdXunIwDtzqh7ciQMhMMuOxooIqyPexAMxq2yVdfji5rmOGGh1JVUhnjPFvEKYGuls2Ij703Nh9CEIExhBvzFiRoY4qihVbTax42sSKroUVy0BXvY5ZKYx7esY4kpCbx3QpMNrDbrWr6cegRkacWWaZhwW0KXZiSo36RrjRiuUA0jggO7cgvKp9t1ST8bh45iiiNav0cSf0qrsfI4Ktm9x961M2bunsdJ4VmeXW/Ri0XpeIjo8dT3d7McIqHmwfwts3ID58RF+3JAaWd8xTVtjvRaNwUieMd2wxKRPPmeRmpTRw4oKQ6adStY/QvsDbQcbgsHMKD2y1jstXoBa06BN0EZGitvo3LURTDe8kzjG8FjLM+KJrevAUA5CzxXJJ+wI1XpJr18c+iJC5Rkb1Hrcrn88coAer9cSElSNL/yBic98F7Tr1SMeK5uITPbnzwC3taDw0dveCxoaqhpxF/R0eLdsWXdEvSjpWFE9OJtczTINrIKlQiYWQOYsxy3R/+9gYkssDgyWTjEq5SQp3eCEBRvS9qtpbytuxP/Z7kyRRtohdmrQRubJpjlFUNRUzaGp01UQkyYh5fqwZLjYXlsE0Forpa8Uriemvit13JWS2whQSQgghhBBCCCGE1D2zVoFRqzKC5pOEzBwShgjbOxH39JSleegDjPHTvVshP9hfdn7SYUwbjZ+clXECALxVWNVUWT2xYN9ZZBUYLJVbxvfcjvCxw5W/i6cSURcul+2Pn0uNqEol4EUlGW1JV5VkrzB7ZRzHkqcTMlcoihUl8m4A7r4Z/uFtyH9zb9n5qqE4BvgmlEX3Y6GycqLlkePI2hs0NgJI00UKd29E+HBlqYbk8u6eT86VS8Z907rSlI8iJVrgrSxnGOj5Maxa2VdC5hxhgKClGUoE0qJVVbYsOZIYYddCALpsu5jUEj+FxJVgNcip8640sk0PQ3OJWgvFKRdZ6ioACLo840fokqdqpPL9GXQvdAqMLGWETZsFysce8ZkXEbRoxYhVkYb5fFr+3Uu5C8zvCXFMxRaZ9VCBQQghhBBCCCGEkLqnbhUYU6WcoPqCkBkkCCAtzQiXLYK6qE0swyWLAUCX1TIrA/KD/c68Kjlp/C7yuUyDLIctrbVuDQpPaROrLCO7olVc//SSPProiSOQJr3SGpt9RaoLb2U06e93n6ua52Xk8oebbtVtHHkh0/cjaG0x7Y1U9QIgZE4RhUB3J4JVS4CresXRrqLGV646BUL+m3tThcbZc/rcIKwpVoRbNiJ+RhvvlhrtAahYEq9UCZV78nnAxooM5YOvhki8OJN1TbfPU15Zgq3aABRHTiAZMgoM3/BvUTeAyjGOkDmNCKRtAWDup2ixuR/OX4C6MQgAUMMjCBbqZ7Tk9P0njY0oGANuq16Ie3oQGMVF4fhJ3d6ypVDG88Ift1hFVvzcC04FZQ3A495ep9Kwx6mREXfvW8KOdqghHRsKp1K1mBocdJ+tiXd8tcd9Xzv2sCXFk74+FzOizg7dxsCNotLi9nu7a8RxWmackFlK3U5gzLaJB1YrISSDQKAa85C+G4B96GdV1AhCyKiZKBhLCm1l1WYQr671IlyoH9yTMb0MOjuQLDQpK6adrBcNAAjN4CHu7U2lmFmTDVkTD0bCrjLMukqvGRgzsCx3dULmFCJAPoegpz+tApCRfiFRBFUqs1ZJ5TaB9D68eBVhpzH/nUClD9ds90KoyMSh3uqmm/alKBkYqGr8mRX3ZFjHgsSPQ14qifIM/bImQAiZu5g0EKXcfW4nBIqOyufc/e/25/LpOMK7J6VZP29hU06SxFUPUV7KRWLiT9jagthsT7znuUS5ouOiINATtB5qaNhNbkoYuue+NQAFADWqzw8WtOquXr7iJlxsSoo0NKSLMTbOxXGasuL3y5idqjhG0GjGOqBROJmdcAqOEEIIIYQQQgghdY+oCUiTa67XTkgdQUPXyoy3/nKtMFaQesWmLKFHr0D5K/Kjr7kLuW89XnxCECK8dY0+1pow+iX2JpHmI3dthXriGf1DhlmjXXVzRm9TQPKKHbrt7x90143WrgZQYpZb1FHBo8mDjBWEGI783V3Y8J8eH/vAOcap994HAFj9/oerHveg+uI+pdRdU319xgtSr4RtbfrDqmUAgPjQc27f4BvvQdM/P1Z+zpaNZceG3V162+UrtV+8RKFceNVORN/ZV/HwaNVKfZxneg0A4Wadvpgc0+lUvrKvWiokAGDXNt32BT22Khw/icKrd+pt396XqhI9ZbHs3IJHDv8NegfO1Ty2oAKDEEIIIYQQQgghdU/dKTDoJUHIzadeFRi+URVgDDuNwdabDmvDvS/fvqhqG0U5ooSQSVGvsaLUTDdas8qZ6f3EYb2C9ZXbu6q2wVhByNQy0wqMoKUlU7mm7tsOAJCHDwAAht5wD5q/q1VwV9+0FQDQ8Yk9xabj5jx7jnrpnZnl30tLtFt/GqCC140Xd0qvBwDhxvV623PHEN52i/5sVIDh+nWIjx2v+jvIorQ0NCEzzXjHFnVn4jnbJi444ULIxInWGUn+6bOZcjQ7cWGxkxfA2BMXFjU8nGmwWU0ur3abwc2eA+nxt6xF0mwe+lUqHgQLFpT1GwAG/sO9AICWf3xUt53LVzXcCxctqlgVgZD5hpXYJkeOZ943pVWA7OQFMPbEhUUND5dJcIE0TvnxxyJ36Zcd9fjTaV9rfKmIVq7IrCDS+3O7AABtn3lEX8OfWBEpS1fyJ2sImQ+ICILGRiRDQ2ULHQDci77cGMqcwIie0/eLvcsbv/oYrBVwxyf26A9BWDSRAJgJDxMj5Af7yyZO5e47IMPafFM9pdMBJJ9zfSi8ykjpPVm/bL4VKq/bjB97qqyvsamYEq1djYKZuLCTIvGx42XjlXDJYsQX9dghaG11vxcr3Q+6FpalDBAy22AKCSGEEEIIIYQQQuqeukshIYRobqa6p15l4YTMNKUrbLUw5aabGaoAAAi2bQIA5P7qGgBg+BXlZUeBctOtsdQ/FbvR3IxHBv8F1+PLjBWEzEJutmp4plNIZgtZKpKyY4zihNQ3pSlERfuiKFUbe8pgayo+kXSgagTbNyM58EzF/dGaVQBQpuAbft3dAID+FXrs0PXRPW5fNRPP62/fhYWPatWSMzsHir5rtGypvuaL6Xhl9P6d2PfIh9DXe6bmsQUnMMisgKk60wsnMGY3Wa7OPtGK5QAA1aJrzMdHni87JuxoR3ytvCZ8uEin6tSazhJ2LUR8teRl33vOhFs2Qj2vpfhZg7Fww636w/U+J9+1clkJA0iT+Q7+hIJ9wVcJgu2bddv7D+tdzc0Vfy9k/DBWzG1OfGA3AGDt7+4Z48jpp++tuyCxjh2tX3h0hntDJgInMGYh/ou1qYSh+gcyn9c2hS4Y1Gkz8TPHyibaw+6utMqXNxZwz+pKL9glqb/hksVIrl4z23TCj/8SLXffARTM9icPpW3Y883YQgYGUTh7Tm8zqYk4fwkwL+ZF/iOmokjcPwDZoRcM5JAeP0lT07gWNkh1xju2YAoJIYQQQgghhBBC6h4qMMYBVQBkrjJbVlXjH34JGo5fBuBJ3pQqk9iHSxa7WfRxrcBnmH1m4Wble3vTbbdv0NsOH3Hyu4Zv7NXN7tiSrgiMgwnVASdkGpktsWL0/p1oeuZFAEDhnP4bSpXJX4tiRYWKBZnUGis62gGgSN0UbNUrecnTz2L49SZWfF3HiuDO2516aDxkXYeQmWZaFRjha/Qzv0KKHWDUexmr9aXP1uTlO5C7aNI3An1vx88cRbRyBQA4s93kFTsQfO9J3cbtGxAfPjJmX4PmZsiCVt1miSloKVmpBMGdt+tr7z+sVQYA1F5j9rlrG/DIQXNg5d9DLdchZCahAoMQQgghhBBCCCFzDiowZjFUhJCpYkZWVTPKAZKJYT0iKhkzjr5GL4A1HTYr0hmlGxGEmSs30dIl+pzzF2ruSzWDyORldyJ/VueNZpWlDLsW6uOu9xatkgNm9WqMlW+XV3tQl7qVMMw0myITY9pjhZ+zXEGBVKqiiJYtdYZg1QzU/LbVfab04MMHyo8DcOJ/GC+I/z51XhCyY4u+9gTUWD5TbhLrU6Oy5KZRb/0h42ImPDDC224pNhAsxYsD1QwJixhD3TCRe7KaQXS44db0O0zy/37N39Gd4HlgGLVpMjycGVPt8zboGwQAFF44UX5MhdLy4cb1ANIysWMRdrQj7u03fUtcH911tm1yfc/y1YhWrdSn9A+433m4+Ta97dQ5SGhK2XrKWvc8GRlxxybGw0vy+aqmq2R8jHdsMa8mMPjCT0g2MyYLH4fkcUoRcQ+rrIf6kY9oWfeGX9ybnpLLY/TlWr7p13Ava7qhIfNBX1qrvZY+ctBO6o2ZihVjTdJNGyKQKFfx2qe+oGPC6p9+Kj2lQgwoJVy0KNMcd+jH7wEANH7tsdq66DvbE1JH3JQJjIxxRLRuDQBA9VzLTKsKGhv1KZOs6lGaTtrzzt3o3qsnXF16ifcs7//pewEUm9IG2zZhtEsbcYf/9kTla61fl6Z8eJMMyct36Ha+/6Trk+1PUeUS83uKli1xJpaE1AtMISGEEEIIIYQQQsicI5qJi86UEoLKC0LqC6uCCLoWZq5EXv6/tIy7+2+muKSfUhi+X69a5L+5t2z37e/XqxP+mubAj+9A85fHLuVXaeU1PHAUAJCMo4+EEIMx1vNNN32uv20XAKD9049M7XWVwuBr9dih8avliohb/ouOW36s6HvjDrR+fux+VCpN3PxvOsWk1lhB9QWZb4iIUxcELaaMuC/nT/TdIws7gQwFhrpDpwNg71Nl+3yDy2pKjXDRIsSXLxdta39+EElLQ/FxHR0uZaHp0mhZO0lzHg1HdApctTs5Oe2pJrzxQXRdp28kble6L/HGI5IzqSQ3BqtchZDZARUYhBBCCCGEEEIIqXvmlQcGqS/oSVI/0MRzYoyrROtN4Mov7kbXR8rVKtZ8Kn7m6LjbDLds1Oceem5ynTPU6g/gMx4lzox5JcwT6qKMaknOe7RyhTOmPf+r9wEAlv7Fw9WbGCMH3pnBZpjBTZSs8ssT4cq79f3Q9dEpVqYRMsXMiInnxvXFxpAlRrD+czu87RYAqG76ibGfK9Z8Or5ytbYvAK3gALKVWOq+7Qj2aSPq8T4vS4nWrgYAFE6cqu0E7/dlv7fkc5kGpcE2XRJaLujvnaWOk1weqjDq2rSMd2wx1tghvO0WyKDen2VUbv+N1OCQ+/e3JWrV00ec2WnRM8F71kS3rNVtG/NxGoRPLeMdW8xICkk9wJfnmYe/+3mKeSDc+Mm70PwlLyXDPjRFC8PC1hY30K/VRbuoWkUWu7bpv23d9Br6OvT6nQCyDfWCxd2QAS3HrCQHr5Wq1RNqJGvyApjYxIU7d4omLiwT+X7jSSHixMXsxnes96XcPtESPfC3lUeS7nbADFiX/e/HAQBZU6P+AHj4ZboiSO7BbENeGR3DWHgC1TGG79YTidG3K5sAF12iQkxY9BltBFxzOhohcw3R44JgwQLE164B8CpYNZekcCzW8QIj+iVaWlvcC6xqyKcHlkyMFsUiG3POnM00DZV2PTkJbwLDH7cEzSbNxU6cdHYC7a36QH/sYOJKMJq4fhdOnyn/+hljIn+C1N+vmhqK2vYnJjKfl15Ms/srPleP67gbD1ReyKl0bnLkeOb2iu2MMXZQp88B+XzF/fFVU+3Fj9lH9WSEKhSyx5fev3FsU3hsJStOXswoTCEhhBBCCCGEEEJI3TPnUkiorCBk/EyrLDy4X8/4Z6wYqPt0adHc8QtuNbWICaxyZpK1YrJDr8CqJw+lx3grL0CxKViwfTPErjZUkYBXkjmWXi/s7kJ8+YrZ6dWlNysj4arlmTXVCZlJbkoKScb9Gv/wSwAA+cNnshVWUxQrsuKUlUknB59N+2djRcnKKqCl0bUol/xyhz5W1pzsP6yP841LvWvbWBFsWDflSilCpoLpSiFpjxap3W1vRNI/gKCjHQDS5ynS1BAZLWSmTkRLlwAACucvVLxG0NgIFWudU5GKwMYnAKFRXliTznDJYqglRgnytFY+hgs7XN9kpxkH7DuUNrd9MxArc86zFfvjp8r5KXC2TRw01+teiMIFrerwlaxWoSGd7SicPF3xOoTMBCyjSgghhBBCCCGEkDnHrFJgPHBuP5UVhEwDdWHMVwOFV+1E9F2tsvJXZ0s5+rGduO1dteWZTxneCnC0cgWA1Egq2Lqp6soKIbOF2RIrRu/fidy3n9A/VBnnHPm7u7DhPz0+ZdetiSqxolb1BiGzgZkw8ayFUqXVjTfdiwXP9RQdEx8+UqawuvLu3c48N1q7umZjzFo9rqJlSwGgSJHqK7LiV2olWvhdHduGX3c3Gr5RXgp+LKJ1a/R1jCElITPNnFZg1NvkhU1XIYTcHKLv7IPs2ATZsanqcV3fq2zkNG0olZo7DQ1BeU7WL/xM54SaDBobnVSUEFI7uQf3IdiyEYFxuq9E18O5m9QjDz9WDI9ADafy9GNvWzihJiWXdyklhJAqiDjTRvXSO6Feeifa9p/HyOJWjCxuRXz4COLDRwDoiQs/PUx5b001V/UAgDjWf8Yg6e1D0tuXTnICCHr6EPTodFYVClSY7htpC8vaKCXcstFV/HDd6WhF3NFaa+8JqTtm1QQGIYQQQgghhBBC5iezKoVkLJhiQsjEmC2y8Knia2f34cdX7Kx8QIaJICGEsaIMxgpCKlK3KSR33wEAUHufKt9311a97/Gnx93uKw4O4gevuxUAUDh7rmz/xV+6DwCw+K8fdtvC9evKSkWPh9CamF67PuE2CJlpxju2iKazMzebyU5esIIJIdOArbBRpUpApcod0aqVANI66GF3F1T/gN6Z09LvpK+vvKKIx/Dr70bD14tzRLNeSIIFC4BEO44nAwNVv1K4+TYAQPzM0XSbcT2Pj75QNqCQKJpQzfCsCgeEzFlsrKgyKRC0tGTen/79BwBhZycSEyukUeefJ319mVUALFn55FmxIuzsdPFqrHsz3KBfZuIjz7tt0S1rAQCFF06UVzzyqoyMB5s+UlQtgZA5ioQBwtY2JMPDCJp0mqX/Am89LsLuLiRme2LTOkUQXNOxwd5p4ZaNkKv2OP2sVkGIaO0qAHAVwaJlS50/RbB9M5IDzxT163vbmhC29RdtC5cshgRa8O5PXFjUhcvusx8b3Pkb1+u+PneszLsiWrXSjY/ceGFwMHuc5fl+1FKFhZB6hikkhBBCCCGEEEIIqXvqWoFxsxURVF4QMrVIFCLs6ERyvReBrZd+5WrGcZEzuPKVCtaZ3xJfvpKuMtgVSxEUOvQKa5adVdP3n0VSS19XLYMyqyQYo2KIymeETm8Vxa78uuO97zSWqsJfJRGzsgQqMMgcR3IRou7FiC9fQbioG0CxE787rrEBGDQrqZ5SIX6+2E0/7ulxqgQ/VsStelvW6k3Tvx+uKVYkt65AcN3ck0bxUZGoPColl66knwdK7m3vO425ouqpLiQXuc+EzHkkAJoaEeQiwDwzi+6HvDG0FYG0tujPRoERNDUB1/qK27tw2ak65cyLAIDQnof0uRxfTscvQd9gbWOLXA4Iq6wXJ9mt2MolMjKabhwqVqqqwcH0+Bbd3wAl4wujfrXtAQDCsc0/CalnqMAghBBCCCGEEEJI3TMhBcaGbTfwwAPTb5hJRQQhs5xcDmrVUoTNzUjadcmuwKw4JkNDbmVAjYxCmpr0534vfzRj1bFUuRCtXgl5WOezq4zcebf6OgZyYwjq4uWSjZLdh4PPFh8DIO7tTbttFRcZ/QmWLtanXe1Jc3a9vPdg3Wrd3vMnM9UqhMxFVC5CvKwbIYB4aRcAQMxqpxodcfdS0j+Qqg2GPa+IDN+IUjVCtHol4h9o074s+/JkYKCqV48lvNKH2KzSjoXvk+Ou48ck029feWUJunRJ1WB01OWq+346sln7fuDwMfrkkPmFiFY2RJG7Z4N27ScTX74CaTbjiSTRxwHOb0bCoOzZGl++UuYjEy1bCtzQCofAqDHia9edkiE5dx6BUT0knirMFkdwigelgOHiWFTm5WPHQr1pbLAKrPicVqJJLu/igP0ufr8lb0pCB60Qo2iVfN6pvKwXkOTzSHqulf9OCZlFTGgC48jB5nk3ucAKJ4SMHzU4hGS/kWWfLt8vkX7gqtGRCUufCyczGgYQNOr0i2RoCENvuAcA0PjVxzL6oMNgZk33kpeYaNlSfawvbS855sov7EbX3+4xfTADnRs33AuYNecKWlrcACe4dY2rO2/dyKPVKyt+N0LmHDeGoJ48hAIAZBjLSWAG+Blmv7VSU6z4sbsBAI1fK48Vrp3jJyvuK6PKRMip37sPq39fm/oVTWCYlxnfnM/FijUrnSGonUiNVizPrHhAyM1gRgxk4xjJ1R6o0YJ7MVcj6fVt2khy6QpiM1HgJh4qxJDS/if9A5kLIMECvRgTX76CaK1ecEi88YOdSLB/x5evuLgVdnbqa/mpH40NwB3G7PeRg+lX7Okpvu62TVDmnrcTNOjrQ7BeG3vGR/XYIVy6xPVRGhvT7zBqJkcXdyGZRNUTQiqRZWxbdoz3vJ3UtSZ1NiGEEEIIIYQQQshNQFSV1YFKTKb+MpUMhNQf462/XCsuVvipGGZ1MezoKFphsKsjdqUi2LoJiTHTzCpFmEWmQsJn1zb9t7fKUUSGfNwvYVZKuGSxM9rMVHCMg+DO2wEAyf7DVftDyEwy3bHCl1Y7eXZJ2dTSkqkjr70b+W/q8qdF5QFLU7i8VK1q9zUAhIsW6f2XLmXun9Aq0j136L8fe6qmw0tjoqW0lOKE+0PINPOg+uI+pdRdU91uW9CldjW8DkFrC5LrOn3TGoVjcVdR6la0cgWAND1Duhc6JaTavV1v23MAoUnZsuklwYIFTikR2rhSYizurpFR/tQ9v4G07ctXXNtBl1Zj+GMHmzaiNq9DeFl/r8IprcQqKkfvY8YHVt1RqtxwpaOfOOyOHyu+zRs4xqobxju2oAKDEEIIIYQQQgghdc9NL6M6n9QXVJsQAiAIIYFAJXqGO2gxxlQlqwQuR7SjXe/3SplWU16EbW3OQNNXXvirtNGaVXp/hvKiaOWkZBY+7FpYcYUWAOILF8s3emqTTFWFWRUO21ohC/WKSdLc6I6x5lxqZGRS+f6EzDqCUJvhmXtE+eUDPeKS0qVWfQHAmdwBKDP2DBobnNmlf1/L3VoZofY+hXDLRr3/0HNVuzohpUOJ8sI35ExepscKwUP73X6b0x92LUSyepk+rilC4eED+lgTKxDHNPEk8woJQwQd7RARBEZ5YD0nCs8cdQoDNTAAZUsVG7Vkci4dJ8ieA+5zqbFn0LYABXOu71kRLtFG3PGFi4hWLNefT6ceNNbPxprwYngYiTXstsc0NyE+Y84JQoSd7cV92HcIyoyF7HjC97rx1aZWtWHLyUYrV0CNjrq+FPZpk3OrApHGRnrmWKi8mLVM+wTGA+f0w3g+vsjPx+9MSBEieqDRvgDxVe16rYayX8rty35sX+TvvD198fek4G6CwwwIkuHhdMBgJZSXLhW95DjzvoyqIE7yGYQItt6mdxujrEpVQHy5tpscsdfwHohFExcGJyW9dAnwBjXOYMxWYVEqUypOyFxEggBBSzOChR2Iz2dMDPqUpGKE3V1Omu1TGiskilLH/lALUJOhIai96cTCWBMXgDYtTO7erD8/fKDqsX4ajJ1wsKZ6RRVHvIkLi7v/XzgBmFjk62ttdQEksTMTnGwqGyGzAwUUCkA+DxjzTeWlitnUCIkiBIt0VaPEVDUKlixCYp7X4fp1ALR5tk01sWkiamgofekPU8F6fPGSu46dCAi79TXiy1fcfW2PC9vbEK7QE5B2nKD6+tPKa319bqzhJibOX4CY2BGKvnbh7DmEbTpNJr6kK6b5KXdRm6lMcv5CGltE3CKNnbiJr1wtH7cQMstgCgkhhBBCCCGEEELqnptu4kkImTkqKaKm3cSTkLnANBl+SS5fvQRhhesWGVaOg5537kbnJx7JbNPywp/sBgDc8pt73Lbwtluw5+THcX3oPGMFIUCxQfUsoKhc72Ta8QxmP3bqIQDAu1a/rOy4aTPxnMF44SswbfpGVgrXeIx1aylF66fLjnW8vbZSqjwVdZb9n51t2H8bqET/5d1r4YZbM1OiS9U/wNTcq2OVNa1k5ip3bdXnD+j/O74pbjUGfupetD+hxyPWNDe+fCU7pdtX/+zahkcPfhi9/Wdp4kkIIYQQQgghhJC5AxUY08x89gAhs4eZUGAEzc3FqxYlq8xhZ6cz+ux5p14N7vz4nuqr4BkeFz6++VatlOat+8SvfAmiHzytu1NtBb0G+t+yCwDQ+vlHvItX/z6E3GxuasnlCpSWV/VXma6/Xd9H7Z96pOq9O9bqqMs1NyuepUxkday0TONEuf428x0/ncYKPwefkHphWhUYwf16HGHjgFFDyNqViA8fccfa535ifLjClcucr1Th1TsBANG395WVYZcogor1szcybVRSu43lV1V670sUlfliAGlcKty1CfnjF01/zDWTuEx54av3KqlB1H2mVKzn12PNR2s28/RKUBdtnkT55lpUJ+NrcPzKklKfJDJzjHdscdOrkMwFxjMpwYkLMu8JQkgYQhW0K7aTvXsVQwCkExee1MzS+fE9ZcdZwo721KQv0LFPJcXVRdzLSMbERdb13L6N66tWIQm/+wRUVl12u3+zNgUtkt+ZSQnJpQMYDA65iQsnzR0Z4cQFmV/YikXmpSFsN/dtyeDSvrC4nz2JbPun0pf60okLP1b4A+74lS/R+7/7RM0D+4nIessmLrwBd/zDpg//9kS638SKoKkRhbs2AAByl2+4iQv3wjI0zIkLMq+QKEK4sBuSz0FMdR7Wq4AAACAASURBVBFrill49nn3YpoMDAKmAlrYrScR1JW0Alr07X3uc+mYJFy6BAVjfOkv9hZVITHjGWsQWnS+OU7dGETSXxyzwkXdbkIFKJ8ICB7aD2Xk/XYc4JsVF0222DHFgla9r70NKtHpCxIErmqRnURBFI2/CkmFsciEqjEZpmziwjU4/gV5TlzMXphCQgghhBBCCCGEkLqHCowJMBFVBVNJyHxEcjlES5YASZLOjvuz5F46iFMenBufISGWLS4qR2rx67InpSZWHsmJMwD0CkjpaoI6XcMqhSlxBpWxQnH5WtkmV5YtChGfMtc2UncACNas1P06fnrqVygIqVMkFyFatAhqdDRNz7gxmB5g06lUkt5z41UoLe7OjBW5vbp0agJd3nDMvjY0lBvjTQQvFjYc12URfV2HVaCgqwPRo1plIiZVBABwiy6dGrxwKtNEkJA5iwgkn9Mfc/pvWFVUEgMmPSFoDVwJVDUy/udp2KrT1RB7sca799XoaFnbVk1hj5PmJteH2GxTIyOuDLLk8k7h4ZujlsW3kdGy6+rzdby0cRNJAgzqsYzK59Jxlv07UdNmSE3IzYIKDEIIIYQQQgghhNQ906rAeODcfioODPw9kPmIGh3VBlQZ5lMAEBrlwdCuDch963G931vdGPgP9wIAWv7x0YrXiJ856lYT/Lx0dx2RzNXS5OU7AADB95/Ufze1AVaBYdrzVzXV7u2QPQdQRpVVYL80lV0dcds8UyxrVgoA8bET5oJJxXYJmWuo0YI2yFMqO1Z06pz2G/fcioZv7C07f+S1dwMA8t8s32fJKl8HeJ4aIpmmnaX+FJLPpzElYyVTvfROyA/2V+xHFoUTp9xn+/1tXJC+PhfbEq/MXnK4ttJ2hMw5VAI1NISktx/Bwg4AxR5X0qL9MFQ+h4L1t/Lu0eDO2wEAyf7DZU1Ha7WyKX7xAgLrn+U9y61vQrRmFeKzL+qmzf0ZtLQAt2lDz9i0Ha1YjtgaiG64Ve/zY9GOTQhOGq8N45Wh4HnmGPWZr/Lw/XScavPUWfPdm9Nyq1HkvrcaNsafDXkqL8isZ1omMJguQQgBoGWeuQhqOEZgBhTxtfQhXLhDG2g2HTyNLEu8ahMX/jWqPoyVyqxGkDus3cLt9EORvDSjvejQcSS+vLMCRdVVvCoitg/upWT7JqgnD+nDWlrcS1S0uBsAUFi7BHjkYOXvRchcQmDMfgtOGu5PYIzerl8Kmve+gKwpw2oTF+k1xo4VWa70+Sf1y4aLFUPDReeUEh58HrVMP/qVlopiRWms3LEZ2PuUPqetzb2chObFbWTrGoTf9cw/CZnziDb9zecgZhLRn/hUNq1ieCRNObN+20kMeeFMxZbtZGLY0Q5VpYJR4fQ5dw9ac81kYADhDZM6YscLhYIzMZcbZpHEW8AI+oeBrk59fpYZrz2ue6mbwLSLP3FvLxCZ2GFSaaSpyV07aG1JK5/YlJvWFoCmv2SWwxQSQgghhBBCCCGE1D2iJiAjcvXaCfGg8mb2Mt76y7VSLVZIFE2oFCEpJ1q5AgBQ8OTlPnbFSNorl5PNWp2eyL9RlhmqT7hkMc69ZT0AYMlfPVx+fos2TUsGhyZURtaWzB15ib5G/umTLDE5hUx3rPD/zw2/XqeFNHy9RF3hqRWA2v+fSi6fqjp2bdN/P3Iws9xxsH2zvoRXonWyRGtWAQAKJ0+X7ctSfvjIzi0AAPXEYcq/yazhQfXFfUqpu6a63ayxhVVgBBvWIT70XNk5ftnh8T5bisqclsQfoML9m3Gc3xdb9jTreRytXY34RW1oPiHDYP/akzDsDDu1MiS+di1bbWZSYmRUx9/C8ZPlXfEUpj5FpWBr7Et83aT42RRbv7zt5tuQtOp/J2UUa0XXM+MkNXDDKd9sH5LrvWnqsNdXv6x9uH6d/myM5iUXsQzrFDLesQUnMG4y9AUh9ci0vpQE9+uHTNZD3+aDHjsxoZfVqngP7XCjfpmNnzuW7raVDmp9QQ9CRMt0zfdqNdSDO293ebW+pNVWH7FyznDDrS4PNlqx3LVpB1kjuzcX1agnpB64KZOdGQPu8DadbhYfOz71L/De9YJtmwAAycFn0/1VXkQy8eTh1Rh+3d3Oz6MoVpS8DIW33aK/N0ysMBOVdnA98oo7nIcQIfXEdE1gtEeL1O72N0ENDuqUCADJdZ3uoUZHXLyQvgHtrQMU38c13NNhWxtim0Li+2d4KanRiuUAvDGBiPPQSC7qykL+i67z1zhzzo09olvWIu7U30HtO1S5P7fdgtj4ebiJhZ4eF7Nw5ITu36JuJOZ6QUuz+/72HOlsR+GFExWvQ8hMMN6xBVNICCGEEEIIIYQQUvfUjQKD6QeEzBwzkUJSEyWrJOH6dZBhbYZVMO7fSOJyKaK3Ahrdsra21QYRBE3aPM+vPpJFluRc7d6um9lzoEx+Hi5aVORiXitlqzuEzDAzHisqGHGWKqrC9euAPi0FTozqSRUKiFZpx/7CaWPi58UKXxVVlSBE0KjVD2PGii0bAaBI0q7uM7Hi4QNlio+wu2tCKU9hd5e+DtOlSB0xbQqMsFvtavoxqJERSF6rl7LuxbCj3ZnuJl6VsWj5MgDps1V2bkHYYyqAGAPQ+MjzTjFhUyPC2zcgPnwEABBs3YTkaU+pZSiLRZ2dkOamousV4ccgk6ZgFVf2OgCQPP1sWTwp6o+f5pKFpzRz38urfkTITEIFBiGEEEIIIYQQQuYc01JGdSLMJ+UF1SaE1EhJfmr8/An0/uy9AIC2z6Rl0OKrPcXnqbSIYc25nkq5VRKMsaoq/YNl23LHtfqjAECGRouPDyc2V9zzcr1KsuAfqMAgBEBF/4tSL5v4+RMYePM9AICWf0xN8qwaIz0xjRU2v3xMktgZ8I0ZKwbLDfhyp3RufAFAab1VMaUQx8vALu0n1Pg1KjDIPEFEqy+SxP0MoChGJAODCNrMvWqUCeGCBSgYg0zrNyM9A0iajQHkczoOSBgChdQoGAAQpzdscK0vs1xyma9WHAO5Kq9b/jhH0gVo63Ej3vdRUfFYQuW9dm3sKFFgOE8da4AJQJnvSshspW5SSMjkoUEomSgzLgsnVYnWrQFQ7PBdJoU3ONds41weLV2SmpgZ1H3bIQ8fMCfof3YJQwS2SknpSx4qp8FEt6zV/fAnikyboW1vDKdu2bEF6skS87IMI8SgsRFipPt+m85dfoyXSTJ5GCvmNsf/cDcAYN3v7JnhngBHPnwPNrznsZnuBpkEN7MKCZkAGZM+fgqMreqllEJiDU09bDotEn1+8sKptNKTIVqx3E0YFT3T77lD/7336eyJ6ZIU4mjpEmdOavHTZYKtmyBndGqxHR/4FapsX2VoxKXOOLPXG0NukqlonLVUG7fHl69Atm7Q17SpwQsWIO5NJ4XI5GAKCSGEEEIIIYQQQuYcdZNCQibPRNQXVG0QUv+ovJaGRsuWOqPSUuWFY6suGQtTji2rTGzu9BXYrc407OgLaYnXkpKvAJCsXQoYBYaveCicOF3eB6lxbtyqP547jrL1lyQuKi0JAMnIKDBi0nP81ZkkS8hLyPzDrYhevY74wsWqx8qOLQDg1E9hd1eZ8iJat8atSGYpwfxyjq7du++A2vuU/uwrwrJKV5ptYaeRuXsxp0h9YWJF0NyMZGCg/LuUxAqJIiizKuzShJSiWosQD2vmiUKMxKTiWpWjGhwqUhg448+Tp93PBc/IHNDKUJumFyxdDABILlxC0KRTVgKTzlI4ew54TMeIohjjKUujxd0AUmPiwsXLCG06kIkrfrqMb6hqTY2locEZp8YmNSjI5xB2m7aNabNVmpT2QY2OunaS/YeLvivVFzMLFRiEEEIIIYQQQgipe+iBQcgUMNuVLMxrJ1kECxZk5r3OCjJye+uBM799H1Z+8OEJnSs7t+DM/XoFa8UfT6yNycJYMbspKzldZ0TLlgKhVmUUzpy9qdcO29rqalW172d2YcHnHpnQuZLL4+Rva+uJ1b8/M7ECmBkPDGlocB5QRdtLyptOmFqfLVWOK1IIlXg9TQlT9PwrVTZNFeH6dUWlYieKNDS4f9dkYABBS4v77MhSfk2CcNEi3VxPT9n/pUr/9ybCeH/3J39/N9b83sS8i87+1n3ImaHe4v9dW7wIGhvLS/Z6Zc6tqiXu7S36/1jq02YZ79iCExhzFFY6IeOBLyX1jZU9B20Lygw5S4nWrAKQyjwlly9/AIogXKil30M7tYlV7luPp9dbsAAAiiYvwu6uVMr5qp36Wt/ZN6Hv47oxiQHl6P26D7kHJ9cHMj4YK+obO4AP2haMOUlRmhKSGSuC0Mm2B+/V6Sn5B8aIFUsWu/SV5BU79HHfe3JC38fvh25w/C8hU9YHMm5o4lnf2PECggDJZZ36oQZ1lbVg3eqiykyl8SJas8qNMyzhokWIb12mP/fql9v48JG0jQzz8WjtameqGWzbBABIDj6bxpb+fn2gUu6l3qa5VFpgsddRN25km5L7L9eAji821ezurfoaB9J+q5GRmoxGycShiSchhBBCCCGEEELmHDTxnKPMNuUFFSOEVCYxKyK+8VyWeV7Y1la2IqJGR9xKhjO7VMqtSuS+dTVtL9Rz2lZpAQDYtU1ve+Sg2+QrL0pN8SSXhyoY46u8kUB6UkFfpm2VF0Fzszvfmm/5/bDXQBgiWGT2e8oLX5JY7xJ5QqaTZFCvevoSantP+fd10NhYZMYJwN23QKqsEBFXktAqL6KlS6BiHUv80srO5M+spgLFqodSeXfQ2IjExIagqUnvq2SuaVY4/fhh73WEYZpu4pmCqpW6BCL8PnjxKlyiTQbHMjslZK6ierW6Ib52zSkM3H189AV3z8rq5YhfOFV2rk2ncAqEQAAzVohtOfXuLsgCreLyY0O0Yrlu5+o1ty05mBpx2rLuVmURdnZqJQQAMcbmPmHXQqgBHT+swsNew/bDXee6bjNauUL3YeAGYNqMjbmoQjrOUlEEMUakduxUyVCY3ByowCCEEEIIIYQQQkjdQwXGHGAuqBdmc98JmXYyci995UWwVeeNxl4ZsaLTvZxWAEV5rWmDcVGbALTxkqe8yKJ0xTRcsRSFU3o11K6WIAjTFZpchGipXhm1fh5+G9KiV0hVf7qy4dQdDQ2Iz75Y/v1GtZJDosiVPXOrO20tiEtKvREyZ8nIxS5SVN1zhz7MrDIW4ceZdXplMj5YHlNUoVDcpsFfXc3sWulq5fq1kGeP6X0mRvkmcFn4JptqgVkdHhj0LqK/vxocAo6V98et4DY0AKUGfDu3QJny04TMB+Lr5aa19vkNAEFnh972XDpmsIrHuKfHKTCs2jK+6o0hzH0sUZTGBlNiPVrSDXVD37dxf7aKwZY/db4XzU1a4QEAxgDVKUAASBhAjMoiMYqs+MJFp8qSXM71y5Z1tYoNBOLazEKam921fVXHXFJgBI36d1JmzFmncAJjDjCRl/+5MOlByHzGT5uw9c9H79+ZaWoZrtQP3PjEmbJ9lrh/oNyQTyn0//S9AIDWLzxaoSPFbueFk6fLX0CU91KVqHIjUu+lJbmgJem+sWewfbPed/BZN1Aqwr60JEgrGJjBD25uIQNC6o4iwzozcTHyo3cVmXGW8bxJRfMnHw1JX3/ZJCQADP7kPQCApn96LLPJUtPe5PDRtO0aqyZIFKWx4ZKWcideipqVhMfnLyBobSk7X8XmeoUCZJlJMTGScE5ekPmGfZH3X8SjNcZo8/hJJD06vUPt2grZoxczbEpo0NICaTBpoualN2htQVxSXUKNjiK8TZuFx0ee122fv+BSVeTGDajh8onXaLk2A7XPcjUw4FLkAm8SxR2/dAlUb7GpZ9jd5WKUKphrJDFim5ay4Va97cWLQL7cP9LGGnXjBkI7mXNOp6fa391cYbZMXFiYQkIIIYQQQgghhJC6hwqMOUatygoqLwiZ3QTGFMtf7chSX0hDQ6a0+8xv3wcAWPlBU/M7ictMs4Bi5UVW2dOwQ69KuJWQMVZQy9JUAEiUc+Ubs1YBkgPPuM+ltcMvvWc3Fn04rX1OQz4yr8lQMti0LHjpF1nqC99M163IiuDcb+hYsfx/6lihhoeBjNXHIuVFRj+CroUAvHvUV3aMETdcs54CIyuWODNPwJmPWq69Yzc6PpHGirFSXgiZ64g1yPYUGKoxVTnaOBAdPoHYpH9YRWXw/7P33lF2ZNd571ehc07oBrobaDTQyDMYDMIAEPUYTY4ovkdFyoqWaOkpPNsiLcmibGlZXpIlKtiktWRJz5RESk+JIimRFkVy5BkGkxzMYAYDYAYYxEEOjdgI3eh0q+r9cUKdunVu6nj79vdbaxZ66lY4t4Hadersb3+7uwuZcxcS5/M6OzD19G4AQM1zRwEI00t35YrUtXXr1KYm3P6RJwEAHR+L78+ooS6xv3k/Z6s8ANhbzNfE38U0HNbbTp1NHyO5/m/3Y+V/fT7n+SupfGQp4kRFPjRM2H954WHJB5lPSu2/XCyMFXODJyf+Ue8KhK+dEhtzxG5nt6hxj14SUnGnqlovDijcpqbY2Xv9WgBAcPZ8fL2N68U24+HurV+r97F1QEmMQdasus1ykcXShx0wymCmM9ba/exe7Y7vawm4I0tFokwm7iwwPl70ixCZGYwV5Y122u/uRPD6afFzrlixa5v4+OVj4v8tsSLR9UN1JzDKxLQ03PDVScSKrHs4NQYZA1y1n+UlQ40NkCUgtljR2iKOly85ju8jyq5pD4O4TG50lLFiAXg2+vShKIp2zfV5GS/mBlVyBd/TvlPqT6euLlmiIe//4Lp4kfc6O+JSTbVPTzcycoFS7R/evK1LP/x+Uc5qdkvz1/Tr/9cx5tJVeLIETL2nRuPjcUc11TEkR8JCe2BNTsZ+PdILw62ugivjhVqU8Jqb9XXc9lY9RjX3wnTGGsNsSR0yM0qdW7CEhBBCCCGEEEIIIWUPS0iWCOWovHjm2pGyHBchlYZWMNy5G0uzDcwsp1Je+Ct7AACZ68PxjvJYs0REZUqdmhpdnqGUF15ri85omgoNm/JCjSEKAsB15X7i2Ml374Y/LrKm3lde0ceY5SCJDCuEwkJ3MdE7uYAjszFGxkN1IfG7V+iMis7s1tfnVIoQUmnobOPtO/ZYIV37g1u3tPLCZsipspVm1tGUfKsYYutolIgVlqylUnDBc3UGM7gj7tHwzTtQ/YbIqprlINnKECDOfjrV1akOAk5NDSDjSyI7KuOLt2FdSj7u965KZZQJqWTUv3e3rg5OXVYXiomJRLzQyoseUQ6SuXhZxxhPKiMyN2/Hht4yXnjtbbpEJbwrTEH9/j5kLgtTcbOzmD7GmHsolUhklINgSnYbW9OPqErEgfDyNR0TQjlnCicmYqPOYanu8jykqg9cB06I+HspZFxx6uvgqg5GSgUCIOTcYtGgAoMQQgghhBBCCCFlDxUYZMaUi/qC/iBkWWGp27ZlORPKizzH6o8spljZJnj5yFXjDgA1X3ip4PG6vaFuiRogms7ax5KFNbebGWStJjG+1+ZD4pF3YqelXtXSLnK2LHRf9ehbnoDzzSMLci2yBLDFCovHRMZWR57nXjAVXEVj3F/5FFHu1w6jUDW51y0ywKr+3VZ/nstgT5mUwmLeZ6ovfvqM+PwPh9YXGM3yxlTjkCWIal/+6BGcIH3Pm/FCPVOjCWOuII+3Pv/VZ4YHlv63Eobxbpb712lrBeT8IxoT96wtbrhTU9rQPJqctM5jplcKdagrW7jayDXXscYq43mu1GQnPyK8gDb9+t2EAm1W5GktrRWrOeZEc41p8KzwOjtixV+R3H3/PrT/6YHCOxYBFzCWAZX+gl+p34sQhTa7NB7Oj77zKQBA/d+/mNgv+wFum2B6He1WY827798HAPYHjOPYF0D2Pi7+fOHVwl8kB96Gdbo//HxiXbhQzPHiBbDwfdW5eEFsi2a2WJFYsFPmdbZY0b3CapR3/4f2AgBa/uKF9CBssSIMEO3fLj5+/mjxXygLb+tGBMdPFbdznheAQnDhoji4eLG08deuAQBkLl7Rcwdn51YAQHTouN7P3b4ZzkWxwKfigbdhHcJzF8W+stTCH+hHcEmUhjh1sotIECDcJss4XhJla+HYmDbIdKqrEU2LbIV6Ic6cvwh3+2axTXUhcz04rrinXWnwGdy7Hy9WWpIQ3pYNwNcOi+vIl36nyheG34hLX6IgEKVoiBctHN/X3yEcHYXjC+NQc9FA7Tv0o6ID3JzOIvIlnBZo4UKRvXgBoOTFCyDH3HKGsISEEEIIIYQQQgghZQ/bqBJSgZSqumFrREJizNaRhdBtGYvMRHptbTM2Fb35r/ZjxX+XGYw5agE5+r0iO9/4qRcRvPVJMcavvJKz/SVjBSELw5V/vx99v/H8jI5943f3YuN/F+aImfMX53JYJVFWbVSlMe2s1XbFKnvy7TcPJYtFX7sMsJUkVAL+wGptRDpbCrWgzub8b+zD2n8/M4XDud/ehzYpuGn7s+LOYRqxKkwVsG6Je+FSot1srjkT26gSQgghhBBCCCGk4qACg5RMpXtqLEeYVS1vVO0motBqeGVmW1J+GWY9utzPrauLsx9ym9/Xq1fTzdVyfQmLv4YNt7YWbmeHOL6pHgAQnDhT4KA4G6W+q9veqtsgKoMtb8M6bf5lGmV5m4fED7fuxt4e5ncu0yzUUoSxorzR924Y2TO8ZqzINoIrFCsk/pp+3WpwzmJFizDiK+hvkStWjAvPD5Wt9DYPAbK9ciJWbN0ofhi+lfYBYqyYc8pKgUFSqDapCANE8h6CJ5Qq4cOHiWy52jeSnhNOTY3OoiuvCKe+LvbIaG0R56uqRijvS2W4Gdy+o/16nJZmfYw5f1HHqzbpTm2NVtE4vhzjyD1EgZgTOJ6LULZXdauFX4XTUK/vcz0e39dxSxkYex3tok07gOD2bbFfFGnDYACIRmUrWPl78hobilZHkMKUOreYcxPPZ64d4YtthcO/XzIXjH/HHtR99mDuHSyT6VlJNlG6JA8A/JU9AOxdPcbfuwd1//Ol4sZWiDzy1oKGTerarpd+cYgieBuFIV1wRkzkEy8kqme7IQU0X0ZK/Z2FExMIr1zN+bnX3Kyvr69jfOcoI828DONAf02/GGMOo09zgcStF4sm+jvyhYQsI6wLnCZyku53dya69oiD43tFy38t5QeZS/ZYkXn7TnHsc4eKHmtGxYor+ffVGLHCbW8Vm+7c1eN48P3CXLT5r1+InwcG5gLJ1NO7AQDVXyrcJYmUKcY8Qb2UuhsGEbx+Ot5HvfTKF3O3oU4virsNwgwyV9cahXr5zdWtwh8cAABkzl0obthV1XDXCwNN2wK/t3E9cONW3msWS76x27oS6YQJoBMG3sb1CLI79zx6BL+vFwAQSVNM87mtf8e1tXDkvWgaP7pt4v4151bm/CW4L+ccymQYxt+TZa7ntq+Ap45V47AspEaPxhHI+YG3QZiLhucu6n8/5jmDm7f0NrWA48puLQVjLZlXWEJCCCGEEEIIIYSQsoclJEsMlm+Q+YCy8KWB4/twZd9xW+bEZOL/3AMAqP2HPCoXE0OabWvFmGs8KguhDSC/ehheq8yMSnmp19MNyP0SWV9DdZK6puPAX7VSHHP1Wup6iXEoKXlrS/r3Qln4nMJYsTRwqqq1QiHRBtXMXKqs6Jt3AAC8r75S3LmN+zCleMozHqUkC94iY8X/Pgq/W2Q1g7vC2NZtaoyl2mb8yWfA6Djw1q8V5zlzLr6eVHMlsrQyzrhdnSkDOsaKuYclJOWNVq20tsBpEPeyWR6mSjrCsTGtxnQ6RftTU21iK3PVJSLV1XFplyzJiB6N6/mBW1ur73UVT9yuDj0OpW4Jb92BUyOvo8pGamsAVQ5i3M/qOsGNm/CGBsXxl67q7+VKVYqaW7hNTYBUVoQTUrURBnHZSU2NVmOodqtOdTWc+jp9HTI7aOJJCCGEEEIIIYSQioMKDFKW0EtlYWFWdQkyyzZpI/9iH4CsllkF2szZDPsWu13b3c9vQPt7ThfekcwJjBVLkFm2j7zzL0Ws6PiT4mOFNS4scqy4/tnNWPkdJxbl2ssVKjDKG5s/hlYzGea3trantjaaXnMzRt+2CQBQ9z+lF04YxD5c2T4aEAqMq/+PUGWt/C9x22B/rfAImU0bYH9lj9W/rBgu/No+DPzKzNqSktIpdW7BBYwcsFSDLCfm66WkxeuM9ta/B+6KTgTXxEPEdKE2cXZsFT8cE4ZW7kCflgMnd0xOgr2Odi0/1g+8XEZa+SbdrgevSzjia0fsHCUL5nmUCVSQw2AyG7M3dgL5vZTRWJTJpLsEELLILMYCxth3P4WGz7w415eccy596jEAwOrvfS31mbdlQ9JYkJBlwLwuYLjvEC/W0thRGW5HTQ2J57F6SXdkSUPQ14Xo5WPiM2nMaCvJdJua4jKHbeKlPDx20jqPiPZtF9c4cFRvSyz4Z89buldgemiVOPc3jqSu7ff1IuwU4w6PvJ7+BVjGkOhAZnyuSjCme+Tv4fl4jISUCywhIYQQQgghhBBCSMVBBQbJC5UoywPKwpcGTk0NnI1C3hm+ejLvvqPvE+0EG//2hZKvY/Z+z4fX3KzNuUa/9ylxvU+9GCtHpImeMr0Ckm3SzBZ2ieyROn+nVMRkqXVSKPWKX2VXq8xSQk9iGCuWBm59PbBhAECODC6g75vJp0WCvOaLpbcTLdReUu/X2aHv47HvEbGi4dMvxiagsg2j41fZzTcLtLv0e0U2W5ny5TTklLHAra2xG48yVswpLCEpc+S/d39FJ6JGcS+q0hGvtQXhmLgvo+mp2NBSKVVzKWRV21N5zyIItEmnUskEt+/qZ7Vp4ulJk3L0dOr2sqYJp4oXynATNTVwfPEdzFIRv79PbLt8JS5fkaocp7pazy20iWd9PaJpobZ1PJHbDycm2y0PQAAAIABJREFU9FzIqamO5yFqvlFdDbd/VeJ3RmYOFRiEEEIIIYQQQgipOPzFHgCZGQtlcknlBSGLT7Rf1tcePK6VF1ZDTYjaWSBWXkT7t6dqXv21axCN3BP/I1uiBWfP68xCODqaGoOzcyuiQ8cT1w4ePNBZlsZPxR4FWgUhz2eqKkx07XJPd7K9qiSv8sLMlMqMT06vEGZTyTIhfJN4ZjsvHEOklBc5VAWqTTGk8sIWK7wN6xBdlf5FMvNq+vcE9x+kxuDs2qY9BnSsuH1HZzMbPh3HCq2CULEixz1s+hzYTPm08kJhqi/M7y9/BznbvjJWkGWEt074huHhmFYRKDVTcONmYn7htIg2qhmpvPA2D2mVhMJf04+gQ6oW7ot7LLxwWXuNRNPT8s/4PndX9SCUvmW6leuJM7FfmKH0CEOp7mgU845c7eTDO3fFeNauQSbLODSamkKo5j9qWxBo5YXbI1qwupNTCB8IFWogzyd2lvONyUkqLxYRLmAsUcp5YYFlJ4TMLeqlwqmvhzskHcLlxCHztp3wv3xI75u5ctV6rEnC1dt8AVHSTyXrNib5avECANx1A+KHG7e0dNy6oFKgRHHiPXsAALWfP5h3Pxteh1x4yTGBMXG3bwYAhEfZgYBUNsoQ0Glo0Pepcv4Pv3UH3K8f1vtmv/TbYoVphmh76dexwijtUIsXgBErbt/VLwEziRWT375b/PCPMyhzaW8FUEQpGsTiC5D8DoRUKnrRYk0/vK0bAQCZ46cAAFPv2oXqZ17W+0ajccIBEIsM2WQuXAIuiJ8dZaqayejntFqUgHEvBlev65/Ddrn40bUVmcNizmGWg0SydCS4l1yASCHL5zJHXk+boUeRLlmFjGnR5CRUBIrkAkx06HhsvpqD4K2ie4r3lVfyj4fMOSwhIYQQQgghhBBCSNlDE09CyoDFVq3QmI/Y8LpX6JayZG4Y/sB+9Hz0+cI7WvA2D+H8+4QUd/V/mtk5ZgtjxdJGmeQFIyOLPBI73tAgwhah6lhoFYRbX5+7tGQRmPy23TMyVgUAuB6ufEiYpfb9xuLECmCRTDxzGbiSkslVqjpbvKFBuwloibi1tdokPHjwAK5slatMQQFYDcJng269e+duquTM62hPlpvMAtt3ycel/7h/xvOCaz+/H74MfSv+oLhzeK0tafNm497TKtk7dxNlfLn+PkqdW3ABg8wJC+XJQeYHvpQsbQo59LuPix72zriQUNomDk5VtbX+vNguA+ZYco0DEHX2/k1RtmKrH1UeHuHtO/rBbXYqcDzxIMw1oXJ2bBWfHxVeITm7DZAZwVixtNHyb4vnjMn0O8U7Z9U/vZz6zG1qsnYo8ppFjbzqTFRwLL2r0t4VpbDnMXHd88Naoq5euID4xSURj5QkPIqQeftOMY7nRAme2SmFzA2LsYBhdscqa/J0vDE7cyz0GMwSUhUv4Pup8lQ4DvzVorwDGektc+duatxeVxcieQ+qZ7FbWwtXvuBmrl23Ljhlv+j6Pd0IH0p/LlcUEERTU7p7iL+qB5A+FpmLl/U59PHS2yPKZLRHhifHH90dAQy/Hj12GdMAwGlq1MeLr+8UjKNLCiM2LgbsQkIIIYQQQgghhJCKgyaey4j5LFOg+oKQeUT1Hd+1Dd4VkWk0nfjNDKPus35XSMSjyUnduSRxPmXYKTsDmJ1Hom+RnQy+eSQ26TQyGbZMq6nUUOfU4zOytc7zR6FyPWqs5ncxszyq/7tSjDi+r028zGyByhhFQYDocGw2CuTpNkBIJSIzqs6Tm+FeFNlB0+jWzBjqWHHrNoCkqkkrL8xYoZReZqxQHZKeP6rjgZk91vHFiAFqW+bqNV3SgigUY8mh9PK6RWeAREnbwdfENgDelg3i59dPi/H4fqrDCaIIbqPIomJ6WisvFFRfVAZLQn0B5O14syDqCwCe7CziNDfqbUq94HWvQEbdb1GUUDAAwtRT7avP19wMyLGr/cN79/W96K/p19cIpfrK6+qKTT6lYWc0MYng9u3EeYKbt+GqcaquRVNTWpVpzh2UWWhw/YZWZWWuCbNQt6ZGx5OM7H7iNjUBU+PiWHNeIs8N10mrxVwvLicpwky87FliZVdUYBBCCCGEEEIIIaTsoQJjGbHUVBKLbWxJSNngyLXmI6eQyUxbPo8zjFp5MZX2s9BKDs/TWRSVTfV7urUSwjskFBuheWwQZ4tsGa7ggTiPW18Pp6cr+eHoqHV131Re6CHKbInb0gznUVYt7eo+4JHIkpiZZKdfZGjciclURgiulzfTRUhFcvQ0AounTSJW3BZ14FY/GVuskEov07vClbHCvLvN89m8MlTMcevrga725LhyKDBsZsI6VrS2wBlJxiR37Wo4oyLra8YZp3+l+HNsHCFjBVnuuFLJMPYICMQTX7UdDW7cTCiooik591D3t+vq+1apIFEVv1Yq3wunvj5uV6qe6Y6jlRNKfQVAe1NEU1NwlQeGUnM1N2qfCkddt6YmVmUC+ufo7j39eSDbv3pKGVrlA2FidgOnplr/nLlhqCmqq/T1lKmmim9ufb1WiZCFhyaeZF7hIsTSgMZ8xIbf0z03JlWOo6WquSTi2YZ680H4JhGHxnpr0fTJF+btOopykJc6O7amympmA2NF5WEr85hX8pjF+f19GPkWISNv+hv7PXrm90V3jaF/9eL8jA85SlZIySxKFxIyI9TCRcLM22LyqYy2U6aeJeBt2aDLvayfGx3QvM4OAPYSL7+vF1GzKG3Ldb6pp3cDAKq/ZHT0KdWw0ljc1CW0o2Nw5ALQ+V8V11j7mQdz9rw1zVSzsf5dzZJ8v2ebybu/pj+dMDJImMs7xpQhx++cJp6EEEIIIYQQQgipOKjAWAKwRSmZb5hVrQxu/sx+aw9vb+N6AEBw+g2xIVfct2Qlon3SpO/A0TkZo1tfn99Y0zKGBz+wFwDQ/FeFVROl9k4npcFYsQSx3FPnP7wPaz90IPcxeVo8AvYM4EKoqBJjkHJys2Ql0f61QJa11BbRpHQWpY1qa0vxf6dz1TpyBuex/ftVmMaWsybf2GwKC8NoU6kAJt+0JdVS2fF9eP1CjRHJ9qbR+ESqjbrX3Ax0dwJItnD31q8V2964YB1btoLRa25GIFViTrWMP9JYHBClbarcJFBtUtvaEIyIslqlMEAmE5uTy7brzqnzcGS5iPlvR7dynZqKW1DLEhO2aJ9bqMAghBBCCCGEEEJIxUEFRplC1QVZSJhVXbqc/h+7seH/fqnwjnOImaGZFQWyvPm49dP7AABdf5gni0zmHMaKpcvpP9qDDT91cEGvqdoZZi5cmt2JZhErbv7MfgCwqtPI/EIPjKWB39+njS2VQe/dH9uH9o/Hz9diFUvKa6r6qlA+ZM5fjK9jaZ3u+H5sFPytOwAAVSevxAqUGdz77hNbxCFHXrfvkEeVMv4dewAAdZ8tIlbueUz8KVs6k5lT6tyiIhYwaBRJyOyY95cSx9EPCiWbxBObEL18TO+r5ZSh2M/v7tIPuXxmRiaFzKXCN4uHo/u1w8V9AcfB9D8Tsuhs+aS+nnwQZi5fKe6cOaCcmSwFFmMBY/iD+9HzkfJ/+cwnCT/34X0YzFeysdAYMdn68QxM4rTc2pB1zyuWlxCvrQ0AtGw8HwmTOTIvzPcChltbq8sF1d99uK43MbdQJrWqRMBpatQLasUaF+pyh7Pn7aWW+2Wp5fNGqWWel2SvrQ3TW9eI8X3jSOpzd9smOBOT8TVTg0ufO3HPGp/7vaJLl4pLJRnTGt0+bGWZytBSzc2sHY1yMNflnm59PSA7m1gNiS0LIfn+/rMxy0nED7MsOyIJWEJCCCGEEEIIIYSQiqMiFBikPGEZzNKBsvDyxia7tGZ8gFRmxtu4HsGps8nzDQ4gc+5C6jrK0MrWBiyX+aaSfLpfL1LVYmPPY5RgLhEYK8oblW1VMnAAuWXORcQKb8O62PzX3L51IwAgOH4q9dl8xopo//Z0zCNlC0tIyhtvaBAAEF2+ppUQ7vbNAIDw6Am9n7+yB5EsMdHtTbduTN3/3vq1cCanxTmlUiG8dx/O5nVi23ERX6LpKa3AcLu7EI0KFVT4YDT+/PFNYturJ/X5lcrNbZStUwsoVhPtWmW8c/wqRIFUYUTiO7k1NUCVUOhEUvnirehEKM8fPnpExcU8QwUGIYQQQgghhBBCKg5/sQdAKpf5Vl/Q+4QsF5TywqmqhrNZZExCmYVMZSRVlkBmG7IzqgCs6gsgVl7Y6t/NjKpqb+Y0NSAjs6kzqZkP3vKkON9XXyn6GEWxvihAiaZchCxhlPLCqaqGu0HW7UvlhbNrW8IboJhYYVNfALHyomCsUK0LO9qAWcSK2ag3Sqm1H32faNnc+LeFWzYTstRRbU39Nf1Ak/SEkcqLqXftQvUzwvsrc31Y+3RplZdFfWX6dah5QjQ5iUiaaZoG4Op+jK5e194ZWtnVVIPwhVfFMYYRsPJIC+4/yPu9lIlncOT1lNdNND2V8hwLJyaALAVKxlCgaLVaFom2zWRBYQkJKXtYijL/UBa+RMhhvKdMysKHD2OZpDSzMk218r04+H29sfnpLHrae50dQJ14YdC94SenEI2Pp8ZjQy1MuM1NiCbFOJUhn9/fh0i+HKk+70BcYhM1NST6zAOA19palKEfKQ7GiiVCMbFCb0yb2+WNFWv64w5EM4gV6np+dxeihjqx7b6IFeG9+0UvbOhY0daK6JGILzpWDKzWsnTToE8ZPUcNdelymbY2xoo5hiUk5Y26h7S5OoBAxYYoSphcKsNOSDPL0LhXVElHNJ3RCwXK9NJtbtYdRdTCQRSEek7g1NXF8UjGBsfzdJmH19FuDDj3o0fNN8TgQvkVIjjV1cnv6Ln658zwDbFJLbQiGS90ssb3ED54qMcGAKiuKsr8kxQHS0gIIYQQQgghhBBScbCExAIz/uUF/y4qE3f75oRJVMpQrqNdZ9n9taLdmNlP3HrOAmUFNlOoguPcJo85lj7m0Xc9hcb/JaSR1rZdJaDMtLIVBAlyZDkT15b72JQO+TKbidazM1DmqeupjMZMUX93tr/DXK1qtbnp9fRnzKguffzeVUlTyiXIrZ/ah64/WsA2qgViRcLI11BeFINWX2RdJ9FqMh/yeqYp8UwIpdleaPzbePADogSk+a/sJSBmnMtuochYsfQwW50qdVG0YTWiQ7ERtZoXqBabbmuLfpaoDLtSCCTO7fv6uWaWPljJZZSbA7ehAcHj68V1DqRNad0ntsAdeZj/mqmD0koqIFYdBfLZbM4NCpVg6jnYwGrdetZE/f7CB6Kkw2yhrH4Obt2Ky9QM001ry2M59sj4DvrvpkDrZ6+5GaqqwPxeahxaVZaZ1ucp9Pdq/rtQpSiB/K65ykrIwsASElJW0NdicaAsvLzRUszGhkTphA2/pxtAvJBgTsLiEzrwNonJ01S3mPSZPhS2mnGvuVk/uDNv2ymu9eVDM/o+8YXsE65iGH+v9LX4HH0tFhLGivJG3btOY0NBeXN2xxLzZVDjOPDWDQAAJlcLKbd539sWjc1SjNn43CSYRayY/LbdAICaL740uzGQkmEJSXnj9/eJHzwX4S0RL9QLv7tuIFFmlZ1ISpSSSbyuLkxvEuesuiNLuFQXEON6ZjLCXBwxO6Bke1cAcRmImhOZnyW+lyoVG3tkXZhMndtcjHhKLEZ5r19AOC59OnIlf2YRl0gSlpAQQgghhBBCCCGk4mAJCbGyWEoIKi8ISeOtlKoKQ8Kp3bqznMAzske7Itj3WMq531/dh/CSyLp6J+PMab5SHVOtZ2Zgo/3bASDZCaVYZNYi1R2hCKi8ICSNq2KFcQ97WzYASGZCASBzLVlzFTy1Be43jiS2+WvXILwi9vPfuBBvt2RSFabiy1RezEWswN7HAdmdoFiovCDETlRTJf68cl0rLm1zC7epCdFostwkaqxPnc9prIc3Pi1+HolLLXQMOm2Ulyn1QhRpxUR4Kv7cGZDqEGMcytizUJpelYp5G9cD2QoMx0mVzjiep0uMnFeF6iSzfQjuKwXKjam8WDSowCCEEEIIIYQQQkjZQw8MkoImpssP1rUvHbJrN60eF8CC1GZe+aX96PvN52d+vQWqH738y/sBAP2//vy8XqdSsNU2Kxgrlg4pLxvXs99rM2mFWiLDH9iPno8+P/PrLVCsuPFvRKzo/j3GimLIvH0n/OdyeyHRA2Np4NbWwukXnjjKSNzZuTVhhqpQaomE8Xex1ylgtK7arE49sU6rt/KpvcRA0/HEH1gtjrlwyW4unCeeWFtN50D569S/IbzJgtNvFDxmSZLr2VEiV39xP3p/yx5bS51bcAGjjKGhJVko+FKytPEHBwAg7ioAw3HbMJ9SxlemU7jCra1NmHYC+V9k0ycQEwKvox2RfPCH6jq7t8EblY7kr5+2v8DIbbpnu+MgnBJSVF1qUlWtJaTmw1QZe7lNTQju3RNjVyZeNVWFuyKQomGsWNo4O7YCAKLD8YuJun/MhVCbgZ7Ca21JdBMoGRkr/IF+RLfFxF+90Dhb1sO5LAyIC3UFUWN0qnyEo2OJ7+A2NCCS8cMWA72uzrizjYxDzu7HEL1UXBcJUhxcwChz1HPXr4JTLcpJzHveX9kDQHQMUnHC6+qMt8l5htvcCAAIRu6nXnS9zg5tKKwWVd2V3chcNBYkso4xDYVVpxO4DqCe/0Eo9mtu1KUf4a07+jsEI/f1eVWnkWg0bdipxuW1tQGROKetU4pTU41w7JH8rk3y3OGsOymVE7bnwEJCE09CCCGEEEIIIYRUHDTxLGOWgvKCKhFCFh9TeaHwOkXLQzNDYFNeKLLVFwB0W7WcGLJCp0o8Tsy+6Xospy8hs3lA7AdYpeO3PidMvrr+L2HYZdMG5mplpjIGZsY2p9yUkOWCRelkKi8U028W5ppmKUCu9oQAZqS+cJuatCTbV0ajlrjlTmYwsXsdAKDqn162nuvWT+0DAHT90YGc18s1fhUDbfJ3qi/IskPGhigI4DWLOYN57wR342eqes5Gbc1iw/Vh/UzO197d/CySyonIc/OWJDgbBxEdkwaaLULdEV64rFUCSt3ptzYjbJSlcufiko+4fC7A2DahIqn5R2nm6zhwGxuTY7x3zzovUYpOt7ExXVIyPa2Vng93iT8bv362YKv7OWEeyv7yKi8cJ3Utt74+ZynQQkAFBiGEEEIIIYQQQsoeKjDIrFgs5QWNRgmJsbU/NTMnirweGJbVdLe5Kf8Ku5FBiabF6r3XvQLRw1Hx8YT0vdi0Bv49cZ4AsGYPut57OjFGx/MQTYnsjsoMODU1ugbWzBZoD4zWFp398LtXiP0a6+mBQZYnluyc+/gmAED4atwesOp/C+WBuXc+sz2vra2gP0U2ZvYyc114XPiDA4hkrbr+3PdQe/AMABkrLCjlhfbAqK6OPTBkRthtaorjhxHvdA1+dxcyl6RKS3lg7NhqVagQUrEoD4wqH+GDtGml19YKAMgM39B+F7o9KlCcB0ZHu/aacDyRN3cyQWykCaSOic4Yz+yH4t52O9r189+rq5PHhXBHpXq0qUnPBYL78RgbjgkVatTRnvjO4txyjK2tdg+MVvH9nZpqvc1tbNA/KyVXnfxzwZqqzofhcj5Vh2XbYqovAC5glA0sxSgN/p7IcsRf2YOp9SsBAO7XD+vt5sKFeoA7G4XzdmS8qFhLSORDy3wY2RZEEoeoiUx1lZabBv+HkKHjq6/oRQjNi68hMB+A8ueEu7iSssoxRgD8XuGKrsz2cpXAKGPP8N59fZ7M8I14hwXqYEBIueD3rsLUWrGI537jiN5uLlyo+9iT3QfMkg7r5FTGCnPxwhsaFNtk54LUIeoFp64WwQPxUhHt3Sau9/xRvaAQheK+DY6fsn8fSycCLXUfG0vFrHB01D7pVsaewzdTn0eHjy+6kR0hC4kyzfa6OhHVyee2vJf9lT2J56jbIs0ra+V+jpMqIXF8X60DwJMLBqFMaACAqwxAL1yKtyXKy0S5R9RYH3dDaRCLqcGlK7pDiLpINHJfmHsiuUiaMDaX97weY1U1vBWdid9DNDkZJ2HkuIM7d+M44Dr6/Po6rlcw/i0p5rEL1XzAEhJCCCGEEEIIIYSUPVRglAlUFJD5hmU3S5/M9WF4su1grrVynTE4VWTZhGXVPZfyQh8isy53fngn2v9UyLn9bx7T48pnFpq4jpFNtfVe120OC6HUGzmypt5GkSUJz15MjL/imKNe7Zd+dT9W/6q9VztZGmSuXoN7/UbefdR9kLlQZKtkm8ldgcyjusbktz4G/8vCJNQ58Kr+3GYebKOQKW8qZuXKJsr7I5q03ydeZ4c433D+3x2RPNcHvJ2GyUsVbWp7bRi+VGIld4jvI1UG4qvyDcs9Zj6DbWaWgaXtaOKZf0OYgEdrt8E5Izcq1VQmky5dcz1tIJ4Yh1EOE3bL0hF57Wh6Cplr15NjMBRnZvltyrgzcVCA6W5haPrwqb0AgJa/eCH3/ksYs63tbLj0H/dj9X+am7kFFRiEEEIIIYQQQggpe5xoBjUvzU579JTz9nkYDqlU6PFR3rwYPYcH0V2n8J6lwVixMJg1mza02V2f8M8ITp1Nn6Ory9oCNZ/xpw2/rxeZa1lZFkMV4K/pRyizLLbsq7d5SFz34aO41aGsvXcbG7WZlsoGAXGdfRQE8AZXi8/fuCDO1962MG3NlgmMFUsbr6sLgL3dsYnN7FPh9/dZFRGl+kd4Q4MIz19KbCvFe8J9You47thESgnitbXBkbX6Zitp7c8TBPB6RTzMXBQKFK97BYIbN4u+PinMs9GnD0VRtGuuz8t4MTfodqOTk1pRoeYL4dgY/B7R8jh6NK5VCspzyvE8YawJwHFFPjwae6S9bhR+T7c2CFXG3m51FRylunzwIKexOACE4+P6PJE8HtIMNBqf0DHDa2uFeqdV97Hb0KC9cpS/BqII4dgj+bm4RpTJAJ6XOBaI51ZwnFR8c3yfSq05pNS5xZJbwOCLMCFzz0K+lBR6IVYSXvMFNR+O71snvdF+YSrpPH8UnuxIYZucRvvkfgeOpj7ze7pLf0AZ/bK9LRvEdV8/bR13uGcrAKDq2khsaqUMJ6NwyZkqkcpnMRYwJt+9GzVfeGmuLznnjPzoPgBA2ycOLPJISCVg6wxVLJd/ZT/6f23xy8DmdQHDfQe8pib9wqxMn6OxsUQniewFfNwZKWp+Yf7+TVNIvWBuSOr9Nf3i84txOZZeHLAs1KvzqXPasBnXKmyLheb19DxragpwxMv+xLfvBADU/sNB6/XI0sbbssE61yyGse95Ck1fFGXA2hx5Bri1tQXLAnO9A5Q6t2AJCSGEEEIIIYQQQsqeJafAIEsbGkmWJ5SFL20SLcMsqGyNOyhaDQan30jt43V22LNS+XqD5xhLcElkjFRrRFNN4nV1IZIr9DaDLG+9aP/qjI0npN+AyKQ5SuZpyFTV94PjwuuUrdtkH3i3u6ugKSkpHsaKpU2xbf+8rRvFfpa2pn7vquINdgtcIzwtzYZVW8QSSkiUws0Zn0zd415rSywJN0rIzBbPblOj+FzGPW9osDLaIZYRLCEpb7SaJDOtn9GmwkSpWoKbt/U9qhQdURDoEgvHF/daeP9BSjXk93Qjc/N2YpvX0hy3P30wajWIVOPQx6zo1HOHBLKkBTU1upRFzR2cmhqd6TdLSKJHoiwFygDUcfKXkLieHq+j5kSeNydxkAiowCCEEEIIIYQQQkjFwTaqZcByUiUsl+9JyFyisobumj6E50WNrZmxMJUX/lqhstCeGoizmkp5YbbE0u1LjZrhhHeHUk60tekWZrYaYKWCyJy7oD1HtImeURdsmgfaaiGDszIj63rpbLHrCrOxLJRvSvhwNKXaCKm+IMsIdU95vStjJZTZ2tBQGKTq9g3/HqW8MDOYyksgMLKp3oZ1Ypuh6krECpuxp/T5CY6firOidaJ+P6eKzBJzdL2366XHEUYIx9O13F5Xp/j4/oOU4ozqC7LccNtbAQBOdTUwLdqVqvvCra/XaoQok4Hf1yt+HjNajqrnubynvfZWQPmGGJ4kysjb5uvhr+zRz21lGmrOR5xGaSo6cg9OvWjhquJKMHIP0bSMLWF8jIor0dgjBDJ+BbIFvVNbA7dZzHuUgsJrawOUGajpw6YVqCGCm1LJZShV8vmckPmFJSSExqiEsvAKwXzZmNkJ8peL/M1lYQr3z/v3z/waZEnDWFEZeK0tCaPDUrEtKJh89IKIFR8YYKxYzrCEZOmhOoEFJ87obRPv2YPazyfNP3MZqNtig2mqHu8YzzecL8vFkbdd1R+rBRPdjWwGmF1IrOSZ84z86D4aMi8gLCEhhBBCCCGEEEJIxcESErLoyovlVEJDyEzQ/dANcyxrlmNyMm7DKiWbZpZEyS69rs5UqYXf34fM1evyREF8HWWQ6XlW5UV2GYhTU6ONNt0VQq5tlrOYMnWF19ysTTmVTN1tbUHm+o3Ed/EHBxC0ic+jQ8cTYweAYPjmjMwACakUbJJma/nFvfupWAHXS/4MwF/ZnTKq8wdWI3PJkhWV2UynutqqvMgbK3pE2VmuEhJFIlbIuOi2tyFzbTjxXbyhQYStMla89Fpi7AAQXB1GpMz/wjjeEbKc0CUbD0f1/MK5F5trq3us/msn4EhDS2WK63a0I3o4Ko6R8wSnqVHHC1We6tTWwjkn7s9QPt/DsTF4raJ8BdVVCJTywlREqPtTbvNaW2MjzaoqAEDmxk09n3CbmhCq8hY5D3BbW7QCw2trE595LhDIecKUiIlOXW3KxLPtEwfgdXWJYzIZPZ5Amo+79fWzajlKZgcXMEjRzFepCRcvCMmPnlhUVetJd0K6bbyIOPIBj6q0v4Q6NjN8I3WNaGpKT+TNlyC9EBDay0p0fbw8Bp4HZ7WofcXoeDzuzLQ8IH0es6OIOl/DBJJEAAAgAElEQVQ0NgZ/jayXVT4W0xm458TkKPHKIR3Q3cHVCE6dTZzb9PsgpNJRCxfmPVcwVrgWnwr5ApC5dj19kYnJeIHUXJSQ97Z6KchGL1zIBRXH9+EMiHscY+Px+PIsKCRihbxONPYI/mopN5eLpc50Bu458VJkni2qUh2ZLLEihySekEpFdeNw6mrhKR8aY36gvSYePNAdOZTHVXT/gfakUgsdkAsayYuEyAwLrwy1IOBMTiKQncK8lma9q2t0CQpuCa8db4WxiKA8dZQ3V3ubnpuEo/FigjpneOduPDa1IOI62ktDefV4tTXaAyO1sAvAqalGMHJP/Fwt41eVb92XLAwsISGEEEIIIYQQQkjZQwVGmVHOhprlOCZClgMq42H2Jw/fJO5H9xtHEqv/OoOoHLW3btQdBVSG1O9dFWdWVdeBGzeBPY+Jcx+MJdcKp8pHNJnOMgRvfVL88JVX4o2G+VcxOLu2IXr5WGL8wb37QJbJoOlcntiep9MI1RdkOWGLFQkDPVusUMfaYsWa/kQXIUBkaJ0dW8Vuh48jG6e62momrGKFJ2NFND0FqOsVy97HgRdeTYw/GBkBZCZVj9EsWzPHkKfTCNUXZNmxQnbwung17ky2fbPYdvSEVjz5Pd3x/SZji7d1o75/VSmFPzgAr1eUpeC2UCyEIyNwn9gifj4mOgdFmUysxGpsgN8qFBPB5av6c/fxTWLbqyfj8cpyErexUZzvYVzuYqIMir3NQwkzUnGwB0TJeBE+GBWKCkAr05xtmxCdviDOZ+kyEszGMJ3MGiowCCGEEEIIIYQQUvZQgbGI2NQWVDkQQrIJV4uMhmsYbbnfPGrd19uwDgAQnH4DABA5lq5UjhPXcW4W+4dHXgek8sJqELpjo858mqai/jekcsI2mCLrQ92zl1FqBWm2ISAhBAjW9gBIxgrnwKvWfb2hQXGMVCVYY4XrxpnSjWL/8NhJrbxQNfKmeiF8chOcAyI+mX46/vPimNnECu/UDGKFGc8KtIomZDnirejUptnO8B0A4t5VnjqZ4RvaABe3xefIWO7EIEDkiXveaRUmnhgZgXNRKD61EeiNm/H8osrX5r3qGpkLl+COCD+N0Di98sjQ5uK5vk+zUHQ4j9LKCcCi4HAdIJTGnwP9AIDo/FVAmoViapo+F2UGFzAWkXJbrCjn8hVCljPKRd8bWI1gpXDSVi8IXlubNqICgODsBfG5fMCHxwz5pSTRV11KOk28vpViP7M044X4JchZKx7w7vnL8YKKdBxPSDoLPPAffddTAID6v3vR+rnt5UihJjLFyDi91haxb1ZJCiEVh1pkHBxAuKI5sc1rbUncA+F5WWYhFw+sscKMASfPpj5318quHkZphopNAOAMis/di1e1zNw1OhHEg8kfK8bfuwcAUPe5g9bP88UKp1Yudk5PFVy4UJ0KgqySFEIqkfCyMMX2VvXAkwuUuuRiz2M6qQEA0bhYDPBXikXSjEySmGQuXUmUqgLinlT3k792TXoMt+7E16gXix/elg3IvC7mJv4aMd/IXLwMuK4cizT9NRddjXs7kkbiwbGT6WRHGGgTT8j5SjSdQaQ6GFWLRYvw4cN48TZHCa2za5s4XpbAkoWDJSSEEEIIIYQQQggpe5xoBjK6Zqc9esp5+zwMZ3Gg8oAsd16MnsOD6K5FPzw7CsUKU148n5Sagc+VzbvyS/sBAH2/+XzOY+/8xD50fOxAarvfJ9v8KfWD0S7Q1r7P6+xAcPsOCCknFitWLDWm37ETAFD17KFFHgkhmHG7x82HfJzYOXNz02ejTx+KomjXjE+Qg2anPXrKeycQhXA8L/FZlMnoEoLg4UOdmS+17NAsoUhsN0wund3C+FqpJMWFRHj0Ng+JMZw4k1L+ZJ/bZr6bjWl2bZ5bq4ZUW9KuDkTd7Xos4dET+poA4HZ15jTEJsSk5FJdw2Q5F36/aJ2d/W+w1LkFFRiEEEIIIYQQQggpexbMA6OcVQ7lOCZCKgbHgdfUJDIhABzZTis6fDyRgVArvcFukd3wXny9qBaYXkc7gjt3xc/r14pznD2fWDlWygubOaVpSKlQaoho33a4Lx3X22zKC6+rS1zz1i0ASKgvzFpvpbzQ45qain+eNjJcMlNG9QUh+bn8K0IR1f9ruRVRc0m0T7YjPZA20L36i/vR+1vxOKi8IGXFDA0IZ6O+mFccB47nwamq0c9Pr0MoETLDNxCqjHEUaUWEMyTmB5HF68V6iYZ6QM5REvOJRmkkCcC9J3xcAtMcVqotpjvE89+aKd40CLwqWwiHAYJblud9lmrGPX9NG9iGtdV6t+CeaFfq1klfh6lpZJqF2qLqjuEzo/wjRo1tpHJwnJmbE+c4tmST9ALqCyCtvJgpLCEhFUk5L5iVI5SFk2WF4wCOmMwN/xthJNrz0fjl09u4HsGppGGh19yM4MGDxDa3tlYbBKpFMn9lDzLXh/NefvLduwEANV94KTkmYO67IxilSgnU9eTvAWEQy67N75k1iWasIMsNtRA9/GNiAWvF7+dfMPM2DyFQhqfK0LCvF5mr1xL7+X296cl81ovExHuEeWnt5w/qz1XJhM2wdD7IZ5BqYksGzGsJCeMFKTP8gdXa7BSt8nlqmJ3a7nm/vw+hnD8oE3S/rxdhh+ykclHGje4uPS9RJUcYn0g8r91tm8R55CKd29QEp0YsdtmSYsXe2+pc5hi9Dev0dzM/UyVNTrPo9BJcvQ5PGb8a393vEd31MsOi+w1LSAghhBBCCCGEEFJxUIFBKppnrh2hCqMImFUly51rP78fq343zqwWatuYC9OQ1R8cAADd4z6bQp/nxaKs8DauRyTb4plZ0LmEsYIsd679u/1Y9dtxrHj4fXsBAE2ffKG0Exlqi0LtW70tG8Tnr6fbXhe8jMUk2l+7BuFtUXqZaH09x1CBQZYTtntt6undqP5SrLYsdW6hYgN6OnWL22i/LGd8/qh136knRLmU95VXrOd0ZSl3eOT19PU2D8WtdCXT79yF2itC6TGTGKQxYl62YosKDEIIIYQQQgghhFQcC2biSchiMFP1BT00CKlc/J5uXXfpy9pMU32BPY8VnR25/4Mi+9rylyL7GoWxqjFz/qL1GNXObkbKC4XF1yLbtwMQmRZblkVhtvc12wMSQoRJdCjbU974SZE5NdUXQFp54dTUIJqSRtFK5ex6iPaJlpvON8X8wu9eoeNQND5uvb6zYysAIDgszKRtGd5C2Pa3xSa/d1XKpwOIDSwRBDnPRwgBol1b4J26LP6nU6ghap49DDUr8NavTc0tvLY2HS/CMWGw6jU3Y3KXaJWLr8p40daij6m6JpRamWwl5ooOcbxUXnjNzdrQ1jTk1HMCi/dWtvoCAKr+6WUEbrJdcfCWJ+F99ZXUebxWMc5ooFdfy9u4XhxjmaPMFJaQELIEmO8FFcrCyXIn26TT6xQTgWK7wahFCWwYQPiqMNDK2UNdTgSm37EDgJgclEous9ArvyQ6c9g65syEbJMvxgqy3HGbmhJlFyXHCmkK6jQ2ILhxU25MmuVq5IvB5NOiCqPmiy+hVPz+Pqvz//nf3AcAWPtLB1KfzQRbly+WkJBlhVkiIecE4RMbEt05Mm/bCQDwvyw6VTlV1fE9I+OAW12lu/R5m8VCRthYi+il18TnOZIN3tAgAGBibTuAmc0tnN2P6evo83avQGZwpfjc0oUr7/nM72ee0+gaCLCEhBBCCCGEEEIIIRUIFRiEVBAzVWowq0qWG37vKgCwSqbd2lqd/TCZfofInFQ9e2hOx5Jt8pWNs2sbACB6+VhyuyXjWfQ13yWSotXPGBmaXFlgidfZgQMjn8H96VuMFWTZ4G1YByDZDrEQqfanc0Tw1ifhffWw+B/L/N3ZKUpOokPHsz6YeZvmse8WraYbPvNi0ceo2PS/pv6KCgyyrFDlErgnTS9v3NQtyp3mJmSuXAUQl69G4+NAd5fYV5ZYuA0Nul1yQhma1ercLIcF0nMCf3AAeCTK08z99PlymAP7a9cAAMLr4hhzPuR1ybHeuqXbuUayzC6cmNAqEOfhmL6u398nfr58RZeYqNbzgFBjHLj057g/MVz03IILGIRksRw7l3ABg5D8eN0rYrm3xB8c0D4WeSf5hqxU/z+Q3FZg8aDgOdU4LZMD/VnW5CfBnseAg6+lNt/7ESEzb/1zITNnrCAkP15nR1xOIu91f00/MhcuASjQtSTHfZ1gjmKF29QkTmPpQpIvVkT7t6e6HwDA/R+SfkB/EX8vlpCQ5YZaRHAb6gCIZ7FarLCVfXpDgwjOnBM/y7IKVPnai0ItSnrX7qSO93tXAWGoz+0PrAYARA9HxbXv3E0sHhSDv6YfmYuX0+OUpSzBSeljYcSUvF2ScswtlL9PJP19WEJCCCGEEEIIIYSQioNdSAjJYjmoL9hlhZDSiEbH0tvux9nJ8U6RD2iwHlyE0rHYbKrE8auspSNOW6v4waLACMfTZTEK7+4obCMIOUsgpCSiR3FHESUDj+7HKofxLhErmqwHL1yscFuEysKmwMgXK/w7Y/ZY4c25MIuQJUeUmRY/hLXxtonJHHsDThDG+9UK429n0rhflWLTS3YBAYRBuNNQnz5nbXxtTE8XM+zUGFLb1fVtMWo6d2ciJ4xQeq1HYajAIIQQQgghhBBCSNmzLD0wlqPHASH5mLe6drc92uu/C05dHcKxRwAAb0i2TsruB63aR22TLaNkK8pCmCZGqj1dODaWMBrK2c5SHg/YDY7cxzcVHEc+I0V/Tb84t1FPqMaCMILbInNwmYz2LHDrxWp6OD4+I8M1QuaTcvLAGPlR4c/R9om5aQNZiOEPiha1PR9Jt6i9+2P70P7xhRkHIUuFefPAcDuivVVPw/FcRDKD7bYL9Vlw42bcwnJiQmewvU3CXFF5CxTC62hHcOcugORzPvoW8f7gfPNI7AtgOWf4JrGf+82jqWe5t3UjghNyDhQG9nlElteJOdcx/QNUq2unWpzDqavF9GbhhVB195H2JVBzC6euVn8vQsqFUucWy3IBg5C5olJKMcrppYSQcufaL4gX2VW/Y7zIFnD5P/v/7QAArP/hw/M6thnjekVJ0xkrCCmefIteubj0qccAAKu/N218t9SgiSch+RNdZtczZZrd/vfHdGmXPzgAANowPHmwhzvvFx2POv74QGI7APFMz1oIy7x9J/zncndS89avRXD2fHp7ZwcAxAbFxX62dSOC46dS2/2+XgDA6I5e1P7DQZp4EkIIIYQQQgghpPKgAoOQIqkUtYUNZlUJseNt3QgA1gxCLsa/Q2RE6j57sOTrXfpVkbFd/avFZ2zd7ZsBAOHRE9bP87ZPLRHGCkLsuE9sAQCER14v+pj7Pyhbj/6lpaVqAc5+VBy7/gOlH5sLr6MdAOasxIAKDELs6BKjbxzJq9AwmXz3bgBAzRdeKu4iRgvl83+9HQCw9vvjFshmqbWJar06+sQqAEDtP8RzGbMlbHbMc3wfUSCVnCWsL3htbThw/+9xP3OLCgxCCCGEEEIIIYRUDlRgkCVFJasgFpPFyKp6XV2pVV8AqXq9RcX14Lji1xJlcreJWmxM89J8KLOvYr+Lt2EdgtNvlDYYY8Xfiuth6p8JP4jqZ162Hw/M3MBUHn/rJ0V2csXHX7Gaty4WZr3rTPAHhDlb5sKluRpSSZSTAmP0feLvuPFv5y4DnY98SpcH378XzX+9MOMoR0yDwaXGXKsOcpEwlixHCvjgODulceSh40Wfct4VGMbzRhtgP3pkPcbMHBeD4/vxs9KYl6ja/cyVq/lNvPPEar+vF5lrw/qc9gHkfhZ6bW0AgGBkxLq/MhAPb9ya3fPG+K7Wccnfi1Ml5xaWZ61TVZ36/XhbNiA4KecWRc71vOZmhPL80ZQ4n9/TrX8O7o7ArasTp7T8G1B/V3AdfYz63To1NYhUK1BzPMpcvrZGGKsDwFPCo8a/fBuZq9eKGvtCkHNOXQRuQwOiTQMASru/5xKaeBJSwczXAk45vZQQsljc+QlhoNXxsdgMq9RFHyD9ouJ1dljNrfIdM1tM80Bvo3Dfj66JF0xlDpYcAE08CSmWmz8j7q8Vf2CUes1g8T37BbiYl5D5jBXKMDC6LRZzrGVnRcYKgCUkZPlhWxA1y0X0fhvWiR/uP0RwS84PLB1nzLhidp8BchtkJsYzNAgAcOQCjbmgVmzpSoI9YgEHB1/Ti/pORnQCCo6fSi28eUODCM6cS51GX3vHRuDgazTxJIQQQgghhBBCSOXhL/YACJlLKr3EpFK/FyELjTLPa/20aGsaTU6i40+kUZWRYVTKC6+rC+hsBQAEJ86I3RoarGU72ZlRpeLIhdfRDqepURxrkxv3dANASRL9lb8vynMiIC4DcuKcRcqctBxKtggpQ+78uFRm/amMD2GA7v8hfk5omOU95HWvAJpEWZ9qR+g1N1vVDNmZT6e2Ju9Y3IYGOH0rxf+cOpv63FpaUAAzVmTOXxTj8Lz4nJuHxDll3GOsICQPK0RLUV+Ws2SuXEX1FaHGiEw15oiIB05jPfzVslRHPf9rqmPlhcJx4JwVn+u4M51UhmarP/yVPYikIsJWyuT19lg/y1eO5V8XsSUDwL0hVSZVVfHnq4UBaCAVn9G1G1YjcadaHBMdfA1eczOc0dI0FVRgEEIIIYQQQgghpOyhAoNUFOWmUKh0RQghS5XauzKLGMTZRLdBZh1MjwhVz9nSCAzfTpzDaWwAChinAkCwshPIo54I7z9AuEGYrjmWLEn4cLTgNbJx60SdfDA9pbOpTo3I7oZjY8DV4ozsCFnuNF6TWc4o1NtUhtKmqnAa6hHeyIoV9XVAEW2MMyvbgMtXcn4ePnqEsKdJjMFS+p7LwDIfboPIFAf3jFghs8fRw4eIrjBWEFI0odRHGHOLqEb6PdyKvbAcT2gIIs+FM5HlQeG6WumkvSIyoVYtaPwslUaY9LWMpqcBL49WIWNXU2mTUxtVxtKBVF5ENca4pqbltcU53NYWhBZFmGOcJ5ycRBSW5slJE09CFplnrh1Z9AUOGvMRElNKpxCvtQUAENy7X9z+RZhu5WL4Z/dj1R+LHu6FOs4Uy/h79wAA6j53MNFlI5d5KWMFIUVgMfQc+VFRitL2iQO2I4piRp2hiiTRdcNAdbk6/Z+Fed/6DxTRbUd+/2eDT9LEk6SwdSapBLyhQYTnRRLCqRYLD+aiYi5DS7dJLEqayZPpd+wEAFQ9e6i4a1vMwr3uFQhu3Mx5jFoccetqge5OAEBw5lxRne2cnVtTHUu8zg4gEAu9U0+sFdu+8krCfDTzNvG9/C8fShx3YOQzuD99iyaehBBCCCGEEEIIqRxYQkLIIrPY6otnrh3BnneVLjslpFLJpb6wtS8sVnmh97eoL7y2tryme0rl0fPfnkdobB99nzAibfzbIjKiBu62TYhOiSxQ3ecO6u3h0RP6Z2fbBgBAdOR1ve3Ov9yHzGdLuxYhyxF/pTTevXpNb5uN8kKTTw4OwNsi7tvg9dOJ7Xd/TKg/2j+eewy5WkU7shytKOWFZOSHhbILn/hk0ceQ5UMlqi8AoV7wuleIn29a2iHfvad/9FdKA83rw3Ck+gGGAqPquVdSh6cUn46j25XaWrU79XV5x+sODYhjT5xJlLhFm4V6wpHnNpUWqhQ1OnQ8ZRocrl0F75YYW/URYWAcAHCv3dI/B3UihpkLEFFfNzCaVR5TACowCCGEEEIIIYQQUvbQA4MQyXI23GRdOyHFc/UX9wMAen/r+aKPufYL4phVv1PcMZm374T/XHG1rwsJYwUhxTP8AXHf93y0+FhRanyZfPdu1HzhpdIHtwA8G32aHhiEFMnwz8p48d/ie9/r6gIABLcsig4Awx+Ux3zEiBcWDx7F2Y/sxfoP5lZTuY9vQvjqyfR2i09HMeRSmCojZAytQXj0RMlzCy5gEFJmLMZCCl9KCLHjPr4JABC+ejJ2Ay8gf83uxV4QQwZqPd/QoDhflvnX7Z8UsvDub4jr5DIH9TpFX/qExFR1VylhDuA+sQUvnPwY7j+6xlhBSBZj3/0UAKDhMy/qyXmhriC57u2cuJ71pUSz93Hx5wuvJjZPvUusIVQ/83L6GCMWeJuHxHhOnEleE8h/3Sz83lUAgC9d+T0uYJBlhfX5b7uHjHvVXyO6kGUuXs577mj/dgCA8/zR+Lx57kvTxNPvkWVtRke0XCVnynRzslvEseovxQukfl+vOM+Vq4n5EQD4a/oRTUyKc+YwD/UHVovjjY5r3uYhHDj3cdwfv04TT0IIIYQQQgghhFQOVGDMA8u5FIEsTRZSgVEoi+339wEAMpevFHeRHNnr4C1PAgC8r76SNxs2/U6RHKr6p3Rmyh8cQObcheLGYcFcqbahsmJ1B04jMAyUCClXykmtVWoL29mSzzTV62gvXnFDliczUDJkM9P2k8Wqx7J58AN70fxX4t/7xHuEMWft5w/mOwQAtJHhM8N/MH8KDO+dcFwHUSB+n15LMwAZDyy/a3/tGgBA5vzFoq7hNjTEbSSN80X7ZBb8wFH4gwPinJZ5QipbbuANDSI4e17uGNn/bWSp5Mw2mTaljGp97dTUINi+HgBQNXxfj039G3Cqq+asDTcpH7zm5hnPI92mJjjSpDxXqUox+L2rEsbF1msZ7dpNWEJCSAk8c+0IF5pQXi8lhCwIlhKKfDWe0+/clVrkmnp6t5ZW5ntB8Nf0J6WhlmtbpdtzjPvEFgBAaHQW0WPs77MuGmaXnzBWkGVHibEieMuT8L6a7CBgxg/t4j85mTrWH1idkFbbyCX7nkucXdsAANHLx1Kf5RqjrVafHhhkuaHKpyAX1jLDN1KlFgD0opW7ZQjRGbGYpWKC6UOhysxw4zYCFW9kLHK3bUJ4LD5nKgHoOHC3bhTXPpb2tVCLXrk6EJmojiOqs0nm6jWdHFRdS6JDx1Pxzdm5NdHFRJ8va85T6tyCJSSEEEIIIYQQQggpe6jAIGSOWYolRMyqkuVOtvRx+h07AQBVz5bWCcTx/TibUUAyfvNnhHv4ij8ovkOBInjrk/C+ktUn3nHgPiazLRYX8bmAsYIsd0xjPCBZrlgSZvljAVPd2cSKyW/bjZovpruUKKO+6HA6OzpXUIFBljvht+6A+/XD+v/H3ytKseo+ly7Fsik5laoiaqrXqitPqiqyjbuVgeijvesAADX/aO9O5K0XigldxpRnvIBQekR1VWIcL71mPWepzFbdSQUGIYQQQgghhBBCyh4qMAipEGbj51GJWVWzDi+fiWcpNYBFXdc0WSuQVVNjRBDM2fUJKcTp/3c3NvykPTOjuPt+0aK1/U8PxBsdBy+Gz1ZcrCBkrpmpaWYh/J7uRBvE1HVz+Gtc/g9CwdH/n0tXcMzku1z4dRE/zv6Hn1scBYbl2TtXz3qz5aWtNaXeL48hebYZazG/Y6+rS/uLJFp1Zn1Xx/fh9Qkfhmh0LNk+G+LfiM1/hcwReeZ9bm0twomJ1HavWRrQzrGZeyFzaeVrET56lPg34W0QCg5nXGwr1lQ/8/adqD6Y9OYJHz5MfL+H/1yYYTf9TWyG7a1fiwOX/hz3J4Zp4klyQ+NKkk0lLmAsJ6wLNHPgeH/5l/ej/9dLnPBa+pI7u7ZhukVMrP3n7CUZczLhNyTZynE+vHHLunBFZgZjBbGRKJ2qIM59eB8GP3Sg8I5FMF+LGnPFG7+7F+t+Pt1hZzawhGRpo15kg9Nv6G0JA1tlRFktygtsL+duU1PK7DZ80xNwn5elCAXmKK7sjuG2tgByESq4IRZygqe2oOqmOHdw5hzcOmEwaT7zdXcW+SeqqrTBptrPa2tDOCo6s5j3p9vQIP5sb9Pd5KbfLkrGaq8+SJtu5+iKtxDkWhwplnxlJVbm+LuyhIQQQgghhBBCCCEVh7/YAyALD9UXSx+qaIiJVWEwC+WFYs3vvoKw5MGkrxu9fKzgw2ZOspJGNiBz/mLO3by2NgQjIzk/d+vri1ZtPPw+KYf8ZHGZy2zpcKmojFA4Nlbc9XZszWvSd/8Hxfhb/vKFnMZghBSiEtUXADD0Jzcw+0gqOPNbInO7/t/OQuUwjxnevq/k/6b5WquSysRUXigSagr5vA8ncv/bsbUarj57HZki5yhKVRAOW9Qd3zyK0JNq0yiyPrdVbNIxyqJSyDUfUM9Z83lb8w3xPA1s53nLDnhfOwoA8NpbxTajhMcbGkRw5lzqOJs6K2X2aahbbW2cndoarUpQvzOb+sWGW1sbl74UycR7dqP2H9JGpEoRPP2UaB3rfeWVRAv3RPmTPshDqYGWCgxCCCGEEEIIIYSUPfTAIGQBKPfWqqxrJ8ROyXWhAMa+5ykAQMOnXyz5eqf/VJSLb3j/yyUfmwuvewUAJFo/zhTGCkLszCRWjH6viBWNn5pBrPhjGSt+fO5iRT5TyplADwyy7CjSQNZsv1ysP874d0hVxmfTyodC3PycUESseG/cYj27laneLlUSmQ3CsNY5cDT+zIxzex8XG194NT64gHm9Dbe2Fi9MfAH3wztFzy1YQkKWFYu1kFCuCxeEEMHwB/aj56OxYemMFyFcTx+Tz4UeEHJSAAlJaWLhIt9EwGKW6m3ZgOjcJQBJM7W5WLgghAiGP7gfPR+JY8X9H5JlWH9RYmmI4+iFi0JdCGzlXcUuXNjK1vzBAYTSCNGUx8/VwgUhyxLLc3nq6d2o/lLc9cu2CJG3+0xri/ihu0sfE+3bDiC5sGDuO/nkegCA/+XYNN1cuFDlYIGlHMzbsgHB66fl+UWZx/Q7d6H2iohN6jMAyYUL/WXyLFyYZTCG+Xw4MYFSBRUsISGEEEIIIYQQQkjZwxISsuCUeznFcoSy8DJHZuK9lmaE4yKznquPu1rVVljNKE0jOHlut7FRmz15XV0AoHvOA4XbJKoMYvjoEdx1A+KYR2KsmavX7aaitjxBgfwAACAASURBVFavajw1NcAGcZ7wVZE58LpXIBpNm2r5a/rl+dy0eecitjWrRBgriI0zf/Ykhv7FK4s9jBlx+Zf3A0DpLaNJQVhCskQwDRyN56W7TZQdhMeMsoO2NgDS+FK1Ua0VbdJt8w2voz02bJTX8df0I7hyTVyugAGwbmXa1oqoQbRJjS4KVWP02BCc8Wlx7eOn4FTLUgxzfiTH6DWK86C6Sn9HNa5ciiT1XbGyC8HJN+TvZEh8lUyYVCOQWcE2qoQQQgghhBBCCKk4qMAgyx4qQphVJcuPfDXnudQm4Zt3AADcrx2e07FMvWsXqp/JXc8efYuITc43jyS2W9uRFUm0X9bQPm/U0BYw3/I2D+HAuY/j/vh1xgqybJiJCe7kt+8GANT840sF9iyN6XfsRNWzh3J+nitGOTUiQ55LuZePyW+T3+WLxX8XVYv/zMifUIFBlhXK2wq3RWvWYGREK2PdtlZkrgrlid/XCwAI747AWSN+Dk6cASB8a5wqYVNpqlqyn/kJdQuQeoZ769cCI/cTxyTGmqN1urdReGjgjvwOZitYQ6Hrr+wBAGSkesWp8uGuFt8FD0bFfjdu6u+auXLV6g3mbVyPAxc+UdLcgiaepOIptECxnBcuCFmuBKNjqW1TT4uJumm4pfBX9iCT9VLg93QXZXoXvukJuN84kvPz6mdeTvRJzyZ74UKRb+HCnDAoB3Q8IeTA0cvHkgsXEC8cwb376RMZE6LgxBlEUekvQIQsZYJbd1LbJt8tX+q/kI4VXveK1MKF172iqAWQQrGi6tlDeWNFrsXVfAsXZqzQZX27xDVw8LXUwoXX1ibKB7IxY4UtlhCyDHAmhCFnaHYc2S7KTkLTdFN+7vStRHTxauIc7uBqBKfOAkh2ClElMoqovwcw5gFunSixUYsewRsX4D4mFilgmS9E5y9bv4O6to2oW4wHt24BsnTI2SHnFoeOI7osFmiUkbi7fTMyR0/EX1suXOjFj+EbCE6dLXluwRISQgghhBBCCCGElD0sISEVwzPXjlBNMUNYQkKsVJgBpuq1fvbDT2Ldz5XY8rDAOW1t0HL1WF9Irv7ifvT+1tyZEzJWkOXElV/aj77fzLp/LK0Si0EpoXR5mhFf7/zEPgBAx8cOzHyws2Ty23fPeckLTTyJDa+zY06ei47v6/vKbB2e3Cl/aWTpF7WcT2579J17UP93sjWyfP6jvRXBaWEA6nW0I5AlHYViSPCWJ8UxXxXmyP7aNdqkPHir/OwrSeNkv3cVAOgylVIoNJfRik/5vR9+3140fTL3PMpsE+/3dItx5VGsljq34ALGHMOXaLIUma+XkpbqFdH+zvchc+OmqMWDCGYKb8M6AIAznUl3jygSW4972z5AMjCbPagBEfjzBX2npqa4+mHzpd/osqEnr6H4zNmxCdGh42K32lr98PU2S4friakZ/04ImS/KaQEj9UI4z6h4pSajibFkxaEfPClksn+5qW9BxkZIOTJvCxhuR7S35tvgGN0zHCmfD0ZGYo8j2VkLQPwSdX24qGsk/AXMZ/mOrQCA6PDxxM/xQGQnrcdlB4/XTqdeVv2+XlGyI/f3WuR4zdKbbD+DoUE9fzLLiNR3VXHQbWlG2N2uT6NKjdR+TlsLMhftpQOELBbsQkIIIYQQQgghhJCKgwoMQuaIpdzNpJyyqoTMN6b5Zvgmcb+axnlWkzpbOY3jwF/TDwDIXLgEQHT3UAaZuUpIZuLqP1P8wQFkzl3IvYOZWbR1KsjKAjJWkOWE2ZEon8mviVtfn+gcAMguIIHIwqvzjb5vLxr/Vkiwc0m/7/2IKC1p/fN5KC3JurdzdV8q+nSW+MESErKccHZshXP6gvi5V5hU4s6IVvL4vauQuXZdbFfKmq6uWOkjlTpeZwfCtSImuKeEEjcaWIXw1ZPic9k9JDz1RuKe1aqfY7KbiefB8YRWIWeJTZF4bW0AEM+N9j4OvPCquI6hhnRra8VYekT3pszFy3F8uxKblZrdTAAqMAghhBBCCCGEEFKBsI0qISWSS2mxFJUXhCxHwgdxXbT7TamWMOqdrS0CbWrFKNLKC8V4dy3q5c8nflv4vmx4f1KBUf/yBXGdGYy9IIaiAgCCywXMvIzabKvHSwWZuBJSKmZ2s/qZlwFYVE1ZSoZs9QWQv40pAJz4kFByDf3r5P3a8YXTAOYnVrhSMaEys7P1komm8ntREVLxuEA4Jlq0+2PjAIBgsFe3MA1u34G3bkD8fPY8ACAaG0t5pESPxuHdvCd+rq4CAIS1Vfrzqa4GAIB3POueDcUffu9KAKJlaTRd4newqE39nm6gTigrIOdHzmQAtZcZO3Q8uSq9ZqIIgc28052dkJMLGISUyEIvVNAYlpC5JfGCIR/U0VTyKW8tpyiC+r9/Uf+84f0vW/dRkslCXP3QfgBA74eL6yJy+g/3YMNPH0xsK2RwSwgpEhUrHo4lNruNjQCA0DCMLAZVPgIAQ//6Res+xXZqsJXC5aPmaz2YfHNxZpZFw8VOslyRi5jKmB2Iy8GmHutFtdwWTU5ibLMonaiVCxi2Bc/w0SOEF5Pb3dExtT6BqrviszD7uGOixATbN4s/c3i1XvsFMbdY9TvpuYXX0Z6KOzffPYjOTx1LbEsY11ow5x7aYPaJLdpUNrhxM+/xhWAJCSGEEEIIIYQQQsoemngSskiUk+knjfnIckO1qw1OnNHbbO12/cEBAEDm3IXEz/lQJlZKSuk2NGhZqQ2npka3A7Qabe19XPwpDbOKQZt5vXoS4bfuENu+flh/nstgNC+OgxfDZxkryLLCldnM8OgJvc2m0PIHVgOQpnWr+/TPec+dFSu85mYEDx7k3N+pqoZTJcTTtsytMvcLjp/Ke90Eex4Tfx58DcFbnhTn+eor8Tk7REtObTRYAjTxJMsN1W5blVCEY2PwhgbFz5eu6pih4opz9SacJqHiypwXhp1WI3Ekn+sA4K1fq0tRbJilH+rcibFuXA8ACE6dTW6XBpuhjEVmnHO3KaPQk3o8zg1ZInPjJvy+XnE9ZdjpekAktSI51hy8ri4cuPtp3J++SRNPQgghhBBCCCGEVA5UYBCywJST8kJBBQYh5cfZj+wFAKz/4AsF9hTY2jfOhPMf3oe1H7K3bWSsIKT8+MGTVwAAf7mpr6j9zVr0+YQKDLIsyDLyNXG3bYq9KQCtxgjOnJvx5dymJgCl++4orv7dVgBA73fl97FQXPml/ej7zeK8uPJx/ef2Y+V/sZ+n1LkFFzAIqWCKNQDlSwkhgL9S9G3PXE+b23ndK1KmU/7Aat2FJFsKnjg2hxw0ex8gRweUOSLztp0AAP/Lh1KfXft3+7Hqty2GXq0tYlz37gNgrCAEKBArjI5Gev++Xi2pLhgr7onuAznl1ln35HyQebuMFc+lY0WulxmvuVmMyyiB4QIGWW649aIPWRSIziLR5CTcJ7YAgHXR0CwD8deIbkRRbY0u69ALHmfPp2KCt3E9EIryjODMOevCRskLJq6X6ooCxIsmboP4fpnhG7qUDttEKYppYqqI9m+H8/zR1HZdVtNch+jQ8ZLnFiwhIYQQQgghhBBCSNlDBQYhyxil0GBWlRCSD8YKQkgxmMpPKjAIKR6biqkQj77rKQBA/d/ZWzErlEn51Z/dhVW/O/tykLlipnMLKjAIIYQQQgghhBBS9nABg5AFRBl4lgvlZCRKyJJh7+Nxa9MicWpq4nrRIth5OMx/Pt8v6fqzhbGCkNJxn9ii69+LpdRY8Z7j8+ebMxMYKwiZGVd+fBuu/Pi2xDa3qUn7T9gY73Ax3pF8nfdX9mifHkU0PYVoegrTe/Ibf06/0y6Y8jradUvlOcFxAMeZcbzgAgYhC0ihG7XcFjgIWS5kvzBMv2Mnpt+x077zC6+K/0ogmpxM9FIHgKmnd2Pq6d3W/Q/tcOGv6demXtlc/OU96Y2uB8f3F3xxg5DlhJJiKzJv26kNcrMJj7xecrcPW6yYfPduTL7bHis+v7Ut76LHmd97Kr3RceBUVae+CyFkbon2bU/8/9j3PIWx77HckwD6/uwk+v7sZGKb4zhwHAdeV5fe5veugt+7CgDQ8bED6PjYAbUz4DgY3bkaoztXW6+x5n2v4cH378WD799rH6+bruJwdj8G9HSJ/+YIx/PgeN6Mj+cCBiGEEEIIIYQQQsoemngSMk8U28K0HKAxH1luqMxjND1l/cy6fYfonR4dLq53erFMvns3ar7wUs7P3cc3AQDCV5OZGX/tGgBA5vzFkq9pPacrsyGWFmqAUKUceuH38fDBFcYKsmxwGxoAAOHYWNHH5GtDOhsefedTqP/7PGZ9qrQtSyE2mzbNM/kuSjn2pQsfoYknWVb4gwMAgPDWHfHnw4daFen9/+zdd5wdZ3nw/d81c9qePdurtOrNkiyruFu2wRgnGJsSHngNrtS0lySQOCRPkoe8kDeUN0AChLzJQzVgbGxjQ6gBDHGRi2yr2pJsdWlX23s5feZ+/pg5Z882aSWttKvd6/v5nI9350y5z5Hn2plrrvu+6+vy0ykH6usAcDq7sJZ72+SmTkUEKxbLb58zeppUq7j4pHHJXrkM09zmbTPOevbFF3nH3fPauMul25uquXC66MKBRu3qqvxnALwqkXKvjZLwqsiyTSfy8SB7rBFrwxqvPbv2jTjecwe/QV+iRQfxVEoppZRSSiml1OyhFRhKXeBy42acTbWHVmCoOUf8/90L/gYWPiUYzdq4dkxfdrl8HealV/wVJq5esNesxNl34KTNmehJyBkbpz3u6zd5bz25Y+zxL1ox/PSncDfRqLdtPA5orFBz0HixYonXvzx79PiY1e2LLxpzHsumi4crt04WK9auwtm7/6TNsdb51VOvvHrS9SZtnPbkxvQI/HZs1cVEbRwdK0CnUVVzT25sCpNMAuB0dSOXewNz5q8XACsSAUCWL8bdf8R736/8tNatzp/fuUpLp6kF/PEpcmPkWBvWjKxk8MfJcDo6ho8zQQUnDA8GbrLZU36uXCWaVVsNeJWfuUE93WUN3n5efHlMdatccQnmxZfH7M9eucxr68EjYMxpX1voSF9KTcJM7g4yU9ul1Izm34zIZX63kG17hhMXIiNuVgDYf3TMLuSVg4x+BNDz3muo+LY3oFbu4mB08qL5LzcDjJiLfaoSFyfrGjNe4iJ//HGSFwCcxSBbSs0KfizIX3AfODxu4iJHunrHLLOa2smlB6xQEIC+37uCku8/7y/0zrPRiYGe914DkI8pAO4UxYqTdY0ZL3GRM1GCxarzbp7cM+jSptRsEFi4ALfX63Yh82oBsF2D4ycu7KpK3P5BAFw/wREYjGOymRH7sQaGsPwkKVk/cmxYlU+A2CuWAmAOjDzX3EX+Mf2//07/INLYNmF7J5O4yLep2ktW5LqsBpYsysdBO+PtpzAlm585Ze9hrIJuJzni+DOtnUEhBWgXEqWUUkoppZRSSl0AtAJDqUkYr8phJldlKKVOLl86uW14QE577SpggieMyxbBqJJtWb4Yck9D/fLrwieluacb5poNyHO78ssLKy/yJhh873TlS1DHKefmyku8/74wTjlnVSVOV/eY5e5QfMwypeaSfCXVgcP5ZSfrxmEqSqF11FPP6nLwy7pzT17z1ReQjx9m8wbk2eFYURhP8vv3Y0VhTDkTucqL8WJFburH8Y5hV1flB+0rlD3WdFbtUepCl21syndFNV3egLlOT483DSngFHSlsMvLvPWikeFz0D8nTXFR/jokMK/eW7Z93/AAmge9LifWxrVQ0LXVOtrive9XgWDZmIXeYKGMM4DvuNcJE3BOePu2Vy33Puv+Q/m2OfO9wTzZtgcJevEyP9DohjU4Bd1chnc4/mDhk6UJDDUnTcW4EZq8UOrCNV7p5HiJi9aPet096r84NukwUbeP7vd7Zd+V3/K7kkzmRuMkiYtcX9ncjU9O7sYqp/Azmczwz8m3XAlA5KcvTHiM8ZIX3kELLjJEGNNnRqlZbrxYMV7iovUjfqz40jixYoIxcLo/4MeKb/qx4tlTx4qTxRMJh4HhPvLDb4wdxyPHFNxIDL3zKgCKH514ppPxkhfAhLMXKTWXmD6vm0Q+iQDIq0e99wrWS2/0EgH2E9vzM5LkEhiF1yKFM4AkNntjZYX+y5u1zOwZ2fVz9N9xO1aM84oXewbeczUwMnEqDV4CgoLkLAzPpJKZV+6t98zO4Tgow8NUZBd7XVYKr1/E7yKHnxNxx0teMHKssUDDfKQtOO56E9EuJEoppZRSSimllJrxdBYSpU5iKio1LgQ6s4BSk5efi31wMP9Es/ce70lq+XfGlnxPvKOJZyM4ldQtVxD++YtjlqdvvgIYfkIz6aZEImMqPIAx3U40Vig1ebLJHyR45958rOi7y3sSWnb/8+NvM97MACepoDgV9/pNWE+PHcDXbPa7iUyi6qPQRLHC2rjWO15BSbvOQqLmqhEDahf8rR890LZdV4vT1g5A9o3e7D+R11rJNp3w9uN3P7Hbe8fMkCbBEKxb6e1vxx4CC/zZQMpigFclOlEF50TMtRuRZ3aO/TzjzKSSk6sgyY7uOsfIGVVGLN+wxmuXX6FxutcWWoGhlFJKKaWUUkqpGU8rMJS6QE1ldYg+VVVzzXhPDOw6rz9n7mkIkB+QK9vYnB+cKt+/fLzpVimo0PAHsQrMqx/Rj3U8dkWFd+zxBtoatb/JKOzvmni7NwZG0U/8qRFd54yrPzRWqLkmN1Bd4TkcaJjvLTvRPHa9tg7En344P53xRLFi1CB6gYULyDaefDBMu9obMG+8sShO92krjKweS93iVXCF/2u79+ZZxArQCgw19+SmWzYnvHjhxuPD1Qa7X83Hgdwyq70nP115ruqisCqjUL6ia4c3+Li9dtXIsbtGVWrZ5WVQWw2As//QmP0F/KlaR08LfbI4UtiG3M92z0B+P6Ovo+zS0hHTpw4fZDiuWJEIzyd/Tp/bNelrC01gzHI6U4aaDL0pubDZNTUAOP4o98C4JccnuxEunNP7jPjHCyxeiEn4f/T8/5p0Gjftz3Nu3JOWQdtV3lzjEivGbRs5an9gQQMm5c9vXvBZc59fImEc/ybDuN4xAnU1p0weqMnTWKHG0/Q3m1nwmXFm17kAHLx/EwAr7hrbzUKdHU1gXEDGuWYYb2auwuuIXHcIiYTzy0YbMWuNfwx79QrcA95MGuMNklsodzNt1ddiiou8bY54XSnMRUuxBhNeGw8ewSry3h8xq4Z/o2z5bZSiCPjXB7kHBoF59bg9vd62BTftuWQhVRU4ufZe7XWlsPtT43aNUGdGu5AopZRSSimllFJq1jmjCgwR6QCOTX1zlFLTZLExpmaqd6qxQqlZR2OFUmqyNF4opSbjtGLFGSUwlFJKKaWUUkoppc4n7UKilFJKKaWUUkqpGU8TGEoppZRSSimllJrxNIGhlFJKKaWUUkqpGU8TGEoppZRSSimllJrxNIGhlFJKKaWUUkqpGU8TGOqMicgWEXnfdLdjIiJyk4gcne52KDXXaaxQSk2Gxgql1GRorJjbNIFxCiIyWPByRSRR8Pud092+MyUiK0RE59BVaoporFBKTYbGCqXUZGisUGp8geluwExnjInlfvYzaR8yxjw+0foiEjDGZM9H286UiOi/u1JTTGOFUmoyNFYopSZDY4VS49MKjLMkIv8oIg+JyIMiMgDcJSLXiMjzItIrIi0i8mURCfrrB0TEiMgfishBEekRkS8X7G+ViDwlIn0i0ikiD4za7k9F5Ij/3mdFxPLft0Tk70XkmIi0i8h9IlLqv7fC3/b9InIc+BXwlP9eLpN7hf/7h0TkVb9dvxCRhQVtu1lEXvPb9iVATvK9XC0i20WkX0TaRORzBe38gYi0+t/PEyKypmC7+0XkX0Xkl367nhKROn9Zr4jsE5ENBes3ichf+8t7ROQbIhKeoE0LROSHItLhf4cfPlV7lZoqGism/F40VihVQGPFhN+LxgqlCmismPB70Vgx2xlj9DXJF3AUuGnUsn8E0sBb8RJCRcAVwFV4FS7LgP3An/jrBwAD/CdQBiwBunP7BR4B/trfVwS4dtR2jwMVwGLgIPA+//0/8I+zFCjx9/8t/70V/rbfAqJ+G1d4//wjPss7gdeAi/zjfQJ42n+vFhgE3gEEgY8B2dzxx/muXgRu938uAa7yf7aA9/nLIsBXgJcKtrsfaAc2+e8/CRwB7gBs4LPArwvWbwJ2AwuAauB54BP+ezcBRwuOuxP4WyDkf/6jwBtP1l596etMXhorNFboS1+TeWms0FihL31N5qWxQmOFvgr+jae7ARfS6yTB47en2O4vgUf8n3NB4OqC9x8D/tL/+QHg34GGUfvIbXdTwbI/A37p//wk8AcF710MpPyTJhc8FhW8P17w+DXw3lHHTAENwAeALQXvWUDLSYLHs8DfA1Wn+G6q/bYV+7/fD/x7wft/Drxc8PsmoLPg9ya8krrc728DXvN/Lgwe1wKHRx3748DXTqe9+tLXZF4aKzRW6Etfk3lprNBYoS99TealsUJjhb6GX9qFZGo0Fv4iIqtF5Gd+iVI/8A94J0mh1oKf40Cun9u9eNnFl0TkZRF570mOdQyY7/883/+98L0QUDNRO8exGPg3v0yqF+gEXLzM4vzC7Y0xLt6JO5H3A2uB10TkBRG5BUBEbBH5JxE57H83B/31C7+ftoKfE+P8HmOkib6T0Z9tUe6z+Z/vr4D6k7VXqSmmsWIsjRVKjaWxYiyNFUqNpbFiLI0Vs5wmMKaGGfX7/wZeAVYYY0rxsmoT9tUasSNjWowxHzLGzAM+DHxVRJYWrLKw4OdFQLP/czPeCVL4XhroKNh3YTtHtxm8k/CDxpjygleRMWYrXqazsC+ahRdUJvocrxlj3oNX9vUF4FERiQD3ALcAN+KVr63I7XKifU3CRN9JoUbgwKjPVmKMeesp2qvUVNJYMfZzaKxQaiyNFWM/h8YKpcbSWDH2c2ismOU0gXFulAB9wJA/OMwfTnZDEblNRBr8X3vxTnKnYJW/EpFyEVmEV771kL/8QeAvRGSJiJQAnwIe9LOU42kHjIgsK1j2H8Df5Qa08Y/zLv+9nwIbReTt4g0G9OeMzKyO/hx3i0i1f/w+/3O4eN9NCujC6wv3qVN9J5PwJyLSICJVwN8w/J0Ueg5Ii8i9IhLxs7CXiMhlp2ivUueSxgqNFUpNhsYKjRVKTYbGCo0Vs54mMM6Ne4H3AgN4mdDx/meeyFXAiyIyhNcv7cPGmOMF7/8EbyCYHcAPgfv85V/zj/M0cNg/9kcmOogxZgD4DLDVL2e63BjzCPDPwCN+adVu4E3++m3Au4HP4ZV1LQK2nuRz3ALsE29U5M8D7zbGpPEG8Wn2X3vw+n2drQfxBhY6hDcA0KdHr2C8aaVuAa7E60fYifdvU3qK9ip1Lmms0Fih1GRorNBYodRkaKzQWDHryciKHjVTiTdvcgZYaow5Os3NmTFEpAm4yxjzxHS3RamZQGPF+DRWKDWSxorxaaxQaiSNFePTWDF9tAJDKaWUUkoppZRSM54mMJRSSimllFJKKTXjaRcSpZRSSimllFJKzXhagaGUUkoppZRSSqkZTxMYs4yIvE9Etkx3O5RSp0dE7hSRX01y3QnPc40BSl045tp5LyL3icg/Tnc7lLoQabxQyqMJjGkiIkdFJCEigwWvr0zh/heN2rcRkaGC36+fqmOdbyJyk4gcne52KHUmROQ6EXlWRPpEpFtEnhGRK4wx3zPG/O50t+90jIox7qiYdud0t+9MicgKEdH+lWrKzKbzHvLXMDed5T5CIvIDf19GRG4oeO8XBbEkIyLpgt//46w/wDQSkabCz6rUaHMlXpwsBoyz7tUi8mv/++gQkUdEZJ7/nsaLOSYw3Q2Y495qjHn8XOzYn7c5lvvdvxjfYIw5ONE2ImIbY5xz0Z6p4k/lpNQFSURKgZ8Cfww8DISA64HUdLZrIiIS8OcvH5cxpjDGHAU+dLKYdqr9zQQaY9RUm23n/RTbAnwReKRwoTHmzQXtuQ9oMsb8r4l2cqHElpneRjX95mC8GDcGjKMC+CrwSyALfAX4FnCzxou5RyswZhi/rOsZEfkXEekVkcMistlf3igi7SLy3oL1q0TkxyLSLyIvAMtP41j3i8i/ich/icgQcL2IvE1Edvr7Oy4iHy9Yf4WfIb3Hzwh2iMj/LHj/ahHZ7m/bJiKfG7Xd74tIs//684LtIiLyZRFpEZETIvLPIhLy37vJz8z+rYi0Al8DfgIUVpjUnsVXrtT5tArAGPOgMcYxxiSMMb8yxuyWUSWd/jnzRyJywI8F/yYiMt5OReRzIrJFRMoKln1eRHpE5IiIFP5xn+/HjG4ROSgiv1/w3if8pyH3i0g/8D5/2cMi8h0RGRCRPSJy+WQ+rIj8o4g8JCIPisgAcJeIXCMiz/ufqcU/94P++gH/c/+h37YeEflywf5WichT4j2V6hSRB0Zt96f+5+0Ukc+KiOW/b4nI34vIMT+G3udfJBbGp/eLyHHgV8BT/nu5GHPFZD6vUhOYM+e9iNwg3vXB3/rn4VGZoBrLGJM2xnzRGLMFOK2HJ+NdG4h3PfRz8a5NekTkJyLSULDNFhH5pHhPtgfEu/ap9N+LisgDItLlf+8viEh1wXafEpGX/NjzQxGpKNjvO/zvp1dEfisiFxW81yQiHxORl4EhEXkQmA/knhj/xel8bjUnzJl4cToxwBjzC2PMI8aYfmNMHC+Bce2pjuG3WePFLKMJjJnpKmA3UAU8AHwfuAJYAdwFfEVEck8+/w1IAvOAD/iv03EH8EmgBHgOGATuBMqBtwIfEZG3jNpms9+WNwGfFJGV/vJ/BT5njCn13//BqO1e5y9/M/C/ZLgk6u+By4H1wCa8gPQ3BdstwKsmWQT83367jhtjYv6r/TQ/s1LTWaDsSgAAIABJREFUZT/giMi3ReTNhX/UJvAWvHN/PXAb3jmXJ96N+df893/XGNPnv3UV8BpQDfwT8I2Ci5rvA014fxTfBXxaRG4s2O3b8c7dcuB7/rK3+duVAz/Gu3CYrHfgxbEy4CG8Jycf8dt2LXAz8IejtrkFuAwvHtwlw6WnnwJ+hvckZgFe/Cv0duBSf9t3Aff4yz+EFztvwEvyVgBfGrXt64DVwK3+zxTEmBdP4/MqNdpcO+/r/TY0AO8Fvlp4kT6FRl8bWHgPORYBi4EMY8/zO/w21QHFQO6G4P1A1N9nlb+/ZMF29/iv+YAA/wIgImuA7wJ/CtQAjwM/Fj8p63sP3nVPuTHmdqAZeLMfW/75rL4BNRvNtXhxpl4H7DmN9TVezCKawJheP/IzcLlXLsN5xBjzLb87x0PAQuAfjDEpY8yvgDSwQkRs4J3A3xtjhowxrwDfPs02/NAY85wxxvX3/1tjzB7/9114wej1o7b5hDEmaYzZjhc8NvjLM8BKEakyxgwYY7aO2u6Txpi4v99vA7f7y+/099nhJyP+Abi7YLus/37aGJM4zc+n1IxhjOkHrgMM3h/ODv8pR90Em3zWGNPrdwn7b2BjwXtB4EGgEq87WrzgvWPGmK/5MeTbeAnOOhFZiJc0+Gv/HN4JfJ3hG32A54wxP/JjQO5822KM+bm/v+8yfM5PxhZjzE9y+zPGvGiM2WqMyRpjDuOVhI6OMZ8xxvQZY44CTxR87gywBJjnt/+Zcb6vHmPMMeDLjIwxnzfGHDHGDAB/C9whfoWG7//x45PGGDWl5uh5/3H/muJJvKTjbaex7WSNuDbwryF+6P/cD3yasbHlG8aYA/739ggjY0s1sMJ/6v2SMWawYLtvG2P2GmOG8B66vMe/2XsP8GP/2ikDfBYvWXtVwbZfMsY0aWxRkzFH48VpEZH1eOfhx05jM40Xs4gmMKbX7xljygteX/OXtxWskwAwxoxeFsPL3gWAxoL3jp1mGwq3Rbzy7if8kqo+vCeX1YXrGGNaC36NMzzWxvuBtcBrfjnVLSc51jG8zCT+f4+Neq+h4Pc2Y0z6ND6TUjOWMWafMeZ9xpgFwDq8//+/OMHqE51r4FUzvR0vMTj6/MhvV3DBEvOP1e3fxOeMPt9GxIQJ2hGRyY8VMTrGrBaRn4lIq19++g+MijHjHC/3ue/FuyB7SUReloLudOMc61QxJoQXQ8dtp1JTaY6d9z3+hXvhseZPtPJZGHFtICIxEfm6eN1f+4HfMvnYch/e09CHxevK+tlRn3V0bAnj3RSOiC3GGBfvyfWpvlulJjTH4sVpEZEVwC+Ajxhjnj6NTTVezCKawLiwdeBlFBcWLFt0mvsYPdL+94FHgYXGmDK8rOu4/enG7MiY14wx7wFqgS8Aj4pIpGCV0e1s9n9uxivfKnzvxEnaqLMDqFnBGPMq3h/CdWew+T68pOEvTqM8uxmoFJGSgmWnOt/O1uj9/W/gFbwnF6V4TycmG2NajDEfMsbMAz6MV5q+tGCV04kxabwYmtt3YTs1xqhzZg6c9xUiUjzqWM0TrXwWRrf5Y8BS4Eo/ttw4dpMJduQ9lf2EMWYN3tPvd+BVbuWMji0poJtRscWv6lqAXsOoKTIH4sWkichivMTB/2uM+e5pbq7xYhbRBMYFzC/Tegz4hD+gzFq8vlpnowQv85oUkavxyp0mRUTuFpFqP6PYh3fSuQWrfFxEikTkEr+dD/nLHwT+XkSqRaQG+Dhw/0kO1QZUjwquSs14fvXBvSKywP99IV43h+fPZH/GmAfxukM8LiKnHMDXGNMIPAt8RrzBc9cDH+Tk59tUK8GLD0N+f9DR419MSERuk+FBtnrxYkzhwF9/JSLlIrII+DNGxpi/EJElftz4FPCgH6vG0w4YEVk26U+l1ARm8Xkf9PeXexU+gfykeFMkXo/XR3/cGQZEJFzwoCPk72dSCc1xlOA9Je0RkSq85OikiMiNIrLOv6HoxysRL4wP9/j/jsV444Y97Cc9HwbeJt7gpUG8m6IBYHQX2kJtgMYWNa65Fi8mGwP8v/2/Bb5ijJmKqVE1XlzANIExvX4iw6PcD4rID89gH3+CV9LUipeh/dZZtumP8YJWrp/4w6ex7S3APn/bzwPvHlWytgU4jDfK/2eMMb/1l38S2IX3VHY33on8mYkOYryxPh4Fjoo3dojOQqIuFAN4fR23ijfzz/N4/9/fe6Y7NMZ8G68bxm9FZMkkNrkdbxyJZuCHeGM/nJPpnCdwL14CcwCvGuOhk68+wlXAi/539xjwYb/fb85PgJ3ADrzPdp+//Gv+cZ7Gi0EDeAOJjssvnf0M3r9Tr0xy1hWlJjBbz/uf43Vpzb0+4S9vBXr8Y30P+CP/KfJ4XvO3bcCbHjHByGqp0/HPeP3Ju/BuwH5xGtvOx4sp/Xhjez2ON/hwznfxbuBaABv4KIAxZg9ePPt3vIqum4G3+f3bJ/JpvARPr4h89DTaqOaGuRYvJhsDPoR3I/+Jwnuns2iPxosLmIysmlVq6vn91Q4YY870qYpSSk3If5KTAZYab+BPpdQ0EG92sfv9vvuzgnjTVn7dGHPfdLdFKTWzabw4P7QCQymllFJKKaWUUjOeJjCUUkoppZRSSik142kXEqWUUkoppZRSSs14WoGhlFJKKaWUUkqpGS9w6lWGVVdXmyVLlpyjpiilptu2bds6jTE1Z7sfjRVKzX5TES80Vig1++m1hVJqMiYbK04rgbFkyRJeeumlM2+VUmpGE5FjU7EfjRVKzX5TES80Vig1++m1hVJqMiYbK7QLiVJKKaWUUkoppWY8TWAopZRSSimllFJqxtMEhlJKKaWUUkoppWY8TWAopZRSSimllFJqxtMEhlJKKaWUUkoppWY8TWAopZRSSimllFJqxjutaVSVUmqmyjgOHfEhsq5LZVGUWCg03U1SSs1AyWyGjqE4ADXFUSKB4DS3SCk1k/QkEhzv6yXrujSUllJXHENEprtZSimfJjCUUhe87kScJ44eIZ7JACDApvr5rK2tnd6GKaVmlMa+PrY0HsV1DQCWJVy3cAkLy8qmuWVKqZngUHcXzzY1EhQLyxL2dLSzurqGK+Y3aBJDqRlCExhKqQuaawxPHz+GJcK8WAkAWddlW2sztbEY1dHoNLdQKTUTJDIZthw/RlkkQtj2Ln9STpanjx/lHavXUhTUSgylZrNkNoMlFiHbHvf9VDbL1hNN1BRFCfrruMbwamcHS8srqCkuPp/NVUpNYEYnMNKOw8GuLg73dmOJsKqqmqXlFdiWDt2h1GyXdV32d3byWncnruuyrKKStTW1hAMjw1ZPIsFgOkV9cUl+WcCyCFkWTf19msBQapZLZDLs6WjnSE83AdtmTXUNKyurxlwrtA8N4bhuPnkBELYDuK6hIz7EorLy8910pdR50JNI8OKJJjriQ4gIS8sruHTe/DHXE92JBMaQT14AWCKELJvWoUFNYCg1Q8zYBIZrDE8fO0LLwCDf2b0DgDvXb6AzHufqBQunuXVKqXNta1Mjh3t6qCoqwrID7O3ooHVokG/v3IEIPPjOdwNgAMzYsk5BcI17fhutlDqvMo7D40cOMZhKU1lUhOO6vHCiib5kki9ufRYojBXG6182moAx57HRSqnzJpnN8PjhQ9gi1MdKcI3hcE8PiWyWG5cuG7FuwLK8ODGKiyGkD0+VmjGm5WxMZbOcGOjnxEA/qWx23HXaBgdpHhhgXkkJlgiWCA2xUg50d9GbTJznFiulzqfeZIIjvT3Mi8UIBwIEbZv6WIzOeJy064xYtyISoSgYIJ5J55e5xpB0siwo1X7tSs1mzQP99CWT1BYXE7AswoEA82MlHOjuIuuOTGDWFscQvKRHTsZxsET0yapSs1RjXx9px6EsEgG8ior6WIzmgf4x9xNV0Sil4TB9yWR+WTLrja3VoNcTSs0Y570Co3mgn6eOHeWr218EA39w2RVcv2gJDaWlI9brTSa4b9cOgpbF/u4uAD7/3BbSjsMbliylPFJ0vpuulDpPhtIZBEYMmPW5Z58m47oc6e0B4PZHH+LBd74b27K4ftESfnPkEH3JlPc0Fbikto6aqN6UKDWb9SaTY/qzf/65LaRdh6O9vYAXK8CrxNi8aDHPNR7H9UsuLBGuWbiIqI5/odSsNJhJE7THPq8VhOSoh6iWCDcsWcqTx47SMjiACITsADcsWaozmyk1g5zXBEYqm+WpY0coCUUIWd4FR2kozFPHj/B7F40cQCsWCmMmqOnUKc+Umt2KgoFxijiZMCbUFBfzjtVraR0cJOM4VBdHz2mScyCVIu04lIbDI/rKKqXOr9JwmLTjjH1jgi4hS8srqI0W0x4fAqA2WkzxOboxMcbQl0riuIaySISAlqArdd7VRmO80t4+YplrvO5kpeHImPVLwxFuXXkRvckEjjFURIrO6bnbl0wymEkTDQSpKNKHs0pNxnlNYLQNDfLVbS8Rsu18VcWXX3iOtOOwecFiFpcPD6BVH4vx0as3k8xk+ebObQDcs2ETNdFiqvQEV2pWq4gUsaC0lKb+fmqixVgivHfjpcRCIb63eyciku/XnhMOBEbEkLPhGkPGcQjZ9ogqkFQ2y3NNx2nq70cQArbFlQ0LWFpeMSXHVUqdnobSMkrDYTriQ1QVRXFclzvXb2RZeQVfefF5gDGxojgUYukUJS0c1yXrumNixUAqxZbGY3TH44AQCthcu3AR80tKJ96ZUmrK1cdi1Bd7XUbKIxEc19CXTrGxrn7CyitLhMqiqRkAPOu6xDMZIoHAiGqxrOt6Y3319mD54/AsLCtn84KF+mBEqVM4rwmMkw2S5Y56XBK0bW5cupztLc3cs34TCCyvrGJjXb3Ow6zULCcibF64mL0d7bzW2YmLYWlZOevr5/HWVatHrFtYHn62jDEc6O7ilfY2EpkMsVCYS+fNZ2FZGbc/+hC9ySR3r99IfXEMESHtOGw5foySUFhnO1FqGoRsmzcuXc7Lba0c7ushKDYb6+pZU1PLtYsWn7PjOq7LnvY29nZ1kHVcqoqiXD6/gZriYm5/9CG6EnF+f9MV1PtTOyezGZ44doS3rlxNSTh8ztqllBrJtixev2Qph3u6OdrbQ3HI5rL588/LGFmHurvY3tJC2nWwBNZU17K+rh5LhP2dnRzq6WZ+rCR/X9PY28vecJgN9fPOeduUupCd1wRGTXExH9x0GVVF0fzo4B+9ajNdifi4fdVjoRCvW7yEjOMgIueshCueyXCgq5O2oUHKI0WsrKzSMi6lplnIttlYP48NdfUA5yVxebinm+ebGqmORikLR0hkMjxx9DC/s3wFjmtIZbPURovzbQnZNmHb5nBPtyYwlJomxaEQVy9cxFULFp63Bxy721p5uaONumiMgGUxmE7zq8MHuXXlKjKOV5VRWXAdEQkE6UulaOrvY01N7Xlpo1LKE7JtVlfXsLq65rwds3mgn2caj1MbLSZo2ziuy+62VgKWxbraOl7r7qQ6Gh0Rs6qjUV7r6tQEhlKncF4TGNFgkKsXLGRrU2O+z2pXIs5VDQtPOjjO2ZZSGWPojMdpHujHtiwWlJbm+8cPptP88uABUk6W+3ZuxzGGD268lJuWr6C2OHZWx1VKnb2JbkhylRdbTzSN+P1MKzGMMexqa6W6KErY9kJjUTDIl154ju+9vGvEYMIAH9t8PQBByybhj1KuJseYNJg4SBQRHRhNTY1TJS+mqlorlc2yr7OD+mgM23+wEguF+PILz/Hgy7vY29kBeAMP5+IEQEAsUuON16EmNBwrihDRyhV14djX0U5paHicLNuyqI0Ws6+jg7U1tWQdlyJ75G2YbVlkjYsxRqvNT4MxGXD7QQKIpbPFzAXnfRaS5ZVV1BbHuKJhIQB1sRil57Cc0hjDjpZmXulo59u7dgCG92+8jM0LFrGsspL9XR2kXYe64lh+utbiUJhtzc3cvGKlBhCl5ois65LIZCgLR3AxdCfitA4OksxmsGW4+ss1BqsgLgymU2yor5+OJl9wjHExmVcgu9cbZFEEE1yHBNZqrFXn1O2PPjRlyc6U42DwbjYc49IxNET70BCpbJbC/4sLO8YaY0g5Wepj+mBkMowxmOxeyLyc739sgmuQ4HpEdDBUNfMNptOEAl7yoisRp2Wgn7TrYokQz2RYXlnJq50d1BU8LO1MxFleXql/D0+DmzkGmRfAZLxZ6Kw6JHQNYmlV7Gx23hMYACXh8HnrA9qZiLOns4N5sRKC/pOS6qIoW5sbaSgtpbG/n2/t3IaF5J+w/vtLW7lr/UbSjkM4MC1fkVLqFHI3H1P1VPWeH/2ArkScP7n8ak4M9NPU309RMMDmBYspCgZ4qfkEITvAu9asRSyL3mSSeDbDvJISFur88JNisgchswusesSyMcaB9HYMRUhw2XQ3T6lJiQaDhPzKq/1dXXTH4xQFA1y9YCE10SjPNjUiwDvXrqMjPkRALOLZDMsrKrWyc5JM9iiktxfEChcyL2MkjATXTHfzlDqlBaVl7O/uIpkZ5EB3N9FggHTW4ZeHD/CrQwd59LbbaR0coHmwn6Blk3VdysIRLqnTByKTZdxuyDwDUolYXjWncTox6eeRyI3T3Dp1Ls36u/P2wUHu27mdoGWNKAHPGpfXL15KSSjsJfcLkp0GCNsBHQV4Eow76N2UOB1gVyKBFVq+pS5YJaEwLYMDHOzuojpaTNrJEgkE2Fg3j6eOHaUoGOTWVas52ttDPJNhfkkJDaVl447PY4whkc1ii2giNCe7B6waRLzYKmJjrEqvIkMTGOocevCd756yZOfdP3yERCbDjUuXc6K/n9riKEOZDOWRCGtravnl4YPURIu5deUqjvX2knEcGsrKmBcrGVG9lWOMYSiTIWhZGitysnvBqiyIFRbGqoHMPkxgtT6hVjPeRdU1HOzuYldbK1VFUbKuC3jXGWnHoSse53eXr6R1cICeZJKyUJh5JSUT3nukslnSjkNxKDRuHJmLTPYomEA+eQEgdjXGbcO4A4hVMn2NU+fUrP9LGbAsrxSxYFljfx8l4TB729tZU1PL3es3Uh2N8uWtz2GAOy/ZwJqaGg0Qp2DcfkzyV2Bc7vhZJ+DywK0HIPI7iF013c1Tc8RUzD5SWF7+WmcnJeEQb121mupoMQtLyygOhfjApsu4btFiQrZNdXExf/aLnxK0bL7/rrHH707E2XqiyZ9CERaXlXN5QwORwPhTts0FxhgwSZDRFxQhML3T0ialztSR3h5+fuA1rlm4ENfAwpIyGkpLCQcCfHDj5dy0bDlBy6a2OMZH/uunBGyb748Tq9qHBnm+qZGBdBqA5eUVXDq/YcR0ixeSKZsVyiRARk85GwSS+P3Pzm7/Sp1jsVCIaxYs4lhfLwGx+M/9rxKybQ739gDw4Z//hFgoxL+86VYqi4qoLoqOm7zIOA7bW1s41N2FMYZoKMSV8xfQUKpTMmNSIONcVxkAHZtsNpv1CYyG0jI+dNnldMcT3L97J73JJJVFRdyyYhWNA30sq6zkukWL2d7SzN0bNmGLsKa6hrU6SvgpmcwewMt2ivQANkgEk9mB2DdNb+OUOg0Zx83/7GII2wGu8sfpyTF4yc9nG4/jGkNPIoFtWfQlk/zRz/4T8C7ak9kMvzl8iIBlUx8rwTWGxoF+Esey3LRs+Zx9cigiGKsB3A6QiuE3TC/YC6avYWrOmIpk5/94+AF2trYAkOnvI3U0y/+6/g0j1jHG5dXODhr7+xCB7kSCgG0Rz2T44I8fy7elP5Xi8SOHiAVC1BfHcI3hUG83WdflusVLzrqt59NUD6qMvQiyR8GuHl5mesGar2NgqBmtN5ngzsceIe043HvNtZSGI6yoqORXhw+OWM8FOuNxfnPkEAABS7hmwWIWl5ePWG9b8wkO9XRT64/Vl8hk+O+jh7l15UU6Y6LVANlDwHDltzFJL6khWg0+m836BEYsFOL6hYv50tbn6EkmyLgurUODPHn8KALUFce4ZeVFLCotI57NUhQYv+uIMYbmwQGO9HTjuC5LyitYUFqWH4F8TnKaueNnnQhdbG0ZBOCOnxkeuLUUY5x86adSM1nLQD9vX72GvR3tJJ0syWyWeDbDp55+gr+7/gbAuyAR4FBPNw++vJum/j4S2SwA/9cjD9I2NJhPejb195NxXCqLvAGkLBFqo8W0DA7Qk0zkl89FElqPST6OcdpBokAcCCLBdZPa3u26CwCr6v5z10ilJnCgq4ueRCL/ezKbpXlggM9seZK/ue713oxniTgBy+ZoXw//9MzTAPlY8c6HH6B5oD8fK470dCN408CCFyvqojGO9vWyMZ0+6exshaas6mEGkeBajHMC47YBUb8iI4CENk5309QsNFXnUE8iwX8dPMCRnh4QGEqnOTHQD8Zw7zXXYYnw2WeewjEud6xbT1k4QjQY5HPPPo0BXANV0Wj+3E9kMhzs6c5PNADe7GjxTIZDPd1cXtRwVu290EmgAeMsxDiNIEVgHMCB8Ov0HmSWm9UJjIFUis5EHNd1WV1dzc7Wsnzpli2CiyGRyfByexuvdrSTcV0Wl5Wzvq5+zCCju9pa2d3Wynd27UBEuHv9RpZXVLJ54aI5+0QVKQHTBiOChPGCCHM4saMuKDtaWygLhSk8jYuDIdKOw/aWZgbSKRpKSllWUUl3Ij6mcLmxv4+U47D1RBO3P/oQ//Pa1407JoaIkMoOT6F4Pm86ZsoNjljlEHkzJnsYTA9YKxB7iY4Wrma8rOuyo7WZv958Pff++hf5pER9rISkk+XFE03EMxlWVFVRGYkQdsdePB/t7RkRK/pSKT608bIR64gIgpDKZiedwJgJpnpQZbFiUHSz18fd7QBZhgSWIVbx2TZVqXPmzsceJu04JB0vPnxt+0tkXZdbV65iX2c7PYkkb1+9msvnNXCwu5tocLj7Q1N/H1/f/hJXzF/AyiqvG3bacRB/hsRCoYDNYDp1/j7YDCViQ/g6cFowTjNIBAks1rH45oBZm8A40NXFCyca+caObQC8fvFSbrt4HY/t2wvAxzZfT+vgIIlMhp0tLdz/8k4E+MDGy2iPD3HLilX5wbQGUileaW9jXqwkf2MyP1bCkd4eVlVVU1M8R/+gBtfwwK2tYNVy50+PYTA8cEsxBC6eu0kddUHJOA49iQT1sRK+fPNb+P+eeQqAP7rsCl440URZOIwtQvPAAPs6O9jR2kxRIJi/ebGAebESjvZ5YzgYoLq4mN1trSOO47jevO5lkcj5/HgzkljFSOiS09omV3lB5oX876eqwjAm7d34GMcbDNDS2R/UmYtn0mRdl6Bt86Wbb81XV9y9fiMHujupLCoiZFsc7O6iL5lkd3tbPk4UBQKkstkRsQIgZNvEsxnKGI4LGcfBtmRSM7VNebeNGUakyJ9x5NzOOmJMFtx2MGmwyr1Eq5oTRp9DG/7jXwHY9Ud/ekb7SzsOLYMD+d+P9/chQEc8zrLySuyo0D2U4Eev7qMiUsT9u3dysLuLXCfWo329vOPh77GmuoYH3/luYqEQIdsmlc2OGOB3MJ0+q67usylWiNgQWIAEzn1XVOMOgdsDEhgxILk6/2ZlAqM/lWLriUZqosX5gbDW1dSytbmJZDaLAV7t6qA2GuX/f2kbYTvAAX+Gkm/u3Ebacbikto4VlV4GtC+V5Js7thGy7REzmWRch8vmzZ+zCQwrsBDXbIbMTr53aylgQ3AdElg53U1TalIClkVRMEhPIsHRvh6ubPD+AP7y0AFioQid8TiJbIZIMICTcOhNJglEh/9gLSmv4HWLl/DE0SMAvOfiS7AR5peU0jwwQFk4TG8yyYGeLqqLorx4ool/feF5grY14qbjXF1EzPYbnIkYpwuT+m9/gC8BAya0CUunX1RnKGx7l0s9iQT7uzvp959+/ubIYZaVldMyOIhrXCKBAC2ZND3JxIjtg7bNTcuW8/iRQ2C8wcKvnL+Aba3NtAwOUBoK05EY4nBPNwtKynjxRBMX19ZSHrmw+rhfaLHFuP2Y1BPgDuJVkOLNchLcpGNtzEG5wXRP9+9y7m/rX137Ov7pmafy1d6l4TCO69KXTDKUTtGeiBMJ2Aiw9UQjST/JWchxXboScR7dt4eN9fO4Yn4DTx8/RsQOYItwuLeHZDZNeSRCwLJYUl6hkw6cB25mH6R3DC+wYhB+vVZ7TJNZmcBoGxwck3D42o6XGMpkeNOyFfz84H6+t3sXv3fRGhzXwKgEmiVCbzKZ/32i0cCNgcgcn/LMCq7ABJb4swuEkfFGA54ixiQAG5ELp6xWzRwZx2EokyYSCBAJBElls9z2g+8zkEqxuLycmqJi6ktiPLpvDwOpNFfObyAcsKmOet0bFpdVcGl9muriYgwGDFy9YCHzS0ryY2UMpdM8cewIb16xio74EDtbWzjY08XisnIaYiV0xuN0J+JUzvWBt05TrtpiMmNgGONg0k8DYcSuzC8jvQ1j1yFW5Tlvr7qwpbJZ4pkM0WCQcCDAUDrtV1YkeL6pkYWlZdy2dh0DqTS72pppGrCZV1qSr7BaVlFF1nV5tauTaDDEPes38mpXB4tKy/i7624AvMrOp48f400rVtDU38/OlmYa+/pYVVFNXSxG88AAx/v6ePPKlRMmMaa628ZcZNJbwWQRu9773biQ2Qt2Pdhze3yBuSB3zuQqL3IJjFNxjSGeyRC2bYK2jTGwp6ONzz/7NKsqqzje34eF8I6L1vByexvxTJoTgwPUx7xKwOJgiF8f9gbvzFVfCBC0LL74plsJWBYZx+H5pkZuXLKUm1esZH9XF1sbj2OMy8XVdbiu4enjR+mMx/MPX05lrj7UOFvG6YD0NrDq8lUXxu3DpJ6ByJu16nwazOq7b8cMT546mE6TyGaoKopiMHQnEvxg3ys0lJTy9tVr+f4ruwGva8mBrk72d3XS2NdHXSzG6upq7r0tCuMZAAAgAElEQVTmOoYyab7pd0m57eJ19KfSpJzsmNKuuUYkAHLuyrON241JvQhuN4hg7KVIaJMmMtSkvdbVyY6WZlzjjXsTtCy+uv0lOhNx6mMxdrW24hiXNy5ZhjG5QbKyuP687Y+9uhdj4E3LVlAXi/FqZyfGGJZVVLK4bLjcuDgUYiCTonVwgIuqazja28Om+vmU+uXgoUCAP73yakrDYb7ux5JzeeEwJ29w3B5w4/kbEvBKTA1BTLYJCWkCQ43PNYZdrS3s7exAgKF0hgde2UXayea7jy4tr+CIP5bFs03HvW4llk0DpTz2qtdF9c3LV7KmupbDvT0UB0NcVF2NAeaVDE97WBIOewP7JpJcVFXN/q5OLpvXkO8TX1lURFcizt6ODjYvXHS+v4o5wbiD4HYiVl1+mYiFkRgmexjRBMackesycqq/lbc/+hDJbJa7128kkc3yje0vYVvCoR6v4uJgTzdH/OqLkkiYtOOwuqqGQz2dJDLDyRHXeBWg4YBNd0G1lm1Z+VgTtG0qwhH2dLRz84pVZB2Xw91dzC+II0XBIPu7OlldXU1pWLuonisme8wbX6Ogy4hYZRinFUwfiHY7O99m3V131nUBw80rVtIZjzOYTtGTSPqJBodv7dpOZyIOQH86Tbavjy3Hj5J1XWzL4rWuDo709rKjpRnbsvjQpss43tfL9YsW82pnB7evW8/B7m5e6+pi64lGfvzaPj58xdXctGy59m8/B4wbxyR/AwQh7j91Lb4Lk0ohkddNa9vU1HGNoWNoiO5EnKJgkHmxkilLCp4Y6Of5puPURWMMZtJ8+YXn6IrH8wnO4319BC2LrOvy3IlG2oaGAEhmMxzo6eT2devBeGWdC0pLKQoGeey2O+hKxLnj0YcJ2TYf23x9/ngBsfJloe3xIWqKRnYxi4XCtMWHzmsyYbYkLs5u9hF9QjIbZByH1sEBBlJpyooi1BfHpmw2sFc7Otjd3sr8WCmtg4Mc6e2mqb8PQcgaL5nZOujNuPUH8xooCYVJOlmyxpDIZgCvMnMok2FNdRkfvfpa3rF6Le946Hv0JZP5Sq0Rn8d1yLguA+k09cUjHwSUhMK0Dw2est2z5fyeFkPfxBBCSu4tWKix4kKWu55IZDOUhMJURc9+kOjRlQtDL72AARLZDKlR3UAsEQSwxeKK+Qv4wvPP0BWPs7HeS6o/tm8vWeNy5yXrqSiK8qPX9hEQi7euuoi6UTEgZNsM+lUh3Yk4Qcse91j9qdSkEhin+1BjTj38OCmX8eOCMFxDo86nCzaBYYzhWF8v+zo6iGfS1MViRINBXm5r475dOxhKp2koKSHtOF65NxC0rRH9UtOOgzGGPR3tvH/jZVREimge6OOq+Q287A/CV1kUpSsR51hfH29ctoJdrS2kXZelZeXs8ueCB3ihuYnfWbbi/H4Jc4BxjsPQ14AgOAe8hUP3A2lM6FHEKj3Z5uoCkHVdnm08zrHeHgKWhWsMRcEgb1w6NUnBVzvaKQtFsG2L11o6sf0/+IUyfqVFd8EUidFAkK5EnIf2vEJH3EtqfPflXcRCIW6/ZAPlkYjXX7pgP8YY0o5DrX8RUh6OkMhk8tMkgjctWkVYu5CcM1YFSBHGjednNzHGATL6RPUCF89k+M3hQ/SmkgTFSzpWF0d5w5JlZ53wNMawt7OdumgMx7gc6OnkyWNH/UTn8FmeixVf3f5i/udEJsPLba1k/aToc03Hea6pka++5e0A+SeqrjH5vuquv25FpMgbi8cf6LPwc8QzmXwXNjW18gMDO8cAMANf8H6P/TmYAcS+fJpaps5GIpPhv48eoScRBwHX9Solr16wcFKJzsneqFsiDKbT3LLiIjrjgzx57BjJbJaySISrGhawraUZx3VJOllsEYqCQYJWgPahofxgwJXRKFVFUR5513u447GH+e7unfzhZVeMSET0pZL5Ks9YKJRPpBYyApHAueu+rUACizDZ/RhTlh8bx7hDYBVr9cU0OecJjLTjMJBKEQ4EpnRKsH0dHbzUcoLv7t5J1nXZUFfHfx89Qsi2CVo2lghtQ0OICBnXpSuRoKqoiKJAkLTjkHa96QwrioqIBILcsGQpS8oreGTvK3x9x7b82Bm5uZn/+PIrAe/pyyN7XsYSGR5fY/uL3L1+I8lsRoPIVHMHmDDraXQKqdngeF8vR/t6aCgp5XPPeiP7//6lV7C1qZHfXXH2A8LGM1mCtk0ykyWRzfCm5Ss51tvDC80nyDgOhuGbkrJwhMG0lxAtCgYZzKRHTHMWsm2iwWD+qcTRXm9Ggc9seRKD4calywhaNns62jEY1tfW89ujh7D8C5hEJkNvKsEbly4/68+lxpebVs2knsA4/f5SA8H1iF01rW1TZ2dXawtDmTTzYyX5WHH3ho282tnBhvp5Z7VvA6SzWQLhCL2pJI5jcAtuFqRgvdGGMpkRz+BcY4gGgywtr+D2Rx9iW0szAJ/e8iRtg4PUx2LcsGQppeEwu9paWFtTy/raep5tOk51UTQ/7kY8m2Ft7ZnPNKBORxrvjrcNgqvBnj/dDVJnYHtLM32pJPWxEsBLTB7q6aa2uJiVVdX59c62sqCxv4+s6xKwvHsMx3jV3wFL2HqikY64V+n9t7/5dX5K1f5Ukngmk48h9+3cTtvQEN/etSNf2fGVF7fy+5deTpEdoGVwkO5knLJIhOO9vdTFSigOhehOxKmIFGGAjvgQtdEYVac5rtZkKy90rAyfVQfBNZB5FYMFYoAgEnqDDvY7Tc5pAuNgVxcvtZzgq9teBOCTN7yRKxsWnPWTkrTjsLu9hbriGIKXca2KeE8p+lOp/M1IcFS2NRoMsrC0nBMDffQmk1REivjjy67EsizqimP5pyCuGXmJ4riu97QVsC0Z9wImN3e7mmJWHUTvQex5BU9IPgpuJ1gl09s2NSUO93Tz3V07sUclBdOOw3WLl4xIIJyJmuJi7v3VLzDGUF9cTCQQxDEujjE4xlBVFCVoWwjC+zZs4gf79tDY38dQxisJXx6JEB60qY+VcPf6jYQDAR56ZTfBgsF9g7ZNfyrJLw8eJBIIsLKymscPH+LahYt4/eJl7GxtoXVwgFgozOsXL6OhVEetPpfEroGit4HTBjhgVWm11gXONYYjvT3UREd2yaqMRDnU033WCQxLhJJIhE88+VuyxqW+OMaKikoG02lcY4gEAgQsm6JAAMcYPnz5lXzn5V0UBQL5gb6H0mn6Uynev+lSAmLx0wP7/W6tfluLimgfGiTlZKktjrGwrJTueIJfHjrIGxYv4dqFi9jd3kbPYJLKoiJuWrpszOdVU2PkwMAGKf8nfxrVMh3o9wKVcRyO9faOmBlQRKiIFHGwu2tEAuNsOcbgGsPejg4yTpba4mKWllXQUFbKbw4fLjj+8DaWCCKC8e8xHNeQdVxSWSe/Tkd8iFVVVWxrbqYnGWdJWQWJTIYnjh1heUUlNy5ZxraWE7QMDiIGllRUcOm8eTqI5DkmIhC8FAJLMU4nEEIC9Yjo0AHT5ZwlMNoGB3m26Ti1BVOZHu/vJWBZXHOWA1LFM2lcA//y/DMc7OkG4P5XduUTFzml4Qi2CCHbJhIIcNf6jdRGo+zt7OCxfXuxLQvHGC6vn5cvVV9fW89d6zdy/+6dWCK8d8Mmjvd7feR7kwlWVlZx9yUbmVdSwhee2wLAezdcyqLy8jk9kOe5IoF5GKcG47bg9TMz4LZC6FINHLOEfZLstXUWf5ObB/p5/38+xtHennxs6E0l4Vg/S37WzFv++W2cGOgn7bi0x4eIhULcvWETH7j0cm5/9KH8k4f+VJKFZWX89WZvzJV4Js27163n9y5awz0/+gEAH7/+Dexoa+a7u3YCuUFA0zx++BB3XLKBt1202n9aY+mFxnkiEoaADn44WwjeDYBrDF94bks+2fkvzz+DAf7HmovPaL+O69LY38fO1hY+s+VJOuJxbBGKAwEG0ikClkXQtllaVk4oEKCpv5+KcJjb1q3nP/e/Cgw/pQzbXvXnghIvQdmfSvLeDZvyXUgAEtksicFBfuQP+PmRq69hIJPi10cOcfvF61lWUYljzIht1Lkm2r1sljDjPGIUvEEz4cwrC/791rdxsKuLu370A1zX6yoKsK3lBCE7wE1Ll9EyNMi+jg5uW7uOnx/aT1k4wkPves+YGU4idgAR+LvrbyCZzdKbSmJbwv6uTtbW1LKqqoZ9HR1cPn9BvstZyLbZ3dbKwrIybly6nFQ2iyUy4kHKVJqTA4CfgoiAVGqCc4Y4Z3fcB7q7+M6uHQQsK3+h8d1dO7lnwyYunTf/rG72iwJBMo6TDyAATiIDYe9EzlVefGjjZRzs6WJJRSXzYjHW19azvLKSzQsXc1FVNQe7uzHGcKC7i/JIhIbSMpZXViLi9TU71NPNztYWnj/RyK8OHeB9Gy/jyvkNrKurY29He/741dEol807u6c/anwiQQjfgMkehli1N1VrcBVY+n3PFssrKrnrkg3MLynNJwXft+lS5sVKzrhL1ivtbWxrPsFQOj3iCWgkECCVvyAIsLSikpJQiD+47EpKwmFO9PfRNDDAX22+nk9veZKM4/CuteuYFxuu9okGQ/SlBv4Pe+cdJudZnvvf+5XpbXe2V616syTLtoxtuTuODbbpBttAgORAQoCEQ0hCEpJDEg6HmnMCCT2hxPYxxpwAgQR3uWK5ylax6va+s7vTy1fe88c3O9rR7sqyJFsr6ftdly+0387OfiM077zv89zPfTNRlogC/Pf7foVlO1JVgM9ue4Cru5aRLhb58a6XuHrpMpbEao7rtbi4uDibx1XxOnaPj1ddN2y7kvLzapFS8puBfg5MTfKtZ7ZX4tMtKZkuFjEsm83NLbRHoyhCEPf5+d5Nb0NKyaGpSZKFQtVcfUs4UqX8jHh9DGfS/PMbb6LG768cBmbf+5MD/UhbkiwW0YTCVV3LaI24aqHXixMzBnZZTOiqSns0xlAmVWWePVUosKX1+AtUhyYneWKgj5FMBltKR1VRLohoiopEEtA9dEZjeFSFd6xdz5OD/Zi2zb7ERPlnDjcuZvcwfJqGKMKeiXFyhsFTgwO87//dzXTZ9NeSNgcmJxlOp0iXSuR2lbh66XI2NDZVihsuJ86xRLS7LC5eswJGwTTm7TTOzJsf33YDErkcByYT7E9MkCwWUIVAUxQ6v7+f/hvb0Lti2LZEVxU8usY7161nY2MzerkzAnBoeorJfJ7b2r+CAJ7MfI4He7q5fvlK6gIBltXGifsDpIpFNjQ0sqNs6BnzeXmsr5eb153DyngdV3Ytxadq1Pr9blf1NUQID0Jf7cylupxxtEejnNPYyO7x8UpRMOLxcUHLseWaH0neMNg+2M8dL73IeC57uB9jS1LJHF1f3IEB9H3616iKwjXfvhWPqvJIbw8DqSQ+VSNjlBjPZfFrOlfF/gZdUdlR+mLVmJiNrHQlbrjzh/SVo9PA6RTHfD6EEDQEgjze10vcHyB8nActFxcXWNfQyHShwHs2bKpEmv/dlddwYVv7cT3fRC7LS+Oj/GTXTkazmare7XShQHMoTFs4wvnNrWiKcKKVpc39hw4ynS/wgU2bSZWKZEslPKrKbedsIubzIZGVtUIIZ/xlpnixOl5HybL4+IUX8ZuBfoKaByFAV1RiPj+P9nXz1tXrXEWni8txcF5zC8meAkOZNArOmaMjGmVZreN/9GqVBUXT5In+PsJeD/2WyQc3bUZTVG5/6QU8qsa71q1nJJNhTX09UZ+PqUKBsNfLZy69kudGhnh6aIBPXbwVRSjcteslsqUSn770cpRZewlNUVgSi1WKs7PPE/2pFEOpFDG/43nRGAzx4ugIYY+XZbWvvRLAVV64LFZes0/I9kiU39lwLs3hw2ZbHzn/QiQQPI6Zdiklz48Ms3N0hH965ilmbCr8mkbz13dj7Ukgr27E6kmy5JdD3Piv7+PNq1Y7MWS5LCXLosbnI6h7eHlijOvr/p5a9SUALgr9JWbAZn/i/1AXcCTHiXyOH+54Ho+qVhQkn3tkG5a0saXkuuUraY+6c+wuLieKEILNza0sr63jiiVdeFWNeCBwXN2Fomnyn/v38cLICP2p5JyxstkWfJqiYNg2bdEoE7ksA6kkihDsHh/DQvKdS36KZds0eZx51uWlPyag6zxb+AKqIqjzH04HuP2tN/PWH9/OcDpN1OvjLavWMF0o0B6JEvB4SJVKjGQybgHDxeUE8KgqVyzpYqqQ56d7dqEqCpd0dB7Xc6WKBX728h52j43Rn0rOEZ6risIbV6xEVxRCHg/D6TTnNDZyYHKS6UKegmmxfzKBRJIuFbGl5Df9vdQEAkS8XlbX1SMQ/OsLz/GLvXvZPuRI1kO6B8O2GEqnK93cZKHAingdAV0nWSwwls26+wsXl+Mg6PFw/fKVjGYzFAyDiNdHXSAwp8l4LAdz07Z5pK+bZ4YHCHu8jGezxINOcoiuqJi2hWU7/ji1/gA5w6DG58O0LZ4dGSKo6/RNTzOZz2PhqDEk8NChg9QGgiwrK0CLpsl3bngr193+fQDufuct/HL/XhL5HAPJaSJeL0XTwKOo1AWCmLbNnomx16WAcaZTSSMytld97SoxFj+vWQGjq6aW7ukphtNpTNtGIsmYJa5esuy41ArjuSw7RkeIeLwIBMlSodKtHX5zO+bVjbR+fTeN53UgfH5WxuvRFIVf7t/LPz71JADv37SZpTUxLFvOsdtUhCBVOJxqMV/c0nShgETi03Qe7u3mjctXnpR8aRcXF4h4vcctBQdnlv2+g/vpnp4ioOvUB4NYtl2Zab95/Xp0VeWZ9X34NI2rvn0rtf4AF7S08fzIMKZtc3BqkqjXi1r2vJlt6GvaNtPFAsOZNFd1HY5tPDKRxJI22ZLBkmiMzpmxEVGdaOBy4rgbjbMTIQS1/gA/fddtx/0cBdPk3/fsIVUs8tTgQKXQqZQN9uoCQd64fCVD6TSr43WMZNK0R6OsrW/gvkMHKBoWh6YnqSmrrCxbkioVeW50mKu7ljGWzTKazXJeUzNRr69KMr6uoYGiaSGEYzruUVRWxOtoCx8eG5lvjt/FxeXY0BSF1vCJj2E93t/HztExAppOjd+PYVnsTyTwN+ooQqAqKn2paVqCESZyWbyaxkVtXYxlM5i2zc7RURDwYM8hDNtCV1T8us6DPd1IJJd2LGFFPM7PXt7DLw/sq3hk3PbTH/PtG97CkwP9TObzhD02QY+HdU31Fc+LjFE64dfn4nI685oVMDyqytVdy+hPJemqrSWs6yypqT2uA0rOMPjV/n3889NPoQgq8UQzBJbGyR1MED+3jQ/c9RGG0inaI1Ge6O+nZFrYUlK0TPYlJpysdttm5/iH+PDSr+HTNHYZX2Q0m+GchsNz7o3BEB8673y8msYXHnuU6UKhEr36by850a2r4nVuAcPFZRGQLBS479ABHu3rIaR7+PXB/SSLhwuStpRkDYNN8Tr6QyG8qspvL1tRUXoENI3vPvcMNpIrO7u499BBvlG8kZDu4btbf4oQ8KO+j5ApGVyzTKvyxDiSsMfL+a2t+Mv+HaZtY0sqsW4uLi6njuF0il8e2MeLI44Mu2iale8pCEwkIFldV8eNtatoCIUI6Dq1ZcVVQNf5zvPPIITgso5O7us+SKpURABSQsznoy85Td4wqfEHuOsd70ZTlDmS9XTZ86I+EKwUQ0uWhSKEmzzi4nIKyRkGj/X28Mv9ewl5PIzmsgghqA8Gub/7EAemJit+OXsTE/Qmk9y6YQNNoTBeTWMilyORy2LYFl5NYzyXxS6nnuVMA6+qOnuSUhEVQdjrndNUDXu9XLtsOVLajGaztITClebvVDHPitqTl6hyNlOdRuQ2RE4nXtMhS11VWVpTy9Ka45c5WbbNf+7fy2AqRapYqJpB9ygqNpKtHZ3s/9pOFN3DYDpFazhMrd9PIp/jW89sZyCdAuDu3TvxqCrn1DdycHqStzfliHp97E6M0RaNsrw8IwdOisDlnV3cd+gglrSrOiIzbuiZ0uEDkouLy6lBSsmjfT0UTYuI10eNz1eWYjvvz7pAAE1R+OMLL6Y5HOZ9T5w75znaIhGKlkmyWOQne3ZhlZUXqVIRo1y4fGZ4iCuXdNEzNc223h6uWboMRYg5M7VfvOY6Hu3rYbrgrFcSyfnNLZWkI5cTx068x5V8urxqiqbJtt4efKpG1Oulxu/nravX8O979+BRNS5ua2dNXT1vWbOWuD8wrxKzIxqjYJlkSiV++vLuyloBMJbLcs9uZ7Tl0o5OXk6ME/F65x1zCXu9bO3o5KmBfma0WYoQXNzegf8Eo6NdXFyODyklj/R2M5ROE/Z4qA0EUIWgL5nCls57dLZRb7xc2OycZdL9lw/eS18qWSmOHjnKWiyrx18YHeG5kWE+e8XVXNW1jN8pp5rNHm+5qL2Dew8eYDSXxadq5E2DsMfL2vqG1+YvwMXlNGFRu0RN5fP85/59fPWpx1EQlUXAU44/DXo8qIrjTH7xnb9PczhCSzhMayRK3jAomCaj2Uzl+QzbxrBtnh4exJKSmx94EzGvj+uWp3n3+nPmbBqawxFuXrse07bxqRo/ePF5BPCpiy9lOJOmLeLOqLq4nGqmCnmShQKNoRC9ySkKhsnb16zj7t070RWV9208lys6u+iIxeb9+Xf8+E6mC3mmyh2VI7nt4ZsAaAk7HdaltbWMZjOMZ7M0hkJzHt8ejfKW1WsZKc+4N4SCRLwLFy+ypRK909NkjBINwSCt4chrFo3m4nI2M1qWdtcFAuxXBKZlE/Z68aoafl1nfUMjN65aTUNw7vsa4G133c50sUCquHDzQlUVkM4Y6pJojO7pKc5pbJx35n5ZbZyGYIjxbBaAxlCIoMezoMFgqlikNzlNrlSiORSmJRJx41ZdXE4ik/k8E7ksreEIfclpTMsm5vdjS6fR8cFNm7m0cwl//+jDwNz3aMmyyJRKWLZN3jDmFEEVqBQsJ/N5mkJhJnI5BlLJee8n4vXxphWr6E1OM5XPUxcI0BGNHdXkN28YJPK5iprL3U+8Mm4D5PRj0RYwiqbJA90HyRgl0sVilW+GBASCW9efw/UrVtIRrSHk8VT9fMjjQVcUArruLCblLsmRMq2cUSJnGDzY3c0bV6ycY7Ln03V+a+lyHu7txrRtFCEYzqSp9fvpjM5/IHJxcXn9cPLdBQLB6rp6XhgZoWCZXNa+hLDfy5JYjM4Fihe2lEwX81Xri1/TKJgmAoFHUymZJqqicHlHF7WBADGfj7FsluwRM6izNzIBXWfpMRhsJXI57u8+iGXbeBSVvYlx6gNBrlyy1E0hOApK/N9c5YXLq2bG00ZXVFbW1rFnYhxFUbiovZ2OSIz1DY0Ljm8UTKOcfHb4QDJ7rbCRKEJw3bIV5A2TlnCEiNdHzjTIlowFi5hhr/eYzH1H0mke7D2EkE4HeN/kBC2hMJd1drkHlHlw1weX46FkWQihoCkKy2vj7Bkfx6dp2NKJOl5b31Cl1p7NLffcRapY5OXEBABRrxfTtvFrGpqiYNk2EmfMbCKXo9bv508uugTTthlKpxY0FvXrOqvr6o/p/g9OJnhqcAApHd24T9O4cslSd9zd5Yxj0e6QhzNp/vnpp1DLSQHg+GpIKfnURVspWhZvW7OO5vD8c+V7JyaYKuTIm2Zl06IAGxqbSBWLJIsF0qUScb+f+kAQKSV7ExOc3zI3K7o9GuWNy1eyKl5HplSkLRKl8ygVUNO2Gc1myJcMor75HZBdXFxODjU+H15NJW8YRL0+trS2MZZJM5hJc/2ylWxsal6wSzmZz/P+jZtpDoX5q4fuI2+arKipZfeEE+nqVdXyhkawtr6e+mAQVSjlNCXPvM95rNxyz11M5vP8wXlbCJdlqDX4Gc6k6Z6eOuYNy5mKbQ6CsQtkCtRmhL4WodS88g+6uCxAfSCIwPmMbgqFCXm9jKRTJFSVm1avZmmsdsHP6lvv+TFSwqcu2cpnHrqfommxrLaWHSPD2OURU1tKfn3wAB5V5c+7LkNKiS3h4//1H2iKckzJB7fccxdPDQ5U/gxw+9tu5snBfsK6l0BZKVqDn6F0iv5U8oTGdM8EpDWKNHaCPQlKHKFvONW35HKaEvX5QEos26Y5FManaQymUmSNElva2riorR2vps37XralJG8Yla9nRt5bwhGCmkbE6+XZkWEsW2LYjrfFPz39FIZt85Vrrz/maNeFmC7keWKgn3p/AF1V+dITj2JJp7D65lVr5h2Je6040dfyWiClBfYI0hwE4UNoHQjFbUSfrizaAkahPDumzt5MlAsRWcNgU1MzTfPItwFGMmm2Dw1Q4wtg2Tai7CxuA/sSiUoUqmHbDGcyPNhziG193Xzk/AvnLWAAxAOBY6pgZkolHuo+xD/85nEAPrBpM52xGi5u73Clni4urwGqonBJewfbentIlgooKGiaynXLVrC5ueUVi4czmwyBwLAs8qZJWzjKUCbFty65B8u2ue3hm/jhiy8A8L6Nm2gKhqgPnpjRni0lhmXN6b5GvV56p6fP6gKGbfRC6REQUec/awRpDYDvOoTijO65nVWXV0vQ42FLaztPDfY7iSNIgh4vV3YtY1nN/F3VI1GFgpRg2BZF08SjaWhCqaQC6IrTvTUsi0Qux7La+Al/9mdKRXKGQVMwVIml/9TFlxL2eOlPTp/VBQzbHIbSA0AYRAzsFHLyFrB6ne+7SgyXV0FA19nY1Myzw4OEdC+aUKjx+9nU1MylnUuOGu8upQQBK2vj5E1HdaUKQdG0kEDY66uMq81gS8lQOsXnHn2YZ4eHAOfwfzwH/8FUClWIKkWWKgR5w2Aynz/hPcvpjJQWsvgkWD0g/CAtpLET6d2KonWc6ttzOQ4WbQEj7g/w/k2baQmF+dKTj2FYFtcuXU6yWOCyJUs4p6FpwYPJgckEP9zxPBIqoyMYK38AACAASURBVCO3X/FzAP7giXdgW4Kwx8NkIQ84Gw5LSsKeE+uoAjw7PEjeNPCUF5DmUJje6SmaQyFWxM9e12BpTSDN/WBnQG1B6MsQwjU1dDk5NIcj3LRqDUPpFEXTpCEUos7/ysqnGp8Pv66RLZW4tKOTvRMTZdNeG1UoVSNnM/LPdfWNrKmrn7ORObLjUDANVKHMK+++5Z67eHpoEKDqQAJg2pKw9+yVhEtpg/mC00mdWSNEDdKeRBp7Ed4tp/YGXU5rVsTjNIaCDKfTSKAlHD6qR83M+/qF0REAvvDEI1jSZmU8TsmyaY9EaAqG2FWWmn/+6mvpTU5TME3u3r2LgK6zfeiwouLIg4mUkoJpoinOWnHn2981Zy3JGQZI6RyQZmHYNl7tLDf8NHYAUYRSPpyJCJKzd/10OXHWNzQS9wc4OJXAsGw2NDXREY0dtXgBoCgKqlD46AVvYN/kBPcfOoiNE62uKxqpYoGbVq7mgtY2vvLkYwB85IIL+euHHmBfeezkRLClREFU9hT7JhMAfO/5Z/npy7u55+ZbT/h3vBIza9eRKrJTrsSwR8DqQagtlUtSlqC0Hak2I8RZvo6ehizaAkZdIMDy2jgHJhPY0vGeaIlEuK5xJZuamo/6swXTRAgxx+8CYFNjM4Zts7GpiQe6DyGAj194ERO5LGvrG0/onoumyUAqxY92PF9ZOL785GPYUlIfDJ61BQzb7IPSo5D9IaBA8H1I6xD4fsstYricNAK6vuBs6nzMfLD+4/U38FD3IcayGTy6il910gm+s/WbtPudIsPPf/vXrK1rOKYuXiKXY/vQAJO5HEIIVtTGWVvfQN400RRB9IjDUsm28CjOhtu0bVKlIlva2o75dZx5GGDnEOoR67EIgj1+am7J5Ywi4vUdtWhxNAzLdubaVZ2wR6EpFKI+EGTfZAJbSra0tnFhWzsbv/k1coaxoKoTHF+Lp4cGSBWLqIrC6rp6Vsbj/ON1N1SaIOCsbf/20g5KlkX39BQAX3ziUd6zYRPLzmL1hZQ2yEmE0lT9jdAnIfNlUOpd5YXLcdEcDi84on4kG7/5NQDSJUeF9dXfPM65TU0EdB1f2f+iMxojoOsMZzJc0NpCfdAZXb9p1Rru2vUSu8fHCHs8rK1vmHPYH0qn2J+YoGBadMZidMVq5h1hbw5HeH5keM51cURyytmIMzbir7omhAdpl8BOgXrse0eXxcGiLWAIIXhDWztt4QidsRiqUFhaU0Nz6JUXlI5ojPdt2IRX0+i0PwbABfXOmzri/TaaovB07vN8YNNmLCnJGyZbO5a84mI10/1YqKu7UNFEznPtbEFKC0rPgKhl5p+bUBqR9gjS7Eboa07tDbqc9dQHgrx51RqGUml6klMEda08fnb4ndsQDFEwDfxSznn/H9lxuPkn/xdFCP78ksuwbJsnB/q59+B+ltTUgIS6QJDv3PhW/tsv/h+2lHzkgjfQn5xmJJtBABc0t9Iajrxur/9EcQ4RORAehDhxFRvoIDxIWap+PpkD9ejFaxeXk82RMcnfeONNfPPZpxlJJYn4/DzYcwjDtlgSq6ExEORXB/ayqdHp8gV0fV5FBTjz6g92HyTk8dIUCmPaNg8eOsS9B/bTGo0ipaQ5FOKSjk58mk7E661EQ4NT7LyotX3Ry8Jnv/aTvVYIoSBFFGnnEMqsEV+ZYxFvb13OcOKBAO2RGJOFArvGxvCoKuvrG7lr90tYtuSda9fxjTfdRK0/wMZvfo1vXvwTWOEknj1TVmbOsGd8jO1DA0Q8XjRFYfvgAE/29xHxeinaFu3hKBubmoh4Hb+9jY2OOl0Rgu899wxCCH701nfQ8jrtKY5cL0+58mIG4QNpzXMdEO5acTqyqP9fU4SgIxZbMP5wIbpiNfROT/PCyDDt4eryQaFs6tkWiXJJRydFy8KvaUc1tymaJjvHRtk/mUACy2tqWd/QgGk7h5mZBBSPqtIRifL+TZv5/gvPAfAnF21lMJNiRe3Zqb5A5oECZL4H1n7nUvorgA3hJnALGC6vMwtJHP/koq0Eh3XGs1kOTU/xF0Pv5X9s+j4hj4cdxf9JIp/nYm2S5fF41XPtHh+rymSXyEpSQcE0Gc9kMKRNxOMjoOsk8jke6+vhjrfdXCmGpIpFiqZJxOs9rdJHHKPNZ8oHBoHUViL0Ddz603uA49u8CKEg9Q1Q+g2yPEYi7TTIEkJffZJfgYvLqyPm97OltZWD/gCD6SR50yBVLGLZNjesWMWn7vs1UkqKlrNZnunO7vj9j1U9z4HJSTRFJVjeP2RLJSbyWQSwwd+ErqiM5bI8NdDP5UuWctc73g3AO+++Eykld7z9XVUqjcXGkevsu3/yI5Ap7nhTDUiB1JYhPOeeuHRb3wDFbUgbhBJA2lkgjaj9l7kqLheXk8wt99xVUV7MjKHf/c5b2DEyTED3sHdiAsO22Dk+hmHZeDWVvmSS7ulptnZ0ArAmlqg8X2CWP0bRNHl+ZJimYLjiozOayfLC6BDnNbfSFokykkkzciDDG1esJOjxsLGpmc5YjLFslrt37cSjqa9b8eJkIGUJaR4Es9cpdOqrQHllL7NXQmgdjufFrMaItBKg1ld8tVxOL06fnfKrQFdVruxail/T+Oivb0Yg+NobnA/Tjz/1ToqmyYfPGyMeCLAyXnfU4oUtJY/09jCey/KDHc8D8M6167nv0AHaI9FKzvIb2juIeL1sbmkl1VOkVN68jGQzrKitY8mrLMKcSqR0XJRPykyY8ICcb+GxQcxvwuriciroiMb45f69jOeyTBcKFCyTb+z/KMnPbkMR/8I7fvS7vDg2wtLa2nlnYS9sbSNVLPLBTZuJ+Ryp4ngui6Io6AgM2wJ04v4AI5k0yWKh8riI1wvHEKW4mJD2JBQfBqXGUVVJC4w9J0VxJrTlzhy7uRNpj4DSgNAvRihnr1ze5fVjvu7h7D8vq6nl0w/ciyUlU4UCAJOFAp/d9iAAnmMoQiYLBXyzHjeSzeDTVAxbYlg2uqJS7w8wkEqRLZUqhY6733nLib/AU4E9DUJFKE2OEsM6gCxxwp42itaOzRVg7HDWClEL+tVu8cLldWd2I+Nzj25zigvZDACJ/l4nEbEEf//owwD89eVX8dw7f4MoGwDPjKqCU+xMlYpIqBQvDMuiLzVNfSBI3jRRhKDWH2A0m+HQ9BTnNDj/5mM+PzGfn39/922vzwufh+NpXkhpIAsPgUw45t122vnacy5CX3dC9yOUGNK71fG8sA0Q0ileeC5Z8GdcA+DFzRlZwADnDb++sZGPbbmIZKHAx59yZlfPqY+T+ttH2O7tJfrd9zCSyXD10mULmvMkcjk+/9g2PKrK/rKvxb88/yymbRPxOpKu3zv3fB7qOcQNK1YR0HWuW76S85pbyy7EXmr9p0f+srSzSON5sPqdr9XOcofE/wo/uTBCeJD6SgjcCrk7nIuhj4CdROjLT8Ztu7i8KhaSOD7QfZCVtXFW19Xz5SceQxGC4UyG4tU1bH5wGoGgP5lk1+goLZEIH/3PX1S6izMqjK9dfwNP9PURK4/XG+VCpqC6swJU4qFPV6Sxz4kiK/vYCKFyy6/SIB9n+4izITteGakQAqEvBX0pUtoIcXbP77osHmwpeX5kmLDHy1A6Vf09wK9ptIYj9KWS6IrCtvf/XqVQOZvmUJjnR4cqhQnDsrBtZ+/i1RxlxUzX0ZKn31oxe52Vdpo73hRFKI56TQgFSQOYB5GeDSfshaVobaC1uWuFy+vOQiNiB3f0YNo2NDmNiflOGAXTQBp7Kt/rDA6DeViN4VU1ZNm8VwhBybIq4QT+WcXPgKYzmc+d7Jf2uiPNIbAnEDOjogKkDEDpRUexdcLrRAdSbQaZBlRXeXGac8YWMMCpQtZ4fTw7PEiyUMAGbCR+TUdVFJpDYYYzacayGZoW8NbIGqU5K0+ivFBMlP/3u88/Q8my2NLSRnM4jCIEjQtEvC5WpDSRxYfAznPrr5IA3PHGfke67futE9oUCH0jEgHB9+I4gljgvcLtprqcELaUJPI5pJTU+Pzzpn3MPG44k6ZvehpVCJbU1NAQrH5/5g2DkXSaL9/1nwAkG51DhVK0kVGV/u3dfOedX8eWkpoffpAXxoZ5aXS08vPpUond42P8xQP38r6N5zKcSRMrR6ZN5HOc19SMXjbqLFommqoSO04TwUWDnQWq59gF4qR7/rgHEpcTxbAsJvP5csfSv6Dq8t0/uYuiZbKjnDjyjrvv5CdHKB6m8nmyRolPb72cLz7xKAfKjY2Zf/dNoTDThQIxrzMy9ot9e9nc1My6hmpFwNLaGvZPJhjNZoh4vHhUlYl8jova2isjaDnDIODxEPKcXuqsuVhA9WtwihgSR4ZxctZCd61weT0wbZupvJNiWOuvLk5+8sq/ca5v2w2A8unNRFfUc2lHJz/evROBIG+aAHx9+2+45tp2mr17AAj6N1Y9V8TrpT0aYyA1TUMghEdVKZWjm+tn7WFypsFy/xlgQmmPzVkLhFCRQoCdBvXE1wkh9LIn31Fuo6y8wNhe9bWrxFhcnNEFjIlcjmSpyKXtS3hueJjk/jHy33uc9IvDJIDbb/0WedOk+e4/xJaSxmBozsYm6PHwwU2baQ5F+NITj1KyLCfSDBjLZaseOzM2clpij3HrL7oRwsP2Yed13fYrgZRj3Pn2c0FteIUnWBghNIRnM1JfD9IA4Xc3Gi4nxFQ+z7beHjKlIgLQVJWt7R20Rqor6lJKnhka5M/u/zWqEHz4vAt4eXKCzU0tVd2STKnIZD6PlBJ71hHc9iqAwqHPn8+62/tQFYV9iQl8qkaN34dSEGTK8s8Z+ejVXcs4MJmgLzlNeyxKTcBPqlhgMp/HkjaWbbO1Y8mCBZfTBq25HGF42ETw9hvagSK3/cr5O1k0Bl4uZy2DqSSP9fdhlruXQY/OFZ1LqTni4GHaNtOFfMW7AmAyl6N3eprOWSOgyUKByXyO7z73LL3J6arnaAgE2NzUTM4wiPh8aEIwkEySKhRojUSqlBg+TefaZcvZl5hgMJ1iVbyOhmCInFliKp/HsG2kkFy9ZGGF6OnAnW9/F7axz/HK4fChS8qiM2IqFrcJqcuZT9E02TMxzv5EAkXA8to4a+ob5vWYGUmnebS/11FDSEnAo/P1628kHphfad38tV34l9Wy/y9D2FIy+62sKgo/H/sLbmz4HAC27+tzPP/e0NrGC6rKwalJpJQsicWQOIEBtpRMFfLoisrSMyGNSAmDVaq65IQn2CetyOly5rDoChimbTNVyKMgqPH7F/zgnoksHctmiHp9LKmpmSPR7ktO8YMdz6MrCsOZNDT7OXB9I+ZlNbR+fTdZo0TeMPnsww+iKQqfvGgrV3YtrXqeOn+AtkiUgWQKG0fGtaWljY5YlPsPHQTgkxdtZTSbocZ/+r7BpH0U+ZksnJTfIYTH2bC4uJwAlm3zcO8hhBSVVKKiabKtr4ebVq6pmOoCJPJ59iYm8JY3IrX+AJZt8+LIMEtiNYQ8Hl4aG+ULb/oiOcOg/kUnrSj3xS3YmgDFWX+kX2P4Y+vwqCqX+v0UTYvzm1vZOT6GyGbmRJ+ta2isdFxnFCCDqRReVaUzFptXUn66IbSlSPMg0h4FwkARKIDnMuCJU3tzLi5AplRiW18PMY8Pr9/Z7qRLRR7qPcSbV65BVZSK/PvzV1/LredspDXsNCvAiVjfPjhAaySCIgRPDw6wNzFB33SqSrLdGAwxkctStCxSxSIhj4eY14euKhQMk72JBHvGx7movaPq/vy6zsamZjaWo+Et22YonWI4kyGo63REY4RPM2+c+RBaJ9I84HhUEAZKOGvFpQhxmhdyXU5rbCnZ1tvDeC5D3OcUIV4aGyWRz3Plkq4q88iCafBwbzch3UNt+TM8WyrxUM8h3rxqDbqq8tlf/yUvDA/R8+avkjkwgWnbCASr6+pREFi2zf7pSRQEl3d0MZ7N8u3uj1M0TDY09nLbEQUMr6ZxYVs7m5qasaTEq6p0T02xa3yMdKlIeyTKOY2Nc84/pyNC7UAaLyHtFEKJlFOLxkHpQCjHFml7MphRWrjKi8XNoipgDKdTPNbfxzeefgoE/PGFF3NZ55I5m/28YXD/oYMkiwW+/8Jz2Eh+/7wtXLtseeWxtpQkcnlM23Lm0GZ+tsWPZ1BSs6mV+r+9CqEojA0OYNiWk8Ps9XJZ55LK44UQXNLeyd7ABAGvjrQlJcsib5pY0kZKGM6kWd/QeNy58osBoUS44021CLWZW3/hpIXcfsNysEdBOb3GYVzObBL5HLmSUTX25dU0ZMHJS18ZP5z4M5HL8q/PP0tPuVM6czB574ZzmcrnyRsGzw8P4VFVVEUhPfN8E0WKcS9aQKsoq3RFrWxmvJqKX9e4ceUqnhzoP+r9KkLQXPoIzb4z64NQCB/4fgtpHgJzEJQ6hLYCocZd5YXLomA4nUZKWZXsE/Z4GcmkmcjlqkY9B9NpQnp1gd2ranz52ce4a/dLfPGa3+blxDitoQiqUBhMJ9k1PoYqFK7uWkpXrAZbSh7r62VpTS266qgMfbqGR1XomZ6aU8A4ElVRaI/GaI+ePqbfx4IQXvBdXV4rhkCpR2jLEepZms7msmgYy2YYzaSrkjqaQ2GG0ikm8jnqA4cVQsPpNKZt459VLAh6PKQzGcZyWRoCQR7sPsjd7/0e6Z0jDHzESdlr/fpuXvrEz1j/1Ztoj8TY/+xTqIpCY+jwc+cVg5fGR7Dsc1AVZc7hefYatjwer0pDO1MQSsBZJ4rPIK0REALUZQjPplNyP2fSfu1MZNEUMHKGwcM9PUS8nopsy7IlD/Uc4qZyp2SGvYkJ0qUizaHD0UKqUHhuaIirli4jWyrxcG83/3bLN4mUSvj/ais5wyBdLBD2ell6XjNsXs6Tg/2VqDKAe/bsImeUuKS9o+r36arK+oZG1s/qqA6mkiyvjaMI6KqppWUBD43TBqUO1Dak1Y+kLKG1h0Hrcr0qXBYVpm3P64ilCFExzZzh0w/c66iv5iDRVYW+5DQ+TePN3/8Aw5kUX7/nPgSCqy/byIUtbTSFw/yvxx9hPJvluuUrKuoOq9xV8apqVRzq2YYQPoS+FvS1p/pWXFzmYNoWynyLhYA/+NXP8apqxYg389g2cobBX2y9nE9dfClQli9LUIBDU5NEvT6SpQLjuQwh3eOY7CHZ3NRC1OdjJJNyxtCkjfNTznqlKwqKcnauETMI4UXoa9zodJdFRaZYQplnpFkIR10xu4Bh2Qs7PNm2ZCidIlksoCsKerC6GKopCue3tBHQdTY3NVPvrx6dMmybkKaTLhUXpULzeA25Xy1CqUX4r0XKAqCenDRElzOSRVPAGEqn+M7zT+NRVPaVTbG+9ex2SpbFxW2dVZ2S3ukpvr/jORRE5bHfee5p3rthE4Zl8fTQAJlSEb+mU7IsXk5MkDONsulfnqjXx3QhT7pUqhpRmS4U+I/9e/noljcQDyw8l6kIccZ1SYRQwHsx0jzEHTccBBTQliO0rlN9ay4uVcT9gUqxYsZHwpYS07ZoPKKQ6FGdDHRFCFQh+NTFlzJdKKAqCvWBIP2pFBPZLEOZND+/bzu5Zmfj8FhfL3smxvjzSy7HtG2iPh9F08KwnHEqAbSEInTVxI9avJjPDMqt6ru4vD40BEOYto0tZeWz3rAsFCHQj/C78mka2VKJomni1TS+9MSjGLZNT3KanuQ0ySce400rVjKayTqjIbbJ8poaYv4Afl3HlhJLwpa2dkbSaXKGgcRprrRHY3RGzpz9govLmULI63HMZI9ASggeociqCzojJrPXk5mGSjwQoHtqEgFs/D9v4c6XXqRQbo6O/NlG7DUtBHSnQLE6Xs9IJkOyUMSrqRRME00oNIcjBDO/i51VznoDyRNNHHE581k0BQzDsuaNGQIwj4gQ82oaUlLVhZWAqiqULIvBVIr7P3QHA9u7AbB7G/ALyJYPJ5qioCgKYY8Hv6YzVO7Q1vh9CESVidfZhBA6Ql8F+qpTfSsuLgvi1TQubG3nif4+FCFQhKBkW6xvaCReNuab6RY8PTQIOIeTpmCIkUyaGr+frR2djoQzEOTlyQlag+Eqcy3DtiiZFkOZFP/j8qtYWVfHAwcPULIsvKqGoghMKdnU1PS6v34XF5djIx4IsL6hkZ3jY+hCwUZiS8nF7R3cst5x/J/dWexPJvnNYD9TxYLzXp8l2/aoKvsSCTqjMVRFsLK2jt3j4yRyOYbSKTyaxtr6ehoCIR7sPoiEiveOoiisqT9+I2wXF5fXhoZgiLpAgJFMhrqyEed4LktzKFT5eoaYz8+GxiZ2jAyjKypSOAWMn+zeyX/se5mvXHs9BxIJbKiow8EpeNhSMl0okDMNbly1mkf6ejFMk4Jp0hAIoatOoVNdZAb3M+vjjFLt9VJiuLi8EoumgFEfDPH+jZtpCoX5ypOPAfCJN1xCIp+rmOXMsKaunvdu2ERTMMRXf/M4ALeds5HV8XqEEBi2TabkONkOfnQtAuj4xRDTn9hIxOvlY1veQF8yyXA6hQ3cvXsnAG9dtZb6YPAMiCxzcTmzWVpTS9wfYCCVxLRtWsIR6gKBBdUQq+N1fOuGN+NRNSJlU7zpQp5UscDQp+9jQEoaXhzG+OhatKCH9924hVXxei5sa6M+EEQIwfUrVrFzbNRZk/x+1jU0VslL58M1g1ocSGkD4qwd9Tmb2dTUTFs0ynAqhaootEWiRH1zu3vpYhGJ5KK2dnyqxptXOYbA777nLkzL5k+3buULb/wSQ8C6f7gRpGB1XR0BTWNFbZz1jY3U+p0Dz/UrVrFrbJRksUhjKMTa+vp5f6fL4kOWG2ZuUtrZgSIEVy5Zyq7xUfYlEihCsK6+gbX1DfN+XmxobKIlHGEwleQzD92PV9N4acyJVP+j//ol/ckk1yxdxo0rV/HzfS9jWDYXXLqcZbV11AUCrG1ooD4QxKNpPDXQj2U7uWfNoRAXtbWj6O6ewcXlWFg0BYy438+6+gZ2jo9hlE03x3JZLmxtqzLMAeiIxtjU2MTO8TFKtgXSiT1a39CIpihYlk3H569F+Yt7GQ158HXV4vnMEkrJFCXLIl0qcePK1ewYHWYw5Zj3CKAhFGJ1XX3lgOPi4rJ4ifp8Cx4K7nz7u+iZnuItX/oGSHjbFet4qKebK5Z0kTdVHuvrYTybJWOUMGyLoO6pGHiqikJLOMKKeJyGWVnr8UCAy5e4I1WLASmLYE84Xyh1jkngfI+zk8jSDrAHAR2prUboa9zkg7MIIQT1geCCxcY73nYzL46O8LO9eyrXQh4vV3V1MZXPk8jlGN8zzFe/sJ1UOaWo+0//C0UIbrvjw4znsiyrjVeKFwCNoVDV2KvLqUPKkrNWSAuUuGMUON/j7BzS2AFWD6AgtRUIfb2TnuZyRuPVNDY3t7K5uXXBxxRMg93jYxycnERVFFbF6wjonirl5mQ+jyIEbZEIiVyeG1aspi0SIaB7aAgGuXRWQEBXrIbWcIRUsYiuKHP2MoulcDGjtHCVF4eR0nBMRmUaocRAaXT3FKeARVPAEEJwbnMLbdEo6xsaynOj0apNATimWhP5HCGvl4vb27m6axlBj4eQx0PRNBnPZlFVweP9fWSvrSfb5COTz6GrCu9at543rlhNVzlytS4YYM/4OPGAH01RWVVXx4raM8/Z90zC6Y4YgMftprosyB9f/hkmcjlKFzmHiAc/dAdv/cEH2dbTQ9jrIVks0BQKY0vJBf/4didL/a8fon17nrf/8FaSxQKts1zJAT555d8A8JWHPvuq7+fIzch0IV+Zf60PBKtMg12Ojm0OQekxkKZzQahIz1ZI/hlw+O9a2jlk4f7yYxoAE4wdSJlDeLecgjt3WYyMZjO8MDpMSyhSmWufLhR4tLeXgmXwh+dfyM//6fvIWY2UqX2jCAR508Cv6TQEj67EOl6klEzm86TLnl71weCC0fIuc5HWBLL4MMhS+YpAerag6MuqHycNZPFBsHOg1AMSzJeR9jR4r3T3Gmc5pm3zYPchpgsF4v4AtpQ8NzLMH15wIW3RKB/82U9RheCPLryIh3u7GUmn2dLWjld1jljD6TStkcic5/Wo6pwxFZdTg7TGkcZLYCdAqXWKl2rj3MfZufJakQQ0JCao9eC9fMFGistrw6IpYIBTxGgIhqq6nrMxbZsn+vvoTU7x6991Nqm33P4hruxayv5EgqeHBvjVB39I3jTgD9dSt7qJbDk+MeTx0hqJsiIer6Sc+DSdc5tbOLe55fV5gS7zIu0k2JOACmrTvB0PKSXSPAjGSyALoASR+iYU7eixdC5nJzPRp61f3+1c2NJFQNcZzWYYy2ZYXVcPOPLRjU3NPDXQT84o4dU0cqbB5Z1dhF+FEitvGOyZGOPQ1BS6orKmvr6cUlS98bWl5JmhQfYmJlAQSCRRr48ru5ZWEk5cFkbKApQeARFFKN7ytSKUHgVzN7ONkaTVB5QQyswmREcqjWAeQOrrF+zEupxd9ExPE9Q8Ve/VmM/H3sQ4HlWjK1bDbXd8GIAf3vJNUoUCHk0tGwgLruxaUjETPhbSxSK7xkbpSyUJ6Dpr6xvoitVUDskz0nFZ80OeHOinZ3oKIRxTwVp/gCuXdM1RpbrMRUoTWdwG+BBqbfmaAaWnkGodQolij54HgIj/DOwkQm0+/ASiCWkNg5wC4Saxnc2MZjIk8vmqtMHmYIju6SkOTU+iqwoKgoDu4YLmNp4c7GPvxDhLYjVkSiUaQ2HaI9FX9TsNy2I8l8W0bWr9gQX3B7aU7EtM8PLEOCXLYkmshvUNjQRO8hpxJisvpDWOLNwLIggiBnYGWbwP6bkGRav2OZPGDrBzlbVCpr8ClJBKE8Kz4RTc/dnLoipgvBKHpibpnZ6iORSuFCHSpSLberqdvGZ/AI+qogjBRW0d1AcDPNLbA8B7N5zLstrDXcPdOAAAIABJREFUxYtjRUrHn/hoXY+ZLkkin8OjqjSHwlXmXy7zI6V0Kp7mS5D9vnMx+GHwXjEnH16ah6D0JOTuBASE/hCKjyDFNQjVNVJ0qeaP/v0TPDM8xAP/7XaAygHEljYc8V4O6Dqbmpq59Mcf5byWVmI+X5UBFzjqixe37a78GQ4rMUqWxf2HDpIuFan1+bGk5MmBPpKFAhe0tlU9z0Ayycvj4zSHw5U1JZHP8dRgP1d3VXcFXebBGgNpV4oX4MQzysz3QGaAWckvoY8B1bJcIRSkECDzgFvAcHEOAPN12KUsx6jOQhWCYvcUmUwRgEf/4P/yKMeuysoZBvcePIBpW8R8fgzL4tHeHnKGwVr1k1WPPTg1yaGpSVpC4cr9jeeyPDcyzCXtbuH+FbETOAXMmsolIXQkCtIaQiiHD5RSppl/O6yUVRluAeNsZrqQRz/CE8Xx27MomCZ/dvFllev1wSBvaGknVSpSFwiysamZ9kj0VRU5p/J5Huo5RM4wnN+F4+WzrmGuIuDZoUF2T4xR7w/i13QOTk0ynE5z3fIV7jnkGJHGiyCCCKWskhFhpC3AeBFmFTCktJ0RM8VpgMn0V8Da73wz+afYSt2iGf05Gzit/nXvTyR44EN3oAhBfzlh5N7f+zdWfvlN7PrEz9FVtXLd+swDHCoVKXxiE4oQdESjnNvUfLSnr8KwLHaNj/HyxDi2lHRGY2xqaiZ4RBVUSsnTQ4PsnRjnmhpnE/Pzwb/jqq5lxF1p2NGxJ5wFQmkCZv5evcjSY+C7sTJTJqUEcyfk7gDroPOwzD8BFlJtcgsYLnOYGQ+ZHVZUtExCHi+KEBRMA592uEORMw0ubGs7LjnnQCpJsligudyd0XEiVvclJlhT31DVOTk4NUnE660qiNb6/JXYxZPdNTkeFv2sq6g+VDqbiJHDF8yyl4ESB+sQMPugYjuRVcprI/l3Of3ojMU4MJmoel+mS475ZsEwMG27UtC85fYP8YN3f4PRZ/vmfa5XGjPrnpqkaJk0llWmmqLwptDnMIsW6LurHltrfojr4/Bi6UuVa3F/gN7pKba0tL6qA9HZiXSqUHMQkLgFW6ggy85Hk7eCUgfhTx/+aSkBGxTXy+RsJ+rzzUlDBFCEgldVsWy7agxUVRTObW5hyxENjGPBlpJH+3pQEJU9hWXbPDc8REMoVOXlky2V2JeYqBp/awgEGclkGEynWFrjFt6OCTsxR2UllBDSGkFWFbgFoMI8sbsurz+nVQFjISx7blfVq6rg8fLlG99CUyj8quTgAE8O9NOXnOa6+N8hgPum/4aJXI7rV6ysUnEMZ9K8PDFepQrxqhqP9/dyw8rV7rzqUZDWAOR+BGiHq5jZbwIl8F42a0GxQWaBI30ClPIcmotLNVGfj/ObWxDfvhVw3qeKEFzasQRFCLb1dpMqltAUQd406YzGaAnPnVGd4QsP/A2fuOKvAfif932mqrMxnc9X4hJnUISTeJEtlaoKGBLJkXnRQgj34/BYUepAKkhpIMRMsUeC2lw23wO0NQAIrR1p7kZa46DEANMZVdM3Lpgx7zq/n300h8Ksrqtn78Q4ilCQSLyqxlVdS8vqqAF0oTiFT8vkv//8k9x+67eA+QsVUkqG0ikEgnggULVfmMjnCGjVRUoB8+6HF1wT3MXi2FDiIHSkLFTe71JaIEyYkzCiAB6kPVLed9hgT4G2xDHpczmraQyGqPH5GctliPscD4yJXI6lsRrCXg+7xsZoCIbQFIVsqYRpW6yMH5+fnpOOVqwUL8ApiHhVjf7kdHUBwyghEHPOGV5VZTKfZ2kNLseCqAGZA3G4WCntLCg1Veo8OfleMHY6nhfhTyPCn6yMkBD73yj66lNw82cvp1UBY0U8ztXfvpXmUJg7bvs2ANd+9z0EdJ3279xGy6zrb/vBBylYJktral+1Qd50Ic8K8cesi6vUqC8B8Fuxz1KyLIbS/8qS2OFVoS+Z5Ldr/xZNUSqPvST8V5Qsi1TxTmJHRMC6HMkCHZLZXwkVKWqd8ZKss3EU4U8i7Slw1RcuC7CmvoHWSITRTBZVCBpDoYqC6oaVq+lPTpM3TZrDYZpD4QWLjZP5HA/3dDOVz4OAn768i4vaOirrQMzvpzRR3Z2RUmJJOWdWfWlNLY/09hDSD5vQThfyNASDp1x9cTrkvQslgPRcCKXfHF45Au8FzxaYvBk4ovjgvQZp7gGzF4QXPJcgNDdJxuUwihBc0NLKsppaJvN5PKpKUyiEV9OIBwLUBQL0J5NIoLUc13z7Ec8xo7yYGTP706s+y3Xfey8eVeXSjk6ay8XRWp+foVS6qqHybP5/MZHPcXPzF6vWoEntH9g+OEjL4XMMiUKOjuirk6OfrQihO+a+pUeQchqkAGzwnIvS+BxAxQNDaXwWKUtIY29ZtaWC53yEtvzUvQCXRYOuqlzVtZQXx0Y5NDWJKgTnNDaytr4BRQhUobBnYhzbtgmXPa2Otvcfz2Z5eWKcdMkpVKyM11X2JlLO6cc6iLmCooDuwUZiS1m1dhQsk5gb23zMCM85yML9SFsglKBTvJBJ8FxZeYydeE9Z3ZkDqxeZ/jwEPgiUQPjcteIUcFoVMJbW1DKSydCXnK6Y9IU9Xi5f0sXOsVH2JSYwbBuJJFUqcuWSrgWLF7aUjGUzpItOh7QheDgJIGcYRzZJAWdRSRUKVdeOVhoR8z6LywxCbUcGfgeUBsj8b+di8MNO/UIc0fXQz4XiA4AJqEh7ErAQ+trX96ZdTisiXh8R79wP8ojXO+886ZFYts3Dvd2oQuEDd30EcMbLHu/rpdbvJ+L10RqOEPDoJHI5avx+LNtmPJ9jWU3tnEjmjmiM5bVxDk5NouCU74IeD29oaz8ZL/esQNGXItUGpDUKgFAbEUoIGp+d81ihBBCe88Bz3is+r514DxjbD/8ZV4lxtiCEo5aYb+yz1h+Yk4Y2n/LCnnW6mPHCKpgG23p7eOvqtXg1jaU1teyZGGcqnyfq82HYFhO5HBsbm+YUUJfVxhnJZOhPJSkfvYn5fK7p+KtA0ZqR6k1IcwSwEGp9lffFjFoLQAgPwnMOcM7rfp8uix+/rnNhaxtbWlrneOZsLPtTmLaFV9WOmlrTn0zycG83AU3Hp6m8nBine3qK3162gqDHQ43fj1/XyZZKlaKGLSUF06TtCCPQkMfDito4+xITlTSzyXyOoMfzqk1Dz2aE2oT0Xu2klNkjzvnDcyWKNitW19xzeOQMwE78f/beO0yO67zTfb9TVd09CRhgEInIBBDMSaRISpRIUaKp7LXvtUVSkqMcr702La99d9da3d29vo9lWV77etdaS7JkMTzyXtlykG1ZAaRIBYpKBEmAmWBAIHKY2FV1vvvHV9MzPdMDDIDumW7gvM8jcbq6uupMY/r0OV/4/ZDy66HrtlClNU90VAAjdo7Xr13H/pFl3PDl/0AlimuBh2tXreacRYu44h9+i1IUsXrBwml6FeOMZRkPvPgCe4aG+Jef/SwAd973C9y0/hzKcUxvqcTfH/pPrOjp5YqKWfP9cOwP2DV4lDesq4+qrutfxJee+z1W9PRyVdfvAPDA4f+LShJz2wm2rZxpSLQETS4zHQzGbc6qSPlmZEqJp4uXo3IrGq+zMnC3DEk2hokj0FL2jwwzUk1ZMamcM4kiRIQdR46wYGmFchxzy9nnsuXV3bx4+BCxc1y+fAWbli6bdj0TGF7DhoElHBoZoZLELO/pbYuMarv5vas/jKZPWyl3tAyJz7NABdafKqE3vWmYNsgwSKmhC1Tg2Hx084d58dAh/v2b/zOlKKqJBlfihIOjo+wZGmLNQluTvOXc83h09252HD1CJYq5ZtVqNgwswUl9sMwBN65bz77hYY5WxyjHNldMFRgOgPpBNHsa8n1W9p1sqAUqRLqQpHHVVTMClGdisFP9MMAZ6eQ0U3Aidu64n02vynd37WBxpVLT4KrECXuGhnjmwH4uX7Gy1ur6tRee58jgGCLgvXLxsuUN7ZqvPmsVvaUS2/btpepz1i9cxCXLlgcBzxmw77pBwNWtIVy8EuKVqPpp+w/Agp1FggPpg3gTEp+4xkmgeXTcX7iIsKQo65x6/FgWrJPZuncPe4eH6nQr9g+P8MTeV7ly5SoWlCuct3iApw/s47KyIsDuwUEWd3XVSkHHWd7byxUrz2LL7l1US1YV4pzjhjXrgnf4LHCli9F4PZRvAInBLZ1xAS3RABLdMLcDDJzRjAuBTkUQ8kkZ175ymRvWruN6NXeAY332Z5rDAhNovh8d/TJIBNIN6dNo9ixU3jKhFN6se/kh8HuRhf8FPfy7QHRGbUZ8thPSRyyAoaDJ+Uhy2SSNkcBs8McQp5hcndFf6eIN68+eIg7XGBFhaU8PSxtsXAKG+sNmgahq4rz5dnMt63oz0mT3ENVRyPeYBk80cMYlUNQPotWHIX8VELOjLV3b9Dn5dGU0yxipVlkwKSEC0FcqsWvwKJdjRgNLu3t498ZN7B4cJM1zBrq7WdTVuCUlco6Lli3nomXLZzWnnMlovhetfhv8URBB3fLi73difm0YvMAClLU2knjTxGPOrODliaA6hmY7QIfNWdItm/H9PRk6LoDRDJ49sJ+v/Py9CNRcS77ygXuIPnEnV660kqHXFHaK39z3X8nVc9GyxVywZEnDCOsly5azfmE/h0Y/QxI53rG654R1N053fPYypE+AHgG3AildXFtciOsNSt9NQFXNs17TQnwoZFJPlcVd3cTOMZZnlCObLr0qmc/rqjLGOR0WD/NdeQGg6Q+sr3R8YRx1of4Amj6BlK9r2n18uh0OfQBQ6PnpQo38+ItxVY+1syUd/W+u/gCM3Q9uIeKW2++VPmUL4fJr5nt4HcWSrm5u++R7WTJJZC/N81rAciqd/HfTTmj6BCBIVIgmShfqj6DVHyCVNzXvPvkedOx+GPoLQNCe96PD/wsy0z2ZaTNjbiYpEDd18T7XqGbo2GbQKrjlJj7tD9l7UrktBDxnQeIczrk6ZyOwwMbKrvr1RDmOWdd/YgGyMKfMjPph+/uly1pGVMEfQMcessTIbN+7Inhx7HsNodlTkL8EdBXVGmvOqH8f9QfR0a8BY6ARmqYQrYXy9Yg0J/RwRgYwZvojmtyDGjnHBUuWcsGSpbO6Zl+5fMJOJ2cKPn0eqt8wG1Qc9PysZVcrtzY1g2HRvpeKFpN+JF6LyJkhoqp+0CbiwY8CYu9xcjUuOXe+h9bRlKKIG9as48GXtluCTyBT5ZLlK0IFRYtQzcHvAZmiUSILIN/ZtPv4/e+xgMW4e8nQPdB7ly1q/HDD8mhVtexutgX8KLheNLkC16GlpJo+V7SN2Dwp4lC3HLJn0dKliITvtNnSVy5z9arVPLLjFURMAcur8tpVa2ZsZw00gXwnyJR+f+kD/+rM5eAniG3eHwTpoWb5LssLd7SZ8dkuSL8H/ghIGU0uQeLzO3Mj4/eCP1pnWy+uH81323wdrTrGiwNg7acXLlnKD17dxYqePmLnGE5TRvKMjbPcawRODs13AHmt2kJEQBaj+S5L/Mnxq7UmBy5m0sxSHUXHvlKsD/ot4Ff9OspVSLKp4XVhfN2zF3QM3ALEda6FjKqiYw8DEeIm5gvNX0Kz1UhyTlPuc0YGMM5fPOFmMm6H9qb/eQcbBpbM88hOPyyj96jZHxaSp+L6i2zq00j5mubcxw+iY1+1MuihzwAe7fklqNyCuOmZ8onxpYUwT6lje+pVFa1+q+jrG19c9UP6LTRa1PQy2jONNQsX8s6Nm9h19CiZz1nW09tQ7C/QLBxIF5a1nLTx0zHbmDQLTZnqgiSSoOJtIeHWTX9J9jxUvwVuAIkWojoCY/ejckvdwr5dmZYlnjxnFIg4M2zQqjm3BGbNxoElrOjpZdfgUQRY2dfXUEQ40ESkF3S0CC6MUwXpbl7Fgz9YWLyXJizfBz8GCMQXgvROr7zI95rwuPQXGd8qVB9GUSTZ2JxxtZi6+UKrzOgap9UGxwONuHj5CkSErfv2kuU5vaUyN68/JyREWo2OgDbSGmvu369m24uWiSIBIwmqFUi3oPG5DSujrTrkftBDxQFF4/OQ0ms6s2pLh0EPIq5REmo7hADGybNp6TL2j4yw8+jhmpvJit5eLmwguhc4Vao2cQx9ovbFb77JHnp/tWl30XQr+DEkWo4SARGQo+ljSPn6hq/x6YvW+z30PwFF+/4DUr625hnfMegROPoH1C+u/gRI0fgipBQCGKdKb6nE+Sfp6x44MUQEjS+yxb5bjkhsi389VGdrdsr36f8jy6oO32uP++6yJxSmWjlDUQqePQ5uca0yQaQLlQxNt3ZEAGMa0Urwu4CJ4K3qGFA27ZHACbOwUmFhsDCcO5KLYGwzqgkiJUtK+H1Qal6rWaP54HjPabYNpKdWySVSQt1SSB9D4/PbfmMyPcOcQeUddVUt1h7jwQXHi9niRLikEPmejWtJoDlItAxNt9TphKjmgJzU3++MGhh+H1Bf+S0Sod5bxVajAEb1e+CHamsIW2s8jbplMwoQtzXiLAgzTZPFQxNbzc7IAEYpirhp/dnsHxnh9V/+j3SXEpZ0dYdJpCWUiize1Mi9B9fEDWH+Igx/xu4yvokf+kvouRPV66b926o/AOlDIANA8YHKd6Fj30EqNzZvXHNCMQlPI2RGAp2JxOej5JA+jqppTVC6rrmtGtFSEwlFGf/8qFZNTDhqVM6bW2ZlWlahAv5w88bVAsYXWpM3JG7gbiQ+G82eM+s4+jA3qBEo3YjI/DvjBALHw8Vr8Ho9pD+04AUxlK5G4ia2T7pF0PsrlsEd+nM71vtvQfcgldsaVzn6I0Ul2QQiJdSnWHVZp1U3xZBsgPRJVPqw9cURiDeAdG65e6u466YPAY0tl2F2riWBJuKWQbTeRH6lF/s+H4XSlc1tNZd+0FeACS0t08xi2nxgz41B/oqNb/wSIqj0Q/YcdGAAQ6QLjVZDvhsi62wYd3+RuDlV93CGBjAgOAHMFSLOrFK7b4fhezANjJ8HHWluGaWUAc94m4qhJgTYIDCl2XYY+isgngh4DH8Wuu9E/WBntZPIQuj5BZscB//MjvX+JvhdSLxmfscWCJwEIoIkm9D4vKJ1pKu2oW6W8rdIF1oqXI3UWy+3OCjd0HBBIxKjsgj1Q3Wq5eigVTJ0ICJlqNxS6HrsMBeo+DxTDA8EOgSXnIfGZxetJOVpInGnOmeIRFC+ER3dDN13WLzT74XSVTO3aLplkG2vLeABazlzvUxt22pHGmWYVX2hkfMCoBBdgcSrQ/Iv0PaIOChfh2Zrwb8EJEh8NhKdfOV9o/lE4vVothX1hwtB8MyqMpJNJ1HdPbOzVbsjpavR6kOmMTKeYE0uBde8tdIZG8AIzB0Sn4sSW5mWPwquD0le31yRmvhCW1i4FUVvKtD9HogvaHy+DjNzWWjWvHHNASIRWroexh7AMjtiZeHx2fZ+BAIdikjS1JLDqbh4DRq9G/K9diBaeuxFRnI5jH0V9ZkFDHUQyI8pztUOjC+0Gm3kRMo2/jb/HQKBYyESTdHBaPL13WLoemchtJeZDo6b+X6SXIDmL6J+v2n36IiVkJff2LEbfhGHxGshXjvfQ2lr7rrpQ2x5YGvtZ5i5EiMwd4hESLIWaN3fr7heqLwZrf4A/KtAAsllM64RRMpotKJwQLNgqDkKHobkwpaNs9WI64bym81UgTGQhcecL0+GEMAItBzLpq6HZH3LfKolPgfVQUi3YZt4heRCJN7Q+AVuFXS/F4lWFpocFCWiI80VCpwjXLwSdW9HS5eBjiLRyqZ7LgeOTfAEby0ztUGc6vXcwN0wy0olF69A5VY02wr+EESrkGRTU92UWkn42wycSTR7ztADPw3M7nMkbgFUbkXTJ8HvNu2c5IZTyvjONWG+CASOTcOkgFuEVG4u2l/dcdfhUnoNOra5qFZwgIdoPRKvb9m45wIRgah12nEhgBGYU1qVeRBxSOlyNNkIlbdYyXkDG8Ta+fEaNF+J5jsxDQlvG5LyGzq291tcL+JCFnU+mMlSK3D6IdESJOo0nZxAoDPp5PlU3IKmOa0FOoePbv5wqLwITGtlm/E81wuV28z62Y9YQsQNtLRSq5Pn1XFCACNwWiHSBdHxBXlEYutpzXZCfK5ZrsXrOiaTGgi0O6ZQPwrE1gpyihyrDeJEaJSVPZXrBQKBU8NKpkdA4oY2gydLmDMCgUAraHZ1l0hsFZ2dmT+dF0IAI3DGIhK3vB8ucGYwo6XWGYrme9DqI4USv6DxBiS5pCmBjNMJ9YOmoyGVEDwNnJFovh+tPmwVkAgan2vVlAd+xk5owgbhdJiTVcfAHwTiwso5tIe2M6HyovmoVk0rAikqFMJ64kQ5nYKxIYARmBWqo5DvM5V+t6SpWZJAIHD6oP4IOvo1kB4kWm5e6+k2VFOkfO0pX/9Uv2jrs7I59P0m5Dvwo5tNzyJqvfCtqkfTRwvNHgEUjVYh5evC3Bo4Y1A/iI59BehCohVmtZc/i1bT+R5aHW7gblTH0P0/aU4n3e+HeGPLNL2m4tPnbcMxbsfoFkD5RtPZCATOAHy2E6oPmYAugJSg/HokWn7sF7aI41V3qeaYLXl5ToONqh78HjTfY4mRaFXTxTPbhRDACBwXn74I6bdg6FOAQs8H0NLrcXH7OVyojtoEJ91zNmmoptaK4l81h5Vo7Wk7YQRmphMj2K1AsxcAh7ieSQK5vwHZ82jp0uZ6rp8SuSlkp8+C6wN/GB39is1tybqW3lmzlyB9HNzK2jylfidafRQpv2ba+Z2cJQkEZkLzl0AVicy2XMShLIfsRWTRnyOuty3+9lWr6OhXC9eh2IIY1QdQvRwpXdLae/uDUP2WJY5cUhw7jI49CJXbGq5z2uE9CwSaheoIVB8EWYC4cu2Yjn0dut7VlKB/sz4zqopmT9n3u1bBdaPxFS1fU9i9PVr9VmFzXALJ0fQHUL6pJh7crLa6diAEMALHRP0gVL8JbhFQlGtJN1QfQqN3tk22UHUMrX4XshctoSl9ULq25YrftrDZbD7Pw38FKNrzc1C5ZWZ/+EDgdEaPwvCnUCLIn7Fjgx+D7jtBx8x+tB3o+11ItyLREnssZVTLkH4PjVe3Vsw3ewakv37zIUsgfw7VK2Yt/hUIdDR+EKRUC3RK312ICCrYXEHvvA5vHM12gD+E9P3uxDGtQPo4mpx/bOvlU773y0BSVy4vbiGa7wY9VLNehNOrPDwQqJHvAXJEyrVDIl2oP2zPxavnbWjTKi+yZ6H6iLkAusSSqtWvo+7NLa/u1HwHZM+DO6tWGaY6gla/CZV31q03Toc5IaySAsdE890w/CmgNGkz8t+BKpSvh+is+RxeDR17GPIdMHyPHej5JQssdL3NFH5bdd/sOdD9Zsda+ziV0LFHkK5bW3bfQKBtccsxZ5/JpdUKEoO0vjJJ1YMOMV4FMiP5bqu8mIRIGdVDlmFt6VirTFfrcqAK+NqRsCEJnNa4pZA+w/jfvNb+/h0U39ut/Fs38dAheyA9M7eD+H0wafMEIBKhIuCPQtS6AAZk1ro7FWGipSQQOK3xoI2O139fnvTVm+Qip6pFZeXSWsBRpILKAjR9ovXtqfnLIL1185hIF5ofAT0CYjpbp8s6IgQwArNgph7PhjPKnKP+KBz9z9QFWYb+B5CiyUVI6eLW3Tx7EYY+i+Im3fsvoPsOVEdbmpkJBNoS1w+lN4LuA38YcND1bkiuaLnoluZ70eq3rQpEQaOzkNI1jS2VXT/kL9VVhFjfamT9ta0kPhvSH4JMWtDoIYhWtE1VW6A9ec/nPwfAfT/2E/M8kiZw5Hcg3wV+d/H4Q1apVXlDXba1Fag/hI59G/QAIFbJUL6usa6EWwB5tf71qhZAaHFFmURnoelWVP1Eu5mOAiVwC+uHeRqVh59OBEvVU8QtAc3RfF/xuBeIAIce/nco0RwEOg+CHwbXg7hFM5yZg44gUz6XSMUEzVtOycYwFVHs/Tq9CAGMwDGRaBna9R5QByOftSxqz68Ch21SaSGqeeGLfAjoReKVjTdAWqVxkEUmsiutQso0DOSIA4JKeODMQjU1oa1kA+gqqH4f+3yUkLjFuhJ+CB3bDHQhboUtOvK9aPUbUL5lWnZVkvPQ7FnUD5leh6bg90ByOXrgp1FatwGQ+Dw0fwXNdxZzSApUYOjj+KFP1u4bNiSB0xX7fj9cHwAQB5Ig8bktvndqc4WCOAsimq7E/VB567QWLonWoOljVrIuC7CM8F6I102r8Gz6Z9Uth2QjpE+jJNQ2KCN/g458AQlzQtsyHrjY8sDW2uMQxDgZctOfSb9Pba3vVkH3u2H0i6d89WO5yKlW0bFvWYU3Ang0Xo+Urp0+T0iMusWoH6yfF/QoRKtOeZzHQ+L1aPYkqlltbOoPWkVIUW3arGqTdiAEMALHRP2wRQ5H7itKkBZA+gPovr2lGRKbNB6w/rbhzwCK9v6aidFMbQlxfdDzs1YeNfgngPXSar4LXGtLtiTZiHbfYfcZ/GM72HMnROeETGrgzMPvAT+MRCuBAVhgCzjNd9tzLfwS13wHkNfaRkQEooGiV/xgXa84gLjFaPlmSL+P5q+CJFC6Eok3tby2TKQE5Zsh3436A1a+Hq9GR/5m4veZlHHt1AVGoHmMV148vOOVuscdW4nh90L3+8x9ZJIGhua7rb0rbqG9ef7qpHnKMF2JXTauScftuW6o3IJWv2drEokgvgBJWivgCcU8llwN0Xp7byRBotXopI2bVY65WpA2zBeB0wVVRce+YZ/JeLXZLWsO5HDk/7T9CK3bjGu6DfyOWvuHqkL2AuoWI8mmaedL6UoTA/epBWf9kBV4JRc2dVyNkGgJWroGqt9Dx1cxbjFSuq7l954PQgAjMCOqOVS/Acl5MLYI6IOunwJGEdfa1ghNnwK/t9BggOo5AAAgAElEQVSWKKou/CiaPoqUb6g7V6SEJldC9WEgA8QWItEyJG6xRodbCaWrIX0Uy6IquDVI6fLW3jcQaEc0pXE1lBbPtfLeI8z4labVhoddvAKNbsM+uxEi0ZxlKERiiFcjrMbvv9OWG+P33Xsb9LwXlYWQXIqL1zT9/oHAvKLVGbpTBdWxGRtXm0NKwwpJkRnnCnH9SOVNqFYZnysm00q9GgvGLkWipRPXrs0Vbweq0PMLaHIhEm+cU9vGwMyMV1uEFpJTQA+BPzyhHxFZgkL9ftOqahKNPqcWrHjaBLYLRAR1A3a8UQAjWgZdP1LsYQ5Acg4Sb6hVQLQal2xA47UW6JESyKJJgp4eWfTn6MEPAK7jA50hgBGYGT0MQx8HEsifs2Mj9wIZmlzWWkGa/AUYuhtFJrQlhj8NPXei+tppiweXnI+6hWh8rm1korVIvK7lPfcigiSb0PgcKL/JfJeLiUpVbQLT8b654EoSOM1x/SA6pV/b20bF9bf01hItQ9PH645ZZlKOeW/7cm+/ailrgxmBsfvx3IyLW1+CGpig3SocxsfRbuM6adxii2uqR/ruAoq5Ao9EAy2+90LAT5+n0OPOU+1XWZkBiYkOV79rwZ+QQAmcNigztoj3/cda5XPrNuOFqHAdrqgCaYy4xUh5/qoeRCowZX/ms52QPmJt9ZV3QHwOqtU2nM9mTwhgBI6B0FioU63cuqVENJ64XINjhkTLWm6bOhMiZSiyIzDeAvMQHP19QKD7p9B4XcO+uUDgdEFcPxpvgnQrKoVwpg5DchHS4gAGbjlEa9D8JZBexgW1KF11QmK6x+qHbRU1rYu9bwU80vdBoFAQl35IH4MQwAicRojrRZOLIX20mCucLa6TjSAzieQ1696L0WQDpE+iUrSk6hAkF04X4Jslc6lX4wbuxu/7UdAhpO+3a8fVrYB0G5psarkIamD2hMqLU0D6wXXVtKpg3Gls1NouW3lrETQ6G7LtEE3S/PMHYA5aQpqF+kMwdj+4hUVixJtle9XPa6DlVAk7qcDMSD/0/qaVaQ19wo71/jroXqTVJc3xBui+w1o0Bv/IjvXcAfF5HVEeqenjhbJ6Ed10y4/ZNxcInC5IcgVEK9HsRXscr2u5Fg1g80L5ejRbB/5FTDj0bCRaftzXWrXUnkJHI0YW/ck8VUwV2dTJSLfphwTmhEZaEx1f7dCmSHIJRMuKuUKReC24FTPbmTb13ldBdBaabQcEidfbemMWqD+AZi8DORKtArds7vUn+n4D8oN1h0Qiq1rVkWm2r4FAJyLioPQ6dGyz2YHiAG8BBLe85Z83SS5G/V7TnyECMmvnSi446WvOdYJEs+eBGCkEk0UcyjLbkySXNXZp6wBCACMwIyIC5deZmCaF04ffD6VrW764l/gcE7fLn2NCW2LlrEWzrBzUNgNzsRiqv7dC9gwM3TPR/jL4R9jvsKBh31wgcLpg/dor6wTy5u7eMZKsA2bveKKqaPp9SLeZ3ZnmaLYFTV6LS85r3WAb0ffvwB+dMsDBljs+BTqD93z+c6ePiCfjc8WK1rajznhvB9EqC0CcAD59GqrfAUogDk23WoVZ6YrWDHQm3NLCGWGit141Nx0P6cwNSSDQCImWQNc70WwXkCLRwJwlGEzA9y2Qv4rqUavQcsuntbE3QnXMAqT5TpBeJDl3fhIjOjgtoGlBDMH2dp05X4QARuCYiOuDyluhdC2QgVt0QuXYJ31fiZDytai/AMpvMDXfSWI0M6GqaPYMpFtMNND1osmVc9w/rjRuvYGGHs2BQGD+8AcgfbLI/I73w2dQfQSNV8/JfDeOJJeio/+KerU2GB2yMvHya+dsDGc6p53WRGAaJ5sBVT8M1e/VbWBUvbVtxOvmdHMi8Xo0fRLN95mmh6bmtlS6uqP72gOBRoiUkWT9PN07KQS3Z4/qGDr6FdMSpBfYj2bPoCN/C9mjQHMrMY55LXcWZC9TH+ysmhSA9E4/v0MIAYzAcbFMxdLjn9iKe7uFwOx7UjV7xtxIhu8DBHp/GaqbUbm1puDdakSc9c11v7+wgC3s4fxuiFrrbx8InK7M5steVUEPogd+HsQhi+89rpCv+j2Yq8BEa5pIbDZk/gBELXYymoRES6Fya9GCtg/cAJLcMGdzV6C9ue/HfiIEVpqI+kP2GSe2SpDjbfz9AYC67Ot4JlPzvXMbwJAuqLzZKkDyl63qIrnR2nAC80ZwHAkAaPYC6GFkUvus6gjo0WO8qjVIvBbNnkX9LmABUC30wV7f0Zp8nTvyQGAKlgl5zIIX+bN2cPC/Aznq1s7pJkCSS8zmqWi9qdm6nkLfXCAQmBlVj1Yfhux50CPmcDD6z1C+qaGFmele7IV0O+S7UbegvtpCYD6+IiVagkRvnPP7Bupp1wBBu46rE5hqdar7/zfGRbYZ/gzq+nEDn2v4WtO9eB6ynaj01gQFARBlPpyMxPUi5WuAa+b83oHAmYLqKJrtBEYRNwBu6fG1+LL69i4oRLl73gcj/wBETa28mGzfPPW6IiWo3GxBlXwHyFIkPs9aczqYEMAItC0+e8UCEnrYSjZLlx0zw6FaBT9MQ+cSPdzSsU6l1jdXugz1g4hbUAh9Hb9vLhAITDCbL2jARPWO/B5QgtwERBn8M1S6kcot9eeqR6vfgawIdI58DkYc2neXOan4I1Za6Vps5xgIBOYRCzpItALFgT9cZ606jq8+Zm2pCOheOPp5VHqQBb9rWVViJJ57HY9A+zBeebHlga11j0MlRmej/gA6+jWsaiFCSSE+G0qvbbieV62i2SsWKNBBVM6tVYGae4ogiz895y5BIiUk2WguT6cJIYARaEt8+iJUvw7D9wIOen4OHfkSdP0I4uot1lQ9mj0F1Scg2wbJdZjWRFS0buw3F5A5RiQuRMLm/NaBwJlH/hymEj6ZGPI9qB+uV9r2uy14Me5ypPvt+NGPmK1i368j5deFgGMgcBpRszrd9y6zYey7Cz36UfToR4v5A/TAe1CS2rnqD0K2pZaA0OHPgu4D3Yce+b9BImTxZ2sK/4FAoP05XktqLXHSfTuQ1JKnJtL/POrWIEl9u5b6YXTsq4UQt0L+NPh9aOmqohq8Cgv+a1ODF3Np39xuhABGoO1Q9ZD9sMh+WiZE3ELUezTdhpSvrz8/3QrpD4tgRw7JZeAPg1tgAldCaN0IdAzqB9H0iaKvuQviTWYHOsduOu3C7L+gHfT8EuL6bEMCZgPtXzVl/kloZu+tiNTL7Uo3uAVI5a0dYdccOLNRf8T0UvIdID0QX4TEaztyrmjXBbjme0EjxI0HMyfNC64PpBuJls3L2ALtw3ilRai8OJ3IQQ/V6ViICCp94LcDUwIY2TbwwzVXJZUKVH8A1e8Dqa055tqt6DQmBDDmCBOLOgxSKvqnwls/Mxn4IRiesCG1DYlC7y/VnamaQra1XvcCAYmhchvE65HkAmvhCATaHNURdPTLhYPOIqAK1W+gOoyULp7v4bU30TmQP4DqpN50PViI803JjkoCePux7y700G/Y8Z6fRbreFoIXgbZH/SA6+q/2QPpBx6D6dZRrrFS4g/D776xrEYPWBjJk0SfM7UdzpO8uAPToHwCCLL5vyuc/AvFTrtBlmlaL/se8WMAG2psHX9fNez7/ubbRq/Hpi5A9bq5WbgVSumRaJfOZQqOWVJieKBl/Hr8fJanNE4bScPucvVCs2wyJlqLZFtAq+JfslQd+GmnB3NZugd+5IOyiW4yqoukPINvG7f94AFDufcf6GYXlAgAxuG6mW5Hm03vSdYzp1qSR9a/HF+LKQdwq0Dloth0YRaLxlqcu1K2A7HE02XBG2+Md7wta4tWoXgDpU9Bzh00fUkFK0+cAidah6VZUs0nBZLX5RUKwM9D+aPYcaD4hTi3dqCaQbkHjc47rvnO6MpsAiERL0eQye68Emyt6fgGpvHFa8FKilWjVoTo2qfRbARc0cgLT+OjmD9ecgtoBnz4L1W+BLAJZbC0No/8KldtCYm9WJEBWe6Sagw4i8XXTT5Vyce6krXX+SqsHeMYSAhitxu+E9AlwKxEZtGOaotVvI5U3z+/Y2hQRh8aXQfd7Jmlg/Dzo8PRWEOkCytD7f8Dgn9qhvrvQfA+EzEig0/D7gfpqAZEI9WrZkw4LYKg/VLTD7ATpg+RiXLy6JfcScUjpNWh8flHtVi6q3abrWEg0gJaugUO/anapjNgTw59Ghz/bkgxJINBU8n3W8jQJkQTVzAL7HRTAcAN34/f/pM1xXe8C6cGnL7a0HcaVLkbj9WaNKnExV0x/z8R1o+UbrRJu8GOQby8u0N+ybGqgMxkPXDy845W6x/NViaGaW3u1WzqR/JB+NN+HZs8gpavmZVzzyfFaUqc+L4s+jo49aE6COEAhubyxrl58AVS/jbqVto9RhWi5JVSlp+H9AidPCGC0GM2e5/YvHkFkmId3WQDjji/CPW+rFu4UvfM8whPDWmEOAtHsfNNPEpecjZcI3GLwR8D1IcnrprmQiERocgVUH8IqMVyhe5Eg8bktGVsg0DJkEejLmFe3oVqUL0/ZrLQ76o+go18C4qLEfRTGNuP1BlxyTsvuK64fXP9xz3PJBrxbAmQTriXTREADgTYlWgzpfmCiZUo1wyoQ51bh/lRRP1isKzjpdpjjlYY3QlwvzGIN5uKz0Ojd6Mj/mlTw2TkBosAZio4B6fR1uuuBfO+8DGm2zPT5VR1F06cgfx5IIN6IxOccV3B76vWOF0ioe77yliK5lIIsrLdQnoTE56J6FNIn0eFPY0KexdpCQsV9swkBjMCssFaYRyF7AoY+bQd7fhEqNx3T2vRUcPFaiNce/7zkbNR1odEq0EGIzkLiC2acZAKBdkXi9Wi2zZTvZSGmB7MXkkvm3HbrZJi8SND0KbMMi4qeUOlBNYZDv4x3A7iBe+ZxpIYbuBdoXwHBQGAmJD4PzZ5B/eGi7akKfh+Uruq49hHNnoPun5nzdpgT+dyLJMjAX4e5IjAj45UW8115UUPKQIJqWv8Z0mGI1s/XqI7JsQKRqik6er8FO91iILeKB38QmaFdfLY27MdCxMH43HSc86R0JRpvREe/AEQTAYx40wndM3B8QgCj1UTrufdt28Gt5I5/NJuue962zJSrO6n6wu+B9DGzHWQ8mpugYw9B5e3zLnon0YogphXoeMT1QOXNaPVR8DtAKlC6GonbX5RvmhCfPwC9v1p3jkgZxTNd3+Yk74ciiz8JJMHyNHBGIa4PKreg1S3gd1k7Zem6zqw8nLEdJp11O8zxSsNVFRgD4iCiHmgp8x64KLAK5Uuh+jDqFgMV0COgHkk2zPfwThjNd4Hfj0QriyMJ6lZC9iyabDppXcFmByXF9SADf92Sa7eaThpvmMVbjESr0GQjpM+iWgXUTDLKr53voZ0Qmr0Mw3cDcc0ZhKGPA1Uov87EgQKBwCkjbiFSuRFV7Ug7xBoSW6ZHKrVDevQPrYc8337SX5RTMyq6752AoP1/bJVXJ/GeNRqD6piJJLrOat0JnDmIW4xU3tj5c0W0aIZ2mPiE22EafZZ9thvS71k7qjh0+D7rSU8fsedPYC5qPFdUzTlKujv73yFw2iHx+SglyB6zRGS0EildiriFtXPapmKE4wQi/aFpOmAiDkVMP4fpAYzZ27BPYG27GpIibU4IYLQYKym6Bo3P475/cwgoQbS840o8Z0aK/50a6gfR7AXQoyY4FK/tiJL5QKBVjC+E22lxcSxMiG9Suac/iI78C+qPFP2f1o/bHKqTfk4Ahep3UWS60O8JojqCVr8P2UuAom4pUn6NaWsEAm1Ip2+aJT6/Ze0w6g/B2GaQXiRaXrgIDNGMKjDVtGitfdauJ71o8hpcHKpBA83jVLLiIoIk6yFZ39xBzQeywAKF045rXaJktkxvL7nD5obu24EcjdYgyeWnVC0/vRIsA6TtgiPNaLWZa0IAY44Qt7jo2epMJF6Ldr/XlHcH/9gO9nwAxBW9+ieP+gPo6Fdg6BOY48j70OxJqNyCSNdxX3/saw+h2fO2GHIDJvbTSa07gUCHIm6RtcOkPwT/qilx938cjvwecPIlim7gbvzoA3D094Go5s+uWoX0cTTeMGNLm6qCHi4GuHDaxk9VrS0uP1AotzvUH0ZHvwpdbw9B1UCgBVg7zJtb0g6j6bMgcU0TSyRCe38bdD+M/D3gZpyL1B8BcpAFDTccFuh8FtzyYq4YhrHNqLstBDwDbU8j15R2SZQ0+kxKfBaa9ZpQv1sMeNB94NYe9/M2q/WGHjGxcekHHOS7Uf9Vs5w9RcMC9YMW7MxfAgSNz0M6RNusXQkBjMCskGhZzTd9IvvpkfKNp6x/oWPfw3Q1LNMibgWav4qmzyClS0/+uv6o+V1rCsOfBjza83NQebNtrgKBNqbdLNlmw9RFgkRLkOgWVP3EPNGMqL4exizNJt1LSqivYj7s0xcb6g+iY980i1UAtwDKN9TPBXoQ8j2TemytpUf9bjTbgbTQPSUQOJNpWTuMHgHqs7MiUaGJ0bgKQ/0gWv2WCSirgFTQ0nV1lRWqI5A9Vwte2O/QjeYjaPY8Urqyeb9D4IykE7PirUSkBOWb0XRLIY4ZQXwRklx4Uterby/JoOudIMsn5h9ZjOa70eyVY373q/oi8JE0rBhTTdGxzeBHwS0FFLJnLEBavqktKuhOptVmvgkBjMCscaVLzDe9/DoT1ZrBN/1EUK3aImH4npq2hh79KKDQ+ytA4wCG+iNouq3I7C5Ekk1ItKz+nHQr4C34QoRZJCZo9VGk8sZTGncgMNds3buHC5cuO/6JbchMQc67bvoQAB/d/OETu6BbAd0/hURLaofUD4FbSCN7Q5/vhaFPFpbMAxCtBc3R0c1FZUUR8NBRGrbEaaHnEQgEWkrTF/PRCvCPUq+vUQUSZPHd0yorfH7UEh7ZDtPmiNbYemfsAdS9baKCU6tYKfiUuU1K5oYWCLQxkxMinZAcGUdcL1K+HtXXYp+/Zs0X3lzT3JTrSakIgs7wqmwnpN8t2tKcVYAml9QJBZv46JG6xAiyHM13m9h5NNCk3+HMIgQwAieEuD44SaXfxkQm9jcNP2NPm892w8j/Z5UVY/8MCNrzXrR0Ey5eNXFi/jIMfdpyLDXh0U9Azx31GeFAoA0ZX1j0lUocrVY5Wq0e/0VtgolgzRy4OFUk2YTmLxWlpL2gI+ZWULp5eluIPwwjf2M+7m6lbTyyLRBfCHjI90C8usg85FB5R4P5Ia0LlgQCgebQ8rkiPhvNnkXzPVZ1pWO22SjdMC14oToCI1+AbDtEq4AUsm0QnWNVGPnLiNtUc0Ci60dRrU4pLx8Cd3IZ4UBgMq3Iirdz28iJ0Mz5wjS7BtGRv5/+3a9jM5oU+PQZGP4c4CxQGi2DdCuK1ldg6RDj221L0FK0voqtXdqITqi8GCcEMALzikiExhtNNGf4XjvY++tWWdHAOlLzvTD8GVtkSISVgJZMhyP9ARqdNan8qwfw1JeaK0hXCF4EarR79uHCpctqi412R3UMTR+z0moUjc4xxfMGwci7bvoQWx7YWvsZZl+JYT3zP4JmT0G+25TV441Ig0yGpk+Bqtk0irPA6Mg/wdj90POLWMvJOBEkF0G6BZUFgLPsS7QKXGdWvwQC7YhqFU0fh+wZwKPReiS5DD34AaCJlobSBeVb0OxZyF+BaACJr0ei5dPHlG0Hhk2vRxxQBhIrV08uKKoualeG5EqofhOVbqxt7YhVhMbrmjL2QAA6a1PZqYjrRZMNkG4rLGcj8AfBLULildPO99lLMPzX4AetUit/yVxekksgexpNLq4FNsX1o3XrjEKPS3yTE8JnFiGAETgm6g+h2YtWPu1WIfFZTfdQl+QisywcdyjQg1C6Ghevrh+L+qIvdcTcShRqehyDfwrdP11co8iGxJug+w7beAz+NzvW/ZMQX9TU8Qc6k07QmLjvx34CVc/tn78HJePed13XIOPXHqh6dOzrkO8DV1QrZM+j/oDpzjRZdVtcL1K66vgn+r0QLYV8F6gvNiauUDPP0MO/gxLX+ow5+hFY+PsWhNEMkk1IvD4EPQNtj6oHvwf1BxHpa1vHs5pYrt8NshQQyF62uQKlGc5mkxHXXehpHUdTy++3bKvsBs0tSSIORr4CY1+HaAme0sRcceT3gCr0/lvwQxBfYULhbTg/BwIwsb5px/VOI1QVzV+B9ElgDKK1SHL+KQv8N0KSK1Hph+wpYBSSC5Fk47Q5VDWH6neBBKJ+s3qWstm8+r1F20l1wvLVLYPhe1GqhXYHMPj/WDK1+46m/x5nCiGAMU+Yb/hwUQ3Qniq0PnsZxh7k9i8eAIR73/Ycmp8F5RubGsQQiZHyNejAF4BRsztrtADQI7ZIqN5PvY0ikyaLiXFJvA5lBNLHoPtOEDHBn/i8po09EGglqhk69k3UHwTEFv2uB8o3WxVCO+H3TRPAJFpW9H/utRLLSXx084dPXgPjRHCLId8J8RrLkoxutgovgOG/Af8yTHE7cPEaOz8Q6BBUq4WDzi4gtoyfW2Aice3mvKUHId9VN1fo8Gdg0gJ/XsTkZBHwMkTn2SamutniKbqn+O8hpgt/lnDl6+dujIHAKWJuXCmgqB9GXPd8D2lGNHvSggWy0LRo0ifQ/CWovKXpgUIRhyTnQXKcPYKOYMGU5ZA/X1R1fgGr+H47JOebk1LtuhHqFta3i8iCk7J+DUwQAhhzjKqi2VZIH7eyZhRNLixEX9onw6eaQfVhK5+SowBIdBaa7yzU+JtfImmT6LEm0qiB33MJUCjfAsmlQIaqs6CIiPXKx+fZxCGVkBkJ1OiETIRm2yF/mfveefHEMX8QrX4Xqdw0fwNrhI5YkHAaYgKb7EH9fqALiVcWgVsFHcNXv1+UXq9uekBXko32PsoAxIsskzpOvt36Viv/xrIn0oMs/qum3j8QmAs0e256UCDfj6aPIuUb5nFkDfDDNK6ymHRMhwGH6igiFQvm5jstUCp9xVzR3CysxOvRbJvNY8kVUH0QJJuIWbiBYoxLgAqy+C/bssIlEJgJ9cPo2IPcc5vtN3Tk79DSZbiTdPJoJapjkD4KbsVEBWe0HM13odlLFmyYD6QEiFWa+l22dsBbxSYplK4CHbOKOOlBRHAD1iLfSS4f7U4IYMwxmm2H6vfBreCOLz6Potz71i2olJBk03wPbwI9yu3/uAuREg/vMkXt2//hGVQz7n3XDlRXF1aGMeIWTLzMD1l7h3TXHW8G4vpQtxR6fg6G/sI2TKU3mnNAcj3kr6DV74IIGp2LlC5DpGQLjLDICHQi2fMWqZ+M9EO+C9Wx9qrekt4iKDsFzSB9EtWD2FeOR9MyWr6ej/zTtWZrmj0PVNHscSi/qakZY3GLofJmNP2h9aj23QVDn7He+2g50vc7NkxKoEOFZdrapt0/EJgTsudhqj24s95s1Wvaa6PtegCts0yVvrvQbCeM3INZGv7vIJ7fesNv8txjRzn30kU2X0gZqJqVYuVNiOtv2rDE9dhcUX0U/A7o+zVrRd3/k0AOvb+DiBRCfKNo+iRSuqRp9w8EWo1Wv104YlhFpLVDfB91i2vH2gYdAlXETWk/lW7we1FdZy4eCLjFtcpw1RHI91v7VxPcEqciUkLjDZBuhbEHrJrF77Yn0+9CdgOaP2SxTlkI5etsHRJoKiGAMddk27j9n44iDE8EBv5Jufdt29D4grbwAzbsA6/TyiUV/CA68rd88Ee+CsBH/vXHoXQtZE9D+iQfvO1BO/7lO5FScxdOUroOHXsQK9UqQ3y+ieb47eBzGC6imj13omOD7ZelDrQd7Vh5MYFjesnyeI94u8wVBW4xRGvQ/GX7GQHdD5QKq7AJgV31R2D474qqi4lFk/rWZIwlWoJEt5hOx4H3WdknI5Bvr1MFVz9ojkUhgBHoOITpc8Xk59oHcYvMkj17oRDMc6AHwCWYqG5pki16oVej1frqEn+4qES7pcljW4hUbqwFVyxjWlgoD/6Rzb59d6GaWptJCGAEOgT1Q5C/WidILRKh0o1mLzQMYMxvhWoFC3ROdQYZBc3Qkb/DdO/Uzi3faK5j1e9QmwulBOU3Nt1FTJJLURyQF7pa42Mbg3wvuOUW7PSD6OhXC7v2rlB50URCAGOuKcoi6xFglFaIV50s4nq5952XQP4qt/8TCMI9b18H+Q7wL4E7a0KgJj8Aw38LeFPrl6KtI9+Opj1I6fKmjovKrVC6BkjBLUSzXXDkz4DSJLvUu4EqWrocmZqVCgQ6hfh8qD6AavfEF7g/APHatmuHEhEoX4dmSyB7FlCILy9KLIfqgrPiFpggb2lK77iMZ4yva3pLnfrBIltzLDtaV5SBBgIdRrwBqt9GXWXis+b3Qnxu04W3m4GUrkXdQOFCkkJ8sVVj9fxSrariR1d+kqHD9nnd8tB+fnTlJzn30iX84ZfeZZVp+Z6WVKKpHwa/r9ig+BnOOtZzgUA74kG0QaLUURPRbyPEdaPxuZA9Z9XXOKvwxkP2gjkKFRan6ofQkX+x5yaJF1vLzNeh653N1+4rXY4u+SJoiu59PaDQfWedG5q4XgtiZDvmr+XlNKX9vtVOd6I13PtWj0RLuP0fbLN9z9uWFuVP7aOBASDl16Jj3wbdY7FMHYRoHR+89fMgj7HlwZ0AfPCtD5ndkPSAlCaO3/YwH/nnBE0ubervJuJQ12dRWJLCY3mGflodbdp9A4G5RuI1qG4yWy511qLhBuo9xtsIkQRJLoRJ/bR+7ABwtO48VaXxBsBjX0vNDeT66mMm5itic5UOFs+Yno55smMifck1Tb33qaI6VuhzlED626hKL9BOSHw26vdC/nwxVxQL+dJl8z20hojESHKB2ZMW+LFJmdPjosU00dx1k0+fm8jgDv/ltOvX5gq/f5r473yjmtn8hhT6Zc11fgp0ONILsgD1g9SAn1kAACAASURBVLU2TRP0PAruirpT59ulbVwrQhZ/GpWKVTtpbo5i7mxIH6/TwBHXg2ZPF5oTyaTj3Vbx6feb6GbTUfTgzxSBFWD4L1FKE/MEgMb1Ap6BphACGHOMJBeh+Q7Uv2pfNnggb2qVQrMQqSCVN3Lfj18NVG3iqz5K483FTNUjOfY7NmeRoZqj6WOQPWmbOUnArYTu9yPRylo5OL2/Yf3u0j5ODbZpqwJJ2wWrAu2JiENKr7F+S3/EBGzdQEf9/Uh8Lpq9iGrfxLj1oGVcdbB2XFUtY5xc0tRNuuZ7IP1hTQhMmVS5Eq0Eqmi+D0itzSU+u2n3PlV8+iRUf1BspBT6/h2Ub2iJhVygsxGJkPJ1qN8E/qip4LuBjgp4SbwOzZ5GdQEiEX+762f5rbd8nue2HODcSxfykS/9+KQ5ZA9E5zW1RVX90UK8fACRpJgrJgdZJ80Vrh8pXTzDleYen+2G6kMw9HE70Psr5hgXeu8DBSICpevQsa9ZRaLGQBWis5F49XwPryFW6XAZmlwM5IiULMg4Y5KjQVWU1v6vaah6NH0CsicgfaL+XtOGVkWipU29fzPQotq0HSv0ZkNnjrqDEdcLXbeh2XbufddekEVIfHb72ZxNom5s8Uo+8s/XI9FZ/NatfwfAR/7lrfYBdkuQePXE8X++sRDQad6fmabbzMFl+D470PtrVq7uKqjfTW3y8rsguaht3lef7YD0++AHQRI0uRiJN3bU4jIwf4hbCG5hy67fUmVst9J0atInJpYQrh+6boX8aUifsuMCxOubLmas2YuYXXVk4l7d74PhT5m1qlsIC/4AdNDKPt2ytslaav6q2ce55YxrEpEfQMceQSo3zuvYAu2LuH77fHUgEi1HS1dC9YcoUriOiSUqpBf8nuK4B7em6YkfzXcBUgQvqjZXABz9L0AMiz5lbS5uARKtbBthVNVRqD5g79F4gFYjdHRzUTrfHuMMzD8SDUDX29FsB+iIbazdsmlJkflyaRtfi5B+p/bYDdxdfC9Hxe+wFEVRzWvf16oZRAtB3ZTjY0Wis7mBPM2eqSVGiNZC/rJVePT8Nuhu0+LAgR6BaH2d7sh8o37YRM0Ly2qN1iGlKzouMRICGPOASFexSG8j15HZ4lZAtB7Ntxe94gq6Dypvt9LVfBfj/tLgmrrAUM0h2wbD90L+rB0c/Jjdq/c3IV4DvQuBGOINSNx8q9eTQfO95icviwqRUYXu91igdlL5bCBwOiIilkGJzy1aIcoTVSTRVWi80Vo6pAdxraiYspSI5jsKbQ6KFpIcNMcl61twz1NHs+dh+LNAPKHtM/wZ6L4D9cOF7XQgcHrhkgvRaJ21c5Hwh/f/xMRmxA8XLmcVC+o2nfG5Yq+tNWoh1xyIcPEaYE0L7nuK5K/C0Ceo1wH7c6AK5esgOms+RxdoM2wP0rl6DOIWoKXLi0BnsY2VDEo3glYhexT1zgKgRFB6fVM1w1QVsq1FIlUmPnNEMPQxZPGn7PubFNzlheVze1TNqubo2P2WTHVFVUj2irXZVN7SNuOcDSGAETghRJwJ9eXr+MiXzwMpI/E6xC1G9Ww028VHvrKpcBdY3WRxrbwIjkyqWshfNlvJ7Dmk8hbrv28zNNtWiIpGkzYi94F0ofGGjpowAqcffv+dddkOmKjEaGb2RVwvNKiIsiqp1lRK2e9ThfItkL1stpKjf49lcjLIHsPvvwM3cE9L7n9qVGlYJitgG6pA4PjMr4vAySGuB+hpcLwbaGHg7siHwO+D5Aabq0a/iAUxMiDD778dcG3nJKBT10V1TwZR4sDJM9fzxvhn63hVoS65CI1WWiUJDolX1cR/NV5dtHpFSLS8BcF+LTQthDpRcD8IEoNbhiu3mSXtOH4v+EP1jjPREqtg93ug3ax0j0EIYAROGJEIiddYxUPd8QRJ1gKtsSAUKZnlWuXtMHJ3Ucq5BEpvAH8IzbYjSfv0r9cYLyWrQyxSTAo0V0E9EDhVGgl4zccG6K6bPgTARzd/+BSuUrLqJ30StLBkrAsatukC362F7vfWa/v0/KKtmaQ9WuMCgdOBibL17xcHBs3tjCmBAa2aDlGbIdEStOf9IMtg8I/tYO+vm3ChGzj2iwOBNmQ2QUJxi5HS9NYQcf21YEZz1hBTri8OdWdB5d3myjiyy54o3wISodkzSLKxafdrKrXAy9TjdJzpQQhgBDoKKV1lfZ3+EJCC3wnVr4F0Q+kyaMcAhlsB3T9li4yayOgvYzNGe1lhBs483MDdDbIdn5u/AZ0g6o+g2QvWa+pWmBDggZ+xJ4vKEnQIKu8EStB7oWkEDP4JkCL9fzpfQz8mEq9B89Vo/goWZFFgBCndFLRz5olOqmZolyBkO6F+qJgrDsDRj4FUcAP3TT/R9UC0EXo2FXPFnwJVpP+PkLg1CZpTQVw/Gl9iTkvjdpi6B0rXFNUsgUCgmUjpMnT0fhj7GrUqjOpXzY0xuaiorm7D72npY6rQqRkMaFuZHsyGEMAItBXqhwohrRyJliFuUf0JbgBKGyF9BLwtzGofuja1KZLkAjR/sShpU8CbC0Pp5vac4AJnPPMl4DXOeNZkywNb6x5PzaJovhcd/aqJcuKg531mpTbV+UhK9r9JIp1afImrjqLpNsQtALe8bRS5RWIovw78q2iyCaQbidaEDUkgcBKoP4SOfsVaKqTLgpo6jPrBKWXruQU73eKa+KUWWhhKbkLidCPxyqb21Z8qrnQpGq1Ck0sAKUrqgwNJ4MxktmuImTD78v2AQw/dBci0ZI8mG6D69QmpHOnDqhtGsTVIewiC1+EGIFqD5i9ZSy1YQjha13Sh01bTHiu1QIDCqWPsQRj+JCBo9/vQ5FJc6dLaOSIOjc4zi7ChT9mxvrssOBCdhea7AS1EAttjcSGuDyq3oumTNm7Xh8Sb2tJWKXBm0m493bNBVdHqI5bxKFw6xK1A/avQ9+9xpYvqFhs+fQKqj9a2InTfCcQ25xCj5JZtrdzUNmrcIpFZuwYRvnmlUTUDtHclxnwHIdsNrW4BpBDGBfLtdvzA7ciSv590ZgSl66H6TVSKnUnXHRYAHfsmtmzO0awLyje3SEz05JBowFwmAoHASeOzl6D6LdAcENPF8Xun64XpEWvrHP4MUOxF/JFC66uK+tTEydvE2QwKK93y9Wi2HLLn7GDpGnPD7LCEaghgBNoC1SpUv1lYRRaBB7cC0i1ovLoukyClSy2TQgo427CQQvY8esSirPT8PFq6ARevmuPfpDHi+pDya+Z7GIHACTFfm57xLMmxsyajljkYvqcmjmstWt76v7mo7mwT/VpTZFUiNN8J2QtItLJ2juZ70PQJpHR1C36rQGDuOdMDF1CUSPsdphEx7cmx2o+TA7kaLTVRO0D9UUi31M8V/hBafQSp3NK6gQcCgZNifM3w7kXvr3t8PNQPwtg3rDphqGgvzV+w/2bb6k+WHpAI24tEaL4fqIJW0OEvFOdU0NLVhYNReyASm0ZHu+p0zJIQwAi0B/4ADP0FkEw4dQx+DEjR5Io6oR5x/VC5DU0utNfJAuv9dP3Ugh/SB9WHUPeOYDcYCHQyOoYf/ZqJb8bnIPH6orQ7niLGWXuBlYgzvbLE2kQW2FnVh6cL3LkByJ6HEMAITCJUM3QGmu9E06csKBGvReJzESkjIqhUgBTpu8vOrQU7f7XhtcR1g1tv5458xdYZdScshPxVVEfapmIrEAicGprvBArTgKlPxuda1UK8qba2UD+ExudDvheiRZDvB90HbqlVjOsojD2Iuh8JLV1NJgQwAnOGZUFeRbMXAZB4XdFzLli/+rTpwmiwSRHXgzizTNV8B3r409R5oA/+v0AVStfWFiGBQKCz+MiX3g3p4+YKgIPqw2j+MpTfgEiCxhug+3YYvtde0Ptr4PfOUgE8YqqY1TTtjEBgEiFw0b74dBtUv2vJC0kg/SGavQSVm62dNL4Qqt9B3YpJJd2ZHT8e4kCnrk/GH4f5IhBoN8arN4cOD9c9/ujmDxd7kQOgg5bscEuQ2j6jaBuBKcHOKtL/39BDH6y7j7gepGhzVx1B078ttLbseiIVVJL/n733jpPrqu/+3+eWadv7rlZa9V4syUWWe8FgGwOmmWZKCARCe0gcJxBCHEp+T4Ix8EB+lAQceDAkJBDTDMbYuNuSLdSt3lba1fa+szszd+79Pn+c2dGutKu6Xef9eoF278zcOXc9c+453/L5IN4hVNgEMEYTE8AwjBvibQXvlWwPqsTuBnclKrRaZz5zPwLiQPzb+gW5n4Cg7cz933LyJmTQQ+KP5I5uMBgmGSIpJH0A0oe12F76CLhLBwlrxrTIb9AMdhXKXYmIl9GzQPekhq4c6nE+Es4i8LbBoLJwglZwV472ZRkMhlFGJI2kD+qMqHiQrgV3cVZ4ExVF/EYkXYdy56GchTob6u3SoYecd4OzAuXMOfOb2QvAfxqR2ImNTtCuqzyUsUE3GKYKIh6SfBH8Y6CUDkza5RC+Tldr2ZWI2qz3DtlApwAKrJLT64VJClCDgiEDhID4mFzPxYwJYBjGDL3AOAp+bWaBcRCchWQ/dlYVpF9BnLlaCCt8PZJ4Gt1PJhmnjivPLJJll0Ls/VpBt/fr+ljuJyFoRtnD9LwaDIZJh4iPJJ+GoAVUIQRd4B8CK4o4cyDozohq+YjfhrKrUMpFhdchoVW6bFzlnNjAnAHlLtZ97P5RdMZFwJ6FcpeO4VUaDIYLRUSQ5AswoKQv/XquULYu5+59AC3U+149nzAPpSxtfegu0c9XsbMW+lbOTESWg7fnRJ2oXYoKXTpGV2gwGC6EoTpawpcf/zBIEkltAf9oNjGqqys8xCpChS5DWUWIswrS25HARs8j74bw1WeeL1QuqBAiyaGBTekFa/nIrzOcFyaAYRgT9GbkWfCP64oL8bRtmZWrFx0AvfcDPoSuBqtA94dFXwfh9fq4VYJSkTO+l1JRJLQOUhs44YHeAqG12gHEYMjg+z7NR1tpPNJMOBKiemEVBaX5Z37hJEbER3/uQ8NE/qcQQRP4TScWFlaB7juPf1+XcIdvzjyvG1QFhFZkX6pUNKt7cbYo5aIi1yBBBwRxsGKgiqacErdhbPBSHo2Hm2mtbyeaF2XWoipyCqa2he30mStawT82aK5AzxXpY7o6SzLZztQmUGFErsh+r5UKwzlWTUj7e/Rri76lA6sqkrFZncJ/Q8OYYvRyJgnig3QiiT/o373t2kbUrkRl278cSB9E3LUoZWGFViLOzIyroYuyK1FWbvaUg93NBqOUjYQu15oXKgzx7wI+5P4VyqkZ+2u9yJgWAYxUIkXjkWZ6OuIUlRdQXlOK407dSxMRIJhU1jvnTNAE/nGUXYVggwpAbAj6TjzHrwOVr7OpmTJOpRw4m/Lvk7DceYhdhoQuAwRlV2ixT4MhQxAEbHliB/X7G4jkRPDTAQe2HGHNq1Ywa9HkcKs5F0QESe/XN2TxwIohzmosd/ZED+280AregzIcKlcL80oCiOqqDOnTlVZBI+K3XJAV8ZBFyIAfusEApJIeG379RzqaOonmRvASaQ5uPcKVd1xKSdXU+6zouWKPbuEcmCvcNVhTdVEtPTCoOVSpKGJXQPx7QAJI6wdSL+v5Mbwe7Auf45WVByYpYhiE7/v0dsSxbIvcwhwTAJ9k3P/o9RB0ozL3eEkfAb8eVAzp+wEEDfqJvd9C+n+JKvkRAMoqyr7mXLCcGsS6DfEOooXGc1CRG8+62stw9kzdXX6GeFecF3+5iS917kIpi49as8kvzWP96y4jHJ1avYkifmaRsRtIIVYVKrRmSm7ExW+Gvv+L4JwQ1rQqdKZTlYF06U1D+GbwdiPuEpR1YdktZeWZigvDiLTWtVG/v5HymhObXi+VZsczu6mcU44bOrvWg8mCpA9CaqNWu7ZCur879Sxihc9OA2KyofLIbjzQfuXS9wMgoUswEz8DHMi9B0gg/rHzCmAMBC4G+7mftq/VcNFRt/c4Xc1dlM8qzR7r6+ln+9OvcMPbrp5ymxRJ79XVCFY5ynIzyvjPIOoWlF0x0cM7d1SUU0S/7ZlAP1qEL0MmayrpQ6jzCGAMN1fAqZlXw8VJ6/F2tjy+g0RfAlAUlufzDe8wtmOxsb4OmHqVGBJ06tZvSaGc6ozQ/tSsNJIgDn7L0PWQPUNrX6VeZOgc4uvWsmHIzgMDnGHtIB2fyJxyn/69/f0IZt4Ybabmp3IQuzfsJ+35uGEXJ2RTNquU3vY4h7bXTvTQzhnxtkJqC8QfhPiPIOhAEo/rL+FUY7gFBmGgT2tbkNJVGqkN0P8DLcxnMIwhzcfaCEeHRsHdkIOfDuhp780ei3fF2ffHg2x9aifHDzaS9tInn2rCERFI78yoZ+trUiqiK5q8nRM8uvNDOTN0Zjjo0BljCRgyh6h8nc2wcsBI8xrGkIZDTeQUDg2ox/Ki9Hb2kYgnsse6WrvZvXEf255+heajLQTByILSE4VIoJ18rPKsPoyeK3IRb88Ej+48scrAKkT8NkQCfY2932DokjYK2JDzYUZ0ODMYzpP+3n42PrIZJ+RQWl1CaXUxfd399LT3nGpYM0UIvFqk/zfg7QG/Fkk8gaQ2ZO7FUxHRQp2Dsau18L+3GQhAlWs9vty/htg7EZl86z3D8EzpCgzf92k43MS31DH2JLsA+GLDVgKEe/aHWbpuEaBLx1vr2miuayMSC1M1r4Kc/NhEDv0URPqh69OAC/4BfTD+PcBD3GVZy9CpgrJnITkfACIQ/44+GH09xP+vLsH0D2WeOWBl6A9/IoNhlAjHQvj+qZ8zEcEJ6amw9Xg7G379RyylsF2b2l11lFYXs+72tZOsLc0H6UNZJ+l3qDAEPRMzpAtEqRCEb0ZSm6HnH8FvAJKZRyOAQuXdoxdTkkDZM8/rfQayICabahiJcCyctd8bIPADlAI7Mw8c21vPlid24oRsbMfmyM5jzFw8gzU3rcCyJlNuKK2zqdZJFWYqoishpyBKORC+QSd9/KMQ//eMWKd36pOlB+y1I55ruHng5GNmrjCcTFOtDlhGck5UeueX5PHndbO59tp1fOS53wG68iKV9OjtjBPJCU+ydcQJRFLgbcxou4QyxwohfQiceefV2j3RKCsXsUqRoHNQJbsFKkcnRHDAsgAHZeUgQa92ElEn/hsFbXdnKy5wrxjy70jzgZk3xofJ+U06S5RS2I4N6ZPCnSI4YX2z9n2fzb/fzvGDTXxTahERPvHSfNbdvpaymSUTMOoRGKF0CSwIOsd1KKOBsmIQvglJbYScuzNWRRWQ+yHdSjLYLUSMW4hh7Jkxr4K9Gw+Q7E9lKzG6WropriwkryiXIAjY/tQrxPKiRHO1eGx+SR7Nx1qpP9DI7KXnt2EeC5RyEFWCBL1DxKX0Yv0MtsOTGGXloSLXE8T/FYI27SwC2upUkoi3G/D0AsI6tX1kpAXDwHFV/F0kXZs5d1xv4gyGk5izfCZ1+44TzYvguA4iQntjJ7OXzSQUdkklPbY/s5vCigLcTPAzrziXur311CypnlxrC1ywCpAgPrRNM+iGs7EQnaQoKwcVvhqRK5D+X4NVfmKjQRhy/iyj91F1nsFOIfAOQdCoW9jMXGEYRLLfw7ZPDVQqS5H2dKJEBPZuOsiBzYcQEWzHZtHl85m3cvbka0MLugB/iFaDUgpREcQ/PjXbUgEVWoe03YXga7fCvgf1A0G9/tdemEmMpIBzF/g1TBxTOoBhWRZzV9bw4c0+3w4fQyn424pLaDnWyryVWpyq6UgLxw82aWHPBt2TllMQY+sfdnLz3ddOnkyJyoGc94Mqgd6v6UN59yBBI1iTaTF09ii7FCK3ZzYKNkpFCbzDuvdMkoAP6d0QWn9eYjkStOvNiKRQziywKqdsr55h7MkpyOHy29ew9Q876WnvRUQorS5mzU0rUErR19NPvLuf0urik14Xo/FQ06QKYACo0JpMi5kHKqYrLxQod2rbdemMx8v6F5XRtCl6EPp/BaS0uGdwBPHyUYOcSEY8F2Q3NtJ6J+BDzkchehco96TsjMEApdUlrLlpBbte3JfZjAjVC6tYeuVCAHraewl8Pxu8AL3YD0XDtBxrnVQBDKUU4q6F5JNIkNLtnUEclDUtLIOVcrPCe/r7noLwq0+4ngVNiLcF3LVDNo0j6VsMPkbnhwBbb3zwEb9pamqGGEadkhlF7Nm4HxHJfq7Sno8C8kty+Y83v43DO46y7elXKK0uxnZs0p7Pjmd2E4mFqV5QNbEXcDLK1q0VJyNpdPv31ERZ+YhVAqRQ4SuRxM8BC/wBmQFB/B6gG0JXn2KeYJU8dFaVFCIJHTBVOdl9iKm8GFumdAADYOHaefR19+PtPoRSirbj7SxcO5eZi3QWsvFwM9+kFqehjt0JXS75ZXbjJdOs61xLfvHkEH1UKow4K8Dbgm6pUIjfClYOypmargKgF0+oExliy51LIH0QuQMSD0P/L8AqI7DKz8k9IfAOgveibklBIbF36zK30JUmiGEYkYqaMl717uvo7YhjOza5g/rcbcdGAUEgWNaJha7v+YRik09BWtnlEL0V8fZC0A7OXJS7+NS2kulAagM4s7RdKhk7SG8rYlehbL1ZHFzqeYroVpYACGX+RvlabyO1FRW5YcwvwTC1mL1sFjMWVNLX3U8o4hLNPWHT67j2sLIKQdonFJ18c4XlVCHqNVrMM+gAdx7KWTy0emsaoIp/iCQeAfFR1jxgQANkt65Ms89105jRF7JLkaAPSb0EkdeaNYaB4spCapZWU7urjmhuhCAQUokUl1y3jHA0jIiwf/NBiioLdaU4et7IL8njwJYjkzCAUQRWCRJ0nHDskCQgOkE4BTkRpNQJEen6LOBmghJv11VoofXgbQJnIecjCymS0m2v6UNab0NFEPcKLGfqOdtNNaZ8AMNxHS695RIevnwBiXiCnILYkIWGG3GHXWgIkp1UJgvKXY6oXF0aLf3g1KCcJVpwa5ogQR94O8BdAsnM4skqhtQLiF2mW0/OdA5J6glHlQGZvl6rakr36hnGD9u2KSg9dZMfiYWpWlBJw8FGiquKUEqR9tIk4klqlkyu6osBlFWMCq+f6GGMKidnPMRvQZK/R6kTVVpK2Qgu4tdnAxgjnQsyC5mgBXL/MitkqE9UCEEDIv7Utq02jAluyKWg9FR3ovySPArKC+hq6aagTM8lyf4UEghVcy+sHXKsXAuUXYKyrxrVc046pFtbJg5aAyhlISqGpGtRgwIYp+tTD1peCwSovHtOnMeKIUGTtnJW0yvwYzh3LMvikhuWU72gkuOHmnBCDtULqigqLwC0tlYy4ZFbNPSz4oZd+k7S15kMKKUgfA2SfB7xGzMHQxC+bnomRRAgDe7KTKWndnEL8LHc+UOeedrKi9QmXc1hleu5RhKQfBqxbjuvynLD2TPlAxgD5BbmDMmmDjBz0Qw+sn02xaWF/FOrVuf/mD2XwjkFk07IUymFcueAO2eihzJ2BK3Q9z0gdMJetffr6LLPq8A6iyqMoDMjcOoOOsdXgDTirpyyvXqGiWflNUvw0z6Nh5uxLIVlW6y+aTklVeZGNJ6csmAYUdX9RKXMmUs9bbRN6+AN6cDvJqNqOHuUUlz26kv44++30VrXBkoRCrtcfvsacgrOzw58IHAxVe0XJwdqBIMi4Zyci/L/LiMiPOgMEmROM7Xstg1jh2VZlNeUDbFmH/xYWXUJ3W095BWfCGL0tPdSvWBytiEpKxcir9bivpIGq2BowH+KcVoxzdxPgn98UHAmilhl4G1HnLlnVWUlQR+kj2SDF6AdngQHSR9Ghcy6cSyZNgGMkSgqL2DNTSvY8ewevKSHAPmz8lh90+l7pw0TwVkuMJTL8Dsa0RFjg+E8CUVCXHHrGuJdcVLJNLmFMdzQ1L2BTwusYrCiQ0QIRXxQae1TP/ipp1EF1/o7zyFWpa7gkEAHVEOrJ5+gmmHSE8uLcs0b19HbGcdP++QV5U66qs7pzLCbEpUHqhgJulDWQCY8AOkfsRV3uDlDOfOR9EFEclDK1bbV0gzOPJQR+TOcJUuvXMjzP3+JjsZOwrEwiXgCJ+SwYO28iR7aiOi274tAEypo19phg1AqhAQdaDejs/mepwB1arBDhbXwr2FMmfYBDICaJTOpnFvB+o5LcVyHvOJcs2CdKOwyyPlTPUH2fkMfy/24rqqwSs/uHKoIcu/RE0T83zPn+GimdHRq9uoZJhc5BTmcXx7VcLZI0JUtVVV2ZXbDcTJK2RC6Dkk+ifjdus9UgNClKKt42NcMex5nDiK9kH4FCTIBUHcJyllyoZdiuEhRSpFXNDrtBAOVFqby4lQk6EX844AP3f8AuKfo3VglD2XK4Ncjyaf13KLQc4W7SrufnSXKLkdC68HbjARpfRJnNio0sh2rwXAyBaX5XH/XVRzdXUd3aw+zls5g1uJqojnTpy18KjBsYsMqAb9uiOuISBKsCEOrNE+DytVC4JIcGtiUOFhTW0x9KnBRBDAAQmGX4kpTzjNWSNCBpOsA0X2mVumwQSKlokjoKki9gI5eAtIJ4avOSv9CnyPTq5c9h9KaIeHrp50omcEwHQm8/ZB6mYHWDSFAQpdhuYuGfb6ySyD6eq1jIT5YJUMtIc8CpRQqtBJxF2WckaJZUVCDwTA5CbzazHoBQGUypyPf55VVoN3PgpaMjWoRyjp3sXbLnY84NdqamvA5zzcGA0BOfoyl64a/rxkmDuUuQdK1SNCpK7ckofcioWvPWqRXKQcJXQGpZxEJAyE9X1ilKKdmbC/AcPEEMAxjR+Dt19mQ+A8AkNh7wV2JCq0a9vmWU4PY5RDKCIrZZee0kRAJ9OYjfAuE1gE+qAIjwmcwTAEk6NXBC6sUpfQtSCQNqU2IPWPEIKRSIbDPXdlb/EbE26k3PlYJyl2pHVwMhkmIqbw4gRbsfhGsYt3KUHQezQAAIABJREFU0fMAEILYW6A/DDjDt4Ao5zwcRzLW7KkdEDSBygd3BZYzOQWcDQbDCUQESR+B9E5dnW3NQIVWjlilqaxCiN6CpHZC0Ki/76Ebz/n7bjk1iLoVSR/UiRF7KcqZrdcrhjHFBDAMF4QEfZDapJ1Tso4gFRkhnFkjqvAqFYFzjFCKeIj3CqT3AT7YNSj3khFLzw0GwyQkaNNae+rE7UcpB1FoTYpRrKIK0o2QehzQvfEEvUji9xC5xQQxDBeMafcYY4K2jCXqSSXdYoOkQJ3dEvb04r6ZUwadSP9juqRcFeuqzuSTBHIt1nQWVjeMKWaOGB8kvRc6Pw44usU8aMvc628b0UVFWcWoyHXn934S6KQIabCKscJXnP/gDeeFCWAYLoygHfoeZKiryFcBDwmtG1UbIUluhKAW4j/WB3LehwSteoIy0U6DYYqgRtTgHXU3EG8bkH+iqkPlIQGItwNl3zy672UwGEYZC1CZygtOrDH6HgSVd9qAxLki3h5QzomEiMpBxIH0VsSpOeuycoPBML6IpMHbiU6iZkQ1VSHityHp/ajQpaP7fkEXknwGgh59QNmIeyWWexYuioZRwwQwDBfGiBkQYTQ/XhJ0Qs/nGBIoiX8fSCHOKpQ7d9Tey2AwjCF2BShH+6Wj0FamgLL1Y6OEBD2QPgR2FYKgBlyOVI7O7BoMF8A7fvaTcbM8PZsKgmmJVZIR2Qs4xaXsLJIWA3+34QQ/ByNBHNL7gRBCgGLAEjGM+J2cvSuBwaAxtsjjh7S/W9/T/SP6954HUHn3gBUDf3Tv9SIBknxWV4bZlZljHqSeR+xCUxE+jpgAhuHCsEoh5yOABfFvAQI5HwBSKKdyVN5CJIV4h7Qg1yk6F1ZGZMtgMExGTt40KBVG3Cuh/0fgt+gnqRBE3joqFoUiAeL9Ebx90PcQIBC7C3GXonBB+rSTkcEwBQja7j7jBny6opSLhK/WgUi/PlOyrSD377Gi11zw+UVEt6V62yFdC4lHtKtA3t9owXFJgjoHVwKDwTBq3HPjfQA88OTnzvBMXal1CkEfuPNHd1BBR8bx8MT+RikXwUbSdaiQCWCMFyaAYbgglHKQ8JUQfxD8o/pg8nmIvBa4cKsoCTqR5JPQ8y9aJTh0bab3NYrKuwfxG8C6CDyrDYbpRFAH9iywF4CygDAEe5Bg7hDRrfPZsEn6CHh7wKrSehpBL/hNIGHEqQbpRpl+VcMF8h9vfpvJqo4H/nGwKsGeC+FrgAgEhxF/wZBNxHAMzBsjziNBE3hb9PndXEg+ql2OvL2Iu1gHTEJXmfYRwzljbJHHD6vkRwTePuj8CKAg8ibEOwBWLspZOGrvo0V+N0N6v+54tUtRA9toZZGtJjWMCyaAYbhw0ofAmgt2RpQz/CoI6iFoAHvGeZ9WRLTuhZDJgoj+WRKAjfhN2lXgPNTGDYYBzAJjbBipfFsV/5vOdlqVQzYGEiQQ7yAqXHzWpd/Dkt6vKyx6vwr+YX0s9TzIk5D39xB+1Rk3PgbDZMEqeeiiq7wYQMTTot125RCXMQk8JL3vgr/Hkj4MKgelbKT3OxA06wf6/wuSBVD4r6av3XBBmHXFuTNQebH96V3Z389YhWHPhuifaBeS9B6dvLAWMmxlRoZzmVcDrxa850AcCJJacyMoRdwVIBZI0uxFxhkTwDBcECJJSB/NlIMf0gfj3wB8LXx1AQEMJA7SDvGHTuheeBsACyKvAnc5yl2MUqa80zB6iAidzV30dMQJR0OUzCjCcaf3VClBH5AElTv23yfxABkmqxnS3/kLJp3JhgxC5QL94C4a0abVYDhXfvTGt9Le0EHt7jpieVFKZhRhWdM7Wz/uc4UIyjqpdVSFdHn4WTLyBiXNsMLBKgoqhrJNObjBMCXwD4EFKnJL9tBAxcT5Oo1kzyNp8F4GVYKyQkjoEkjvgvQxIAJWPrhLMm6MhvFieq/KB9HZ0kXDoSbSqTQVc8oprS6etgsNkX4tXKMcsEqH2BWO/pv5IzygIEiNwvlPsitQISAAq1hfn3gZkS+D4dw5WYhPRMj/4iZS/Une/Mk7ECCnIMaVd1xKTn5sYgc7Boh4SGoTpA+jvU1txF2L5S644HOPVL4tEoCKIpLQdsrZwfSAvey0rz0rnHngbdYtZj0PAGlwVuiMTGojgkJCl2G5iy7wCg0XM6mkx8uPbqG1vh3LspAgoKiykHW3ryUUGV1XrMlQeXHyXHHvbc+ClcsDT31p7N5URcHKR4I4ysoZNJhucFZd+Pmt2ZA+gkheZr74knYWCF0J7kok8QjiLNd27WrkTK7BMBJnr+NgGGDgb3VOf7v0oVO1rVQR+PWIeEOCrcNVeJ52jpVuEA9l6Xld2WWIdQV4R8AqQEVendlrmTliPLkoAhi1u4+x7aldPPx/HgGluPNjtzJ7+SwuuX75tPvABd4BSL3Mvbc9A8D9v3s1hK8f0lc+qqgoWEWQ80GI/5s+lHcP4h8HZ86FndrKRewyyHm/1tgACN0EwVHo+0/o/28k9qcQuRFll1/ghRgudg5uPUIQBBRtrwXgl9/6HQBv/uQd7Hh2N1e+dnStuCYDktqq2yyscpSyMmraGxArd9TaLE5eGChlIaErIPk0gqODkhIHuwzl1Fzw+ylnPuLXa30cSYH06uBF6NKMMF8aUpsQu3JEf3iD4Uwc3HqY9oZOymeVZo+11bezf/Mhll+1ZAJHNjYMzBX33r4BUGx/rgVo4Z4bPs0DT/3vMXlPpRSELkcSTyB+fyZZEQdVgBqFIKtyqpFgHqQPI2JD0A0oCF+JUnk62OrtBLtS/89gOEckEJL9KZ7/xcuEoy6zl82ibGbJRA9rTBFJIekGIKn3HlbJOOy1bJD0SR0jAkpxujaSsyMECCIy6DocwAfpQ9IHtCGjbSowxpNpH8BIJVLsfHY3RRUFOCF9uWWzSql95RizFldTUjV91Ogl6NARRav0hMWY2EjyOYjcMSZCVEopCF+BJP4ApABLbxzsGSjnwntHVWi9FvEklSknPQruCkgfyDwhhqQ2Zq5vegWjDGPPYCG+GQ/voqu1h9qTnpNfmkfLsTaS/UnC0elT7SOSAv8gqPLs3KCUi6gcfUMewwW75VQj1m26B116wVqJcmahTrJGPJ/Ms1IhCN8IQTNiz9QOA+7irNiWUo6uwvAbTQDDcN7U7qqjsHzo56ewooDaXXVTMoBxumqnwXPFKZsB6R/TcSm7HKKvzcwVXWAtQzk1o+JYpJQNoavAWYikD0H07RBahCKTaVUWoiLaXcAEMAznwD033oeIsOPZ3QB879M/4u2fupO6fQ2sum4Z81ZNT20VCTr0fkCSgEIIwJkPoSvOeQ9yusqLU6oznEXaylQiJ95HWsCef0oV+rlWeCorF+n7DyCJ5P4NoJMgBK3gXAn+cSR9EAldg+XOOadrNJw/0z6A0dXaw38/8CvcsMuRnccA+Pe/+w/SqTRL1i2aXgGMdB333vocKJftzx4H4N7bngBJ8eUnrwK79AxnOD+UVawXGO5qkD6UXQpWxRDRrfM/dy5Ebs9kYZ6Bvu+Cd+CEJkb825DzLp3BVaa33XD+PPDk53jhly/z3U/9CNuxeP8/vhPQmhgKpl+ATNIMr0XhnlN/+fmirCJUaGzmX6VsyAhqiV9/Qil86LPG5L0NFweWpU7pcBSZhvMEDJkrvvy7NwDwV6/5BYjP/Y/ePuZvr6x8VOiSsTm3UmCXa/vU9KFs8MJguFC8hJf92XYs8opyieVF2b1hLzMXVY16q9lEo4X3XwQclF2UPUZ6P2LPGJUKy+EEPgG+/If7EGkDbx+CBQRgV43evGHlQYB2LvJbQDq1Q5Glq2lEcsDbhDgzx7Zt35Bl2v+VHXf4TbQIhCLTTfxRTvNYMKbvrFR0VEo6hz+3DXYl4sxi2E2HoPUwDIaz4OTI/WCV8FlLqtnyujKUUrw/c6yrpZuKOeXTbrGhheryTu0vpxucNdOjd9cq0RuTQXobMiAiajKqhgtgzooa9rx0YEg5eEdTJwvXzJ3AUZ0fQdvdp3f8GXGu8MGZNY4jHUNUQUZvowdl5QEZvR7pH5WNl+Hi4oEnP8eOZ3fxlT/7Do5rZxMitmMTBNDbGae4cpqtKaQXpBNlnbi3KqUQlQd+LYzh90gpCxW6HHGWaD0tFQFVdNqA8lm5j2T1MjbpfxO/gqALcj6Isk6I/CoVQoK0/huowgu6FsPZMe13fQVl+bz3828n1Z/ip1/5FQDv+syb6e3qo2L29OpXUvYM7v/t1WBVcO+t+lrvf/Q1IH1a9HKKo5x5SOzdWum39+v6YOxucGqGigEaDOdJ9YJKZi6qItGXpKWuDRDyS/JYcc3iiR7aqKP7y3X7l+4vD2XmikLuffWP2f70HmBqi5ApFUJC10LqGSToAiUgCkLrspsUg+F8mLuyho6mLppqW/QiXYSymSXMn4IBjDMx3Fxx/2/WgVWIcuZN9PBGBaUsCF+NJJ5C/EadKxHAXW00tgznRSw/igRDE4siWkth2iVEAP2lGS5gIMCFV2TDmQU+9X19LO/tNig3U5V2ApEArbkxfdqMJzvTPoBhWRZX3LqaTY9tw0t6gCLZn2Ld7WuJ5UUneniji1UC7krwdmjxOtCRyND106KkSdkVWvzP24LW20BrbYQum9BxGaYO99x43ymlhwM3wLf/9D9J9qd4Jd4BwHcjx3nw1a+nqLIQ2x6dm+9kQ/eX346kj+i5wqrIZBs3TvTQRg3LqUTsN4DfDIhWC7emn6OMYfxI9iep23cc3/cpm1lCcVUhZTNLKKoonJItJFbJQ2fsBx86V3SDVTWsbs1URllFEL0DghatuWUVm0Cn4bypmlfJXfe+PqudFQRCR2MnlXPLyS3MOcOrpx7KykWsUiTo0N8lBqqY4ihnagZ2h9PLkKAT6f8tIv0ZYfAApBmceSg1zfaVk5ipv6s9DUEQcHRXHfs2HyIRT/Lez7+NBavnMmNB5bTckCilUKFViDOL+5+4HN2HVjmtFuuWuwhx5kD4ZlBhs7gwjAppL013Wy9e6kTPan9Pgv7eBKXTcK4YjO4vH2pJ+MCTn5vSlRcno1R4+pS6GyaURF+SF37+EvGefmJ5UZJ9KVqPt1NSdfpy5cnO2ZRTDzdXTDeUcsGeMdHDMExBTr5nxvKirH/dZWx7ehet9e0oBdULq1h+1fSr6BxAC+8/hQSNgNL9+u4qsEa3ZXO81yWD50dlFSLhG8F7CQmadIGJswAVWjOuY7rYmdYBjP2bD7N7wz6+49ShLMX/6p/Pzud2U1xVRE7+9NnUn4yyirLRz+mIUqExEyQ1TG9G2pg3HG7mw/5MyuaU8sWGrQD8TckKdj63h6p5FTjutJ4qDQbDWXLklWP09fRTOkO3ZcbyovT3Jtjx3B5uuOuqKR3EMBgMo0OyP8mBLYc5tuc4yoLZK2ay4JI5xPKm794DBoT3b9MOHZICq0gfm2boys47dNutckfFFclwbkzZVXmiL0kiniCWFx22l8xLeRzYcph/devYm+wG4OvqIF4yzdw9s1lyxcLxHrLBYBhn4t197PvjIY4faCQcdVmwem5G9HXo875411fwkh4f/NK7+buq1dnjac+ntzNOYVkBFxvTofLCYDhbOlu62PvyAVrq2skrymXh2rnMmH9q1rDpSAs5J5V/R3MjtNa3kexPEYmZhazBcLExuD31L2/4e7pbe7jz47dRUFaABAFHth8l1ZfislevPsOZpj5aeL9ioocx5ihlGffDCWTKBTB832fPxv3c/yffBOCt99zBgrXzWXTpvCGZj2RfCgmCU7Ihlm3R1dI9rmM2GAzjT7I/yQu/eBkvmaagLJ9kX4KNj2zmvZ9/G6uuWzbkuZZjIYlTxbYQwQ1PN7cig8EwmO72Hp57+CVCEZeiikJ6O3p59mcbWPfatcxZPlQ5P5YfoaOpa0igwk/7WLY1ouuZwWCYHogIib4ktmMTGmFtkE6mSafSlMwYEM+3KZtVyvGDTXS39ZBfcm6tz+/42U+AoY5pk4kz6ecYDGPBlAtgHNl5lANbDuOEHEQEzwt4/ucbkCAYUlURyQljOzafKlrJP7XuAODvqlbT3tBBUaWxuDEYpjvHDzSR7EtSVFlEw8FGGg414fsBh3bUYjsWy9YvRinFPTfex4HNhwH47qcewrIt3veFd9DR2EnVvIpp3W42mZhOmhuGqcWh7bU4jk0sL0rtrmO0HGvD99L87KuP8JZ7XsfspTOzz527cjbHD7xEOBYmFHYJ/ID2hk4WXjrXtJoZDNOY9sYOtj+zi572OJYFs5bMZOmVC3FD7pD21I987U/Ys3H/Ka9XStHX03/OAQyDwXAqU+puKyIc2HKEX33rMWp31QHwnXt+ACK0N3Zh2RaLLp0PgOM6LLp8Pjue2Y1vB1iWorO5CyfkMGuxEWgyGKY7nS1dhKIhWuvaOLbvOPnFeVi2BcC2p3eRV5xLzZKZQ14jInjJNB0NHVQvqmL51UsmYugGg2Ec6WjsIpoX4di+47QcayOvOBelFI8++ATbn36FrzzzBUqqtK5U6Yxi1t6ykl3P76O7tRulFPNXz86uPQwGw/Qj3t3Hhl/9kXAsTGl1MfHuPnY+t4euth6ufeO6Ic/NLczB94NTziEiRHMjZ/2eA5UXG+vrsr9PpiqMgcoLvJeG/G4qMQzjwZQLYKSS3pBjSun/i8TC7N64n/Ka0my/+ryVswlHQ3x6ayH9vf1UzCln0dq5RHONzY3BMN0pKM2nfn8Dxw81kVuQg2VbPP7Dp/E9nz/9p3dxYPNhapbMHJI5uf+J++jv6cd2nWnXyz6ZFxens7c1GMaawooC6vc10HK0jdwiHbxIex6WZWE5NrWvHM0GMABmLapmxrxK7rnxPizL4itPf34CR28wGMaa+gMNCEI4J8yh7bW01LWhlOLwzqPYjsW629Zm71l+2ie/JJf2xg4KSvMRETqauqiaW05Baf4EX4nBMD2YUgEMy7KomF3GbR+4me9+6kcoBV4yDcDGRzaz6bFtLF23MBvAUEoxc+EMZi40FRcGw8XGjAWVHNh6mO6WLrZlNsctx9oA+MmXfkHaS3PTO68d8hrLssgp0AJ9ZiNtMFwczF81m8Pba0nEE+QW5eAlPJ748TO0N3QC8M2/+D4FpflD5oK/vuXzvPL8XsDMFQbDdKevux837NJc20LLsTbyS/O0xp4EHN1dR2l1MYvW6ios27G58o7L2L/5EEd312M7Fosum8+C1XPO6T0Hqi0mqwbGQDJkMidHxorB13wxXv9kYEoFMACWrlvIsT31WmBvkJVAKBLKCmkZDAZDJBbm6juvoK2hg02/347jnBDY8z2f8CD3oum88ZgKZZ4j2dsaDONBfkke17/tKlrq2uho6iSvODdTqakDGMM5nRkMhouHkqoiju6up6m2hZzCGEopfD/Asm0qaso4tK02G8AA+Mxr/z/g4rifTaa1xLiS3q3XU5N4bTWdmXIBjLyiXF7z/ptI9CZI9KV45qcv4oYc3vv5t9PR1El5TelED/GiRsSDoB1QYBWj1JT7iBmmETn5Me74s1soLCsgSPv87GuPEPgBb//UnVx95xXDvmZgIz19Wxp8kBSBdxBlV0yoR7tIP+K9AulD4LeAiiHioZRxfjGMLyWVRbzpf72WDY/8Ece2ufuzb+GrH/o2Sim+9uwXTgliXAxBNwl6Eb8JYBLMFSnE2wXpffqAswDlLkep6dXqZ5icVM4tp7C8gF0v7KWgLB8v6ZHsSzFvZQ1uJES8u/+0r7+QKorJVnlxMXNyQoj07okbzEXOlNxd5ubHuP3PbuGlRzajFKS9NF3NXay6bhn5xReXuq8EnYi3G4ImUAUodxlqgvyXg3QDpJ7n3lufAOD+R2+B8HUo2wSVDBNHXlEuN9y1nmN7j+O4Nk4sxHVvXU805+zFtKYyQ8o8pR8ir9MPpDYigISuwHIXjPu4RNJI4kkIusEq4f7fvQGkFUm+oOeNkyywRxOTKTEMR9nMEq5/63qO7amnr6efOctrCEdDF2UFRuAdgtSG7O+CyswV4y9WKhIgyWcgaOHe214E4P7f+kjQBuGbUWp8K2/N/HHx4YZcrrzjUvp7+tnz0n6KKgqZt2oOBaV5dDR2UrOkGhg5AcLHlg17XsPYIBIA/tgnQ5ylOojhLDXzwTgzJQMYAEXlBdx897WsuXkFvh9QWF5wVhuS/niC3o44oYhLfkneKYvkIAjw0z6O64zpAno0kKALSTwGWBB/EAiQ2N1I+HosZ9b4jkX6IfUMqDxQA4s9F0k+BdE3mIyqYUKJ5kZZdOl8/nXbA2d87v1P3Efb8Xb+9rX/Gy+R4qZ3XsOMBVXEu/uylqoiQtPRVvZs3E+8u4+axTNYfPmCSb7R8UF6wCrJfh9FPEi9hNiV459dDZog6EDZVZkDNqgqxK8H6QBVPL7jMRiA/OI8ll+1hHtuvI99mw4CI1dhfen3f0/LsVZe+OXLtNa1EYqEmLVkBgvWzCUc1ZUBQRDQcLCR3S/tx0t4zFlZw4LVc3FDk/eeKEEfpDaeZq7IGd8BBa3c++qfggqz/dkGAO69bQNIii8/uQomKGljuLgIhV2uv+sqwtEQ3W09SBDQUtdGbmGMhZfOG/F1j622iA9yEoHpU1UhIhC0I34DKBtlz0RZE5dIFvGR9B7wdoOkEKsMFVqLsktG5fzD6X5kqzIM48qUDWCAtkotryk77XNEhLSX5ujuOjb+ZgvNtS1s+t02bNfmQ19+D5fesopwNIzv++zasI8Xf7GJ1vo28opyWXfHpay9eeWk9XYXbw9goaxiBAXYYBWBtxWxZ45rAEbSjdx761OgQmx/9jgA9972eGaBcRXYRkjVMDkJgoD2hg7aGjqxbMWxPcc5uPUwNUuqieVFaT7WRuALLcdaue4t64nEwmx/+hUe/9EzpBJpbMdiy+M7mLP8Fd7wsdvIK5q4MuvToQr+GUk9NySYqJSLKCBohXEOYEjQzYi3oKAPrNEPYJxJD8RkVg2nw0/7tNS10dXag2Urjuw8yr5NB+lo6iKWF6V8dhnpVJqWujaufdOVKEux4ZHNPP+zjfiBj7IsNj++gyXrFnL7B181eavAglZQwTBzhWTminEOYEgfgzXPTn1sfDhl/mi61GReLzIisTDXvGkdzUdb6W7rIb8kj/KaUhzXGRLoHPzzFZ/9Z+KZ14tk3BOnCeJtA+8V9L1cELYgoaux3NkTNJ4d4O0EqwxluUjQgyQeh+htKGv0HGAGf+fN939imJw78wskCAKO7qrjwJbD9PX209XSjZdK871oI848m8qUh7IUHU2d7Hx+L5e+ahW7X9zHow/+gbf92c8oq2qhtbGcH349TjKe4No3r5/oSxqeoBniD+rghb9fH+v9JsTuBjxgPLPB/sgPyWkeMxgmEN/32fLEDur3N+CGXY7urqe9sQM35DJjQSWWZdHV0k1BSR4oOH6gkZIZRbz06FZsx6Fyjt5kB35A3f4Gtv5hx+SdLwBkpAfGX/xYWQUI3vAPWrHxHYzBcBInb0JSiRQv/PJl2hs7+fnXf0NfT4I1r1rJ5t9vxw053Hz3dbQca6OoooDu1l6aj7ViOzZbnthBJDdMblEur7nzQUSEh77+eva+fIDVN6yY4Ks8DTLMLusk8fRxQ+Vw/2+vQdlV/NVrfgHAl3/3hkzWd5yDKYaLHsd1mDG/khnzK4HTtI1kfi54ehe9H1tGOBoi/C/P8cF/vpu2ho4h1sxdrd288sJeGg81UTqzhBXXLKG4sojJjPhtOnhhVWTbuERS4G1AnCqUGt+KVJEUpPdkxqNF25WVh/hJJH0IFVo9ruMxjC3TMoCxb9NB9rx0gF99+zGCtM/iy+cT7+6nd6GHUopQxhrtZ199hDd+4nYWXzaPrU/t5GOf/RbhaAqA6tnH+It//C7tLb+iu+1h8ksmobaGVQAEgD3ooIAKM97/aZVdxv2PXgOqnHtv/TUA9z96uxb0tEandMtgGG0aDzdTv7+R8poygkBIbTlCNC9Kw6FmSqt1cCInP0bz0Vbmr55DZ3MXlq3oaukmf5Cfu2VbRHLC1O6uZ72XnpxVW3YFKBuRBErpzK9IErDAPn0l25hgVYBVqkUCrRIggKAN7NmoMai+gJErLYarzDBZlYubk9tGDu88SmdzN+WzSrEyjkabH9tGR1MXAE889AyBH1BcVUTZzGJ6Onr5yge+TVtDB7e+/+bseZVSvOPPHyYc+w3w2/G9qLPFLs/MFcmsSKZIEpStHxtvrBKwq5CgEb3mQf9sV4I1fhpb2XJxlafb8aQHMPOFYSheMs1HvvY+LEvxpff9/9njvh+QSnh0t/fw/MMbufbNV1JUUUj9wQZ+9c3HaG/oIBQLseflg2x+Yjuv/8itzFs5ciXDRFcMStAEOEM0aJQKIYGv1/525TgPKAEiKMseelxFIOgY37EYxpxJuMq+MFKJFAe2HObX3/k9R3fpnrPNd5QRlAXEa3SJdCojpjPzsU5A6O9NkuxPDVvXFQRCIp6YlAEM5SxDYu8GlQ/xbwMCsXeCu3LcRa2UVYA4l4C3FUQHgQjaIHQFymRTDZOU+gONxPKjQ47l5MdIpzySiVS2jx0g2ZeioCwP27F56wd+iu1Y/P6XH8g+LiI4IQdl6Xmkr6eftJcmpyCGbZ90Q50AlAojoWsh9RwSdABKb0hC16BU9IyvH/3x2BC5MeMscFCPxV2NcheN+1gMhjNRt/c4+aV5PPiZH2fXFk5omCWUCL7n842PfJeDW48A8MRDT/OZb22mslonTxL9IWznxD063t2Hn/bJLczBsibeCl6pyElzBaAcCF2bDX6O73gsCF+DeHu4/7cRIABnEcpdMu5rHYPhZAaCnPfceB+9nXFufte1HNp+lJZjLcxZUUN3ey9lv2jilvfcQPzOGbTVd1Axu4wDWw+48bKVAAAgAElEQVSz9uZVbPzVZno741TOq8i2frc3dfL8z19i5sKqSayt5TAQUByCgomo6kRFQTnDOJnFwR5/oXLD2DLtAhiJeBI4NRYxWA8iFHYREe78+G0UlOaT9ny8ZJqvffaDvPeTv6a4tIH21ir+63tvIa8oh7v+Kkp/PMHxAw20NXSSW5TDvFWziUQn1r5L2aVI+Gbwtui2ESsCzkqUM/4q4QBWaAXizOD+x9eAslB2FcoqnJCxGAxng+vaBL6+AVuWomxmMS31beSX5NLb0YvtWPR29VFUUYiyFJ0tPdTtPc6KFWm8pOAlPdywSyKewEv4LLtyEb7ns+mxbRzccoSO5i5c1+bqN65j2fpFEy4MbDkzEPtOCFoyB0on1IZQqTAqtAZCa8b1fU/OWBkNDMMAw5WDP/Dk57BDTnauGKCgNI/O5i4KyvIREQI/wHEdwrEwyUQq+7xEPDmkfSuSqfRMN7+Tns44j/zX3XS19BDJCXPdW9azcO3IgoBjwXCf+8k3V4RQoVUQWjVhYwAzVxhGxkt6JOJJymaVoJSifv9xCsvzSPWncFydyIvlx+ho7GDm4hl0NXcT7+qjo7mDcDQ0ZH0Qy43Q3dZDZ4uu+jqZoO3uEbWcxgtlz0D4IyKpbLuIBL2gYhNSea2Ui7irIbUBUQXaUCDoBiuKcuaO+3gMY8u0C2BEciMopXjv597GD+77CRIIb5IaOpu7eKTERymo/OERnf34zBXEu/v49K1fwEumKZlRTF93P0UlkE6lSfUlWfG6yxARnvjRMzz0+Z/ipdIsW7+I4qoi3vKXdzBrUfWEXq/lVIJzm1YIZ+KdU5RVjAoZ9wDD1GDWkmqO7q4npzAH27aoXlhF89E2CssLKSjJp6WulUhuhKXrFtLbGaem8tPMq7EpyG8EIPC/zX9/9y2EYiH2bjpA4+Em3v7pN1Jd8teUXpPiX794CyAc3nmU1330Vq6647KJvWD0RgB7Yuctg2GqMWf5LLY9+Qrv+8I7+P5n/4PAD1hyxUI2P74dZSm8pIcTclhy5QJa6tq582O38z//5xG8pEfF7HK++6VZ/OP3/wcF2VbV7o5e4p1x9mw8QCrpgcDhHbXc9ddvZNW1Syf2gjFzhcFwLnz4gfdSu7s+uw5XSuGGQiy8bB6zFg0Wslf09/RTOadMV23aFkEwNDia9nxCEQfHtTO/p+nr6ScSC0+aigxl5SLhq3XAIPBBCagcVPj6rAbFeGO5CwlUTGthSBzcBShn8YRUmRrGlmkXwAiFXRasncfuDfsI/ABlWVTNKUOCgE/GyvASKXLvW8bS9YtoO95OW30HbtjFDbu86u5r+cHX8nDDLrOXzuR1H17NossX8PKjW9m36TCJeJJP/+4gtnOQH33yOn721Uf44D+9i5yCiReRMjalBsO5U1pdwrKrF7N34wGdIBVh9U3Lmb96DhIIOQUxCssL6Gzu4rmHX8I9qWQ8ryiH1/zJjSy8dB6ff8sD+OmAg9trKbosgWUpYgVRJBC6Wrp58sfPsvzKRRSUjp4StmF0MdlUw+By8MG/1yyppruth9qdx/CSaUC45k1XULu7DgT2vnwAgO//3X/S09HLh778XpSCUMTl9R99DYe2H+Fg/duZu3I2pD9E2kvzyH+9mW1/2Ek4R8gtyMH3fTpbunj8oadZuGYO0dyxX3RPhkzuVMT8jQwn44QcgvSJQETZrFIO7ailqKyARG+CUCREsj9FLC+Cn/Zxwy7PP/wSXc3d1O1rYM4Ki9zCXNKeR19PH/NWLaGwvIAjO4+y9alXaK1vJ9mXZNFl87jmTf9GmA8CE/tZtJwaxK7UmhfYYBVPWPDixJiqwTGB1+nOtAtgACy6dB7R3DD5JXkk4gmq5ldyy3uuJ5Qp0XJcBz/ts3vjfn7xL49S+8oxAC366Qe8+x/u4oa3XgVo27Sju47xqjv+jRtv9bmkRpshhe/9LeFoiMM7r2XF1ROfKTEYDOfHwjXzmLloBj3tvbhhl/ySXFL9KdywmxXjTPalsCzF7r2fBWDp4i8A8MeXP8rCeR+heZvF9qer+NJPD+CGH2X+Mi3s955P/AqA791/Kx2NXTQfazMBDINhCmJZFquuXcb8VbNZd8elRGJhcgpi/Nf9v8zq3oAW6hvoYX3/P74ze7yovJDyWWXkFuQAD9Fd30ZT7X8iQCRjp+pYDjl5MZqPtdLe2En1ApM1NBimCpVzy9n70oFsa2lpdTHNtS34aZ+qeRU017Zi2RZL1y8mmhfm4LZaiioKWHvLJYjAvj8eoqy6mHA0zKLL53P9W6+ita6NF3/9Rw5tr6W3oxcCoXbXMer3N3D3x4NJopkTGn/BTsNFz7QMYCilqFkyk5olMwHobuvh2L4GgrRPxZxyiivPXpdBWYp4dz/9lZFMD6sOYPRXREjYFt17WkwAw2CY4kRzIkRzItQfaODlR7eQ6k/xDe8QkdwID7/nPUTzIgSBICLZ8tBYrJZ1V32NkrJWAL700/iI5/c9n3BOCD+dHpfrMRgMF8ZA5cXJ5BTkEMuPcWhHLc//4iVufte1uCGXZF+KSE6Yv/nBx9jwyOYhrxHRc8dgweC84lw2/nozXjLNpa++JHs8ECEUdvHTw4jjjQFZZw1MVYHBcCHkF+ex9pZVbHvqFfx0gIiw4NJ5LLl8PiIQioYorizET/s89oOnKa0uxs44Gq1/3WXMWT6L4spCLrlhOYXlBSil2PHcbg7vOEJ7Rn/PsiyS8SRb/7CLS278NGtuXDnBV20wTAzTMoAxmNrdx9j25C7+5+uPoIA7P34bC9bMZdn6xcxcWMWbPnE7D3/jNwD8yRffQfOxVmYvnTnkHOU1pfz5w7eTTqb5zvueAOCTj7wWlOKb1xiRSoNhOvCJq/6WrtYemv9yFZal2B/EoTvOm374Q6K5Ee6Zv5j6/Y0UluezbfunWLb4C7jhoa1b73rq9bhhh2/PehyAv/jDHdy4IYUdssktiGV94w0Gw9Tl6J46dj6zm+IZRTiug5f06O2MY1mKkupiiioKaK1vp6A0jyDTQlaztJq8oowTWiLFsb3HsR2bvp4E7U2d5BbkZDK3DgWl+ZTPHj97UIPBMDpUL6iibFYp3W092I5NQWkeftrHsq2sG1l/fz8SBNngxQAFZfmEc8IUVZzYV3Q1ddF0tJXiyqJstUU0P0q8p49dL+w1AQzDRcu0DmAk+pLseGY3RZUF2d710pmlHNx6hKp5FSy6bB497b14SQ+A1ro2Zi6somaJ7p3qbu9h06Nb+cmXfsHM5i58z8d6u49tW9y6XSisKGDBmjkTdXkGg2GUuOfG+9i9YT8ADXtKCEVDUKUV93u74iilqFxZTl5xLnX7GpAgoCP1DeYuruHgczcCwl+/ZQZ8TFdbDOB7PtE8Xd1xzZuuIL948tkxGwyGc2P/5sMUlBdkW8zcsMt7/uEukn1Jju2pZ/byWRRX9dJwoBHLsVl13TJqlul1RSrp8cIvX+b7n/0Jbce1Ren+TYdwXJvLXnMJkdwIN9993bi6nJnKC4Nh9AiFXUpnFNPZ0sWLv3yZ9sYubMdm3iWzWbhmLuFYGNuxSXvp7Bzy4Gd+jJdK86kffiJ7nqDt7v/H3nnHV1Xf//95xt0jucnNTiCLQNh7qIAIDqwbtY5aW7ustnW1ttaJVjsU67c/bW1rtWqrtm5QURwoorKHbEgC2fMmNzfJXWf9/jjhQhh2OIB6nn/Bueec+7nnkfu5n897vF5MndHDK38ePuD+SkLF6XGiawYWFl9W/qcDGJGOCM/dvwibw8aezabOxWO3PE00EqWrNUxGXoDMwiDX/+n7CJJIbnEWwYIMBEFA0zRWv74BQzdw9Jd9lY0t4QcLx+Bw2/j2yHxOmDeVtEyrn93C4n+J4keqyCvNpvHq4SSiCeSfLSMpiaz6VSa94T6WPv0BNrvMPYtvxma3UTammGRCIb88G8ej1eSVZnPn0Etprmmj7PGdDL/pXEbNqKR8rGXjZWFxrKPrOrGeOJn5gX3HNJ367Y207G5F13QS0SSx3hi5pTlk5KUTyE1LZV+bqluIhHqxOfYtv/yZXjpbwuxat5v5L/yEQcMLD3pfCwuLY4drp99Kd3s3l9w8j2BBBqqisXN1NUpCYdQJlQydVM7Hy7aw6A9LEESB2i0NAPzxhscRJZEFS+djKFtxu3SgkpbdbWTkBcAw0HUdh8vOkAnWmsLiy8v/dABDlA9Wwk30xUnGFd556gMkm8SoGZV8+NJq3D4nF/3sXMbMGkF+aS7htghPzv/ngOCHJEtUrEpw1W+/wZgTR+JNP/LuIxYWFp9MMqGgJlWcHscnCl45PQ7ifQkS0QQAmqIS64tjBxCgra6DSKiXWE8MfC4+eHEl42aPYtCwv7F7xU5E8SES0QR7NtcTqguiKiq+dA8zLziOrMIv3hPdwsLiPyMRS6BrOk6P87CW5KIoEiwI8Ocb/4Zkk7ji7kvoaAxRv72JYGEGugGte9rpCfcS60ugJlXqtzUy5YwJZBcF6Wjo5KX/99qA8nFPmpvujgiB7DSKRw76oj6uhYXF58ANs25nywfbAfi/7/+ZvNJsrrj7EjILMqhev5v0LD+eNBfjZo9i0cNvDtC7ESWRK299m1jjNhxSL7IMP/t/a+gLR3ngphPIKgricdspGJLP6JkjjtRHtLA44hxzAQxVUeloDNG8uw1RECkYkktmfsYhFxuBnDS+dtsFGLrBP+99GV3TCbdH0FWNpuoWAPq6o/SG+3D7XbjT3Kx982PSLvKhawcLaOmagSRLZA/OsoIXFhZHMUpSoa2ugx2rqwg1h3F5HDg9DkZNH07O4KxDXiNK+4IbuqaTdvca6IigagYoGs/f/woI9Iv5dvHnn/6Nq//vCvLLcnH5nBjGvnLO3Ac2oyQUGoG7LlyAzWE7rCighYXFkSMRS9Bc08r21VX0dvXhcNpJy/IzeuZw0rPSDnlN5dQKWutM8d5YX5x1b28i3NaNy+dk3ZsbifZEKRtdQjQSw5PmRnGqbF6+jVkXnYA33Y2hGzTXtKXuV7ulHsOALR/uOMi+1cLC4n+DSEeEh3/8BJIk8t37vk4yluTy+ReSnuXn1jN/jSAKLFg6H62jCjW2GfpjnNl57eg5BgXlueQUZ1E5tYLRM4bj7rdZtkR4Lb6MHDMBjFhfnO0rd7F68QbaGzrY8uEO7E475/5oLhWTyqmcPOSgayRJYvLccax5YwNKQiEeTaIklH4Pd5Pu9m40VaepqoW/3fksSkJl+NQKiobmc8ENZ+FJ9/CXm/5OMq4Q7YmZtqrbGikeUYTNbjvoPS0sLI4skc4eVryylpqNtYSaO3H73OSVZuNJc7Py1bXMuGBaamOi6zq94T7ufOlGPGkerptxK/G+BOdeM5c//fhJRFFE1/ZpWjCg5VRAUzWikRi5JTlc9LNzeOqeF2ivD+EP+gg1dgIQbusmPfvQGyELC4sjR1t9B6sWr2fXuhqikSieNDclIwaRjCt8tGgtsy46Hqfb1KLQVI2+SJTbz/kNoiSSjCUBeOKOfxLp6OHEi47HF/Dy1pPLMDDw+D0EctJ4/PZ/IAgCZ119Gsl4ksKKfOZdfyb/+M1LtPQHMcx+eHOeUZMqsv2YWZpZWHyp2Oso9K/sS11eJ7HeeKqi85Gb/s6oE4aZLwoC8b44z9//KmDgTnMT7YkBZvWGqhRzy8NhMoOmLlc0WgzApNPGESzIYMLJYw58OwuLLx3HxK+kpmqsfHUtoaYwbz/1PpIsoms6yXiSzLwMdq2ppqgiH2+6h0hnD3s21xNuj5CRl07JiCJO/OrxjJ45nDefWMZLv3uNWE/4sO8lCKAkVexOO6NPHM7bTy4j1htHtpkLDEGAjxauprmmlTlfm0FucfYX+CQsLCw+CcMwWP/2JjTVnB9yS3IRBGipaSM9y4/daaduawPpM9Poag2z9q2PifXEwTAI5Kbzi1duwuN3s2t9DRf85Cyaq1pZ8sS7GLrB+JNHk4gl2fLBDtSkSiKa4Ln7F/H239/nt8vuYuoZE6jeUEssGkfAnCckSWLo5CFkDwqyafk2Rhw39Kjwbbew+LKjJBXWLtmIKImIgkB+WR6aprNnaz2jpg9HU1RadrdSPGIQTTUtbHpvG8lEkq6WMKHmrtR9Qo2daJqOrmhgGCCAYEB3qIfCoQUIgoChG7zw21dY+vRy7n/3TibPHcuutTWsiK7BAEZNr2Tz8m1oqs742aOYdvakI/dgLCwsBmAYBl1tYWq31NOyuw1V1QnmB6icWnHYKq0BFZ26Qf22Rhp2NKJrBpqi8ejPn0aURPyZPvavH69avxuAFcuv4dQzbkSS4ihJlTcXfotErJWsQfvaUfdWXqCsSv3fqsKw+LJwTAQwQk2d/PWWZ0CAzv0WDmAq9+q6wcRTx6IkVT54aRXP378IURI5/7ozqd/WwPHnTsHlceILeHC47alrRVnE6XUiSRKB3DS+cdfFdDSEyC4yJ4iiigKyBmfh8jrRNT2lGP7hy6tx+918tHANx50ziZxBhy5Jtzj6MAwN9FYMvRtBTAMxG0E4Jr4GFv8G0UiUSEcP3gwvGCCK5tLA7rYTauoitzSH3u4o8WiCFa+sweF2EizIACAS6mHV4vXMvGAahm6w+JF30DWdvZ0h0Z44YKTuCeBwO1ILFYfbwaDKAtKCPnaurWHWRSfg9rtJxhUQoHrDHoIFGeSV5Hyhz8Ti02EYGoZaBeoOMJIglyLYKhEE15EemsWnINwWQUmqOFx2M3MBSJKIIIh0d0RwepzEeuJ0d0RY+8ZG/EEf/qCP7973df70kydp3NUMQF5ZDkpCZe2bG+lo6kq5EG16byub39+GoZsTiNPrNEX4AKfbSfm4EjYv30a0J0ZWYSanXTGbWG8cRJFtK3aRmZ9BZl7gECO3OFoxDANDrQN1Kxi9IBUg2EYiiJbY+7FKtCfGmjc2sGttNc2728wqrVGD6enq44OXVnHihcfhSdvXUq4qKr9eciuyTU5VdM677iss+NYf+tcS5vygazq6ptPZ3EVv2I4gCuSX5eBN9+Jw25kx5/cYuo4g62QGdzF69C8xDNj4wY0E8zNTbokWFl9WjolUYKwvAYJwSJ0LXTcAA9kmsX3lThwuO7JdRpRE0nPSEASRqvW7U7ZF3/7V1ygYkktWUSYnXzaDYZPLsbtsaIpGR0OIsrGDB3gw2+02HC77gP52JaGy8rV1LHp4CbvW1nwRj8DiM8Aw4hjxNzHiSyF8DUbn5RjxtzCM+JEemsVniAHYnXZkhw0lqfLWk+/x/nMrAIhGYuQMDtJW146q6Li8ztR1/kwfPZ29XDf9Vh665lEadzXTXNNKflkOuSXZdDSEaKpq4Tv3fp2C8lxKRg3i/z74RapX3WaXkWQRVVHp6+7D7TM3uGpSxeV14klzU7+j6Qt/HhafDiO5BpKrARsIflB2YsTfwTCUIz00i88Ap9eJgNlOZmL+1ivxJJn5Aep3NiHZZOxOM/khCALfvfcy7C4bbr+LO1/6KWdffRpdbRE0db92swOWK/HeOB+/t5UbZt2OzWkDDC6+6TymnjEBx942FUXF5XNid9lTOl0Wxw6GuhOSy8DQQEgHrQkjvgRD7z3SQ7P4L1n/ziYinb1Ee+Lklebiy/SzZ0s9giAgCCJ7tpgi/6YWzscsfvQdFj/6Duve/ph7Ft/MH9b+hoW/f4OsQUHmfvskbA4Z2S7jC+wLeggCGLpBe0OIHaur+Pi9rbTVthOJFKXOMQAEaNnTzj9+8xIfvbIW1fOoWXFhmwy2yVb1hcWXimMi9exJc3PeNacTyA3w+2seBczyLFXROPdHc/H43QRy0+l4rYuFD72emlAevfkpDMPg/OvPRLbJVEwqY9OybUiyhNMjkVUUxBfw8pXvziYRU1GTKvG+BKGmToIFZhVG4bB8zvnBXGo+ruX1x95J6WfY+oMk3R09R+ahWPzHGMpW0LsQpDwM+vVL9DCGsgXBPuHIDs7iM8HtdxPI9tMX7mPQ8EKq1tWgqRq6piNKIp40F4UV+dTvaBpQ4rmXvQsJc4Nh0rK7DV03UJPmd3/hQ4tpbwyRVRhk6dMfMGxyOcUjByHbZEpGDWLLBztMf3ZBMDV3kgo5g7LMAKxu+bYfSxh6L6jVIOYiCP1/L1I2htaCoTYh2AYf2QFa/NcEctKwOWR0TSd/SB712xtZ+epadE3n/BvOJG9YAZkFGdRtbxpgeQpmEOPK+7/BzAum4U33UDJqEMGCAJqq09EYwjAgtzgLURRprW0HQUhpZgC4PE4KK/LZta4mVaGRjCUxDHjj0XfQNYPrH7nyC30eFp8Ow1BB+bi/qrP/90PIwNDaMdRqBLulW3Cs0dfdR6ipi5cfXExfd5RTv3kSALJNJtTUSfagLLo7etA0jdWL19Pb1UdGrlk11bK7nUioh+nzpmIYpntR8fCiVEWn3v8Pm0MmEd07N+yLej78izlc+OOzaG+4Cgy4/izTnSh70CYmnz6e5uoWNjltTJgz+ot5GBafKYahY2iNoJktQ0jFCFLhvnWGxb/kqA9gJBMKaVl+sgdn0VbbjtPjINaXoHLqEAI56aRlpTHh5NHY7DZcXkd/RcY+dM3A7TczoaWjBmN32lEVhV1rdvP6X97B5rDRF4niTfOw4tV1YBic/YO5VE4dQmFFPqWjBtFeH0LTdLwBLz2dvaRn+/n2Ly9FSap4A5YbyTGDWg3RJzEQQDPFkYg+CZ7LwQpgHNPEowm6WsMIgsCI44ex7u1NPHfPy2jqvtav919YicNt56SLpxPISUdNKhiGkarsMrOnAr9+6zZcHic3zLqdZFxJ3bdltym4F+noITMvg+/d93VURWXFq2vpaAyRV5rLoOFmxqSxqpm2unb8QT8blm5my/LtnHHlyQyfVnFEno/Ff4lhZk4PWlQIdjC6ACuAcazR199mJtkkxs8exbq3NvHKw2+gKVpqrnjv2Y9wuOxMPWMiOcVZNO5qHuA8lownsdttePxufjLbrMBq3GVWTAiCAMK+/9tdNvLLcvp1uxS+dtv5NFY1M3RSGaIsUre9kY7GEOvf2oTT40hVaf35xr/xuw/v/iIfjcWnwYgDyr7gxV4EN2gdR2RIFp8OTdXNSgtRBMEU7ZZkCVEy/x3rjZFfnktXS5hwe2SAXXogJ432hhDXTb+V7SvN9eaih5eQX5bDWVefxnMLFhHvS5BZkJES8y0fV0LtlnrKxhazYOl81r+zCZIChnGAK6IAgbwATVUtjDx+KA6r8uKYw1DWgbINBJ95QK3FkCsQHFOO7MCOIY7aAEZXa5jNH2wn3NaNbJMZPKKQjNw0Lrv9QgQBsgYFKarIx5/pwzAMOhpDyHaJ486aiGEYSLLI1267gHBLmCHjSwFzYZEzKIjL42La2ZPYsbaaRF+CcEcPalIDDERJpKuli+cfeIXKKUNIz05n1PRhZBVm4E13s+z5FdgddpIJhURfggknW9HPYwfpEMcMjpFOKovD0FjVzPq3N2EYZvWEklQYNrnc3HDsF3hweZ1gwJ7NdVRt2E3L7jYadrUweFgBsl0mGU8y4vihuDz72krUpMrFN52Hpmg8/csXiPXGGTq1gvRMH5qq0lzTSsOuZlp2t1M+thjJJjHptHFcPv+rXDf9VnTdoKnK3Mi8+ue3OO6cyXS2dJGW5UeSDvX3aHFUIbhBYECgCzC1MATLWeZYwjAMdqypZufqKgRRMJMdhsHwqRV40j2IgkDLnnYAnG4HhmGwc2011Rv2UL+zkZY9bQwaVoBhmK0eE04dgyQf/B22OeQBFVu+dC/fuOsi/nDdX0lEk3y0cA2FFfn9AZIJXD7/q6x4ZS2bl28/IAFj0NXWTVrQZwn/HgsIDkDGMNSBulpGDGRLr+BYZP68++hqDaeCim8+8S4nXXQCsZ44GXkBc29SWUCoOYx4iBZ3QRDMasx+VEWlvaGT5+5bmJpr3D4XDredYEEmZ37/FF783Wup8wdVFrL8hdtpb+wkWLgMURSZcsYEAjlpOF12ejFQFQ2HJcd0TGHoYVNTS8xLJUcMwwtqNYZtCIKYcYRHeGxwVAYwesN9fLhwDQ6XnZcfep1ENMnwaRVkFWQyae5YKiaVpzYZuq6z8d0t1G9v5IXfvYYSVxg9czhFwwqI98QZN2fUANG8cHuEfy5YiJpUaas1o+IdjZ2Ikulssvf9b3l4PXllOaxfdyOrXlvPrItPIGdwFqNnDifcFsEX8BDISWfVa+vRNJ1BlQVUTCjF4XJ88Q/M4t9DHgLuS80Wkp4F5jH3JSBbWfFjlWhPjPVvbcIf9PHEHf8k1htn/JxR7Fxbw+xLZzBsSjmP/OzvACxYOp8tH25n47tbSMtOY/i0oTTuaqalto1xJ43k8Tue5b1nP0ppWixYOp8PF66mYUcTjdUtpg6PKCCKAk01LTTuaqG5po30oB8loRAszCQeTbDqtXXMuWwGgdx0lISaCmAocYWPFq5BFMDpcTLxtLGHVTC3ODoQRD+GVAzqbgwxCEigd4HoRbA2JccUoaZOdqzcxaI/vomh60w+fTzh9m6qN+xh7hUnMXrmcP7v+38G4L537mD16+vZvrKK9Jw0Rh4/jPrtjbTXdzBuzigGVxbhzzQzZ3vniysqr0FJqkw4eTQOj5Mlf11qCvMNyaWlppUJJ4/B7XMiO2xkFWbSG+5j9eL1vPaXt8GAX71+C6qics+l/0e8N87JX5/J+8+vwJfhZdKpYwdUgFgcfQiCDcM2EpJr++cKOxgREAwEeciRHp7Ff4Cu60QjMXTdrLzei91ppzcSxRvwMHRSGY/f9g+WPP4ut/zjenRdHxDo3mu3etfCn/KLi34LwM3PXMd1028lGd/XTtbZ3EVa0M93fnMZSkLh5MtmMHh4kZnoCPoYO2sky55bgZJQkWSJla+uxeVxkl+WiyfNndLasjiG0LvBwKzs6Va4UMsAACAASURBVEcQRLM6XO8GK4Dxb3FUBjDqtjXw3IKFyHaZ2i0NgLn48GX4yB4cJNQSZsa8qcg2mY6GEHXbGskqysRml7HZZSaeOpZYX5xZl56A3T6wnE+SJbSklsqOpOgPnv7muSrsThuV47uBbsaO/TXxvjhvPflTxs8ZzcRTxyKKImve2EBzTSsLH3odBDj3h6fT1RLm+HMmHzIrY3HkEWzDMPQuDK0BUAADpCIEW+WRHprFf0moqRPDMLA5bMSjCTRVIyMvg0hHBLvTxs7V1dzwl6vIL80h1hen5uM6gkXBlJPI4BFFdDSGyMzPwGY/eDpMy/LxwYsNpOekccrlJ9Kws4l4NI7NLlO/oxGH20k8miSYH6AvEqVuawPNu9v4x29ewu138/9W3MONc+6kq62by+d/NSXWF43EWLV4PSddfAKy7aichi36EeyTMQSfmTFBAbkYwTYKQbD/y2stjh6aqlpweBwImI5CmqqRXZRFJBTB7rKzYekWNEVDskmE2yO07G4jqyjYf7VM+bhS2hs6yCvJwZ/p44ZZtwP7Ahjf/c1lvPf8CnyZPgTBDHQaBtgcNuq2NuLwOkhEk2QVZdLT1Uvtlgba6toJNXbh9rvIHhwkGokR64kh2+SUDldPVy+r39jAzAumWZUYRzmCXImBHdTNYIRN7Rz7GATRd6SHZvFv0tnSxfp3NhONRDnp4hPIyAvwzK9fRNd0fvjgtwkWZJCRF0AURf4mP4eqaDTuaqY33EeouYuiYQVIskR3R4TCIXmkZ6el5oiOpk7mXfsV/EE/j/78KWJ9cYZNKseX4U0FNdpqO9i2YheVUytwuOyMmTWCedefQU5xFt3tEV575G1URSPRl2Dy6eNQEgo2h+2QJgcWRymHXTsYn/CaxYEclSvnns5eBFFEU/f1fSkJlc7mLl5+6HU0VWNwZQHedC+71tbw0oOLkW0Sezab4p3P/OpFknGFyknluP1u/EEfHr8bAHeaiwmnjuH951Yg26X+1hFwe13E+syNiS/DC4QBM8Mb6ejh6V+9iKbqFA7Np2zMYB665lFsDhu1W80Ay0sPLkaJm6Xr2Zat6lGJINjAMd3sXXdMB9EDQsCa+I9hdN3g+QdeQbbJtPaXZL715HtMPn0cgijiTnOzadlWWmvb6AtH6Q33kpk/0Jrw+Qde5YUHXqVhp2mLeO0JtyDZJH7z5m0oCZW6HU201baTUZCBN81NZ3OY+u0NXPajRfgyvLz67OVk5GewY9UuBFHCm+5BssnE+xJsfHcLSkIBg1TwAsDtd9HREKKrtXtA36zF0YcgyAj2UWAfdXAricUxw0PXPkYyrtDQXw7+wYurAJg8d7zZ1y4KnH/9Gbj9bja+u9l0PzsANanx01PuQpTE1Hrjhlm3Y+gGZ//wNOp3NJoi4PkZTD1zIo27WqjesIfq9buZNHcc6dlpeNI8bF9ZxUeLVqNrRsoa/upJP0OURC644ayU3SqAL+CloyFEJNRjVWwd5QiCgGArB1u5NVccg8R6Y3y0aC2ufsvjhp1NbH1xJ211HXjTPWTmZxAsyEwFLz9+bysAC771exAEZpw/leoNeygfX8KYmSMYNKxgwN9Ad3sETdXZ+tFOkvEkuqajJFWad7fRWmuKfqqKhifNjT/Th2STWLtkI7MuOoHXH30HZb/5a+Ef3uDZ+xcx75qv4Pa7GHH8MHIGW3uPYwIxC0Qfht6FIJpzvaF3m3sSMfsID+7Y4agMYAQLMzj3mtORZYnfXf0IgkDK/UMQBJJxhXf/8RHvP7+CRCyBrhkDxDQ1VSfaE2P++QsQJYF5155B5bQKCobk8dHC1dgcMn3d0VTLCJhCW5Is8sITF1E6ZjBzz/srfd0x3lz0bZb8dSlun42sokwadjT2i4Ie4odJEOjp6rMCGEcxpsBahlWi9T9CsD8Ysb/NsWEYfLRwDZuWbWPGBdPoaOzkmV+/hKEblI8rRpSklIe6klCIRqLY9qvUCrd348/08fGyrdRta6Bs9CAioR5CjZ3UfFyLN93NzQ+vZ8SkPiDCRd97AU1R2bHyPD56xdwUdTR0AvDgD/+Cy+vk/OvPPHjwAgNtFy2OeqwNybGLw2UnfkBQQtd1Vry6lk3LtzJ+zmjqdzZTNmYwkVAPu9bV4PI6UwHGWG+cHWuqScbNjOdeVEUl2h2jq7WbwZWFRCN9tOxpIy3Lz/R5U3ju/leQJIFgXoCy8aX85aa/m0FNQUC27avWjPclcLrth3RHMucK/eDjFkct1lxx7NGypx1dN+3V67Y10FzTRrAgk2lnTaJ8XAkrXlnDzAuOAwauOeT+6s1hk4fQXh+iYnwpD1z5J2BfhRaYSYy2hg40ReXUb55ER1Mn4ZYwNrtE/bYG7C47nnQvWkTD5pCJhHrZs7meV/60pH9+2JcEifWaCdfMggzifQlWvrqWE86bknJBsTh6EQQZHLMwkiswNLM9GTGIYJ9ysAiwxWE5KgMYBUPy2b2pjnB7BH+G19Sn0HWcHifn/uh01r29idySbGwOGVESKB9fSkFZDstFAQyoPK4Ch8vBhrc3pdR6t3y4g46GELGeOJVTKnj/uZUIgpnBtTlkLvn5PDxpbrIHB9m1uho1qRLpiLDkr0vpbO6isxkeu+VplITKdX/6Hudd+xWyCjN59OanALji7ktor+8Y4O1sYWHx+aDruvkD7rBxxws3sun9rfztrucwdIOpZ05g3Vub0HWDum0NjDh+GK8/+g4Ap3zzRHatqSY92483zUPNxj1MOnUsU74ygSfvfBaAi286j3hfnPrtTWQVBbE57excXU1WUZD67Y0YBzih6ppGW32I919YiWyTcB3QkypKEpq2z8oVzE2PIAikZ1sZVQuLzxNVUUnEkix4dz47Vldx7zd/TzQSY/yc0Tg9Dta++TFqvwPJ5Lnj8AW8/O6qR9A103Fg2hkTke0S21fuxJPm5soFlyNKIo/e/BSqonHmVacR7Y6SVZiJYEDNpjpyinOIRmK8/ODrdDSEAFjz1se8/+JKkgkFQ9OZ/bUZVEws46m7nwfgrKtOY/TM4WxcuhldN1JtbsmEgmyTSQtabQgWFp8n8b44siyhKiptde34Mr2IooggCNjspuXyrvU1/OSxq+kN93HXhfdjd9q44u5LUvdw+ZzUbKolEU0iiKAklVSCxJPmpnl3K1pCNZ0N09y07WmneuNudm+qZ9Lcseh6D4MrC2mr66Bue6NZpZFQOe2Kk0jPTsPAINwW4Zu/uDjV9ur0OFASCtUb91gBjGMEQfQhOE/G0PsAA0H0/strLAZy1AQwIp09VG/YQ2dzF/6gjxHHDyPc2k33N08kEupl49LNyDaZqg272fLhDmq3NlC7xSzh1FQdAUjGFQzdYMWitfgCHuq2NQLw+G3PoCZVZlx4HB+8sBJBFFJlm7JdxpPmJj3Lz4RTxuB0OwjmZ7B5+S388cdP4PIeKMppkJmfgRJXaKpuSXm4h5o6Sc9JJ7PAyuxbWHyetNa2s2nZVmJ9CQTB1LGYccE0Xvvz2/SG+1j7xkaaqlsB6OuOsuy5Fanv6VO/eAFd0xgysRQloSKIIqOmD8fmGLgIaa1tx+G2IwgCgew0KqcNobm6lSmnj6d0bDGP3x/g6vR3Afj9HRPYvHw70M2gygJESaR4pOn3fvbVp3HK5TNRWi8h2hNjzcrrMXTQNI0xJw4fkFGxsLD47DAMg92b6tixugpV1ZBliYqJpaRn+3G6HeSWZLPk8XdTIrvJWJKVr64jrzSbRNSs1Ni0bCsV40tx+ZzYHHaGTysZUCEhyRLNVc2kZ6cDkD04C5vTRktNGxGlB5tj3xLL7XPRVNVCNBIDYNXi9ax5Y4M5Vt1AtksUlOfS3R4hN/BjRFFg5YfXousGE04ebWnlWFh8zmTmZ7BzTQ1qUsUwQBRNcX9BMF3MOhpDfPjSagaPKEJJmtWbgrgvaWkYBo/d8jSGQSpw+d0xP8af6eO2Z2/go0VrSM9Mo6mmhdqt9didNsrHFdNY1YySUJBlieLKQjLyA2x8dwv+gNes6i4K4s/00d4QIhkz9zkHanY53HZ6u6Jf6POy+PTs//dj8Z9xVPwiRkI9vP/CSiRJ5Ln7F6FrOuf+6HSmnTWRb8y/iNqtDYyeMRxNUelsCeP2Otm/Os/usDFsyhAun/9VlITCbef85hBlmGa5pmEYCPu1f+SWZHP2D07juLMnpUr+nG4HLp/T7KMHDMN8jwt/cja6ppNdlElucRb+LB9OjwNdMygalk/5uBLLGtHC4nOkuyPCqtfW4cvwEkz3oGs6C771e5xuJw+vv5dkPMn1M2+D/gCG0+NIBS+AlFDWrK8eT/agLNYs2ZgKZu5FUzUcbjuiKKb6mP0ZPvwZPjoaQ4yfM5r3n1/Rf7ZBtCe23/h6cHoc2J02NFVn6OQyHC4HNp8Lu9NG6ZhiREkgtzgbf4aVUbWw+LxorGrm42Vbycw37Q5VRePOC+7HG/Dwxw33Ee2J8eHLq1Pn2512kvEkzTVtqWPdHRFO+caJ+AJelj6znP2Lr664+xISsSTd7ZEB5eSBnHQCOem013fwrXsu5Z5LHgDgl6/fwg8m/4zdm+oAiHT04PI6uez2C4hGYow4bihi5BuMHAEoVQCceOofsTts2HKe/vwelIWFBQDBggzyy3Np2NmEmtSIdPagazrFI4owDIPtq6opGzs41VY29ztzaN3TTjKexO60E2rqIhlX8Gfuy6YLgkBPZy8b392My+tkxHEVIIDdaSPRl+CDl1bTXm8GO3ZvquPjZVs5/dsnE+2J8cFLq7A5bEyeOw4w2+C+d9/X6WgImS0lnn0JkGh3jKJKyxnL4svDURHA2LWuBtkm4c/0IYgCkijh9rvZ+tFOZpw/jdLRgykdPRiAPVvqcHldZA8Kpto3zr/+TPyZPvLLctE0jctuPR9Rlnj6ly8A8I07L6K9IUTp2MF4071kF2Xy19ueAQPOuPIUhk0Zkgpe9EWiLH9xJZIssfXDHcR641ROrSC/PAeHy87YWSOxO02V2IrxZZSNLkZTNUsF2MLiC6B2WwOyw5YSxBQlEdluOpDsXUQ8uPJXKWG9U6+YxT9+/TL1281qrMKKPMDMtNww63aUpMrsS6bj8DiwO2zomk6ouYvKyUN44Pt/It6X4Fv3XIIoiUQ6esw2s0HB/r7W+exYU83p397FooeXgGFw6hUnUbulnqGTy5l4yhhyi7PRQ18DZRUyUFp0ixkU8fztCD1BC4svB1Xrd5MW9KcqF2SbhGyTifXEAbMi4qFV5lyhqRpzvjaTh2/464B7CILIHefdy4Kl8ykfX8q6JRuR8zOQbRKqohFu62bMrOE0V7fxxx8/gWyTuPzOr9LV2k2wMJNAzj4Hgg1LN3PmVaey8MHX0Q2Dky+bQcPOZgTguLMnkVWYiR4a+Blkm3RoTQwLC4vPhN5wHzUf1xJq6sKf6aV09GAKynNxeZ1Ub9xDXkU+6dlp1G1vQrZL5JXkpPYeX7/9QpY8tpQtH2znqzeeQ8ueNi644UwGVRYOaC9vrmkl1NRFYUU+AEPGl1C/vRFN0wdYqtpddtJtaTx55z9RlX1uiY/d+jRX3H0JSkLFm+4hZ3AWK19di5JQcLjtRLtjIAqUjBr0BT89C4sjx1ERwOho7OTZ+xYiiMI+J5Ffv8hZV52KpmkDqhoKK/Jpre2gZU8bSlIFw0AURUZNN60wJUliwiljWPGK+eUGgVBzF0Mnl1MxoRSbTaZ6/Z5+UVCD4pFFlI8tTt2/bmsDz95nWrjubUFxeesYNX0Y086amOpl01SNnWuqqdlUi67ppAX9jJpeSSAn/Qt5ZhYWX0aikSj2/QT0Hr35qVQr2Y0n34kkSyxYOj+1aYiEenA47dx7xUOAwOXzv8r4OaNTVsem7fIYNr+/nZ5QDwDlY4upmFiGJ82NJEvEownUpErh0HyGTChNzQG6rlOzcQ8ZeQGzIkwQKBySR2ZeILXQAQZkbbs7IgCsfvU9xs4aSX5Z7uf4tCwsvrxEIzH8QT9AajNRt810DTvQAlWSJSbNHcsLDwTRdYPGXaYj0ZDxJan7PfC9PxLrjXPmlaegqRqiJDLi+KEUDx9EQXk+T85/lnhfgr5wlCHjSygbW5KyPU3Gk9TvaDLdRQQQBYHBw4vIyA2QnpOeyuiKmX+js6ULvfMyDE1nxfIrsDltTDg5ZLkVWVh8xvR09fL+8ysQBMF0BmvspKmqhalnTuS0b55EqLmL3Ztq6euO8d4/PiCZUJh+3tTU9bJdprMljADMvHAaNRv30NLvhrY/Lz/4OgYG37v364iSSEZugEB2Op2tXUyfN5V/3vvygPMPFBvGMAMtkiySV5aDy+PkhPOmULOxlp6uPooqCygZNSjltmhh8WXgqAhg+DK86LqBJO6rYNA1A5fPdZDvuWyTmXTaWDqbuxh5/FCcHidZRcEBm5pATjqzL53O2FkjUZIq6dn+VLl25ZQKSkYNZsoZE+juiBBui7B1xU6KhuaTnpVGuCOCcMB7ipKIbJNJxpKpzcuWD3ewZ3Md02f/HkEQWLP6Bj5cuIYTL5yGJ83qabKw+DzILspiS/2OfieggRwqU+nP9DHzwuN46cHFAMw4fxo/PukOqjfsoa/b7Be9/zsPYxgGd738U2wOGzfNvRuATcu2AfDGY0tJxhUu+PGZvPPUcoIFASomluPP8KIqKpIsDtDPkG0Syei+rIqR/jjhnWcDULX7DgC86Qpr3/qYtKDPmi8sLD4HcgZn0d4QIq0/iHE49ncJ+PPm++nrjnJZ6dUIgsBvl90FmAGP6g17KBtbzMlfn0kimsDhdiDb5FQwpGr9bgBe+eObxHpjzLvuDHJLsqmYUIYkm3OTKA6s0pRsEonYvrlCSSqsfG0dU6YJiA4bwcJMEtEEqxavZ87XpuNwWZo5FhafFVUb9iCIIulZ5hxhd9qJ9sTYtmInWedPIzMvkGoBq91qBj/vvvi3JPp/328/9zcYuoEBzAteQeXUIcy+ZDp6QE+tCXrDfch2yQx2NHeRkZ+BKAqoqoaa1CgbMzg1Hl3T6e7oSf3f5rCh6zpTz5yAy+tk1PRKXB4nABm5ATJyA+i6ftA+ycLiy8BREcComFDKWVedSlqWn6fufh5dNzjzylMYOqn8kG0ZoigSLMgkWHD4jITdaSevNOeQr9nsMtUbdtNa18HLDy7G0OHcH81l/MmjCeSkM++6r5CRG9hXJnbHhfR29aXK1uPRBLVbG5g+5w+k+bcDMHHSAtSkSv3OYoZNKv+0j8TCwuIQFFbkUbetgY7GTtx+F/OuO4Nnfvkivgwv97975yGvEQSBB97/BbBvI7I3eAGkNiaHCyQkY0kinb0oCZX0LD/dHT188MJKpp8/lexBQcJtEZ67fxFglov2dPamWt7A9H7/8L0fEdxP4NfmsCEKAq21HZSOtgIYFhafNRUTy2ir66CrJdzvLJTg2fteJi3oHxC02B9JkvBn+Hg5/ETq2P5zxsfvbeWnp9x12OvBzOrKNgl/0E9bbQdtdR1MnzcVb8AzQC8HTJHh/eeKzuYwakJlR9XtqWMOt4Oezl46GjspKM/7bx+HhYXFAXTUh/CkDaxacPtcdDSEUBX1kMK5Ayzb9YGWZNtW7EJJqMR6Ypx33Rm88MAriKJI/Y4mwAxuJmIJ5l13JrJNYtxJIwkWZKbmk6oNu9m+soqXH1qcuqeuGwQLMph8+vgBidr2hhDbV+0i3BbBm+5m6KRyq6LT4kvFURHACBZkMuUr49n20U7Ouuo0nG4HQyeXUTjk8/mxbq1tp7W2g+xBwVQpeXp2GpuWbeP4cyezZ7Np4WoYBoZuEGrqYtSMytRklowlEQQ4MLQiiAK9XX2fy5gtLCzMwORxZ0+ifkcTrbXtZOYH+O3yXxD4D+xIy8YW8/F7WwHT1qxsbPGADcnef+/NrJ7+nTlIkpgKYPoCXiJ6D9UbdlM5tYIPX16NklARRYH2hhDedDfF+/Wi6pp+6IEIAqqi/kef38LC4t/DF/Ay44Jp1G1roKu1m5zBQR5c+cv/qOLpUAHPA9k7X1x/4m10tYS54p5LkW3muiIty09nSxf1O5oYM3M4N5x4Ow07zfaUP97wOLJdZu63Tkrd6/Bzhem2ZmFh8dnhzfDQF44OcPRIxpM4Pc7U3uDA9cBdC3/GjXPmU7e9kbIxxf0OZPtormmleGQRE04ezVtPvIfskFMBDG/Ag9vv4vhzJxHvTRAJ9VC3vYHc4mzsTjtdreGUMGdzTRt5pdl8+5eXEmruIt4XTwUwOlu6+HDharxpHoIFGcT7zCqtSaeNtYKcFl8ajooABkBeSQ65xdn9JdnSgJIowzCV/kVRwOU9uHT8P6WtvoOXHlyMbJNSmhtP3vksSkJl2lkTOeHcKexcW828a8/A5XNSPq6EgvI8NFVDEM1eOUmW2LT5JkaN/CUA23bcSkdjiFHTLQ9mC4vPE7vTTtmYYsrGFB/0mqZpdLV2o6saaVn+g0qu91+M7K28+KRsqqEbRCOxAdUTYFqqdbV248/w8cZjS1M982/8dSkOp50TzptKd0cEh9uBP+hHkkWUhILNsVc/w0BTVLKKgp/mUVhYWHwCHr+byikVh3xNSSp0tXabVsk5aYe1KT0w4Hm4+ULXdAyDVPBiL06Pk+62boZNKictKy0VwPBmeGnY0cSNJ9/JHc//BJfXSXq2H0EQUBUtdR9N08GAjFxLX8vC4rNkyLgSlr+4Cskm4XQ7UBLmnDBu9sjDivK7fS4cbgdDxpeyYOl8ThYvSL12YELkdx/dAwzU3FGSCqtf30B7Qwi7w4aaVNnm2sW0syaSlmVWbV1x9yWpCnBN0xEwBliu71pbg9vnSrXSOj0OEPzsWFVFflmuZShg8aXgqAlggFnqvVdjYi/h9m7Wv7PZrGwwDIJFmYw9ccQhAxmaqhFq6iSZUPEFPIftfXV6nKY36kGY3sqeNA/jZ49OHY109rDy1XW0N3Qg22RKRg+mYmIZm97fhjbMDGqEGjvxpHusEi4LiyNEJNTDqsXrifbEzAopQWD0zOEMGlZ40LmGYVofXz7/Qnauq6agLPegzOyCpfMxDIO3/76MRCyJw2VPvRbrjZOZbwY19tfecLodxHrjfHf0DQDMu+4MikcWMXrGcNYv3YwACKKIpqiUjytJ9d5aWFh8cbTWtbPuzY/7K6AEbA6ZiaeMOagtde+G47rpt1K7tYGC8lxiffFUH/r+3Pv27Sx5/D00VUtlbwHivQkKys11wQPv35XazNz79u1cPelndLV08/7zKxEEg7JxpQw/fiib39+GJEtmMEPVGD6tAm+61WpmYfFZEizIZPLp49i2Yhehxk5sThtjTxxxyDXDoao0Ad7Un/23EyIA9dub6GgIkd2fvHj05qfQFA1fhpdxJ43kl5f+DoCG/qqNP9/4JDf9/ZqU+yGYYuAu38DWF6fbQUdjCF3TB8w/Fhb/q3zqAIaiKDQ0NBCPxz+L8QzA0A3i0QTpZW5s3eYxh19i+44dA6KRYDoCJGNJesNmqae7y4VcLx3S3lR36/zgiW8giiJ93WbLh9vnRpRF6prqoOngMdhzRAIu09u5z4gg6zLlM4uoj9+NoRsMmmYKfVbvrv7Mn8PnjdPppLCwEJvN9q9PtrA4CtF1nTVvbABIqfWrisqGdzaTnpWGP9OXOldTNS78ydm07mmnuaYNVVHZubqaKV+ZcJDSvyAIDJsyhDVvbCQt6MfhttPXHSUZT1I+rhgwFzPnBC4H4IZHvs+qxet55Y9LEASBjLwANRtrGTZlCCdddDyttR2oqkpWQSbp2WlWpsTC4gsmHk2w5o2NeNLcqaBkPJpg9esbmP21GQP6zBOxBB8tXENvuI9gYSanfXs27/3zQ447e1JKGHwvNruNIRNK2PrhTrOiw26jt6sXUYKiYQWp86o37CHWG+eHU3+eEv58+aHFXH7nRWxfuYtxs0dy4kXH01bXgWEYZBcF/6UQqYWFxX9HfmkueSU5KAkF2S7/R4KYB7oZHY79X2+sasZ3wNwhyRKhpi4kWSIt6CMa2aeV09US5s8/eZIF7+67R0ZugFBz14B1Taw3ji/DawUvLL40fOoARkNDAz6fj+Li4s98MZ6IJWmqbkEQBDyYqr92l93MnpbnDyj57A33oalm+ThAsCADJani8buxOw/emCsJhWhPHEM3+0plu4zdaUPTTEVfm8OcyOLRBM01rQiCgFcYOIbBlYXHvEe7YRiEQiEaGhooKSn51xdYWByFdHf00NsdHRCAkG0ysk2mZU/bgB/65t1ttO5pI3tQVupYPJpgwzubmf216QctYAqH5COKIjvXVvOnnzyJbJe55ZlrqdveyI5VVWQVBSkdPRhBFLjtnF+jawb1200L5r/e+jRfv+NCajbuYcj4kgGCfRYWFl88oaZOdFUbUFHl7BfK7GzuIrc4O3V89+Z6erv6+N59l6eO9XT2svXDnUw9Y8JB9x4yrhS7007V+t08dssz2J02bn7mOnatq+H3P3oMu8uW0tPYa/+8F0ky3RCqN+zhpIunHxQgsbCw+HwQBGFAhQOYiY7qj/ewZ1MdqqJRNKyAIeNLD0qeps7XNPq6o0iy9Il2pvJ+zkOP3vxUqo39+d++wtKnl6cEx/cmRUrHDCYeS/DBiytBFBlUWUDp2ME0726lp7MXt99FPGraNx9qTrKw+F/lUwcw4vH45xK8AFLBhUO+tl8LiK7pqEmVcHuEZP/E0NHYCYbZEnKoAIbNYcNvl82+VSDeG6e3O0q4rdvsN81Lxxfwomv6QWKdqffV9WM+gCEIApmZmbS3H+xdbWFxrGDoh/meCgKaqg041LK7Dbf/EOWXXX30dUfxBbwH3Sa/LJf8slwW/WEJybjCxqVbiPbFScaS/O7qR2ir6wAwN0UHDESSJdSkiq5ZdmcWFkeaA50DPum11wnOQAAAH5VJREFUpqpmfJkD5wNvwEP7YVwKBEGgeHgR/+/qR1K6OLed9St03UDXdfoi+84NFmbS0dhJXml2ynJRtsv0dPZ+mo9nYWHxGbBh6WYadjYTyE1HFAXqtjUQaurkhHOn8NNTTHvlvdo4Pzru55xx5Skpe9VgYQZjZ408ZKvZ4OFFrFq8/qDXHG47wn42y7Fes6p9r537o7c8zSU/n8faJRspGTWI6fOmsnNNNZ0tYXwZXsaeOOITnRktLP7X+Ew0MD6vMmjJJhPITkO2y2ZAAsjMz0BTVCTp05dJCYKAJEskogni0QSaYvoyg9m3CuDyukg/1BhU9ZgPXuzFKmO3ONbxZ/qwOW0DtCp03UBJKOQMzhpwrt1lQ1MGBjX2BkQPFODby95S0b0Llo7GTnRNY/r50wbcq3BoPvG+BDaHObVecfcl9HT1EizMOKxIoIWFxRdHIMd0LNpfq0JVVERBID1noJuR3WknEUti32+vsbfHfP/NxieRTCjomsH086eiKRrLX1xJVmEm37rnEh784aMDzo2EeikoP7T9u4WFxRdDpLOHpqoWsooyU+vjjNwA7fUh2upDB58f6sXmsOELeDEMg66Wbta/vYnjzpp00Ll5pTkMnVhG1YbdnHXVaTz/20XYHDbufes2lKTGtdNvRZLFgxyJREnE7Xfh9Dqp3dpAyahBTJ47/vN5ABYWxwBH9YpatpkaFkpCAcPAADRFxel1DggeiJKIbJcJ5KQd1EJyYFnYoUjEknS3R8yJqn8jE+2JYRgG3jQPoiSi7t2kGAaqouLyOKxsqoXFUYJskxk/ZzSrFq+np7MXQTQrL4aMLyGQM1C9v6gin92b6lCSaso+LdzWTU5x1r/tcqRrOrJNxuN3c+oVJ/H6o+8gySL3L7uTjxauIRqJ4fQ46GzuQpQEhk8b+pl/ZgsLi/8cT5qHEccPY/PybYiSBJh26WNmjTgoK1oyahCrX9+Aw+1AkkQMw6CzJcyQcSWfmERZsHQ+1xx/M7vW1WDoBqddsc8qVRBMu/Whk8q56GfnoCRUesN9xKMJHC475eNLP6+PbmFh8W8Q742DcHByT7ZL9IZ7B7iZxXpifOV7J6daS/a6GnU0hIh09hzUCiYIApVTKygeWURvOMpbf1tGMpbkvWdXoCQVOlu6BsxDdpfd3AP1I4oCggB94ajVZmbxpeaoDmAIgmlZqiTkfjFOsDntAzybQ6EQs2fPBqC5uRkBgYxAJpIs8v57y1OZ0E9C13XTInW/2m9N0YhGYmiajjfdQyKWJCM3HUEU2Lp9C53hTubOnfvZf+j/AFVVCQaDhMPhIzoOC4ujgeyiILMvOYG2+hBqUiUzP0B6VtpB5wVy0hl30kg2L9+Opuqmu1FhJqNnDD/svfcuWK6feRvbVu5CFAXmXDYz9bogChi6gSAITD9vCo1VLXS1hHnkZ3/D4XYw91tzPvsPbGFh8V9ROnowWUWZtDeY2dTsouAhXT7yy3IZNmUIVetqALPFpGhoPkMm/OsggyAK6LpxUKXmCedOxpPuxu13MeOCaTTuaibcFiGQ46dgSP5he+wtLCy+GJxeJxhmZeb+QQw1qeFNH9hSpuvGoYUzBQE1qR72PVxeFy6vix8/ehUfvLSS1tp23nriPQxgwsmmC6LdZaens4eOxq4B1xqG+ZqFxZeZozqAAfvEdQ5XSZGZmcmGDab7wO23347b5ea6a69DlKTDloPvj6Zp2B02JMksCd074YiyiCAICIKAKIm4vE5cXjMqumnLJjZv3nzEAxgWFhYDcXldDK482ALtQAYNKySvNIferj5ku3xI3YtDcf97d/L18quJ9yVSixtVUZj6lfHkleUiSSJ2p52SkYMoGTkIl+/fq+iwsLD4YvEFvP/yey8IAsMmlVM8oohYTwy7y/6JAn17uWHW7UiyhKZotNeHeOvJ9wCYecFx6LpBZl4AQRBweZyUjy35t90MLCwsPn/8GT7yy3NNDYycNERJpLsjgj/oJbton87EgqXzaa1tZ8UrawZcryRVJFnEl/Gv1xVV62po3d2BzSEj9e9ZRFFk6ORyrvzt5fxg0k0kogn2bK7nLz9/Ck1R+eYvLk61wllYfFn5wnsgmsMxXt/czFMra3l9czPN4di/vujfZK+mhd1pR7ZJnHnmmUyYMIERI0bwyCOPAGbVQnp6Otdeey2jR49m1apVLHlnCafOm8M5l57FXffO56ofX4k/04fdJ/Od732byZMnM27cOBYtWkQsFuPOO+/k73//O2PHjuW5554bMIZNmzYxadIkxo4dy+jRo6mpMTM3nzSW66+/nhEjRnDqqaeycuVKZs6cSWlpKa+99hoA/7+9O4+PqswSPv67Vbe2VLaqFFlIIOxLyE4gbKK8BpCw2OAuovaivn66Z1TUEW1U2qZn6E8r0t2+LaO2M/RHRMd3FBxBQFTEjU2RCGHfQkIWKklVQkJqvfNHoKAISKIBEnK+/2hV3brPc0vzfKrOPc85r732GtOnT+faa6+lf//+zJ8//7zXv2DBAoYPH05mZibPPfccAPX19UyaNImsrCzS09NbzFeIrspgNGBLiG118OLRcc/y6LhnKT9YRW2lmzX/8QmrX/8En8ePIyWOAUP7hAKtp48t+qyYos+KQ4+FEJ2POcKELSG2VcGL8/H7/Ph9AQKBAPE94hiUP0BqTwnRgWWPS2fwyP40NTRRV11Pz8EpjJiS16KWlSPFTlKfBKpKnNTXnsB1vA5XlZv0MYMwGFs2EDhXTYWLL1ds5svlm6kqcVJV4mTjB9/w1Yot1B2vJ/qsIsJ+rx+DycCwSTmyhV10eZc1A6PcdZKPiiuJMqs4Ik00ePx8VFzJ+LQEkmLb/07lkiVLsNvtNDY2kpeXx0033URUVBRut5uxY8eyaNEiGhsbGTBgAJ9+vJ64mDjuuvsudDoFg0llwYsLmTRpEkuWLKG2tpb8/HyKiop45pln2LFjB4sWLWox5t/+9jcee+wxbrvtNjweT6g44A/NZdKkSSxcuJCpU6cyb948Pv74Y7Zv384DDzxAYWEhAJs3b2bHjh0YjUaGDRvGlClTSE9PD427atUqSkpK2LRpE5qmUVhYyFdffcXRo0fp1asXH374IQBut7vdP2chuiKz1YymaST3TSShdzxDRkmdCyHEmUyKR697loa6Ribccy2apmEwGkgZ2J1+2b2aXz+nOLBkYgjRMehVPQNy+zIgt+8PH6fXM3RCFpWHj1NxqAqD2UDKgO7Y4luXIWFPsqGdU7CzucaWHi2osejz+Tw67lm0oMaCtU9jNF08KCJEV3BZAxjbS11EmVWiTrU1Pf3P7aWuSxLAePHFF3n//ffRNI3S0lK2f1NETm4ORqOR6dOnA1BcXMzAgQPpP7AfwUCQe39xD28sfYNoexQff/wxa9euZcGCBUBzy9iSkpIfHHPUqFHMnz+fI0eOMGPGDPr16xc2F4DS0lIOHDhAdnY2FouF8ePHA5CRkUFMTAyqqpKRkcHhw4dD5504cSI2mw2An/3sZ3zxxRdhAYy1a9fy4YcfkpOTA8CJEyfYu3cv+fn5zJkzhzlz5jB16lRGjx7dDp+sEFcnr8fHge8OcaS4FIBe6T3pm5WKwWgIK9wF8K+rnuKEqxGDSW1RTOvcY+UHiRBXl5MnTrLv20OU7SvHYFLpk9mL1CEp4cU9FbDGRDDpl9fTUHcSi9WENaZlrQ0hROel1+tDrdbbanB+f/KnDEU16Nm08luCQY0xM/Lp1iOOSNuZtULRKRK8EOIslzWAUdPgxREZXqDKalJxnmpZ2p7WrVvHhg0b+HzDFwQ9QcbfUMDxcid+rx+z2UzAH2xRI+N0NxOdXofuVMXx5cuX07dvcwRW0zQC/gCffvppKLPiXLNmzWLkyJGsXLmSG264gddffx2v18uGDRvYuHEjFouFMWPG0NTU3OPZaDxT20On02EymUL/7vefKQB0brrpuY81TWPu3Ln88pe/bDGnrVu3smrVKubMmcOkSZN46qmnWvsxCtFlBINBtqzeRk25i9j4aDQN9m09gKvSRf7koaG/ubODESbLhQvueZu8PPnGP6M3qC2KgQkhOi+vx8fX72+lqcFDtCOKgD/A958Xc8LdQOY1Z4oBn71WnK/D0enXHxn7NAF/gGfeeZToOOksIERXkZDajetuG8X3X+xmws/HYY4wYjAaGDyif2h7q9wAEaKlyxrAsFuNNHj8ocwLgAaPH7u1/avput1u7HY7SlBhz749FO0oAjhVLVjB0+hBjYkgLS2NPXv2cPToUVJSUnj77bdD55g4cSJ//etfWbRoEX6fn68/30hKfE+89X5qqmvx+wItgiAHDx6kX79+PPTQQxw6dIiioiKSkpKw2+1YLBZ27tzJli1b2nw9a9euxeVyYTQaWbFiBUuXLg17feLEicyfP5/bb78dq9VKaWkpZrMZj8eDw+Fg1qxZREVF8cYbb7T9wxSiC6gpr6X6WC3dUs4U6XKkxFF1tJraShf2RFurz3V451F2frmLYBDQNGK6RZM3MZsIKeopRKdXebiKE+7G0FqhV/U4Uhwc2XmUftm9W/13rmka+7Yd5Po7rwEFPnvnaxzJdoaOz/zB4KgQonMI+APUVLgI+APExse06DKkKAo512eQ1CeB0j3HUHQKPQYlk9gr/grNWIjO4bIGMLJSYvmouBJozrxo8Pipb/Izok/cRd7ZdpMnT+aVV15h2Ig8UlN6kZmehc/jx1lWg6Zp+H3N2Q0RERG89NJLFBQUEBkZSV5eXig74tlnn+Xhhx8mIyMDv89Pn959+H/PL2ZU/mhef+Pv5Obm8PTTT3PLrbeExn3zzTdZtmwZBoOB7t27M2/ePMxmM6+88gppaWkMHDiQ/Pz8Nl/PsGHDuPHGGzl27Bj33HMP2dnZYRkahYWF7N69mxEjRgAQFRXFm2++SXFxMXPmzEGn02E0Glm8ePFP+ViF6PQutK3j5IkmzpcjoSgKJ080tfr8tVVutq/fiT3JFgpwuo/X8e26IsZMb/vfvhCiY3E76zGaw9O5dToFaF4rWhvAOH7USfFXe3GkxKE/1W61pqKWHV/uYWhBZntPWwhxGbmddWxa9S2eBg8ozd8lMq4ZTGpaj7Dj9Ho9yf2SSO6XdIVmKkTnc1kDGEmxFsanJbC91IXzhAe71ciIPnHtUv8iGAzy9NynUXTNP0HMZjNr1qyhrrqemvJavE0+AFRVpeir78P6NhcUFLBnzx40TeOBBx4gLy8PAKvVyquvvoq3yUfZ/nIURcF70kuUJYr3lq5A0zRS+ocvOHPnzmXu3Lkt5rdmzZrzztvlcoX+/ezuIqqqhr3Ws2dP3n333bD3nnvM7NmzmT17dtgxvXr1ChUCFUIAWvNdkQZ3Q9h+dEukGY2WW8M0TQu1UG6Nsv3lGC3GsOysmG7ROEurOeFqIDJW9sAL0RnMvu4Zgv4g/7b6t2FbQKLjIvF6fGHHapoGbVwrDheXYo2JCAUvAGLjYynfX4F3zKALto8XQnRsgUCALR9uQ6/X4ziVqeX3+dn+WTG2xNgWdbMuxllWTcmuUrxNfrr3S6B738QWHVGE6Eou+//9SbGWdi3YqWkankYPTY1n6miYIkyYI0woioLZaiImPgZ3VR0ozRV/A/4AprPSuF5++WWWLl2Kx+MhLy+P++67r8UYFxy/3a5ECHGpPTT6txR/vReAXw+fg2pU+fOXf8AaHYE9yYY90UZ1WQ2xCTFoGtRWuuiWEoctIbbVY/g8vlN3Y8+hNFcXF0J0fP804kl2b94PwIND/wWj2chLm/4No9lIYq94IqIOUlvpIsYRTcAfoLbSTa/0Hm3aJub3+tHpw9sh6nQKGs1BViFE51TnrKepoYm45DMZ5qpBRa/XU3nkeJsCGAeLjvD957swW02oBpXvPtlJ2b4Khk/KCbsZK0RX0unDd94mHycbPKgGlepjNUDz3U6dTofJYgzdwdDrdad+PGhYYyIwGM9c+uOPP87jjz9+wTFUgx5bfAyqQcV5aoy47jYCvsBlWTx+9atfXfIxhLjanTzVz/00g8mA3xdgy+ptjL15JDqdjuGTctj37UGOFJei0+non9Obvjm921SAs3ufREqKy4iyR4be19TowRxhDqsqLoTomFzH3dTXnAg9NhgNeJt8fP/5LoaOz8JoNjJq2jD2fnOA0r3lGE0q6WMG0iu9Z5vGSe6fxHef7AgLejS4G4lxRGG2tj6TQwjRsQSDGsHz3OFUdBDwt/5GhrfJy66Ne8K2pEZEW6g66qTqqJOk3gntNWUhOpVOH8DwNHpC2RXek14A3FV16PXNAQwAo9mIwWRoTpdQWnbwuBi9qsdkNeFp8ISyMQK+AOZIc1jqpxCi46oqcTLj4Sm8/7fVAPziD3cC4Cytxn28DltCLEazkSGjBjFk1KAfPU63HnH0GNSdo7uPYTAZCAaCKDrInzwUnU7WCyE6uqN7jnHbnOm8u+gDoHmt0DSNYwcqSBs1EIvVTESUhezr0sm+Lv0iZ7uw5H6JlB+ooKrEGQqoqkaVvIlZ0rVIiE6gttJFZYkTnU4hIbUbMY5oAGIcURjNBjyNnlDGdzAQxOfxkdDT0erzn3A1EAxqLRoGmCxGqo/VSgBDdFmdPoARDGq0qLynnHr+7KcUpeVxbWCOMGEwqqGFyGBSZf+ZEJ2I56SnRbo2AIqCz+tv+fyPpNPpyB6XTs9ByTjLajBZTCT27nbeNopCiI6nqaEpLEsTTt/4UPB7/dBOiVSqQWXYpByqy2qoqXBhtppI7J3QolOBEKLj2b1lP3u27MdgVNE02L15P5nXDKZXek9Ug0puQSZbVn9HfW0DiqIQCAQYMKwfsfExrR4jdPP1HH6vH0ukrBOi6+r0v8ANJhVbQgx6VY+zrHl7hy0hBtXYvpemKAqqQYIWQnRWcUl2dvv28fP5d4Tubgb8ARQFouPaVlDrYnQ6HY7kOBzJ7d9hSQhxaSX2iqf8QFUoSwvAc9KL0WIgIrp9A5F6vZ74nt2I79mtXc8rhLh06mrq2bv1AI7u9tCNEb8vwI4vdpPQOx6L1Ux8Dwf/584xOEurCfgD2BNtbfqucbpj2i2PTaP6WA2x8bHodAqN9SdRdDqS+iRekmsTojPo9L/GzREmTnj9zW1RtTM9BOQOhhDibPbEWHoMSqZkVxmWSDPBQBBPk5f0MYNkvRBChCT1SaBkVxlVJU4ioi34PD4CvgDDC3PR66VonhBdXU2FC0VRwrI6VYOeIBquKjeW3s01bCxWMz0GJrfp3KcDF0WfFQPNXRYb3I1M+78TCWoaUTYrI6cObVPBYCGuNp0+gOFyu7i+4Hq0oEZFZQV6vUp8fPOdjM2bN2M0tm8bsmAgiM/rIxgIoldVDEY11Lr1Spk7dy4Oh4OHH374is5DiI5MURSyrhtC976JHDtYgV7Vk9I/CXui7ZKNebKhiaoSJz6Pj7gkG7HxMbK3XYgOTjWo5E/OpfxgJVUlTiyRZlIGdG/3TK2zNdafpOqok4AvgCPZHtpLL4ToeFSDCufrUKjR7sX9dTodUbZICu6+loA/QESURb5HiC6v0wcw4uLi+O677wCYN28ekZGRPPbYY2HHaJqGpmk/uYBewB/ghKuBmgoXALb4GPQGPdaYCCnOJ0QnoNPpSEjtRkLqpU/Xri6vZeMH3/DO8+8DMOOhQnpl9CRjzGD58iFEB6caVHoMTG7z3dMfo/xQJd+s3c7/X/g/gML0hwoZOKwfg4b1u+RjCyHarluKHZ2qP9VhrDmDs7H+JCaLEXti69uun88Ln/4OOJOJcfqxEOKMK/Kr+7Z//5rb/v3rSzrG/v37SUtLY+bMmQwZMoTy8nLuv/9+8vLyGDJkCM8991zo2JSUFObNm0dOTg6ZmZns3bsXgE8++YSsrCyys7PJzc3FWeFk/WfrufMXt/PzB+9h2OhhPPLoI3hOdT852+OPP05aWhqZmZk88cQTAKxYsYL8/HxycnKYMGECVVVVQHMGxb333suYMWNITU1l+fLlPProo6SnpzN58mT8fn9onk888QQZGRnk5+dz8ODBFuPu27ePiRMnMnToUMaOHRu6lrfeeov09HSysrIYN25c+37YQogwgUCAbz8qIiLKgsGkYjCpxCXHcaioJFSrRwghvB4f2z7+nqi4KAwmAwaTiqO7nT1b9uM67r7S0xNCnIfJYiK/MAdvoxdnaTXO0mq0QJD8yblSK0+Iy+Cq/ivbvXs3//jHP8jLywNgwYIF2O12/H4/48aN4+abbyYtLQ2AhIQEtm3bxl/+8hcWLlzI4sWL+dOf/sQrr7xCfn4+dXV1+Br81Nee4Luibaz8r9Wk9kpl1n13svzd97hj1h2hcSsrK1m1ahU7d+5EURRcruaMjbFjxzJt2jQURWHx4sW88MIL/PGPfwTg0KFDrF+/nu3bt3PNNdewYsUKXnjhBaZOncrq1auZMmUKAHa7ne+//57XX3+d2bNns3z58rBrvv/++3nttdfo27cvX375Jb/5zW9Yu3Ytv/vd71i/fj0JCQmh+QghLo36mhMs/cN/YzCpHN5xFID/fHoZfl+A3hk96JYixT2FEOA+Xsd//WkFBpPhzFrxzFv4PH6GjBpIbLfWdywQQlw+juQ4CmaNxe2sQ1EUoh1R7Voj59zMiwZ3AweKjlBdVkuUzUqfrNRLugVWiI7ssgYwTmddbDpUE/b47QdGXpLx+vbtGwpeACxbtoy///3v+P1+jh07RnFxcSiAMWPGDACGDh3KqlWrABg9ejQPPfQQM2fOZMb0GVhNzftfszKySemegl6vZ8oN0/h688awAIbdbken03HfffcxefLkUPChpKSEW2+9lYqKCjweDwMGDAi9p7CwEFVVycjIAGD8+PEAZGRkcPjw4dBxd9zRPM7MmTOZM2dO2PW6XC42btzITTfdFHrudPbG6NGjufvuu7nllltC1yqEuDSaC3udb3+sJndnhBAhP1RD67xtn4UQHYZe1V+WIEKDu4EN/70JLahhjbFQU+Hi2IFK8qfkkiAdjEQXdFV/k7ZazzRr37dvH3/+85/ZvHkzsbGx3HXXXTQ1NYVeN5ma97Dp9frQj/65c+cybdo0Vq5cychRI/lgxUoiY6zo9TqMFiNx3e1Yoiyo9eEfo8FgYOvWrXz00Ue88847vPzyy6xdu5Zf//rXPPXUUxQWFrJu3ToWLFjQYnydThdWeFSn04XmA/zg3nlN03A4HKGaIGd79dVX2bRpEx988AG5ubls27YNm00it0JcClG2SO79/e14m3yhGhh3z7uNmopakvpK6zMhRDNbfAwzf3szOlXHWwveA2DWs7fidtaR0NNxhWcnhOgIDmw/ghbUsCU0Z2QZzUZUo0rxV3uI7+GQulqiy7ms4f23HxjJ2w+MJL+3nfze9tDjy6Guro6oqCiio6MpLy9nzZo1F33PgQMHyMzM5MknnyQ3N5fDRw+hGvVsK9pG6bFSvE0e/mflCsaOvSbsffX19dTV1TFlyhRefPFFtm3bBoDb7SY5ORlN01iyZMmPuo63334baM4mGT16dNhrNpuNpKQk3nuv+UtQMBhk+/btABw8eJARI0bw+9//HpvNRllZ2Y8aXwhxcYqikDchG1XV4/P48Hl8uI+7yRqbhi1eUsKFEM30qp5hk7IJ+Pyn1go/9dX15F6fgTXGevETCCGues6yaqyxEWHPWSLNNLga8Xn9F3iXEFevqzoD42y5ubmkpaUxaNAgUlNTW/z4P5/nn3+ezz//HJ1OR2ZmJjfccAMbNmxg+PDh/OGF5zh46CAFBQXceOONYe9zu93MmDEDj8dDMBhk4cKFQHOXlOnTp2O327nuuusoLy9v83U4nU4yMzOxWCwsW7asxetvvfUWDz74IPPmzcPr9XLXXXeRlZXFI488wqFDh9A0jQkTJpCent7msYUQrRcZa+Xa20aRNS6dgM9PtCM6VK1cCCFOi+0Ww/Uzx5JbkEkwECQ2PgajuX1bwAshOq9IWySuKjdGkyH0nM/jw2AyoBrat22rEJ2Bop2vj/EF5OXlaVu3bg17bteuXQwePLi959VhrVu3jpdeeqlF8czLISUlhR07dhAb+9NaNJ1PV/vvKM5PUZRvNE3Lu/iRP+x8a4UQ4urSHuuFrBVCXP3ku8VPU1NRyxfvbiIqLgpzhAmf109NeS2ZY9Pok5l6pacnRLtp7VohFaKEEEIIIYQQogOyJ9rInzwULRCkuqyGk3UnyRybRu+Mnld6akJcEV1mC0l7KSgooKCg4IqMXVpaekXGFUIIIYQQQlwZCandiO/pwOfxoRpVdDq5By26rnYJYGiaJhVwO7G2bCMSQgghhBBCXF6Kokh9HCFohy0kZrOZ6upq+RHcSWmaRnV1NWaz+UpPRQghhBBCCCGEuKCfnIGRkpJCaWkpx48fb4/5iCvAbDaTkpJypachhBBCCCGEEEJc0E8OYBgMBnr37t0ecxFCCCGEEEIIIYQ4L6kAI4QQQgghhBBCiA5PAhhCCCGEEEIIIYTo8CSAIYQQQgghhBBCiA5PaUv3EEVRjgNHLt10hBBXWKqmad1+6klkrRCiS/jJ64WsFUJ0CfLdQgjRGq1aK9oUwBBCCCGEEEIIIYS4EmQLiRBCCCGEEEIIITo8CWAIIYQQQgghhBCiw5MAhhBCCCGEEEIIITo8CWAIIYQQQgghhBCiw5MAhhBCCCGEEEIIITo8CWAIIYQQQgghhBCiw5MAhhBCCCGEEEIIITo8CWAIIYQQQgghhBCiw5MAhhBCCCGEEEIIITq8/wX/giXXeZl6IgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 1080x576 with 8 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "param_img = {'interpolation': 'nearest'}\n",
- "\n",
- "pl.figure(2, figsize=(15, 8))\n",
- "pl.subplot(2, 4, 1)\n",
- "pl.imshow(ot_emd.coupling_, **param_img)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Optimal coupling\\nEMDTransport')\n",
- "\n",
- "pl.subplot(2, 4, 2)\n",
- "pl.imshow(ot_sinkhorn.coupling_, **param_img)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Optimal coupling\\nSinkhornTransport')\n",
- "\n",
- "pl.subplot(2, 4, 3)\n",
- "pl.imshow(ot_lpl1.coupling_, **param_img)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Optimal coupling\\nSinkhornLpl1Transport')\n",
- "\n",
- "pl.subplot(2, 4, 4)\n",
- "pl.imshow(ot_l1l2.coupling_, **param_img)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Optimal coupling\\nSinkhornL1l2Transport')\n",
- "\n",
- "pl.subplot(2, 4, 5)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=0.3)\n",
- "pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n",
- " marker='+', label='Transp samples', s=30)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Transported samples\\nEmdTransport')\n",
- "pl.legend(loc=\"lower left\")\n",
- "\n",
- "pl.subplot(2, 4, 6)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=0.3)\n",
- "pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n",
- " marker='+', label='Transp samples', s=30)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Transported samples\\nSinkhornTransport')\n",
- "\n",
- "pl.subplot(2, 4, 7)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=0.3)\n",
- "pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n",
- " marker='+', label='Transp samples', s=30)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Transported samples\\nSinkhornLpl1Transport')\n",
- "\n",
- "pl.subplot(2, 4, 8)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=0.3)\n",
- "pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n",
- " marker='+', label='Transp samples', s=30)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Transported samples\\nSinkhornL1l2Transport')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_otda_color_images.ipynb b/notebooks/plot_otda_color_images.ipynb
deleted file mode 100644
index e2bd92b..0000000
--- a/notebooks/plot_otda_color_images.ipynb
+++ /dev/null
@@ -1,337 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# OT for image color adaptation\n",
- "\n",
- "\n",
- "This example presents a way of transferring colors between two images\n",
- "with Optimal Transport as introduced in [6]\n",
- "\n",
- "[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\n",
- "Regularized discrete optimal transport.\n",
- "SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n",
- "# Stanislas Chambon <stan.chambon@gmail.com>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "from scipy import ndimage\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "\n",
- "\n",
- "r = np.random.RandomState(42)\n",
- "\n",
- "\n",
- "def im2mat(I):\n",
- " \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n",
- " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n",
- "\n",
- "\n",
- "def mat2im(X, shape):\n",
- " \"\"\"Converts back a matrix to an image\"\"\"\n",
- " return X.reshape(shape)\n",
- "\n",
- "\n",
- "def minmax(I):\n",
- " return np.clip(I, 0, 1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n",
- "`imread` is deprecated in SciPy 1.0.0.\n",
- "Use ``matplotlib.pyplot.imread`` instead.\n",
- " \n",
- "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n",
- "`imread` is deprecated in SciPy 1.0.0.\n",
- "Use ``matplotlib.pyplot.imread`` instead.\n",
- " This is separate from the ipykernel package so we can avoid doing imports until\n"
- ]
- }
- ],
- "source": [
- "# Loading images\n",
- "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n",
- "I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n",
- "\n",
- "X1 = im2mat(I1)\n",
- "X2 = im2mat(I2)\n",
- "\n",
- "# training samples\n",
- "nb = 1000\n",
- "idx1 = r.randint(X1.shape[0], size=(nb,))\n",
- "idx2 = r.randint(X2.shape[0], size=(nb,))\n",
- "\n",
- "Xs = X1[idx1, :]\n",
- "Xt = X2[idx2, :]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot original image\n",
- "-------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5,1,'Image 2')"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x216 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(1, figsize=(6.4, 3))\n",
- "\n",
- "pl.subplot(1, 2, 1)\n",
- "pl.imshow(I1)\n",
- "pl.axis('off')\n",
- "pl.title('Image 1')\n",
- "\n",
- "pl.subplot(1, 2, 2)\n",
- "pl.imshow(I2)\n",
- "pl.axis('off')\n",
- "pl.title('Image 2')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Scatter plot of colors\n",
- "----------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x216 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(2, figsize=(6.4, 3))\n",
- "\n",
- "pl.subplot(1, 2, 1)\n",
- "pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\n",
- "pl.axis([0, 1, 0, 1])\n",
- "pl.xlabel('Red')\n",
- "pl.ylabel('Blue')\n",
- "pl.title('Image 1')\n",
- "\n",
- "pl.subplot(1, 2, 2)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\n",
- "pl.axis([0, 1, 0, 1])\n",
- "pl.xlabel('Red')\n",
- "pl.ylabel('Blue')\n",
- "pl.title('Image 2')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate the different transport algorithms and fit them\n",
- "-----------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# EMDTransport\n",
- "ot_emd = ot.da.EMDTransport()\n",
- "ot_emd.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "# SinkhornTransport\n",
- "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n",
- "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "# prediction between images (using out of sample prediction as in [6])\n",
- "transp_Xs_emd = ot_emd.transform(Xs=X1)\n",
- "transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n",
- "\n",
- "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\n",
- "transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)\n",
- "\n",
- "I1t = minmax(mat2im(transp_Xs_emd, I1.shape))\n",
- "I2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n",
- "\n",
- "I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n",
- "I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot new images\n",
- "---------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x288 with 6 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(3, figsize=(8, 4))\n",
- "\n",
- "pl.subplot(2, 3, 1)\n",
- "pl.imshow(I1)\n",
- "pl.axis('off')\n",
- "pl.title('Image 1')\n",
- "\n",
- "pl.subplot(2, 3, 2)\n",
- "pl.imshow(I1t)\n",
- "pl.axis('off')\n",
- "pl.title('Image 1 Adapt')\n",
- "\n",
- "pl.subplot(2, 3, 3)\n",
- "pl.imshow(I1te)\n",
- "pl.axis('off')\n",
- "pl.title('Image 1 Adapt (reg)')\n",
- "\n",
- "pl.subplot(2, 3, 4)\n",
- "pl.imshow(I2)\n",
- "pl.axis('off')\n",
- "pl.title('Image 2')\n",
- "\n",
- "pl.subplot(2, 3, 5)\n",
- "pl.imshow(I2t)\n",
- "pl.axis('off')\n",
- "pl.title('Image 2 Adapt')\n",
- "\n",
- "pl.subplot(2, 3, 6)\n",
- "pl.imshow(I2te)\n",
- "pl.axis('off')\n",
- "pl.title('Image 2 Adapt (reg)')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_otda_d2.ipynb b/notebooks/plot_otda_d2.ipynb
deleted file mode 100644
index 68d3b66..0000000
--- a/notebooks/plot_otda_d2.ipynb
+++ /dev/null
@@ -1,321 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# OT for domain adaptation on empirical distributions\n",
- "\n",
- "\n",
- "This example introduces a domain adaptation in a 2D setting. It explicits\n",
- "the problem of domain adaptation and introduces some optimal transport\n",
- "approaches to solve it.\n",
- "\n",
- "Quantities such as optimal couplings, greater coupling coefficients and\n",
- "transported samples are represented in order to give a visual understanding\n",
- "of what the transport methods are doing.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n",
- "# Stanislas Chambon <stan.chambon@gmail.com>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "import ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_samples_source = 150\n",
- "n_samples_target = 150\n",
- "\n",
- "Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\n",
- "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)\n",
- "\n",
- "# Cost matrix\n",
- "M = ot.dist(Xs, Xt, metric='sqeuclidean')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate the different transport algorithms and fit them\n",
- "-----------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# EMD Transport\n",
- "ot_emd = ot.da.EMDTransport()\n",
- "ot_emd.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "# Sinkhorn Transport\n",
- "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n",
- "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "# Sinkhorn Transport with Group lasso regularization\n",
- "ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\n",
- "ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n",
- "\n",
- "# transport source samples onto target samples\n",
- "transp_Xs_emd = ot_emd.transform(Xs=Xs)\n",
- "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\n",
- "transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 1 : plots source and target samples + matrix of pairwise distance\n",
- "---------------------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(1, figsize=(10, 10))\n",
- "pl.subplot(2, 2, 1)\n",
- "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.legend(loc=0)\n",
- "pl.title('Source samples')\n",
- "\n",
- "pl.subplot(2, 2, 2)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.legend(loc=0)\n",
- "pl.title('Target samples')\n",
- "\n",
- "pl.subplot(2, 2, 3)\n",
- "pl.imshow(M, interpolation='nearest')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Matrix of pairwise distances')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 2 : plots optimal couplings for the different methods\n",
- "---------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x432 with 6 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(2, figsize=(10, 6))\n",
- "\n",
- "pl.subplot(2, 3, 1)\n",
- "pl.imshow(ot_emd.coupling_, interpolation='nearest')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Optimal coupling\\nEMDTransport')\n",
- "\n",
- "pl.subplot(2, 3, 2)\n",
- "pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Optimal coupling\\nSinkhornTransport')\n",
- "\n",
- "pl.subplot(2, 3, 3)\n",
- "pl.imshow(ot_lpl1.coupling_, interpolation='nearest')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Optimal coupling\\nSinkhornLpl1Transport')\n",
- "\n",
- "pl.subplot(2, 3, 4)\n",
- "ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\n",
- "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Main coupling coefficients\\nEMDTransport')\n",
- "\n",
- "pl.subplot(2, 3, 5)\n",
- "ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\n",
- "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Main coupling coefficients\\nSinkhornTransport')\n",
- "\n",
- "pl.subplot(2, 3, 6)\n",
- "ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\n",
- "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 3 : plot transported samples\n",
- "--------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x288 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# display transported samples\n",
- "pl.figure(4, figsize=(10, 4))\n",
- "pl.subplot(1, 3, 1)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=0.5)\n",
- "pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n",
- " marker='+', label='Transp samples', s=30)\n",
- "pl.title('Transported samples\\nEmdTransport')\n",
- "pl.legend(loc=0)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "\n",
- "pl.subplot(1, 3, 2)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=0.5)\n",
- "pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n",
- " marker='+', label='Transp samples', s=30)\n",
- "pl.title('Transported samples\\nSinkhornTransport')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "\n",
- "pl.subplot(1, 3, 3)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=0.5)\n",
- "pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n",
- " marker='+', label='Transp samples', s=30)\n",
- "pl.title('Transported samples\\nSinkhornLpl1Transport')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "\n",
- "pl.tight_layout()\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_otda_linear_mapping.ipynb b/notebooks/plot_otda_linear_mapping.ipynb
deleted file mode 100644
index 4b6713d..0000000
--- a/notebooks/plot_otda_linear_mapping.ipynb
+++ /dev/null
@@ -1,339 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Linear OT mapping estimation\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Remi Flamary <remi.flamary@unice.fr>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import pylab as pl\n",
- "import ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n = 1000\n",
- "d = 2\n",
- "sigma = .1\n",
- "\n",
- "# source samples\n",
- "angles = np.random.rand(n, 1) * 2 * np.pi\n",
- "xs = np.concatenate((np.sin(angles), np.cos(angles)),\n",
- " axis=1) + sigma * np.random.randn(n, 2)\n",
- "xs[:n // 2, 1] += 2\n",
- "\n",
- "\n",
- "# target samples\n",
- "anglet = np.random.rand(n, 1) * 2 * np.pi\n",
- "xt = np.concatenate((np.sin(anglet), np.cos(anglet)),\n",
- " axis=1) + sigma * np.random.randn(n, 2)\n",
- "xt[:n // 2, 1] += 2\n",
- "\n",
- "\n",
- "A = np.array([[1.5, .7], [.7, 1.5]])\n",
- "b = np.array([[4, 2]])\n",
- "xt = xt.dot(A) + b"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f3ed402e748>]"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(1, (5, 5))\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'o')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Estimate linear mapping and transport\n",
- "-------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "Ae, be = ot.da.OT_mapping_linear(xs, xt)\n",
- "\n",
- "xst = xs.dot(Ae) + be"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot transported samples\n",
- "------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(1, (5, 5))\n",
- "pl.clf()\n",
- "pl.plot(xs[:, 0], xs[:, 1], '+')\n",
- "pl.plot(xt[:, 0], xt[:, 1], 'o')\n",
- "pl.plot(xst[:, 0], xst[:, 1], '+')\n",
- "\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load image data\n",
- "---------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def im2mat(I):\n",
- " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n",
- " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n",
- "\n",
- "\n",
- "def mat2im(X, shape):\n",
- " \"\"\"Converts back a matrix to an image\"\"\"\n",
- " return X.reshape(shape)\n",
- "\n",
- "\n",
- "def minmax(I):\n",
- " return np.clip(I, 0, 1)\n",
- "\n",
- "\n",
- "# Loading images\n",
- "I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n",
- "I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n",
- "\n",
- "\n",
- "X1 = im2mat(I1)\n",
- "X2 = im2mat(I2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Estimate mapping and adapt\n",
- "----------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "mapping = ot.da.LinearTransport()\n",
- "\n",
- "mapping.fit(Xs=X1, Xt=X2)\n",
- "\n",
- "\n",
- "xst = mapping.transform(Xs=X1)\n",
- "xts = mapping.inverse_transform(Xt=X2)\n",
- "\n",
- "I1t = minmax(mat2im(xst, I1.shape))\n",
- "I2t = minmax(mat2im(xts, I2.shape))\n",
- "\n",
- "# %%"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot transformed images\n",
- "-----------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5,1,'Inverse mapping Im. 2')"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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YmFUwFXpPcmh6oNhZyOEuVKxfSZugBymBhN9YQRWyB6Er7gW4JYptSpzrVH33zk9jXpkIJpbspBqXtMFyOrqfE2ZVJjWu0Zm2MdOElmuBqXBOaqxrufScmiBVqk4iiln170Rpo2SaS++WBrEioaid6vhcMZ0QmchWAFNi4kUvZAo9T+Ts4L30YmPeE3Esy03eLFhSiUw8yrkKDVGni9BokIkmuAimJySy3K/hTGpghqrhg1lFh1dAlJ6BezH5JoZNwtrAo6NfOSX1DwRclW0TWc9HduHZHZVPE97GoFjU6x1obJOEPbpA7O77/ZzDnbX9epvUXW9sDtxckGHy8F7Gar10Ck/uy7v3vrmqttX/7tbj4Zq+1DabbUzbVs7t29s1Q3lwPteWLvftcfss89Y+mxXzU82TGZjIY32eGEP2tnlkbVAh8nate/mDvo4B5trubhptedfG9+fb74tPMcJn7RPG03lw4aoqGUWti/hnGSr/nn7aQPVO+mTpAmSA8bHof3ArfqmuIrdr3Wy/7/yOSQPIGnREH1mjSZTVYvPkfdIW9fpW13vbWLjNG1gLQN3B7I3h2uqxgfFR14GL91LHi46APt9lPx4TVTDBx2A+GXwbxnfNMRqq8EYwCUy9ExGYTQiJSkAkfehFMgXpnbM72ZQPXfDJYYIXFr5J4aNP0BofJ8HX4OTQxMmlM7XSmWzMqGr14ntgGoCtRyAqvOhC7x1rJ8SD0NILTa2YB8ZiSKX6NzPRob8Kr/vy7EL2ThKYFMj+aQtUg495Zrk6aUkPxyVpU2PtIM2Y+i+KvUql5Rhv1msB+NTShplxiY5KIhHMCO80eMkkpguaTue0L9AsYaLWzt/hOLWomWmEB6G1qJumE3Ne0bWu6zrGrAIACz85n1m8hP5qxYhE1qJEhyD86gHnibxcUA/aZLh2XlBEamxwgYh1Z5/WWPHLWhq18YRNrdx1aUoLmEjCnTZpabY80GYIDplMkqj0AsFhZAQ55rlpXZlNWXvfAyTUSgO4EQgbedFFAcPVmVKJuHleZJoAhXW7BwLhRDdF5B3pCZpEBiZTeaF0uC3Hc9FS8LgDqbX6RURpPTBT+lRu89Wj5jAz2lgEz5F4CqF68zDAkIXUWG8IMkF61TtiInglmxFfecOPHwS4ShHQatii7R4nzW2wNthZBrhprJDBGtgYbOTGQiBSmgVuE1Psg/+dzurOLbdNMumxu/hcinVxEvNEB+OQtqOcnb25JwUCLQJoYzqi3FRTlm7HB2NxA1s+/o7JJ+pGkyF83Nig7Ya/Ac7xd/t8BzQbQxIPn9ugQbfoGxEZkSDs5W6szLZKhZq1dzZtYzl2d9FoivC9LaoO49q0puWI0uRsn99Yvrj7/1e7C0XkjnHj6cWw0X++o78cAGqwfZ9DOLq5Nu/gan4GEcoO0cclGh5erBPl/69vhe65i0SrzZUY4mPNGgh6xn7PigwGKwFJ/M51m4MlWqx0D3NUmcUGFgMbVveuiBTYR+pZqAqMtru7np2EbagouTN7Q+8w3A4ChCdqpSvcn1MvUOf7Nfqu03IEZXMxC8VMfgrkfkymqszd6VFjUkTyCw2WdbAGDkhjkRJFVzMHL7r1Yw7NyMrcTpCKToJlR07C4gba8CxQ4hm8nJQP15UP7kR21iJ38X6FVhGITZTsymSveJYObAnhLMlJjClnvEHYxCoX3jxoAeCcpon0K8pMrVpgRhEXQpwmMGsBB6STKWR0WIKf2ztYhJ9NV76dnaThvdMTfGnlwopiYoiE5iBBXyGkogqvVuxMMU9JzxlUkZ5IBle18hI0LYmAOirGWQPVpHniKJIBnlylY7MRmVy8xn3XhmpHIjlZTZ3fWrDKuaIFzYrN7SsnUTYqRDIxhG8miH7lrSlxdT4oLAlIicq3hZdbo2+sTQZtFTI72gTpS12TCd849DEXnlXI64RNncCIdaa1hZRaIjmOMNHFSssVUXMujbX3nVU+WRuC7+Q8TYQ7OdfzO3fIXFFPruta2mMq+rE7rNJKm6WK2bnAV+RNY4WxRNL0hJggeUUiUBLPV4grmknGGc11n6OcKzD0fGqYwKQ25s7SLLcor42ZgiRXOsK0g/0u4N4Rq+CPcwivCud1Lf2b3+bUr2U/CHC1m5b7Sp9myW0SjdzBLPDp5Hsv5n0QAT9rtz6nBXpiQ3bke8emKLUa6U3Gyue5kE/rZAmr3rEAg5EIKer2ngfgrlY3cHQDhA676P95bnqeqj65xqd67XiJm2tsw5fPTNE6PrgJ2OXh+8+dW+TTdv/kexurnmcN0NPxubNwdaaNdXvGRp+ye4PKviNa6pj9zvhs3R7KGOTV8z35fPwnWruncnYX9+5qvLm+8yEwoX67X6PXTbYB0G2VJuMcXWJEFT1e0nb/3vcxfNq2cFt0bNFon7SB1b2+sWUxdDXlBi39WJDY5mLdWKwN8O86ty+D5R+TmZeLQrSTCZdMZjdMVyDpurm7haCTKiDKmxmG0FpD+0qzMzHAxtqNX+rEJIJqhf7PU00ori/4NZEGTbImktVLZ6JK5sorE2pGU2c1ZVqM1gz1V6QbfXG8vSLthPaV96qcW+BiXMIhr5xaQwfjG56IRC3YsrOGFyucFUfYSNyCcDjnwtSEJWANxeOViRMmHUtIFmY1TJKlNc4uLKKcW0VQXnzlHUGTiTdJgsEMCvS+YKJ8NwlTzJyzY7mCF3B6YUHbjDQjlpV5uKBmnYhYaMxjjHIiBbVGaLJ4pYN4lYkM50VmMi6EJFObsAxepaFRYEAluPjKZI1zOPmeAr89aNaYPVBR3lJYJHiVIDI4p0BrFXDj9QRNIURdAkbQTegpiK+QjZwdie+ItdirNMUwZnvlHKUL+yggecItyD6VQLw5KVlpP4CIIdXwpYBph8U7TRRaI3ECKUYtVlxAuiDe6WlgJzSuBaSbsvZEMTQvtBQmXYg8FfgOw4c2JkNw6agGqe+wKOG5UwuRtBpvZtpYnATQkZyQhAtGSrlKI9aS/LhDM0KgRUOkc1od8+T13GhpvEn/3OP6d20/DHA1XASO05BS+lOTvAv7oJ3b+Dzeb2wS3AGEYX2s8DNzwyf10I1XW/mb3bMgWwSa2UQM5mjL76LWbgyaADtD8Tix7nWRigaJu3PZWDXeT+07C4cypezswYZQkuG+y9skeX+eVWqgEYZr9NmdpDZWGrpfjzHYsw105AbkNjZmRN9t7Rcy8qoMhmlnPG7lbO2cw6UmsH+no75NZIu2Llr5CSlu7Nzehlu9NHfGywJk3DebyF/H3xZwadVXI9i9vh8gYIccG3CNqAls9M3dKZHBBm1tvrs+46mB+bTfx0U8Xtt+sO38UDFJMRjYofHb7gdTZNz7FsN1muWei8h6Xu7KL7dC0HQMkNvptvp9DlDupNYAT9rHImfar1VGXcgcDGcxUcW+5Q7mNhZOiJ353ICjj/YxSYgfr1vwZLUiN4clvbQhXVBm1ixmpDQvQmLlRsxikl0qHF60leBXhqudcm9ZrCwiOEG/GmYntF+YWyMjUZu4Lr3E8JlYCh/0hEkFFF07TLxDrbNG0HQeuZWC0BxAGSKdaA1d4d3mbhcDArOGyGBOPUs4nSM31sZuxkqEY9KKddBysUlA6DsyF/Cs3+iMuoA6TRYklJiLyUgtD4QpSAizwOorbSyKXF4GWw2nKTgB8xZdh5MaaAiLF5PVs9IqaCqIld5Xq7CAEQWUgz1J6AUmVwKxykD2XcgAUcFqwmSbkN5qAaaKs9YcMhk9o1g5EcxqvP1pnwiCVRPJclduXgm1ITXY8jZR2ii1BVW4uIBViozVJ7wnap2+zsyTk5m87xMiK6smKSOflpcw39pc7Piy7GOaROnDZk/CgvQLkxkSQrjXKKTgTYDkFAvdnT4Jk49UIcPbExEEwhqG6DqcIoHabV5Vm8Z90jEN+tD7zlktoTKzCqiUOB+USwMUdMQlR1j121j4tnKUcrGoSMNT46Nfdgxh9ieQubqtqrcV7z2AqGPuhbY3dunza+HtdybyEPV20ydtg8HtN3cyHGAwO8I+6e6w7Ik52ifeO4Xu/cr/c+H19++37+9TO8gnqpZPLWSbzO+uGXmYzEVkv0YLaGJ3k+uXWZu9rlvI/fjeNqZJHo//UuTkzrDwSJztYuun89798P70txrITU8kcheBubtU6+9ilfBw3Zi4J9YmnpnMZnu77VW5q5LAcM3dvt8CEHYw9oV+frYvRfXtLta7a60f3I57BuT77+Xx/N/nTv3ks/E3dLjXaSXsf2IwI2IsVtr4/OZGvi+3iZYOZru2Uc40XI4FJH91O/1Jtp+LI9I4iXICzhFIe2OmEkF+zIkpOtFaLQWsbnZVBTMmraAQWScufcV8uNu0JnUl+ZCgrdP7hZM12uuFX56NJd5x1oZ154ohAleFUySeQWjwXV+RXqzkbDPvToF00CwQ4+FghvUSc/uQHBC9XFvhzOI18U0AivoEckEQlvACRVF5tzKK2YjQcS8LbbjFzGZeLAvAR+mofFakR2maKiQP9YZkcpKkTXpz+Uuy5isqWq46KlhII7EGFwO8km6KgSxgkhjFlggdkQmxej40lXloeN2DU0s0hVdPlhF0dHJI0WJarPpqSuElk67B0gVsZtJKibHqxMREypWO4d4JTVSEFkFrgonSQwmBSqaSWNZz6KJ0nO5Waz4vB71nkup0nYnUkt0slXOKTGYL5oswD/dlNxA9V0BQlkyAwU4LJTUIBYleYpYMIgQTLwaNE3MI89SRTCbvnHLmynUM2FbMmgQiE2Q1+EQlvg2tRccalUpDwmkstahzp9kZ1ArfSqVkkBQkkjDDvFI+rBKVCFZKXefZcZkRrQXfHIqbErlCm8k0ltxkC1/PfjDgahP+esQ+gWzC5oCxcsiRr2XTCBUAavvkOpCvCo0RTZi5r5ztCbK0JxBUnmcrKlprdR6DXgT2hG1bnTeG667I3RVz04k96sOmvLFuwa1umymbKP/zHb1Fl+UIfRR5mrAHU2YM6YY8gsPtSBu6GVR3sfWewmLz5z0xGhvLssdrbvO/JF1uQAO55YFKnoTNd66qPdpwfLZdcU9hEr3phEYUmqTu+avCdklDZXFm6JESRAxvoBsTM64nNvfZFvW4c0e3SM/N/aZ3156MFTU3ACebtm3TyFklYAzyIU8aTwDjGVTv76NKvX2/abeyUhdkrcds03ThiAp99+/eLVBE92v7kjvQZbilkz2ceYu+Fer+i9GeW1khBQL3+yoq/N0z6OQehcmIF9MRpSMjDBoGMM8RAfkjNfcS/04tuVrjfA3eScO40EmWVF7MeXOnTeeSEFhgJkAfofCVnFKik63Re4Wl57oQInyckjkBM35vSUSNVw/O+sorgMAp15JarMGbNTwD7zVuqE4jC7uzvgV/7sOMRjFJpkU7h3RA7nLDjQjbCFwVj5u2TwRohnTnA7BuiyMTkCSyoxSrktHpekJdmZpDeKWH0MYljL4GH7TGZe9JBHQrXqdkE1ngyYzGymksXLYxdc1gUmWJxFLpfoGpXK7aGtFX2ogaFpuJqPtVAVOjEUxNh6A8Ea1/RDFikw2NliS5dpAr7xRmUf6AzrfnM2+9gJaFjujRCxkTqtXPs4L0GElIi1w4yZbupSKE15E9/oRw9b5rjHNqrLHlzdvmrhqzXKg7xxqX7ERTJl+Z57kGP73S8jRSI1xHGpAhjbh3+2+AS2ukiPaO5m9MTWjMCIFao6VT2ScElcoP5lZ9lHQyZi4aEIFGgasexX6qGk2CNGGeDFAuUgsCi857Ty4KlW93jGHuNFWWEYgQ0dEMzvGRq5RHxdWIqB0QZAKisIZvESZfyX4w4ApqRSQi2F3+nHvsobJpnR6jCbcmuU9Cel/u/ug//e752I39EcYk4v4Amu5ZgS8xFTtguGPM4AaWNtHiJrTbc3MNrcuXJsMNnMmdpqWA2Y1dEm7aHLlLsbCd96Ee43wRsbfbjZEaLfol0dSTTRvV/4UJ8wbaPv/7vf83QMBNV3RfsY013JMa6qf9vYM4Pu3/W5lP5fNpe6sIvfcHHRV3ZT/X/f6ccqvyF4XbX2IyP/e+Xg5Qzg0Afc7u77/793uU4xMj9tlryTswxR07+3CX0CPFAAAgAElEQVR9QpjQI0fCW57a81MX5O2afrysFdR9ntn5pTs/85n3JyPWC2uecV55sRUR46cWBAvKXO5Aiq1hWZi0NDsvk7BEpSKo5O2lgSEMSa+F4tQqK3g23H0kU6yUGh+k9FkeyR/QsZjR6Ih0zgQpDdqZ71bnnRrWyi1cWbKF6C/IVG6rKYPZnEhjTWERwTIQSZJAPFCZaPaGpEEKPb2yuUtlkRdJZIKeK71VZm4RUBPeutNyZhLnMiK/VBJttcXMiiNZi2N0bKWiSs9abMc2vxCIGi3LnaWaMLavUVW6CppG04ZK1PjVdIyVK23IERrKPPV92xa1JDNwUcKEb1h5MaWHcZoDT+Vn2XjrwRlhFUNMeSfJSiXiPLUJX97oIpzmZFmVq4wIWwHL0gvVgjDAGpdwft6EszdaFGtlWAGZ1LHozcovJlquZu+YFtPVdeJ67Zyscob14tyG5CD3JMyRxfw4gmlHpCEGp4TMtTL8Jyy6QpSeb84gJ4ieyHD1KQPIt1auw0had1yq7dSMRZMpCih5GkLiuqJhZGSlytEr5lONMjkRUhpbQgh1uipzj0rguvV3T1ZqmyGhtIcStUjtfxKZqxsTNaqz5f5R2REp1JAcPLIxdVyFAu9RYffpFLjNIzmGe836zh8G/3gAQKQQ1vY0BTfB8kZtBOq5u+du54zdr0zaLWXCHfDYGK/6aOiFnsTQOcrZumhn8Ub/T3LbX0pkpE/Q0r9kVjZyGfUuULlFBjIAbOzux1uC0xo0YuR8msbn/XliHte6aZxuGbo3wJkPAu9tkt0Ysc2NoDfsiVDupH1i3+st3PKUleZKeNTibXXzp3iPHfAMVBeMZJZbpN8T47d9tieea9N+7TZ+y6432zNXDe1fDDCruz5PBvjNoW2pOmwdWVGAetevdtceD7nHhhdNR4hxAXF7ADDbNe4aCdGdmSzNxG0FugvSx0r4IW2EyH7NRJWeox/bYMk2cN/ooNCjtqlwKS3c1u97mPZ+gw4G8nPI7kdkt0WU8otMXlcvPQ4TEz/hLFfO3jlJMutKeMelkVlRotqSyyV4j9MRGsbkwirJZMqaDgiq02AmA9Fk7YlHJ/pwqRBcRp2+ZeEbwPPCm74UqKFYmBOd6MKrKRrKNyw0EcI7p/k7PKS2Hsmx193YYy99JWzzJCTSSq9p6xltxfpK73TJu/G7AFLPkchSjZPUhDrLwmQK9BqrM2gjokzRffuTSRRLH490MGNDN1ruLm+l5ckIRBVtW2R2Qi7MplgmUFGNqkpIx5oUaNkXZ4KZ8U1LmgsyAFiG1e4ZTFxMaFGaglN2IqVSKbhwTWfNFYsZsQpmMDrTnFxCeOOEnhJZnWZbugvFpVhFb1u+LfggDdMFE1hESwsFnFmLwTHBWek5AI42ZmtcWBDvnKeGhQymLYaeKncvC8PNP4Z8Wr5D27Jv+aOy8sZ7mgbnELqVd8SWN84Y16Yw9obMHAmer1fMhTmCbKUtlalSYKw0zjinVH5hiUdy7sKJX5AycdVEeOGdLcji/H4G71x5s8RFaddkUiNNyVRUb3NhC+e01uvXlkgIKu0rxwr+QMDVZl9igb7ENjk5kqFtE/m9fPfL5bpUKoXH3D+frqprNf4rmC5hT7L2wLBt+pnUu5lrQ1n+UMZN//zIGlTXfLp9ynYep8DDcwb3T8thb5utziLbBpsjL9LTXHfTUt2YpO+zvHO/bkDgmbXxZyJsZ6Tq7eeyj29gArjlcEIe+mYHOjbEvfHcFuM2f0r29Fn90WijB5c0nyF6tvQTMbRQG+Dfk3U+Mqw7cxSP57nv14fqyMOfh37dwMszu3Xf3nVPfnp9z1GNn7Ob5u72G4AtlmZrmz5A6n3qlG3Bs4G8e/tcJOKP0cQ7PifTUhF10YtBOLFiWskjVZWVkR4g4Jor01SK3a7KPIF0mOjMYlxzpVN99y4Ej0a3BZdGGyxVZqDYCEe/VkJfqUn7daRbeCdCk19gvENVmVBUSlQce/ZtoaElqkY5SZReC6V7uYOFmYkrKsIlFZlPeFaOrtAC8TKXO1wqDh4TJ0p0WhtRqxKqLNt2SjrTETRnlBoPkMQia8HmBSIaC/NgnZagoiBlWzExBhwZAvaxiIyk51oLtBTEtCLkNGjpYx87Hfe38AsV3jMx5UdsUl5UmaOxDIE4FHvVFaRXioM5S6N1DcVbYC4sOdVzr40I4TUCmGtvR2qVttqEjGz+a3bS4dSMDK3oTlFWBGuNzEp74VnMTJdpLGiEJhTb1QyNQFh5p0NHJV5gF0rsTWnpwnVfmEOrxNzyiidkGuexx2GKcJLKsi6ivAculpymE6+5AkGuoKYjUGahNQMJ1hAgSTXEjYUiLjzhO+qeaXMDMaZYK3t7Trxl0Dq8SSKeOCualQ7irOX+u6SABEsk1x61xY3HiKY1TlrjWI/YpRhfy34Q4Gp+Sii4ZVKPT458vPg99Ht3D91yPG0TyL1rbhvYK+quHrreO601nt2tRR+zZziX4ZaxzT0TMhK9JapW2hdgiwJLEuSmQ7m5ZZ7yI+lj2v37uUdEHpiN+2vgbuKu693KHFmwbZvcbSD2x98rdps8uT//rczb61ul9ihJ2POEyfjdjSHcK8vmn3+caAejtbEt3FYVrtBy9KMWz2Oy9WtR9E3agJ5eItNRnu004diiyLf7YEQL5iNIepjm79syPxXdA0PzVHXZdw1QRoqOOvc62CgZCelytLPG0PSNKsbeplukI+PaRz2e+qTYzEdwtv31HHt3jdV8/X6E83uBnx0ob9c5GKuHlKAij8/cXTDH1n82jomImzt4F+cN0D/uRdk2kx1gV8eqP77gPv6x2D+gb3y8KAsg6pCNyYP3UtnPQ2zPl9YzuUwCeeItF7JX8sZTGMaIMPU3fiKtNuuNIGT0dwRLrCyppEPTci16OF3qqZlzRa3qklH5sFXOLF5ap+mU9CytkUi5yfsQM2sUk99H/jprxtKHmNqvtFZ52F7UeA3hxRtYsZ3iC4hzMuMUxltfWLOyxkeP2kNVAiG5uNLUsOxY04p6pFiQliWqV2CaG0Tw7enElJ1ctFyMIwjHrMbGprUFjSaVhiSDJqVVioCuxfyaVVystc6LnEomkkulwJDaouiixgXh743kdb7wbdykGUs23nDSJhbriJ0HW9YRMZoZH6PYrjMlIJ9kIUPoTei9GOrwKPdn1jjRGlg4KbVJ9hQT360rJopZY0kfKTYqealZG3n1oGXpkLACrH0I4wXBh9tOtp0YtFVA2JZ/cKSMmVIrwlWURWossVbjxzmViwRXVy4IYo1MWGlc2sz1euWdJjpP9HCIYJqh99J/9dVZc+w/SOVfMzPeQlgySJmZQnBz/qC/IBEsNNpUQTgmjVUrgryrcPUgfC39mpUY3qaGp5fnJIKXDi5B/0wE+B/FfhDg6jnabANX25Y0sA3un2eknlfDmztic6N8abW8iei/V4slxZFU2gB5EPfdNE7bpPhYt3j6ftsE2b5Q92e7RcQ9vv9U8P75G+NeyyVS+2U9b4R8r895bNPcP78/7vt0Z3uNvuf7+/JVdTzUX7yUT8vJ56i1G/soIvtg+iU9071b+Dky7u4iHj6Pp/fP17gFE+xRpXfnvP/kxkg+sk+7Nu4u5QbUM7FFLH6OFXy2uM8Nprf7b7+szzRyKHs/3Nteh3zsrz1312AXt8CS7d7cko3eAzp+Rf/+WOz/upYGZGXhvFTS5DBhTeGnCs2LnQBQm5llxANQkV5NlJcWuCvitTgVrU2HPYVrKjNJpmISNL0yS4GySx8Lx7FdUiVkrii7VKkIviVoZnSS6wrZErpzzgYOKZXraFKY3HfGOViYRoSZ2gBKTKQKHwykgfRKqlxxgxNLrEisTFaurNIsFetfmxKXG0mzvBSegVmBoZZKk6DlLZmmtbbnOuotK4O3gBLMCuRU44xWWs2XbY+6phDCNTunje0bz4PqjPekWTLrhMvYk5EFzZn/B6Nb588b/Oy84F6JKX8ZjbnPtUFyO7G4FJMyTcwrpC3MGCuwpuKprHZm1uAbu/KzZqgHfxtlFeWS8CJa7nizsZNEFtN3AvOakyZTzAXXbY5yMo0XW7F0VBtr1AbRLsncSugeGkyi6NhuyTTxCLBt8TySB8tM0ySloWIoHRWl28THdJaoBLlrN36ZDcW4erXftTUuK3siTxEhV2dFOV3f0H5msUTVcZRXeQfR6VJavsuQ3JxSkHjjD6T6M5vjMbEmvF69IsavCwvBNFaS4qXTu/pKem1YrlLpiVo6ptNXfc5/EOAKbgP2PpHfAyvZQnSfJrZ6sw/WOxkglKCRLP3WdpKx593N7XG3R57c3DhFow6GQp6YAG4sTenERp3viIgSNm4DjgzdA+O6eNA5bRFt2/n1aRLf2kZEdh3VUCCxRbIhQrLVn3KVDrdTut9AITcmT+9A5/bZPXwthsgqOshGdvjkM/6r21yp+56AnwYdPB+XoWw7+G4gqAbV2/E7yzY0YFtC/fsMDZK5K622ZKe65ZCS+m8DFltetNjyd93VcycEeQQuMvZRW2Uwnrtey0d0KQ85vjaGSQeq3vKtbRGgW66aLbeKW+mUUiunztbvex23PqIi9/b7+q5pbWw1ApRQmJvrLkderMjaDX7LJaO5bWYpiJZAWdFtFr9dyz3rqJUnZmNVbs/qI+BLShvXdbDLurG+FWVmP3LmapHE0/E0GsoUV85S7rY/6M6fml94GQK867qiTTlLRaKFJKwLfYLmzmzJea07a/WVs534KRc2Tde1r6ycWGXhl2qcp2r716ios01p4kCEkrpQkdNWk2y+o60ds2KCzaxcO1LM0tQUX2shu0a58h7czlqpF2QdC10NjI1NEsgK+82sLVyKre+QycpUDJLUBtYqcE1BspiMn2pw1opYjRQuWmNBRKfpmUqSvowoYaGWUytNG9YUjWCy0h6u3lGZOc1AJqfBXr/24CLB2WbQKIZPdY9WPOnCZf3AzzX56N8i1ws/a84HOj/RlW+0cdHkEkHXiW5CW08wr0wSnHTi6kKn8XvunFCmhIwTva28S/jTJ+HnXq6/63Jhk4ip1BZJTI1JktDa/Fkj6Dkil7UV49QaEU5nBq8tbJjqvtki18U7ao0TQYoReGXJ99KW9bhWzi6r3VSQXseJELzwlkpmbVukudAR3kep+iaRypq/BJO/Yq0iLNd1xaIBwUdPJoQlF+ZTuavDX7EG7/uZ7yRZrQ/W6czMSuOtmMZF+a53ZJppvbRhqwYvOqGDiXtbnX5dSHEWndB0/lQE2ZLL1Ph4Wb7qc/6DAFf3+gwRqUnzC8d9zr60Cezn2IXnKL5Pyk7dWak/TH37AB/cTUApT264fJyw6sXTNWwA6jPnKW3DI9PyOTbmc5qWNk3fq3V51O7Iza2aNxE1nwFLn5b5q9vs+bhfxRbWi/rj+tCEnxy3AZu2A9qnWm3MmNzAcRWvZAYitWkoFNOzAYUb0Gb/7VaNLeoz5ZbE8eGcmyYrH++XzR5yrN2xQBv7s6c7eGLUPtdin7tXn5+JjTkTublgN9Z2q8MnYDjvUincnTufNG27tP/hPqp/qrKL+29Rh58+fz8m+4kJV3eumVhOhAqvBN+KcG4nJFeyNTKUeZZyn2kxQS1eeZsK6J8x3rVraZyi8hatGQVes0MKL+L8MoxpbLa+RuDSeBkLj6ZXwis2bLXOJamtjASSxhqXYiDdUU1Ug0ilA46RXvmhMhOxVskhSSam2qRek5liWDqBe/C2LHXugHfpIFIpeExxr21mZi8mLJnJVAJFQwYLJWDCR6BraaPmWn/j2bmachbnZHDpyRodrPFdGibKT9OxNXnfjCmCVYRmEykw6wnLwDVwjHlKLiJMVFCKqpI2cxXHI/i9fOHvTLUXoUnHVfhbcqbFwjcoP5saJslFjFMoL2r8vAWrTHxAseysrdXG3Gl0CdYU1Ax3+P1U1r5wOht/xoU8n7lGx6y0SbU/nnLKGYp8qw2T5yHKdx96M6djI2DFYbgAW3oxeUNK0iII70yTEGuvZLFhZHRmq8VRRrVFpLJ60rXxXXbcYRLnBET/yKlDyDy25qKS2VJzJt6xXil3Uood0/ZCz5UzDdYR3YlyvQZXu9JNeL+Wfiz9DXfnjaCJ0+yFOZ241thrRdgjoZALsgbvvNjZOYVTBu7J/yvBSzbWS2d5kh39Ue0HAa5StIRyD589Tij3wubnqWTPNr7p/TdGii1a6Wkjxz0q7Y7hGK4+r7y/iG5pHwZA01GfEUG1C5pF6TLyb+mm69FdHiaD7dnrXAzlPUwa2ct3yLEfV0TFiLAb3+6bLA/2QgYA8o2hGqUuMtw08egyk22Vwn2dBkM02ihj5FGWG0tnd5F8pWfyOxCyaW90P5WI7EChP14Z9SQVWyjcWDp9mqz3tt+AzihgStnL3OpzX77ILSIOitkZBY0jhh5PgKGr2PNWaQ6mZXNN2wBCOTLfB12SectfhlBTTa3Yu28Ot0+jQOFuU+odEN5p9O6uxdVKxLs3Wjxeq8twFVaU655Ha4SgPwcJxLhXIkG0gdTmuK3VynZDQyKyb9i6ZSz2se/h7ore94q8sX/37a+qFZ0lK5XSsnSFsd2Td0zbj9G+JVgS3CaWoETTumJx4sVXxBqXvvIaJ34ZMz07q40kovEeazNXgklWWGaQ5JtceUvj0oM5CkT8jr7x56SzZufvmT/g6ys6Vbbrxa0YaS8msdvC5HBqJ3SBjxZ4euXUEkFa4xqV+0/lBfStImB1aOrmE+ErawgWQVhwFcNZeBcnhAXByO6oGinJm3ekg6jhsaJjYTN5YpNgnoQOPWEGOtWWKxB840KbFYmK1v19abz0Cy+SNO98wwsuF85qCC+8klyi85od18Y3+oZH8K1MvD8llgtXaUy9sqp/gxGtRM7fpIN1XISrJy/qrA7MZ/5GTMQ606eVj5LQRv4kPXHxxkVOoFdeJmVqgeSV+TwVi6Qn8CS74JMxyxXJhsbK6kmw8qInPmKoryy9BPspJWI/h7EkWEuu3rnqC3JK/vTitOXCa1cu1rm4MXNGRFjEeUeyesfmlwJEzVjXikpFFfc+8uf1SvJqAEISRL+SdqarsKzJwop0mMe4o1Gj2SkXWmusIkzhsHTcV1aCtjbanIQ4F6sM/doT0bGPLgALE3AduciaVBqFyyV4L6WtmyxoPVCUi1yQUEyVtgZXXTn1iTUqRcQlEkxY+0o2Y1mp/S6bjOvUx3Dtr2A/CHD1vQzGeP2l48yGTuRpvH7+zXMuqYdjt+O/oOvaAMOdrOfuGLhFsH2mYLiLyHt0Q336u6fz76v98esnJmP7YEsKKlvYPDIm1uGq2ebFrcTPzG1/WIYrM5kGI/ZQlye730/vS+UJn23Szx67wZRyh37KfvldI92zMve5v77PPscy3Z9n6689/mEcGlrC8g0Mf9KPG3CUx2830LxptL7kMdtB1+Z6s2KENvfibePufFiY3DNfwo3l2oBT5ed5doXe+hluLr/nun1OtwWMyBtAWuWtGQuAfAb1P1KT6BUtl1nu+ggkZlYLVltRS5qfOBn8MoJXOSFRblsjeb2suCYfx/ZEJskfuJEphBrOigX83vKe/7Ul3/rK3+jJn23G76gyIczWiVhYVWkO2isn+cdY+f/Ie7dmWZLjSu9zj4jMqr33uTTQDQIgZyRRpKSZedGDXjQ/Qz9W/0CmN5nMNBI1GhpJAARxaXSf295VlRkR7nrwyKza55wmH9Qyw6Czrft0narKynt4rLV8LZ0npI+7NqdwwRbhmJWwe3kkayYLWDJWzSwtvK6U6OwyN6o3tCkm625+GyitcVl7dAJ6J7UWE1vNtIGWaneQmYsJRYb2yYRZZwDWtHIQDSd0N+7rKaj1lMEbj74w5/DsSrpw5yl0bMMsNaUHHlvlIspDjdzDKQlTySy2MkliMlA18qQ0hNqFNScqRi8TXYUHX/lyBjBy2iZtmZMZl5R4V2BO93TxOIeSOZqSvPCkiaVGsRbCyBzu8ZoQdRJ3uHcO6xTNPJlwt+8zrUPRFvdlj6Lm3oRaF2SKKLlDarwuhYskVg3Ka9XEUTRE7VKpYtQeodrVo8ErZWVtDZU5zFK1EhrWBAkWlKe+kj0iedwMbUHtZgnZR7IJuxjzoVJHV2fGqa1SU6WZkDUxm6CEHKJLbKPTsR7X9jSibaQ7UxfOGh2eaMWtI1pIokymmApL6zSMg4akRTSzrJ0F5dQ7jcy6hoXS2pw7FEE5c9U5fl/LH0VxlYfI+uPZ9i0N9vzh/5EjhY0aY0Nahrhxd2ZXxmxi6/SKQdJu1z8KnGd+U371JdoHq2Fd4Fy9nLa2YB2ZSQD60RByWwS4hGA5Mv18CI7HNm9IA89RuoRGkbB1i23blbduQGdrRTOuzvC2ZeZtA+YwRhW57qftK9UrSiXsXX77MdgG6pTCm4yti217f9Bo4yFz2yX4vGjZCtwtYkUjeuXj4u5jSmy8ZRrb+DFXmAaCF+71smdA9lHQcrNd+08MVEw94PT9mhmH5BPHdk37evGgO8TL1SB1u4a3OKNdn9T3fUk+tFhc0b5rekAgZHlckNv2bOvv6eoVlSBc4fc6O7pZb+nlbd/dI61exzlae9snGhtVuH9unwBcaVFUsX41VTULmsrlWjwbKdyjb7Vx0tHhrZM8gYyQZ364S1O46/CYWuhbtLA2QaxjlwdkNn7VHmjpkS9EOOvwpRKhGhRmTC4xuI37sGnQKG4NUcebsuS45t658q52/uAzD12Q3vgLnflSjQc11vnEgxitZ+4pvLF4XkRnaAxKIsIxCW6OJ2XFWHoCy3Tto7tv2DwgZDtgfqHl0IOuKM2MRoNmZJzLkjgflHmN50Byp4lzyUJKmW5bvEsHU1RLeEVpp0yJuy707Lyt8NO5cGorCxmkUIrwzQXWfIR+5mXOzFPs+5Qy3/qZl4MCfNecpHfkXjlHGRU6JY6hn5WVl+p0n1h9YdGZap2SC54r97ZyOBxY2wVV5dGdu1IoLWOaOaXMRQTRTkmZ9XxmtRhDuoWlwGROyaFpFAPrYRydWuYxOZMkqhRkPZEVSovAZlxZyRiGieE6022hy4GkGhRi7mRmMp1ZoUjjOCm1XmhJuPgUz8nWaDm0Vj+dZlYsYnU8UEeN5j4W7TxQWHslF+i+Ylk5uGK20jwsHHoS5tap1fC1oTTucwpbEAsn9kk73UJ73C0mfdWmaHqWSldFrEXqsAjmlY4zS4bUsQZLdy65o9XJKbG6kroyS+OCsdjKk2bOtRM5TnH+jPAbM2vP5CLf1/JHUVzZzWzb3a96Ef8UvYq//meQkNsiZiAYZm3Mvj/9/LPX/HOu0s+XW0PH7beeIV+faJQ+/a0NsRK53bfr+8+WjxGrj5Znx+nm1z7+9GYeeZuFuNFm2/FxhrnkR8jU/o1t0L1B9bYvBkIxXo6d21z1P158IEAfR8N8bp9i9f/8udmoQBvbFtTpVevzue9dz9s4DpugfGOYef6nP1vHp9fLtnzcAbtdL5tlh4wZwXVbrkXoLZK5ref2V57fE9frZ29w4IZi/dj5f6eAr52fm3M1MKii/sl3zD0oRBs05E1xvnebyjapiNdFQ/fh/pym/Vzn7w9p+WWb49q0MA993SuvZsCgahRcXx2f+EOf+IDyWlaaBY3XMrydGqWOQG+c5kP3531MJgRu0F2HyG5zZ61GkcR/Wo1v1Xg5Oy9toubMH2qlGfxoTpQxaTMxiobH09qvxbQ4YVwr0CTsEVwiugoHs4oUIa+ZJypCpbqTLaJpjpoQPXNpGcfJWVDTCOhFRjrG1g283WsrF5xuE8sH4d1B8JOiarxD8F64k9AjlTU6CVfvZL3n294oHl2QszW+4sghObMqv1nhyaMJaVZDuOfSnPRy4qmtfLhMrMDrOfEyZ7KvEWPWYmpkOnNeeuikLET04kJPidpXDiOEe0mFb+rCfZk5+MLdKF7VM7M43y7KUwdEsbZw1IzQEEvkkil95ZAd6RfKpKy7RMDiWNuJCaVnqDSmUqgG1mfMGyUFtV9wqE4hs3oKql6EnBMv7cxqint0hNa6InnCOqwCtjW8jE7KoylGCuNXcdCJXsNmQ1bjvSqthx3ElDKWFO8xKSh0irKjdcAIJV/oOdFaFNxYJ4tj3UDD3Gj1Dm0ezT0ZaSMLshlzyvQkvG+V2SeaTJyb0TXsH7JHWkBR3ZNK/v+QKfxRFFcwBhUPemQfDPeR7YbC0E8Ri9tZ9u1BShKeG7PkuOF9DL5jXPAbmii8fK7WCepXjY+ZPU/MljEr9+iog0DGmvi1KNr8rm46qm53qm18jgVKcVXqPB/OUwAk2LClslFwbt5DarBqIBQH16Hfkqtb+rO13RSQN9Ex/eaY5XEc8qbN4YreIfHZxEcI380+pZuCWIY2ypVw/5VA6/IuA4sqoptRSLvPqo8TY7BnW8XHx3rH969F9ravMYAM4IqNSPRx4Hazy43U8xQapTHYdzemQSfu18Yw0LsK3e3aFCHgrjT1/XDuxY19bFtxRVc7TkLRpCFAdqiiTBbO/Inrtm9Y9UYBJk/DQX5cLzeSLpFrx+S8WYOkcQy3z4+CV0iB6PZKStPYH9snJNd1xpZsyKeOVvetyAqd30BxJTq5tq7MQP8yIpDz1c+tqNwgdT+8pbiQU0K6YZ5Y3Hi3Ki/zhMqK+cxiK69EMJ1pLWJZ3uLo2igempmcM24hMO8dunZ6C/TbvOPekab03HeUvVHA4b6sfECx6nxQOLhzJ4V1yvxTz2HhYI1JE4mMWr9JHQDthA9drpiFLxGDrlmaUawg1lmkgmfasLBBlHvrNDquGe/xbOvxsOQgzp0K72pC0oJb0IGSCLShFaDTU4/sQVeURjdFLQWr0Rs1KV6E3oKeBDAPewlS5pdmzK68bCEi125cFO69cKbzy1b41bvKao2SMpNM/PYSFFAAtDUAACAASURBVNpXk/DjWfiz2vjrDN0r75qzWMaTIn3iyQwW4zQ59bxG9mgGZ0akcdGZkztIY10zD7QxKYQjxlwmusLCRLOJi194oc6DZg4588LgjGPS6NmYCa1X7/CQG5eUx5gkHCZHXcleAj3T6BQt2ilLOLg3ItbmgHLXjLOnMDSdDliLZ5wgXFJ0HVu7gCYe2yOtC1knnupChPWNRgeRiMARQaSDRDZqLj0Kj6rRv6mdyozXFe8rKRXC40zxbswqIAtNE9KdEzGWKJXsGdHO0TNPaiw43Z3HuvKQcnSOSuLHaqwugcDTWVXJalzdCPueRPF9LX8UxdV1Ni1jwPqIYxnL55ypP/f+x1XorkcZaMaui3kGLEWJsJl+bmswQIb5GFwHmU+QjIFEfRfC9AmCtG/zc/Rr475uUaHIePuInhz/37KgFg/UZ/lx31GJbyjN5+RHWxxKH78x9U/3RT6zb8/eu3n/+nqvo/b/vz0okhJ+M3u41ZnJzef45Bh8fIwHApWee3k908oBt5mJt1qqXfjOTUE3Xl81Vdus+vl52F5+rIH69Fg9N6XdzkdxnsXh9JsiP9YT29K5itOfb38sx8124lnR9SlyZR6I1VQKvY3u0O8Ak27/2obSP6W0F223NOJWvqrqGDE/vR+7O58IuH5Ay5GKeWhYDn4CnQey1FGbaIfOf0Em5UZj5X06sNK5XBK1x/TL3alrVOvCCh1a6wiZTjipdxFSGpPWHnFX3ZegrlohifBWjNdkFpx3atylmRep0xp0ojPNqRQJ5/XeGqo5nNtdadUjNUaUzkr0Qih9hPRWK7gpruFTpeNaMY8pm4iGR5UZc46JdafzoCkQOJUQvBOUT0vxUOp+HBMBBwnNVMpruJeXCPo1a6xe2Ia5sxu0zrIhtSL8gZXqSi6ZyTtftx4T600p4QnvziVVLmrcl5kPFepJ+JXAE8rDiwvLCms78ZAnlESfDpifwDNrXaK40syk0A4Fs0zFaKTQyfUV85CVHD2RNHygXDpf+cJJEyYzj/1M65XTBA8kjpJZRahudD9irky6cjdmoEeLyKRkR04Ik1hM4KxxyIkDnZKEpYX32EEUkzOphYzAzMnaMInmnZdVOaWGd+FSV0QCkah9oZSEDuPmluOZpdbD5DXBEaXWytmFLJ1JE6s13BJP3sjq5JRR79xJoiFYSkzWqFaw7lRWtMswUj2QpzpigQpnDqytIjmyIc9bYLQ5WRMPo8GtA3hH3Ui7kXWjfc9ihT+K4mrTnWzOzVvr5ubUfe28yrTW8Kz7SYRr1IePQsV10w0JeUQibF1t7tG2LDk9Cxq+ampCgNvMh3jzhm4aTr8QaMv+eVVW62QPg7wk19l8Jwa6TRS6oUCZUVQNyHvTRm2t+zuwtVk71EHvpI3+2eXd0Wm5CUHHoJ2H+3wb8O1mPlmHFX3XrcNSWL0zm4yLLgb6OCjxYO6E7koRSrrReHF1JE/jX1OB4SuiwpbEhYuP4pW9srw15IyOvW2f9drpKDdv7EP383iVW4pzP8Y3yNzuQ/Yd4/lW7CTfKOob8bcIorrrwZx42F+F4bfn8UboPn5+65g0CYRyK5L37s9tm1LQPAy0MNlArEb79+bGvTlLb1mCaVx7YbiotK1sTiDdmCyE712jAN+u86JBg3gPIW5oyIYmUT0eyG1zstxCa3V4fV0L+E2vJgj4yAkdNAibkel2SDzu001j+UNdfppaUC9aeZig0bHsHEmkJKQ0sSThIJlL6zyqIyhzUmpzvCSyZbqN4taUKUE2ZekNxKnDkV2kow6HeY5n43gOqrfhjyZczDgj5J44uSEGkzqrrUxZOTBzkM5LqfSUOEkb9J6SyYisdDO6JxaJZ2V3JTV44Y2u0MQQKRSxobmLgbsk59zjHlstMF1RQYuzUCID1UO47HRoQheltTVMThEmVYqA24RIA4RTNUqZyTkiebo2spbxPEl4FU4S+tzNpPSiSsoF94aQ0BT0eYC6wr3Aj9qJrwq8PRxZDP7KhcculKR8fYR6PsEq3NmJc2/86HRPnoVvOPK2NlaPhoQ0Henrst8Had7yDY03vfEuB1o5YXyTNUw7qeRcWKRw6Re+XuDSIvPw9VRwrzwYfKiVPM+4VR6mAzknumWSCyfJrJq4awtvzZlKZ/KVMwtpNAfMeeYBIwk8NOeUlbU5Z4ezX6BPEZKtOcY6MSw1JiX8w0YzhNO4WOLRG+taeEhKlkImUgku3VAtTMU5WMgOcrXhjO8s1jlb520PT7WYqyVScizF+HFpQuuwuiFy4qhKicwhkmYmD/uQ5o2ihZ6VZoHCTSlhfgHPgSZ/zxYxfxzFlV9nvgCzPx+oNs+gjbLYjBm3ZaMc+va+9b0AiEEg9DTRJZIQt09m07fb4qPA8Y/WH35T/kw/Aje6FgIZkwFN7povDMYsf/MM2gfr8Zm9+21b6W71MDQ4Q7OzI2W36NDNsUhj4OruoLrH4LjIHpA99nTf3xDlXws/ufmNrbZRkd1gso/vmEWEwmc9lUT24kn9eTF0+7nbZTumzbZZ7Ufw0Xd9d/vYVqF9ZnuAZwaZwJ4HGMdhW1XQpluHnPvnb7jPaQFvX+8+aDcVXb/x6NzQ073A/I71i2zr+/i3xrnoPXyJbnSL8e6g94aDs8rz4srF0BRGiNb6rqeCmMocXK80rbD7nn1u+cThf+z/tm+bjYePoisJz+6fH9oypYn5YKwIxWdyarvh6lQ0MgRlCrp/SvQPiYWVVSo5Zy69RQaf+vWcmVE83MaRmDiqbN5lhvVOltAnicAigmGkLbbJnZQjELhooClaFnpLPLWV8zTzpsFDMv6cmYfDZb8eVhOehBF9ExTzJHDRiIgRv04iF9szDNCp0iMQMehtTYPqL8OsNlPG7KMC3qBnp1h0LbobWSHToxBSSJIRbH8OiveIjhnrNAt9EilQvJQS0hiNMDBN0zB2NjQJLsraKk5m1sI/6sTvrFDbCrnw29ZoZJKdeNGPFHlgKcpTWfirdMdvqvKoCU+FtDyhqiwuWK2UlEg9tELLEtRlEediibZcBkJeyMfowJvmmSJKtwvHpXOqztIqelF+70qdhFSUeW2kpwv384ScTxw1M6dCywWXzvtDpvtM9YatmWNVJu5oqTNbeKpNIrzKnUNamZvSR7Jo1QPkxB2J1YzFIgYnW0dbR71TJWEdfEQtFemc85GzCzTjMDl3nig+JrVtparzfhmpAT3RurBKgp4pEhKEpJUiIGKsXfkwMh9NlEMS0rjeMxVNlYOs3KmADXmKhymqacbUmVQ4G4PODq3p97n8URRXKYUYbhs32o5o6F4YaI/CpDJQpE0L0ywoEIEhtd0zz/qm/9nDkwWtTs+fjtZXNyilWUfGrPtWrNs3bUlOqMPRo2smtlWQ4UbsqmSLrq+Gky3eaxIDYHbBBwKUYcRQxPu5j3U5w/wqRKBsMT16FS5H8CpAaJ105LiJyC481TFT3UZnFyDF9o0dJm+IExtasxVOTkshXvahO7st7lyi8JWxr6IaURRjQBdCr7NRllvBtOnBNlxy03zZ+P000KK2uckPvVMdRoi3fljFZUc6N6BzB4NEbmbrsncx7jo4H3o2rjTtcDnbXdBlc+aX58XAXlD2obPb3OH3EOMrwhcFvu1WGc86X7fVfFSwb1skOQwWs25eUVsRk8axCg1cG0ikaDxIuhjZJTqKIBAAie1LKWFtTDh6DyHyQPtqrRzyyBgb14GNgmnbPPHIAks312SczxE7NWjD1Xr4vxGmiOJtoLn6yfH8IS1vJOEUXIwuiVclc9gsyFMmJSUfJkShn1dEDatxDzy1E90UcaN4PMDr2tHhYj5LppGYk+/3SyIGZ3piZh3gqNF7ZstOTc1ofSVpeAsJircCIqQ8Ixb5fiYTv+owuXKUHEaTyTn2CadxMkEk0zz2TXsU+M0KpAjqzQguGbMJ1YWkjluiyYSIM++mpAlJM+YXpHfWRUDjWaXaA5WQzJxlXIuM56DgXeg9aHzV2J7WHE9O7hllQSSjKKaNkjOYYK1hrixiJElxjtwoU0YmYerQe4QO92ZUwmCz2cSyu+k6yY/8TYcpJw5Z8dpgmqi1UrLSuuFuuIwCOSVadRZvTCNKwdRIPbGuK707jSO1VsSd966k1Vkt46XE2NLD2NOackzKu0tnLomnKmTv5FKp1XmnoPMDPWfq0vnQhNOslJMzTRkyvHLht2tnTsqdOtM0sVoDSRTimTwjFHcu3hFRaqt0jfNfbaXlRPOC5MqDNUwymobNBmtUw1Zp7kiHO4t7wKSz5kKyTlc4CIQ97cQqEeXjvTOJBIrrwizCSowlVQ9MCSYXjroyK/GcLonqQu0xybUEsyROBj2D5z/B+JsNnfHNrHC3FPB99Nm6rZImrPWBCPne/Qbso+rujj2+rOlqsqlw7aK5qbHShpp0Q9LVrXofiDckYaMHkWhHHsVediK+YddQBR2z6VN8iBXVQvjbrO/i/K0bRiQ8u8YPR8GCjPDMoKg21KwNTyXZj08gRXuI9Q3ltmmOoqqHah8hd9v7N/hJUFFCG4LqZ27eOdHG8excBdBb2/6GplmguRHme7P+j/VrenOsb4unHb1Jw8hUr78D0WVifiVIP162Y/VdiJmM/9yCXcKnrz+7bG98x49/jGz5oO0Q+ezndsH3OAa7eeh2P3wGNUopIbZZPAB+RRszSuFaRPvW/bXRccIu/N++t63z9r6QpM+upe27eSqjSeDGzd6u1iS99xjYEBQLB2hhFFn/cjbin/Jy93AMmjZ17smkKTPlCPI1q1woXFBMJlJ2Uv8GtcbpKTyBcPDUweOcHopxj/AoiWaBn4s4mREhYgbiPMjKYWocemdKhTfSOPuIRRmTVbPGg83MKfF2vUR8ioQo4DEp9bKgyZGlsJYWxUzJzC3y/Q5J6C2KanICjc6xLJ1IcQvKX7amiSrkPCaDslDUMde4DpNR65nLckJVuRNjFdntRjQpQiAbmx2PUHdrnKyKSqG26zFAOpqh+AvMwzphTlNc2yUMgVutpJr4IJ27ciRNgfJ4j4mWS2IaE3xVw3N5Jk3YGIBGbNfldA76Pwl3kgJZy52iQskRCH0eEUKdI3SjeoWUkZJZa2eajrTWeHx8RKcjU06k4uHmvvoQpRvkzFOeOaV4/uXTEh1+kmAxXvYzcznweH7HFxl6Nb68z/z9U+eyJLIVDjrxbWlkm/hSlK+ts9LJJszeSEVxSWgp5BQ0sppGgQq8JTpSp0WYyoosBZHOSsXbAl74IC0Cyt3DYT3nmNS781KUWRKLKosszDiLOG4evnkpUbjjThdE475Z2wXlSBsyoEUOnFk5psIBcDqLRGfrcUSOWRIuXXjEgLuI9Pkelz+K4srd0aSU4f20DYrFRrcfUBGKQ+09bqpxk4XZ94g3GTM1bBvMr0tE1IwBf0Oy+lVLtXfrpTFL9+t2dTcmUc5eeRhJ4Vte3bQ5l+tAeAbahAcy0yXWmZpTNNG9xwxVNPxMhp7HhyZm0za5CK4boiHhixX8ZuiTVAm0/VrM3ZnymLaC4npswUcRMIoSlJ6eFx/mEQgb+xzoWPVBLfjwS9poVzdI4ZkkYz8b1/DlqxYnUL6A3KM47AqF6JTatGqbwaAMlHKn625oJohuz0D7NnRoUI9bQWkbSnT9c0PFPh92PN7jVsAuZL8pvLfidRzrvqOgO78XP82mk7vSrXBTyN2I5WWrhGD3BtuKX99UWXrdn6tQHFQdxnXSvaGjW1UZnYAe7vpdlXU8UwEmEis9cjo1GjTMIZHwkY4QqGxhHQXYpLLrGYE9NV5kdFZqFNkBxyu6JSOMz2+dl3no0sQddaMi37N09D+v5Uevj5QEBy2sXcizMGtmVmWlQe3gldWeuKwNXDmYc5iUR4MPLiObc3RkpYmnZtzZSkqdpU98aI77SnVj1olkRirK0e+wZLztFdy5lwLzxBMRjDvJHe6Ne1n4i/vMWi/8aOo8GnyzNt7LS6wRXlN13FeLcSEaIs4heaJ7NMREuHBB8ogkcUUJfWrvHZ1KoBMkzhKOe3lMUM9t5eWqfJGB9cKaE09+R6OhCoek4UPVAe+03sZzOzph12Z0DW7hMCWOIjQKhpK9sgKydnrKNJxjXUl5wl046IXehepwUJAaOZAwCkUP4byNa//SKpNM9NpYZfiXtcTijuTOnCdqb5xy58uz8aYk1tZ4SgdeFjhY40kmUn8KN34OXLohxTjmI602Lv0SRYiGPrm50xanZ0fEKK44Rlsb3hzzlS7R4dxUwDue77n0GAPfLJ0sd/z6/YKWO1QqrTqdR7QJ2VdOErE4c1+ZNYpQXy5RsKmyYCTVYGhyRteFLz3E8H4IX7GqW0h4II2ihCOeDoNYyYg3DlkRGq9MWDiTu2Ia65o9gxpHgWqVnoJqPkvkPeY8gXS8FLwnJlaKHHjqxns13nenMpEQcm78JHVIyrEXntSxnljSx+PD/7flj6K4yjmzWHs2q3cPQ7kiyta6YUk46kR12+kvYPdw2TVMGgXRd3W1bcuGht22iWfzoZORZ11nHefADXKyIUIfDaK3tBmw5xRGcEQUR5v2SSQ0N1swMgRV1XWIn+W28+05msOoOTZNzhIyhkC6PrPfu/h/dKTsiAg36NZAV3ygZZvPlenQ8Ow6pIFYjCggg72ZIIk+24aP0aNnCMs2OGy7pEqXq8HkfkxvCh+Rq1WHbecwKW24PD/rjFPduxA/5z5/+3pH9OMEf3L8XLjGxHz03XE6vhvl+ueWnboeL/1anOkoqkSunl2b6i6uD919sz63bNcYBJK0edNstLJvuyrXz/ferya+vT/zybpFc7eifkPj+rhHb4/D9rluEilMP2Aq8Hb56sUdhyJRZDand2NRh2TM4lQzLrXw9PjI+3PF1s5EpjWiw8ziOljVyD1o/YsFnZV7R2Sh4ohMkTWXYEkRD/atCBOdkuPZV7RwWhe6JzRlHKF151d55jdWsXTHfavULmQJYfhdE1IpQ3spNGDJTvY8mi0i93CSRCKo4N1Z1mcSRi9GuZtRiwmCNGfCsDlzfjwDIJfOU66sVXg/NeQysw4NlZQz63IgUTmkgvQ2NIYd6w2z8EmaciZlwftK1hH3U+IKf+krL3XhosfQyFLg0tC7id4L3RSncrDMXBof+pksQTm9W54opZBUOa+VaZ7w3Jla5yXCURM/Ts7vTk+8ISMqPEjmx5b4W+1M50ceVDlk4a6/599m4W9NICVOKfH3H77hII3+OPEgkYjxTxomLWuNqCCrjUkyRy+s65k1GaU6W6S8eceniWVZmOkgnQ/WmWrCiuwhz1/drUwODeXJhDWliKRh5ZCUV975IiUu7YmFA0/WOM6F1lrIKFrjnokHP5FSjA8LypM3RCdcM90ahyTkFFYufYAcas6dBPCRkjOlhHYjYxQTHlIJu4bRdPNAo2c4ufAknVeWeEphulo1s6A0aWSOvOPChSPenac0bIqA1pQiT1Q58vVgj8xh/Z7B9D+K4srEyCr03UE9uufEnT7yzyaLAme1SA6XUTWHQPY6W3YzutzkDLKdSGEas6U+6IuUo6Mup0Rv4VidkpK7RRfcoOMU2QfBLW4B9Z1izCW6GFWvdpU7JTmKRB86r2th4FjRwT/f0jIKvdPH5icENJCFTYeVbCto4vdlaKg2DyOAYhtqtlGseaTNB/KVCaQjDEWHP9RQO/u23RoDbJaCwO46v0enDN+SQEyi3bWKk0beF4PemvtoC3dI3WkhAhmaoRCn9t5po6jcir5KpwwXcHffi06TK6KyiblTSnvn3Z5lKONoa5yN3nsII7drJSnWoiV4GQaMIopsHZCjOQJiwMCvETCbn9T2OlBX3+mBvZdTRteoRNbix4gWwvjeRj+HP1S/odtul01rdXWTH8Xutr1j3YpFgOn4vKWg6W61heaOp5hdNobWTaKwTgaXgWS13pkkGkK6wBEhm7De5AnJQFrFbgpqCb2YeMxq1xai2CSCfieZ+6e/nOcjv/IILtaiTKnR1XlzOVHrwvm8oOeONOOVXeh3yvvTmZoOYMJ9bqzNAOeVGnfq3AMHVf7QC5YVpXKSxNRT6LiyM6U5uktX5WQrU4NuF5o2JjOaVeqYwGRrZIkWf0NxcY6e+LF0/K7ydS2UXHmRoFmgN9UqljOTCqstgOGSgURuTnfjsXwIwfuS6ZeVM53WQ0v7I+u8eJf4Mj/xWo/8QTrvq3ICUp0Qr7zMQyOWZiacLzB+MnVWUS5eOcmBBRl0X48wYhFaA5XO6yws4rz2xFEX3tqRSzrQspKWSubCq7Xy0C+8mA78okem4KveKeb8+YtH/CR8mCd+d+4sybmvK49Pj6yHIz/tFx5mYfYX/B/nDxRVsnfuq1K98+vu/MUhozrxJjvvk3M3veTvTx3Rhq/OX+UP/E/3Z05deTM3ftETLS/826fMP5pzsoY0oTPRJXRJqWRyU7oYZYi3uyb6sjAxMkgt2IRqkWGYLQrjt+8TP587L3jkJ/OB5o3XOvFFXkNH5xn1M19kyPbIkzunyxJAQT4QXYonjpOSpVExHvsBOKC5cDaYdeJoCWxBy4Q0Q6VzlzY5S+j5aq2Q4MfqeDEsVbwb78x49MRCQmXCe40YIW0cXGhz5rheOM0z/6klHm2lSObPmmEFvmGl64w2oyT4Kk90X/hZ69T0knODr/8Ui6sNCdkQHVUdNgCjK42oGVQiENbMh0xI9hl0fEb273vru/ZqCzZug35hrNOHKHcvjAZCEK3ksW27JuYGLdk6q7ILfUNsbjRJQR9t31em5vtgvL2vckV6xlEY6/eh+RqffaaEYiBm189fUZobRAvB0+fdw3e0agict8/f1GXbajigkCJzDIiiCBhmNnuXIaP48UGnRQi37REuqNCH8/KG+I2yIFbnoXNTYY9n2bV3drOPN+c85xwWEb7ZQlwLh/27Y/e3Q797M227Mc71RmlGW3vYWPTen6GfO1KzZaTdHihGoYp88vdsSN32z36QA01LI8R2K66iSeDm69vv79+KA7Fdb9t1Kzslej2HQfdeNVa3y4bmqeozhGx7zz2ueUWQnGG0SoNHhFIOmmRrGIjr6oqG7odhFGv1hl7cfuOHupyZeWHOO3/k6V3nsrzn3buKnY1+eU8vheSFQztxSkJ2IcuMWeeYlezCXWo8tgNP0vhWNDrcBKQIx+Yc0pFLO3FRZbIJ7ROSOvPxgNQVzCjS0aIkS0gxPvSZSYce04Q7ie481LmrRtfGN24kK0x6pvSZKgkm46F31qOSPEw3a534sb3nhS3c58TrlFi8kgTetScOMiMK3/TKgUxOZ/6rF5nf9HuowqkuTKLkImQvHEtBzDlIQgoccOShIJ54TJluzpLumeloj8zGUg4kW5laIw2t1V8w8a41/q6ufCXCPCkmneXpwtLgdTqhy8ScGk+pUNYLX+XMLzB+4pVfnzK/T5nDpZGK8DNfWDRx/2LCvJHme74Wx73SEF7Md/zrOXE+rbSi3KvzdZk4L2fIM34xuh44zMbjGc7N+d/6kV/m1yzdeESgGb5WTDr36txpYr07IJdGOl+ioUeUH/nEvLzj6+lItc60dFou4KGdVfOdBZLWOZF4wROXNNGkcn9f+TMCyfnGO48t8VJnfpoqeNhzqCqv1opp5mKVd5qpDh/KgQsRPi4enlFhDSPMWfGL8XTs3OmB2itMhbujIKuDZRzjIImIy1RcJxYyS6t4alw8c3FnISFeURGOJC4I4oVVQOfCg2b+G12ZZeWY4d1UcJu46/APovQ752GZSebMBaxXZj/z8pD4eb98r/f5H0VxJSY0FfLoGKwAg94DwIxNFZU0rA2CrhhajzQ6YogBsMvwFbotiMK0ZGg/xkx7UGlTLix0tAd/LEmR1jHxvfPQNNZZXDjTKXkYi26DIjB7dLRFN+OmUwJKQvu12Js60YF1a+ewUTF0TGNWmwgfogT0PPyiPAR7YgPh2GqqgewkC0TJPRCC6kYh/I9EM7nHTKffaIA2hKSpMJvQpNMFFnVKZy8ZZAymWbbMryFWJ9ZZXHZfq+JKH5BrTY6QdrbNiLb83SATH4gUUSRsFCaB+G3u6GUrjseSdm70+X4UHwWHB8XbezRAJMK8UHZESPaIHjy2KW1Fmm45i4GSLeocboT7W2mweTb5uBZ2OvRKEA9/qOvrDZ7eumEVrpXtTfEX58RJxjNK9op6jVwsUeqGImkU2hNBddeB/Pbx0NQe3z+Jc8yZgzlnHRFFdtVMtSIRqyHDOqGE0WG484/InKSkm9Bnk0C0blG9rWi9jYtS5yrY/wEu9fzI8vt/5MO545bx3rjrzmE98/Aw82ZZ+UJXfp8F7wqie5v4CmEkmjJeErWt1BoIr6xBW6+A15VJSlh/lJXaKiAUbczaeJ0qRwG3aDRwTVQzVibAOXDhIFDMo6MuhQnnpMpBlZ+nxNsWeXaPHaolUjf+la3kXpkmeOEVVFjNOGfndWq89cx9PvCzXLmTlZ/3A08Z3q4P/EeBb/3M/3DI/EPK0AqqylEVs4ol0BcHknfEVu5T4skP0cWm8fwVd35C42Irq63c98qrCUgT35467fjESzL/JnWOsvAhw2/fxVjz77zyP5Z3/OqLL/lff6l8wQd+njK/ax9Y5YG3U6b6RHfnaVJEnFObkOM07H9ScLAaJq7TfM85Jf6vnlh8oq1Ol0Q5reAT9X3joMa5LlQzqsnoQuz0bxdeFuVklSb3HHJmfbxgGSQVvvjmLfNcOMwr3yzCv3/V+Q/2yF8ejF9Vp8jEF+XElA78L025pBb3/mip/iqvrE0pBpJWVjnw+8fE77VyUeeFKC1lSja+ssqfpwPqT/xYTugxgTVWlGM98SELaVUuPfPBGms6ol6RqTBZJUlB1JGq3KczT55oh8Q3l0TSTM+Jo8OPvI2MTKfpkTeXyoXMg064rMyhLOXimYNBoFO+QAAAIABJREFUxdEiXGhMLXFJ8NRXHnyml8QJuOvGb8SoUwIyxeCro/HQ43kYaQmZC52X37M9zB9FcZVzjkyprLu+aNd/pHRFlfza7RQ6qU3/FOvpbjuKsiFet8ttN9o2QI7uT/BtOwLRSSXTvX/WVux2Br7P1M2HnUEgcDooqQ2hCrFyLPVGXK7+0foGBblZBNQy6EK//t4moI9tYf/z9t+taEiad3fmWEGM7FthAzfibZ77jSmbKP/5/u6Yw0ZlfQSL7EXAbuT5HKXYCoXnnw/d1m0jwr+kmfuXFiPO70Zjdrew8xirTfY8LmnTKG3bv215zplKx813hHSLl7nahsizPX3WGSnyqcfWR8jNd+1r+Gxd6cTtXthUYiLj/uB58SVjolFK2e0WtuJPRJgJoet6c4WnXYCehp5tmOgC2LUrNHG1CrlFX3WglNO4oJo817p9Qon+QJfl1/+ItXCIfnvqHPLCVCstJd6vDXH4rSmLJI4JcEUnobWV3BtPfiAtneQXLjGbQunMGt169Ibi/GxaOCeh2kyjUVXReuan08QLNR5lxkw5jNy/n/qKauObDscEqXdeHye6GyvK/TBCfimdh0l4xcLPX8y8XztPXrlPhyjosnJajfd25I0ZbxqkPjHLHefW0Jz4j1V4kRofyEwX50QPc+j0gv/dGr8nus/uk/AqQcnKfTXW05nX9yvZCl/qGyapuB0gC2/6E+oFZ6V74+GQuVPhD5fG7y+dr+4UTFly5VgSvzsVfnsOx/zXNfNUnP95/Yrz144dE3e5cEmJX5xH2HoPGsrXxpzCS/69TPTzicM00fqJXiZSH+OKO5pByPAgpAvQK60ZtMqxZP5SO8cy8bePJzwlequk1nlU5XXv/CuUX7d3/Bt3Xj4Yjz2T/C1fzJXDPLGkxAuM//Do/CzDKkf6bPyhn/g7zzR3Eo371vmy3PGmrSzeOYtybJV+UE5tYlljQp0JY9djXsnmLOcz36bC33DmXhIv9ciXc4HceeHClDtldV7kM1Mq5LVz8cYqE+el8yF1tAlfqnKSJfzZrPP04QOTCafk/NQTU4E/VKdOmUWU6VSZtaF55gUfqAiuE4XCInDpmbdEAsDjeEhtRt2SlNUKHeELCojzdlgGLWT+pjt3PVM0kQ6d3juzCi/9T7C48qHJqFaDutm8nm5mvdufG4UXlEbidiafxkCCbW2t42F+81tRXG3+PYOWUgKN6T7yswbN1q9UU7T/xkxPk34SP5IRLsP2NOW0D6a7SahHTIg61LzlrnGlM8dgXTw6L+pwND+O8Liqzwcmc382+98Kg+3fTX/VPBy4xWSnK50QwW7LVjzJRmFtx5NNm3UdHDcdGrBn/GV00GJjn0e34VZp5DHYbtsr/bndgIru3ZAW9Wn8vui1MP2M/qiJP1t/KYV1Xfd96IMZ3PycTKJgmIdXnOl1vVuTAvgzijeJxDVXwizv0ipJdUdnfHQ9flJcje+3wUPm/Rhvwdlj37ci7KOQ7G3Zjo1wLazjXA+0aJzXg+bYx2HRsU1M1tZ2Og/3WJdIOLfjpOOMDCd2GZOXSQOVTBqUhJTR8TmKK7G4Nrcmko3KFY/rdOuozP15l6YMOlO/55bn/9yWb1Fmj+OfD53eEzlLRG+1hddp5i0W9EoqsPmHqXLkwuSVTiJ7odMQLcxuvFbhR6ny6hi6TDTzqzWMQ6ccyG/3Ax8uK/lwAKJj6pumFBEkZZ5S42f3hdet02icG0xUXj28oF5WXuXGS3UkFdJx5t3SKZNyV52nDssEuftu1qw9h3wihf3C3TTx3leQwm9l4u7SaNk46ESzCEM+5cLrWXb/onvrSG+cZudfacXTimqhHhPLGXLqPMyZn7eV3z12jsU5Z+WtCb9c4G1N/L5m/lwn3tSV37lyV6GV6ID96+QkTiQ98rUJtTuNxN+36Hp0y1zwMGlt4Tl36p2SMxmhWEcvZybgtJxJKY8xovLz9MBpfc9pUdbZEYsuW/KZtT3wC5noDf5yOnA+r3xrldfaueSJp544ceaVN570gV/XhYs5P+PA3XTH784r77sxS+K/e4D/+zFh2lkNHrry0oysRsXwlPnQ3nEsmRfdoa58OU/864cj//B04dSigC7iiK38WZmYW+Xve+Fdq7xvxjc6U9LM39WVf5c7x2niDBzMeeiNe638qFS0J55s4X2D317uuCTjV8BDmvhA51hSjClT5tiNr1PiaJVHFSbN3Lnxak68njsfuiFNaTrzmApve2HxxO+tBRWsTrEJL2BURGCSgmZFzXmTR/wNCdLITpXMO1/IOEoOTWIpvNc/QYf2TYy8SCZZdA1AdCSHhcIVicp5NMR6UIhp+PC4SBRVwIQMWjCEuS3dzJo9BMRJlWlDvZzoGBB2V+Ot63B7qMkYe72E4H3XZxHrDrNGDYTJh1moRpEVgmuNhG+guBNNkGG+EDsSx8LDmph5c1rPMqijHMhLqLvDeA929GjvJywBjW+D+0bhpd3cchO4b4N9HDfPSulOU2erblScNfkuZDfC6DRtvP0oojZfW5Xhv7Wtd9PEjSpsd6TSKM+2wkJGSHbGcI3jVTSNQTz0Y13ZaSr3oBG3AnE7F2tv0ZUUVRRC36nVQNx8uFT77vJ/W8xsNNa2TnenmaE5kyzMakuK4+m65VSmHT1NyDUEe6wnuQW1ONapQzhuQzS+78/Q8bmMotJjEpFvk5mJ7qy4lrftHPeK9SjeRUY6/VXoHw8UGQX5uHZToFu62pWdFhk6idEJ6jZCr+MaVYtuNd0Hz6tOrKeYGcY1p2AeYv6xTVHYx99l/WGjV07mMRuTraSeEDpIoltFZeK9w5wUutGsxT1eElaNlI4c3HHpPJnzoIr0xn1y7sc9/mgdzYmzHtAZbInrcBLnJ9lwT5QcwcWLV85aMFe+7gKuvG+Nh1z4q2OnIrxZZ05a+fnLmUNviIRNwguEMsG3lwu9zzyUE80yDyq8FDiVxiFnvFY+eKGvjYZxoEDqvJBA8ZsI01opU2Ey44WEZciaj9AqFOeFFmY5ox1yPYB1zIVmwmNP/O7kHPWOORlvbeUf18K3NY37L5p5/qkZyTN3Eo0Vc1O+SuBaWMuIQMmVZBGCbF0x61itzMMCXjRSDTJBD4g6OnUepgNtTahdIAVt3nLh15czlpVXxWmqgQyVBOkV2jtZoFjjg8P91Pmvk/Aqdf5haVyA/9LhbAJ95d/rxOFl41fvVpYV/nLq/ORu4s3FeHM+89f3d8xL5XLXue+ZhJNxagKas5jQtXK5KO9HxMwvPnzgLjnHkvlAQyTTe+Y3i/PonXdaKOZ8OSeWvvDeE0t3/s8mvO6V+wt8kRK/98a9CIeSuBfFpyisvzoaZ4G7rryVhdoSOp4J365n7royl06alJ8DpZ2YcqHR+E1dWVvmrTzwdsvwagtvPGQOUS8YLXXcBdUU0qAhc7iUkOCIbG4AISXCQyoRY3UnY0zu/Fn/E0SuLmPwTRIC6SYxXG8WCZuf0a24+PbRnEd34eZILMPtuMpwrvZhaeCBHO2I1mce8FvcyfaOamQHer+JmdhMQbnOzAM9sRsqbHyWoTX5iBKRnbZLexs7DJ+tFO7km8WEjw0qw9vGPQqcU3KSX5Gz2P7RTbODA/H3F3UOrrsTO2OA3GkbCbrQuMm7Y3N7HzThiETZBtFPOtn61V0dIugT2LcxDerqxsgYYKfpdBQ32QefnsJ8c7agoWzT7bRO1sQqQ0zdA03ctT/j/G+de9ux3knKFOLr7ZzAVa+3OaBv9OeGcn5OEO44NujePNb/MUq6/YOEZ1nSrQU+BPxt8wBj0NIjumka8R6uV/NPEaGO0NnNC3TTcplsGjEwi+NzS2/CZuvRPt0+ue7T7dJGcZQGZd5rQ7NiDt3CHX67N8yv3lUNpySlj4YRGUXkIeTxXMvxH+byZRKqC6dyN3SYE61WxCbm+hj2BqOBwC3xWhdmr+QUOaDHlHihjZortc/k6nwjiakkikCWwrdtxkqlLM6dKllgmjPzQA/fS6MZzN5RyxzU+W+t03XmPTMPh5nfTK84dOfwsvBQH3nosBZjzvcs5yeeOvzW4NuL83BX+O/v4ItU+X/eGb9cE4s6Pztkfu6drIFwrb3xTi58oRNTER7XxkkminZyPfOiZC4mIMpB3jJPwjfnTNXOnB3L8BvJFC18eHrk7m7mXieSdX7tM67Cpb+i64JMnWqZszdynqh1wdx50RL396H9cys0TVSvnE2plpFikbVqLeJWiqP/L3nv0iTJkuV5/c45qmbmHq/M+6rn9HTP9IAIjAjCY43Ajm8wS/Z8Mz4AezaDIIAIIAjTCAw91XR11a37yJuZEeHuZqp6DgtV84is6kEEkVqU9LVNZKSHu5upPfTo//wfZjStWJv6oiSVq/HlHDdUoGjhToymAmqEWbceEMGSktrG58lY5IJE5sCJC7CYcJcLd60QEny/GX85b7xNTp43tBYephse7hp//d2FyRJ/8xz81SXzRU38/KD88gvloBfugV8/VZjveXeufKeBbcFtU76TRtqUkEYqwoHEOQHDLkMt421QDDxQnXlQuT4Ts3bVswcsWbm0gCyYOw/D2uPsmbNCvThrm3n2oGBkDb5Q4/tJeTN1v6xJF2p2PMEqzocmHLPxHuV2mfll2/htmvgFjc+2whdz410xvt0axYXH7YzmRIlC9URaO094k62npBCsBhbGNE1gilnGBabm0Hr6hCDUEnzTzn/U+/xPorhiuEdL9BW+af6Eq2HSEYFrnIkMtd3ehBlGnzZCb/fJMV8RnP5ToJNwf58YzKui5Aoh7VDVUFXZi7lDL3bk6gfUHdMDC70S7zX6JO6DLyM7kdl37ox1ErL/nsJqRzIGd4tB2u4TbZ+aRDoCdWCgBoNTI4x2Di/2E/NAk5YAxF8CoU0HSaYXczqI5FPrJFWj2068LgwJRpbgOG56YRGDmxP2KTk77z3s6+H1c2CRO4dB+ySrLi9q0Rg8qVGUhAplbzeOcRXrsUP79+8BwddzZw6jCIkIZJgEEm20Ivv5d+kRRmfxvvJJGfEuPEjRJdu7G3Q/DUJNvdirREdKpSNijIgiwkcA9Mt1uIdpq9r1u4u34VXVj737Fw0TROAcgrnQtIsqAmgq2BXJ2snnA3Eb5yEQ1PvPNhLpZdwrHo4O5C3tLfGBTqkq1nYkM/qDyOkmurQurMhTty7Z26DRC7VQ6Tlhsls59IJ6mpbRFhcsKhVFpV3biD/WbVPQ4z2zJPx8JpGGTUgjh/HZBE+lC2eS9kiTsMaDVG5yl6G/C+fDmlhS8NlR+DIv/IAQvnEg88Ux+FiFs0KeDpRyJtnMBw2sBmFTz1lLxpQCaU6jL4yO0cOc8yr8sJ3QZxCci3zg5EYtJ7Z5Jp4qTYyiE9+twX/znIk0I+dnfn53YPPG//5oSMz8e29WHuTClznzT6rwvp744jizzg5+ptINOY9SubPgpCun8w332VmkcSnd2uPDmikCT2Jc9MhpSzxLt6S4tUptQqYwJ+ODCLX04nWryleT08w4V+fcGmFKjcqCkUxYRHlHom0bijBLkCIo0m0diISnnmW4TAnX3l4PTVALb8WGE30wT8Ld1li1sXlfoKep8WUIX90I3z1VHiZHJbhNymNR3sUCpfHVVPjJDIeUeJgunPNb/rvfCd99m1F9i+SVn30xc3qqfDwL57Xw66fEXGaKC0+SOEUhWd/XI3BWmCRh0tgiiElZI7jLCi1hKtypsFnFQikIH1rhPDodTYKzZw7SCxlXJQFHLSy74IjGhpHp2ZLLEiQ3zumGi3VhU26ZUyvcLhOkGOKbmaZwS28Dn+cbZhPe+8SsQvHgKZRTq3x0h1BSObGEcDoVbm6Vj6Xz9/pjpT9PTRKHcJbWOF1OXBAsJaY5kVRJU2Jdg9ULXqMbo/4Rtz+J4mrxYYgoL/yXF16VXhGBSnf85veQE9cB0b7CDWQURjvfpLWuwgvpE/TroFrGZ5spNnyn9jy8vdhpo4hJw+I/IX/AB3qNXDjBhL5E9LxuY/KCGpjp1cph35/db2lvh+68LR/Ix2vX7KadF6DD/2oa6N9UB9JBd4R//fnQeUj75yaHR3WmFn3iHKq53eNpb1td+UT7Mcq1K7l/KOMNV2Tv9fnYJ9QqL+HYQM9E3Pk7o8UHXFPar0W26pXvAzC5sMkLl6v5y3chLwas1xbo4Cftx7NGo7YX5aKIXEUNtBcriOv5GkWuqqFDeDFEJyyWX7y3xhi9JpdfB2qcb8Y1+5rorqrXZACRfk5LOFsMH7jwqxBhP7ZdIbrSFTBVXq6hXrS/2GZEvKhfu5Cz+8jNkogWHS3UbgOyeSMbuFcsJUxAopHS/n6hjviVSTocLxFcxIcBoSBRropfsyC8mwM6wp7B+GPcbo9HJtu4bGdmCscWKJWWoegErKgEbzAsFZ7doBnPOtN85WEK3raZhzlYm/GDKLUFRZw3OnGO4NIcx3iTKkfdiAXOFtxEhUwn8zosM5xkQm8ya7Tu+7ZBqStFKn8hma/bI+dquC68zco2GVsTLnNjisR96ouc1oJLOFVveF8nntn4YnK+NOF5u+OHeubGNmqb+Gf3hXfrkWwnNDkthKjObyXxv23CG50JqXyzQa7Kcc48R/BU4UtTqvTwadGVrSWcxMErdwnsUEmnxPtw1qScWoFp4kHgezcuAssyI7XgphykspTGc3JuvaMxuFBDmEzAJnKuhAtaC0tS5pwo0Zii4FyYBN7WlZKgoeQUHI+Zc+3u4601NqncTZnLWsjRSHHhc1toVN7EiT+bF6ZbcM38sMG/eXfi35xveB9wsgNnv3ArGXThbV3RUN5PweqZ5I1m1tv2sVBUSBFkEVZTqjoPbpxEqRgWjbdzT3GwqT+zDuKowZSdXOFNKCeCWvpcePLC956GQryQRbg35W6IKmQR7qPPjU+Db7lEo+mFGxdSynyZKydXJioP2jspc5yYMS4qYLd8yTPnFS618Y03UnFojVDnsDo3OB/cOJkSkvn2FCNM2qmtUQJogtjGW03c5+C363g2VqdeVtQSOTZybbzzQDXRpn+AbcGQPuEmdv+qbiApe1BwdPTq6k+9+1fJSyssvTKb3AsWtS4vh9FuGqRe0Rdi8L5lBsIwEB9/VRzsE6aZMHsP9UR7GC0RwzohcBsTeH9Tn8Hcr4Wajf0o4SNQube9RHv7rw1Pmx3F2S0dduRDpCMm9mr/J3QgLh2+sEH6X6cehOmj3yz6Ytw4aGEv/l+qHDAk7QVQRxB3rlloApSQ3a37UwsBHxyznfukA+1ReVUo8kKAzjA4ZrvqrE+8u+psLwCMF0j6WnRY9/TpocQd6drnaZPd3b7fSPsY+chzDNu5c90WYpYejjqLdjNNj97uUhv2EeDmMPL2GmCuI5y7t0v3Hn8bBVOTHkLaoqOd0Av3fXy6oFWvrdIQrv5rEUGV/nBK0n3K8mgpdpuKl2vfGQc+hB1LA0/KMAi5KkDNpmv80E4yhk7kFRE0JRojVmhvgUcwh/V7cRS8oYFhr4rMHtyK7ApLxxUO/YLtyCkZC6FjIhkRB+8RrPEjJrXfdrY5dzk4LgemesbuJs6lwCasq3CQxixCk8TbGOfejCYLz+4UNbImZnO+8sphETaMtVXezInWVlwWWqT+/GnCZ5xJcmaqyik6Crm2ypd6w5t64eiKTME7bawp87w1Pq6NB2ncpYlwgwbJC+caLElIUvm+NowbTCo3phgNscKqjdqMEo01PnKcbvmmTcSU+O1lYVJj8beUsnKrM8hGssRkwqMIOvypbOqF40PKPKQLZk64MGVj1h4FFGmhnAq6OG0tqFUOEcx3Z1LNPFX40JzL5czPpr64mhbB28b7C3gS1BIHC1IL7qRymOESieyPHLwym4EGljKXbSWsECEclpVjUcp05Nfnxr1Vzl4oTyf+8a0g3vjFPQQXJM/UyxPhzvfxlr9dAynCU73nry+w6sxTFT7LlU0W3hzhfoMPXvi2TdR6Yjtmnksnkk86k3SlRJA0MVnnabpv3YG99SL6LEcsOa7BY3Nus/HbTcla+AxBE7zzic2HH2EYD4fM86XgObhrjYdFmOsKZWM1w0en6bEqmwTHajRRDhI0nSjNKZJorqzTRNHEhNKoFFc0Up8/A3JAWOJyeSYlJbfMrcEXtTJNzqGeeGOJb5ryr2Sm2MzTGiS5QDQmr1gYW3+6kj0wF9Za+J0HBeWnS+bxslFN2GSiaOXeMp/ljVu/8Hz5B0hod+3tnz07Tkd7YxocnaZ9op9r8CzeI3FebTImuT0MeW+19Ant93hO/V8d0Xi1et6z4/5tyjSNjrDU4RNldLLcqp8q9faKLOj8KRmI2tVzK14y+ES4HstuAbEr3V4rDIWXY9h/h2GKKQLerkjGtRWloOjVu2iXzM/e85j248w5f4JoRcTgJNG5BdGJ7CF8Qkz2iOu+7/uTht/SFXnstez12K9to9fnbpyYT5DEvQsZu6Fr34/KIHzvLcMdENrHdCA+m8LsL5FCS+oO+vV6LbxcD1dSewtK6q24UGUuzqkrjj+9DlQxleu5qdrVUTt65irkCjL4gopci1iRF2PXq+EpvRC5usYP/7auvnwh/WdVWrxSXO6hyaNISuM8yvi+3YjE68gA3NWm+7G/uqZe799+XcR+ftWuqso2Fi67fYjGzt/qj5FewINF7XVfj+iFUFoLxOR6jf+Yt8+mhZqEiEZdL6SY2c5nfhDlICtTEo7NYVJaNTYD1kaLRp4mVJX7KIhUlmSEKDkaU4JbMVIMDzIpPEnjPjJmifCViERRuIsn0pxooXxoT3wd3erggzu6CVWcqolfLJUZ4aSVdQ3OnvjgG7rMFA9qrfyjNJPtwlNpFL/B2EhW+fOpcYuzzgd+fZ74Xir3VfhJrqwEYZlFVmYRRE/cpInWCucWTNPE2WZWq2w1sARrc46HmYsb26YcyzNrKiQzzM/IZJR6Ysqgswz0P5HE+fwQfFUu/PMl98VRFJoJUymckvLNCudYmU25p3F3NJJs0JybfMY0sySjbM67TflVzKz1jDZj2iqP28S3KXO0R44m/GKeOHvlqfYC5799H5R1poXxdXyBROEsD7yJ7/jHh4Vfrxs/xC2bGBEN2SoilerCd9l5syn/yZvCfd5Yqbw/O6ELH+sTm88UQKSwpIa1iaeWOXkhJrjIxOQrlYkqM5OuNFXaklnJbHXlszCOGbQF1Y1WhfPWaAgaxre68hNPHJOyTPDdZeJXrXEQyJZZI2gOYhkHbq1yYxkTYRXHZnBXqsJzXjBRtu3CR89EPCBzYTInTws/943GM1Uy7xfj7x4PpOj8t4cQ5vLM07pxpBFqZOCQ7qA4uW0sClMSbtvKx5zZsnLn/X4zhSzObVw4WuGrcO7DaXykTfmPep//SRRXpoOYe/WMErIMU0lRzANvTs3dQ6dFkMU6v2lHgHhFgN//zx0GKrQXNK59lS0jGXvnO4X24qaFY8nw1ou6PTxXvaMPmPZsK0b8DmMQB4qwB/QKXQXn8kII35V5NtpJRFwLAJdB6H8ZghfUbhRaVbqhnwTg3lGs4T90Jdxrf6jchnCWSt6RDjNq68fd08e7JUUv5l5mu5xfbAgievK8e8O9YYOvg2gvhkrtKNorH68FpWkn8ysyCIN9S/aHcb07QtiRE6N4uyKS27Cs6PNxP+f7v7sniw5S+RjDgVRm9kiejuBsUZDc7Sj0VWHeXECsd+as89MiObhTcjCrEtG5aS9j0pWGaZxD3TlzMgQMIWz5hZxfcMwbon189oK+jnbn5Pv11D8/a6YNU1IZfDkY94fp9Xx76qRbs9zHyys56VDNCLWOlmTunMEscjXXnRsdqWPYWLxaZLxuSTJEAnUsemwgqHXcY0n6w6hnhQlpENU36y7iGj05wfTlO9xAxa/2GT/GrSaBZHgNkiUOBPVwZLk881wXZne2EXheJEgVJGXOrUHdcLo6ajLlQ2mEOO9QJoLJgltbubXA3PgiZ4gzWhqYUyVhyUmx0KTf+19YprrBAe6kccmVWqDWle+3hGjpdhG6kXE+m2e81L4AzmCyEh7cJsH5wOezMPvK3WwsGC195OfTwiXOXPQNS3liyUfwZ9w6rSHnzOPpjKhwZ5lprpRyRpJxmIRVhW2tzNPETRhRVjwn5mXqxsYITmUrmepKqU5KaUz8gtTA3Uh0/6Rs/T2bgUbj3pQvEoQkPpSN794nxJSDOrrNSFUkJ4yNNzHxl8vKgcbFG7d5QWxD2zf8ds08rvDbs1NqsLnzkcxv6oFbq6DCW2onyMsTxWf+7w3ydMtPPVArNHeyGAcVUmv8PIBj5kPMvH/uHLmDVL6cIR2EWM5IqzyviWdptC34uTaeSkO08X2D2hLFhLdaCLdOixDn6xCemXgWpWwbIUZyZzJY2oXPtD+fmsKpwsmFtWSmyfmpBKIJcN7sC8JYaUVgyrg4FxWKK+3ccGvdUNsU37o45pet4Xnl5JVYnbY982/qxqoLp1aoRXB/pkpCWiMOPV7pdgbUeKNnclVMnpnzM3cHJ8eR1RLbqqz1ia9a5lKFUwrynCiaqdqIEpzTxpMLT+2G93/kSK4/ieLq6nc0fr+uoHnh7qSUaGOlbGZ8GovSf+4eTjsJ+TUassdz/NvGb//OTP/snZC8E2h2DtgOf73mCF3//cpgc//7HTV67ffT/6ArBfdswRjKvJ2v7HU4a4/w3I5GySd8s646M7Za0IFitZ1fFoIle1HqNWdT6ehN7Q7Pe/yKvnJr3/d5d4Tej8vMaKV7JqkKpZTeohuI0evzoO5kS6NA4BoA3a6vv3CqRF5QwuYOptc24hQ9xPm1n9VF/OoZ1Qay9Zq3ZGbXNqqPYgLtGY6SrLcI4+UcXc8/fVzCuiP67tWVUYp/qgKF0eqjo3k7qf3KDbNuI7E/9FUEN7kiiwALdkWdRHhRk8YeBeVkmxEQAAAgAElEQVTXVh5A3j877UrFnTzeC3wb13qtlWmgG/318bd0vl+9CkVe4nJ2pHEXOzR5ub4BjqRe0I4x2Yv95nvhx2gNp270G9BCULqdxKfEPJhFmP6eQvvHsq2uxOogvQ080WgX+FomzvXMgx641cIUzioVoqIOb6YbHmvhvla+s5nv0e4pNZYvTTdyJGQEkx40MW2NmcokMOvCjQk32rikwMxJdSLVSkqOtW7VcUwJaERyfoZziUpEoYhybg7tGZl6Gz1lIQ2PteYwS+ExFr6OmbYmkkOrM5aVG71F4sS5Hfn8WJizcQhBSvBUE16fmVRIaeX0NLFQiLUg4fzZ3cI0r/zu3I9tmoxEo5XOt4lWuMnBBrgdEBfW7T2lLSTZUDekZUI2Vj+haaGGoP5MxA1TgtKCxd7zZ3cT+f7MU51wzyMeqwENFeMRZVXjI4aJ8teRkdNEbnC3CKBMxfGkHMK4U+UfzY3bqLTtmWW+pQ16grczWOJZjNkzsymn1nhiwVrjnc6s68yNBG/tkcuaqTLTrPKvi3JKC4fLhb/QC5Jgqd4R+MjcHZ54Lgd+wcbDdOFZLrxbj6Q8U2VlOTmfqfLoiXc4kzb+o/sDH58LGsLv6oEgcacVL8qBwtvsNG1UMSbvvKumcER5tt7iVBqXqmwxEZOxeXBpmexOeOLjpaIWnKPx1OBcnpFLoeSlj9k5I/mJN+kGv7vhRi587s88rCsl4I0cWOU9dbrhEM/UdOC7p8rfxA1WnK+0keszTw53OXHfzjyshfl44MTGx1OwqHJL4m8vmZxPPND4d1j+qPf5n0RxZX/Q5gvY+VHSW4SdGP6iBmzDLPFa1EQMHym5Krv2nD7jpcDxIS3vr7+0w/Zg3SY+7ACExAt3JXwQr3HmVzJ3j+GHRedyyR5JI93LSqJH1yjd1PRKSJbuEO6yc6esI1H7BDfiWlSNKp0LpCg6wqYVIckwSt1bW/TC0ix1cQCAKqUUShZS9LGINNhr1vkx65hwF3khveecX6EmvYBoU5exRuutWRVDWsCY2Bu9lXT1iqIXC1t0o7srhrXbJshL28sC5FW8DPQCt2o3MbWQ0WLsGkER6UR+BDftRcxobyHDwym6SlRDxnVAD6f2ztnarhyyXqB57nstg7tWwgfJewRVv2ol2yvkxUU6uqBpEEoZXit9NedjDJPYiCaKEVvU251Jegu1r6SdNO4JE72azartRqOjnQy4xVDeyYhTsg6/vyqMNEB02I9E6x5aCrNqz+i0a+DPCFD1fl5FWKNxlE7ctWRXhPHaNhznXQanTUVxvHMbJTC6geVLW7qPtYmxtR+vHUPZzr3wdkei8W0L3svEXcqYBl97JTvMKmxt5qALiWDb4MkypsoXEvylJt6zsuo8kMWFKsFTGwH10iNhjnLDYpBr4WCBklmbMM3C51F5mBeaVM5aUFeWaqxDOWqqaOvPA6nKfYKWhl2IJYJKSwmTjCGswFuHL+lqvHOZeKeGpLmLM/yWXz7MPITx/OEd34rwwYXaNn4Z1pWAp8Y/eVPYXJmmI1MtnL3y67KgnJimiYvdUS9nFhG2rXHejO/XHjWltTJPnQ+VuaDWACfNK6qJKQ6E90gwpgckzrw9GDcCno5sDucwblyozbpoxQeKWytvstEu/R6xFPxEZiIb79rGKhMprUzHGdZO8reDsBb41UX44Ld8lozztqKS8XaD5r4I+QHnri5Uhb8uxvsyM+XCsTnuQcRbfjqdWR3ubWKh8Xk9UVrl12rUaCSbObZCkpWDGUuF33owJcOYAVhrpUhwezS+LM77FjzW4L0m/sdvz3w1K+dwPrREKUGTnk+40PgzDnymBbRxyImnGl2gghOrI6kvJjuKXVlr42LGivfg97r152l1brVTa25cOS0zh1NFi7LcKLdyxwMrX9avsSWxAb+zyod44F97YW33vP8Ixd5SmbmT4Ezl1JRfaeaNzDwSbGvDNBMZvvpYucsr71riZyL84riSN+FJHNEj3uT/4679/7/9SRRXv4/q7MR01QEUhV25IPCHiNX+79cKw9Ya00BnnJfV9o42/T6nqmu/OppUa72aRZYx8aQ94uNVYWVmQ0X40n5EenGlO+8IWOjcq1UaU9utG0abLV6I1/HKdf5aWI5jvqrDWueV7S2a10jCJ8ezH95w6s7eUZa1dWSjv9RRscOYTOuY8PI1L6/7uwBXryd3J6cEtY3vbdfwZDX9hKhcwpmmiezdgHJvj0bzHo4c/ftcB7E9uCon4QXh2t3qdaCZewvNtBstXgOkx3UQo3iz1sUCu+Jw5xUVg7m9WAfs1yDE1fm+tTaQObmOOcAybsA9xHr3oOq8QEGj74vHHuY9kNMQtknItV8XW6vXa120Sw2yJSIqKQaBPOAyvmdhV7iO98Wuqu3FlY2w6a5wDBo+EDT/hOfWWhvt5z0cXEnjGt+GgGR33n+NxO3vXTRdkcT9+ktmwxm+IdqFKQAmXam7o6F7kbV5vX7mj3H7j/OFqr0oPbuzifDdaeG3l40imTvvViNrFWx2pugeb3HZmKtgNvEYlcemaMwczfHLRtOJYCNV49aMTjoINDJNJ5zEo53AF+oEDeNXDjfFcN841iOzBQ85kVPFqnMbK5ruOvE4G9UCjf4MmGnARFhjso2IA4f2MFBjYUU5pMwvgIsKYZmtCE8n596/Q+WGvCh/cdl4uFkJEheMh1h4L0FY4qkVnut9T19YgjVmzq1Q5soXhzvelU4wpz5TpXWOU8BxRNT8xC7c+YVDBrXANVGKUdQpZC7VWfUtf7OB4qSSO3eorkz5CFJAjUwCf2T1ieeyYOrMFS4psbqxJGGVM78r0ou3CD6IcEoOdUbbQl5WksOvbeFpruDdu+y5BtoK3oQnCe4XRy8rt6mS20S0lfVgcFZ+tfY26G+eCv/BfWNaZk5uvH9a+Gp55tEX/sYXWt34ZavcZeemwQdPfC6Gx4lLeeCb2NBp4jMq72rjIpnWjHc583xxqnT3c4upN3y8cE7GRxcWz3yFknPmnCr5EuS5dK+/CObk1KKcWyW7UGYDP/fIoJuZWE8kCe7XM1IdZOJQK5afeaMrSR9YDb6tE/+D3yCXyqk6lYW37p0+0oKJlaVOvJML080t02Ni0wtZExcVzJwDyl3Z+H468utakHpDFuFbb/wvH51oFdUDIsKNKf/lH/E+/5MorvYHbxmr96trOVw5QREvD+jdOyfixcMHGMVJj4/BhrrNukR/orfE8kApdtSkSSfqpjF5lggWm6jsq7NP0RQYrTPrlhDJdDBNhgRfFRtFUh3Bz+6OJWGJzhPrhZyy7W3DzvklhdLoRYyp9RaOdoTqdYsoYnzGsKnoE3xcj/0QikcbxV+XxstQUE7TdG357UXZFb8zvfqHOV19EiLXQi6PVSyA5NQLqT0PcC/ITK8XlSDgrRPalasiTlNvo+k4rokhkx3tV66F6R7E3NumRAwPpS5WUBgeaVzJ4+huVNljhrS92Hrs4zfRiyOTV0abo724KzF3/h7SV+1tN/EcQdL79XAVULTOnyleuwu899bhJHp1g5/HcRSvZNvDpcdgJYFoZECtH4GlHvh9FTNED98eLP6rKahIjJ90RyyF5P1BF8OIVsY5nKdptCc7mlWjdI8h98HP25WoiglcvBfCHXHysQ8dxeqK3B6knoZpq6qSXqlKe5DtWPUPa49PfN1+hNv/lL9iisYhzpQl8fGstOPKcT3ws/qOGwnKpHgTvCktVVqpxNQncYa60wmkFbZI3bNNPuAtc1HHWFlEyFNGJ/CoVAuK3/dFqynFlUtUJq/gkCcji7NIoelMnZU8Z44sqDcKpQugpXtO1ZT6M2arfJSgBNg8UjSSkosQvtJa44hQcfLgDH+IB47TDbd+4pCFmh5wUfJyy0pvxUWDUnumXWuNm+WAnZ1ZMhFwssYhKaBMKTG1jlBF7c7yQfD/yJElZSZp3Ce4SU6euy2Pt85VnUU4BmyhnHFqC+6nAxHKoxxwETw2fLul4kwZPCbO5hzCeTM5pwarLxzMOeTeLbiVRCtG4ULKz7hPVHXisvHFlEkaLCl4MuEjSi1CRKWlhc8PCq5Ua5xs5hfmPGbhUisRicsE/9eqnNfCFzHx53dn3p+D6t/yvhz5Z7m3XZtlmlYuTfjaKoe0gBXuxUi1kVX42SFx8uAjwRE45u7Dd2nKRZ0mBdNGq90PsWriW3XYCktOfK+N39SJjHIU5/MKzSvqG0mc+7JhZlQa6eOJu5YRKselYRlSPnEqgB/4Xb1DMZ5zoohwTBWvmZYU31bqtkEyHglSE4RGTsplfSLn4MtJ+Mwr7bIhOA8IH4+JbJXizkkbjcyUe0GFCtu20So86R8XTf+TKK72bfcEukr391beq5+vEaqXluBoa5hQI16UecNIcudolRgcI5PRqnn1oB8TukYQw/G7F3w7cvWCdKWURibgy35rwGbdVM7MBqn77w8Gfr1ZDHQFwF4m2/2Y9+11rtzOkRERmu98rf7aQidXG9aLJ+9FyI4u8apI64f9UnzkPfpn7MTOz6ljovfag4DXWsZOdRRwb2Xubb2dY5MGwa0OT6VpJ6DzKQK5o157tFMon6AdV8+mobCsAnMI21CH7pmTXXX62iuqj/euqtz/TvSliHytDv19JMvdO//KXxA8bcEmwSKJx+S9kHVHUifjZ+nk8zSED4qARHe3HwVq086pYxSw+5CZyJXoLgQRbbh5NGSP2VEfhH/7g2ukf9ALV05EPuEI7j87StrbrIgSqgRC2zMJ41PftRpdhSgxKuRXPMZetA7X+f17m0MEq7feIpVe7M7spql/iBz/mLanrTLFylkM2RzRwPTAJT9j5ciNnZlwnlU5NyFtkFvQtNC8t1q1KXPaOMzGEhvbAaxl7tOE0flzTRvLsgBOSOaDVz40eL8VIIPA57NxUyCScKmJc2z8Nm4orWII7/3YF1U6Uc1ItaP4KSWOAZMHLoaIUVtlfToTWigG96pMApNOnNyZtBM8LBpTmlitt/1Id50sHsalFkopUDckz0xNUeskdI9MXrqX09qce11wgc2VtVWqzRwcNoNjc0QKhzQzaSe3h1YQwwiObGxAVDhVxR08nFstJDXcodTAWsJLYYvMlibK9sSldJ8omnKSbnVQA7IxFsK9Rb9irFEB47u24dFQNXRSzlOjrU6ujSbGROOLqTHrzNlWpoMjeqG1qce1Bdy1C9NhqPjcebd1q4hZnccquGZc7/gPzdkaPPvE3z4691PilzmYZSG0cC/CJEJOhpox1ULFKLVxnBt3nriY8sMWPDVjDcdbxu3CMjioK4lLKNkLb8R5Q/cE+0EyT+7cy4WbQ+58Nz0zy9IRew1+aBcqwocLbCXQWUlpInRm05Hs0Spbyjx7xnG8Gs868bs0nksBeUrdzoFgK4VzvfD23HDp2SCSjCzBFx580YKnlPlQV9ZoSFS2FjxvQZYu8il/ZDD9T6K4Eh28oyuPRa5oTzdtTIg2kinh3XMIFUz6at5s55eM1tCVpJuvar9mMJGGm3XPqysafQBqwSzTovNdJO9Nph7Z0ifgfW/HRD9MSUUEGfyovCNm0ZG2joZJh9Svbau+7zpaYNV62ylr59hsu5GodkPQKnF1Re+FT1eR7Y2eLL1tZpKuRqljN/ukH4MDZnolkO1Bxjvpv9ZK2EDSkH7MlkjaC6GDGJdWSMmoY+J97UG1F0smg2S+m4mONlCWbkzai7vOJ4qIYasw3L6lEnuhg36i/NRBzG/iPUxauq/UXiTKQMN6a3FHlIRJlCbtWlDs6FXE8ClDKbVCNqz1AiqGynEv6GKYdFbt/KWWYKEbLqZrYdsL0my9Ddz9rzqSWrXnGcYYh2EDRUo6PKDGwoChiIzAMYiBUgrX8RTpqQMQVJGhLJX9jhmFVvqkCN/bzXtr8Krs1O5TpghE50EFDU3dOASPl7DpkE6B1D3qx4aQo3GYhBiO/jQfIb2dCziNInk33I3ROncJtF3x0h/dtm0bKnWY//aWX63OTyzBbaDF+TwrX2rlKJWvnxs1lCWczUFNORt8xR1VKocbeKtG0oyY821kNg1SM95H8JsC77fKqSwc/ASuvMmJJom/umyYQG4TUhspZqasFBbScgDpRrA5Zw7bTDEnJFibU1seNskXcqxkdyxXapuZIrMKVGlsYszZOqptQhYfLv9KU8WTUYDaNqw0jtYXi0vu9ijPcstKIov2BUA4OgXZt97yJ1+vx6TBsQayPpM1k33l+zxznDMaN3w0eJKgcOx+crmioVB9CFp7YmwKx12gNHQylhKoCzYNekYNTIUqhVIbb6fE/dywsiKWqd44lDPcBH/3PHFrB45zP89LasySEVuZ00yJoDXwNvHUzvzShKLgzbg5CN9tDUrj5qCc3GjbBTfhy4PwzWr8zhvvLys/z8rPs5AvwTdpZm6Nv7yBJzny4Xzilw8b5olJjFn6dTalxoNukDK+Nm7mymfD7/C9OR+3jSLQag8Wdy/8QOaxnlFbeJva1Ztxs8KXGmxb4kPRbnh7zEz+wKk2vt82ahirCk+nC2ThORk3w66ipMYaUPORPE+oTXgMm5cIDk2R8oyokKUxGUh5xrwXaStwcvggXQ2ZqvCbXLjzCSLxsZyxPHGQxnG7MGMcspGLc26V7Y98n/9JFFdXztAeyVFf3KXNjOLSXcMHEV009s4IIsI2vIb2iWZfUXsaaMeYcJsMBMpfEBsZLaAWQc3dB2pvlb12Tt/5QtPIXWoqr9AUoQ4TyZ1zlHPun1Nf/IOg+1qdxTF/aX2llEjNKSmY92KObr3gDFn8OK61dR+kzDgGEVJPnL6iWcC1mNh/3+0o3P0TZ/mdmNoI4ve4W9cJeed1+at8x/7KFRGC3pJ6rTK7Ik6fIEmf8uUkuCosX1DJ3/ud/kc7enf93PEdQi9aX5vKAtcCbG9hkUa+5JWQ1q+91r/sijYafBLg/Xrr/C4lRz/e3XJBoheyuz2IXwucnsUVarSgKyO4XqqYKBfaMDfdrSJeFHY7x1K9Cxf2EOx9PK7n6g/GcC/aestzTx5o8oeoUb+felEbY6xfn9frGMhOSO8FEwYS1xAqPOnwxRlFp794t+3bdb9+vMAVd/VE04SKMKeZqXXpu2rt5OC7hQPPPLXEr9oD59w41zNvF8VaxUy4lZmv28Y7N/Ilca+Nz48Zj8yTF97EhLeV9+2Z+5b47GisbeNDJCImojgXggc79IVVZMwaWQo3lvhZrtzIxp0/s9XGZgE+0ci814qmTGtKUSglc7GFs0PKjXIQrE2Y9zbiXVSmDBGNzWEDlggsChkn1zoEH5VpnsmaCVl4jsBl4o0Y1gofLLiYM7WOzEVOiASzB7elsW6NkJnZTtjBSHNAMybZSDWosnKIhGTlY8xo6/dH1iDlSqmOx8xqsHoPl55UgETeAN+A1L2gwpHmmFfexArAA8LfavDXT8oWRtIDbwRu9ZlfLnDLe2o78/lsHKbM+xWeLu+ZNbHGRJmVr8R5XJXbbLxbK3UOzCtpTp2ztDpxFE51otbGnBtfVEPSDWaP/LAd+D6cC0qKM8kFVeen2bgpwqM2loA3KZhvDNNKC2crzg847y7G11QW6/SJszW0duL+QuMmGj+dLjxvyiPCbermtZfm1GIc55U3c0JSYivGcxi/2YT3mpjyzNkr09p6TJIqd9qvIcy4FeEWOEthKgeiFOr0xOSNKBOtPkFd+Ylm3gh8Y3BfgnlJvBfnY0s8SuNelA+uvLcMYXwWGZHA2szFoU7Cg5xpNTi3A0+1UUSw7R+iiWjrZMN9MzOQSpPU87b2uUakq1NIPZ5FnQllpqvAHMXCrxOK0HtimcTmDRvtibA+GWXpeWs9b7B7WTWBrnsZKIcGOXq48K5QQ4QUkNS4DGQkaecB7YT2HbEJk+HB0rdGkDxoRnffDidHRyemgTKJMFA6H8T4Ubyxc82CopB9FDmjLYP2PLwUMkwpe5ts59gkupLw4rVz23a5vg+l2uBpMb5Lo6NTTSGQa04dClvp8LmMAs+BVYLDnu9XO//KQq4+VDvvy9SpYcwabKFQNp5T4SAzaXDsXvOMFAgJFBsFRm+p7Zw7Ge3cGu1qbBoRTHlYcYgOQvyL5cG13Sy7oq6rC0NfkMqXnMZRwNnOy2u99Sp7G7eHi3ce2EsR/RrVC+2OZtoCt87Rcg0klAlltcbssGmQvV7HLA0UbUf+dp+ujtq+3EOxo757K3AURzKK0Wsk4djHqwnpXgxLd4qfRv4hdM83pMeSHHIPoFXr2Yn9nPT25C48EPZg9P43m4/XR6JBANNYyPyYt19SKArJgqgrqyZ8jFPShce18a/0Dd835Qd12jRxkxbetwv3YhzEeDyfaTifb5U5n/jMhPt6w+WgPMrMUwhMM1u6wRwONTjaxucW1EhEUb6ujYsntlLJujJLgFeknPnNCUQa53TL5spGYC24sWCKG+58RXNlcThZsEbjdhYOEhybkfxCyQ0jMymUBjFnDmnixpU36Yxq4ShKIyEeXHzh4yj+Hs04N+MxpGeU2sy5GdqcW3UWC966cKOBCqwWTEtCaqEygQWzzGxTR2i/14UnyUyj5dTKgujGTcw0f+K2VSYJHssFBd5wYM0Vyou6d1JAKnhjHvzPU2T+5xMkh0mMz1Pj351PHHNPJkjWkZrvyom/eqyc68Tp4z3P0U03s9ygAQfdOKRMxlks8wWNj5757qmS7QAF5qKEbdh8h6WCVudBeitv591anLifMmszLixMOQhfeF8rvy4zb5bKB575whJ3skExnsIprkhSPrSJkxSsZm5ycLoEj75hFnyGsOTEGwlSNs5N+diUNTrwcVyUqBPkishEzMqhrZynC3/eMh/lzBt3ztqYrSdkiAjHWojSOxlnGrM7WSbylEhNya3AUEDeHBberxurJLgceJfB1wvn6cDfFSHrkfet1wAhwp9bkPUZNecWeCyF2y3zLo5cREhU0pRJ9YLO/xB9rqxL5VsbnkoA0lfBuwxfRK7oi9Jl3Rkb8Gm7coMYrSYY5F5ekJe9frvaK8RLuG/bEQiio0+j5VW8e0lNo2CKvb2kyikqM10p1RQO3qvwFs68FzHSeWT8Hl8qOjDHrGm0I3dEiOv+uPUCqbwiFe+T4UI3q/TivSAc33NAO3dnFChG7/9bvKjuzKzL/31XKvbxyqnn4+0IUDc6rORXHLdejDlL6k68yig6W+PGMnVI7tUM0cYqjey72WQlmXb4VYQnFaZt5T/7Av777y6s87FbHEglwvE2+FQji3CnG14DodktOPr4N/+UN9WGUnL3hNpFETaCwl879L92kb8idcQ1pug1v2lHz7oY4feQrVFFr9LVlvvLaZh/VnNmlNCgyYs1x75dx3j/jx2xGwjQLgJofOrx9RqVVNVPrpVPv6AX5KovyNQLmjisN0bx45FoCnNKqJex6IEs3RCjaPdUC32FQnoMgnxHXHek6hPE8+/brx/TdhR+at23L2ni1oPL9sQPKbNK4te+8n0BD+PBZo5yZmkJk95yP4dzyH1VLtmZpx499B5ouvDblniqwqQKrSOyTYy7uHDfGlEuUIOIjTvJpNSwVtGoLGrczp3LeCGxxQbTxPebs2lvAy7NqSpQhGbCYpmfTIHVxmNL+FS548wUjsjGijCbUdtMMeGDJN7HTPaJ31niy9F0uMeYElwSXGqPMiGEZ29Mw3LkgrF6wrzxHYJUuEvCMsjIRYwsyuwr2wQSyjErf+HOQ5y4JOX/2Bbea2EOWFVAFk7qbK1SNNhq5VQ37FwIv/Q4LhcmNZL2hVfvKCjnGtweM6y99frD2ak6sZ4qbaWLXGSCulIlk1Nvh964kCc4aDc9Now7cw6WmKRSRHl7TKSpcllhXc9dEGAZYuXn2bnTfp6KnJnSAx8uytmCoxXe+MYlOn95vTzz+Z1ywnmqmW9PMx/CWNpC2EaJidac5wqaMqCIJ765FFIUXBISQlUjtY2/rcLFg280cYzM07r1ReCpcatKVSWaE8mZpbIW5ZeHMw+XC6dDxh67V+GjDKV1OAllawUX+CfHE/lp5UGdmG9Z8y1/9VH4XV3IUmC54fFDgTmzFbDphlIKczG+WCo/SxuPl0YcBF2NnJ8ozThKorXgcIB/mi/kc/RAaVdKapz+yMbGfxLF1TwUe3nI4TuKpX2VQQLdJ9qXCaNF7AI0so7MsxGqKyKduJxSzz9qnVhbxnuTDG6Mpmuxk8ckFR7dvXqgHZmXfLpKXL9rz9ADyGZ47w8Rtb2gInR39kJHQLoZtnUXeBjL/94a2gnnkZTeIRXyeL+/atHIbjVBR0HClEl7FqF4/1un+zCpdn7abn4q0nlIeMOlI29eKjWPgsKDWVNHsEZw9KTdDyOpXiNarPUsKRkTfxv8qR3Bk4BiPe9x6QcIgA+/qbk2/vnDxodvP/Jf/Rf/lP/83/+S//PbmX/xX/+vbFqY/MBfyPfcTAF3X/L+1+/4F//pn6M28y//+jf8y+/GhROd4O4jB2fSTmDfbQp23pbQV7ciLyRwd+8PbOlCB7TnZCUGqiTBNJA74FUhEqRk18xKH4WejutjN6ntzcwgUEyDwDrp1fl/yXuXX1m2PL/r8/uttSIic7/O876qusvVxtVuW8ayLeRGtjAPGckTkMXDAoQ8gRn8Acw8YIyYwQQJT5iAxMAzBBJiArJlubtp3O9yVVfVvXXuufc89t6ZGRFrrd+PwVqRe59megeFb0hX59x99s4dmRkZ67e+T05aSR4aFdi/P4piQZhMsABDv9atPork6BuM/uQRkdY031PZ6YibyEbJtudSlS5Gf/h9RkPxzmn20LQn7RPY/lRIXhiSNvTPG6hZpFGgJXchfuwPYIpLC1dtoFYliZCt09R92AzxIb/rW3l4ppgwH4V7zRwZ+TJekw8n3qpzXSNJ4To6137iVbnij2XhohhDMGUBbFEAACAASURBVPamfC9Fpnok6MjrYyB7IUwTX66Vt3kgDJW5tGFMMC5j4bK20uF7dqisoFBJmGWuhsAYRk7ZUDW+WB1NL7gaZsxPjGFANBLEIBjVL/uA76wyspQZYSJNlTnuqFyQvKJamCyxSiZL4qSRQ1TuLUDaoThvSrumhxBRK+QaiWQGhVXaXfjU8+GoXasqF6hnplBwMrdE7uqAGRzVeCl7ci4M3gZEXyIhRnRRZnUu88icCqEUPqJpQi0IKoEYKi8vjWNptU1JlHW5wxDuQ5N7xLwwZGcfhJj6/by3WxRgyStX4553ZgwKUQaCW5NgVEfFScCUAkcvqETmqrwSZ/bEc1WGvDDUkYMUduPE0O8DowRe5ZXX/oS8vuN9fc59yljdE4oSdKZmYxHtEUFKOCWkHHAtfBWcy0W4iUPLMZNKGifGMnEq76BMDMmQEriX8ZwT+b6u2DpwOTS0/0JhPqyYBFYpLX8xGp8oXAJjdYZR+Hw58Pm7kUCl3hu/nJybVPm1IfFeC+8Pyn5sAaM/cfg/D1d8WZ/w7H5lXYRI5rZOrFoZcmBXMkNsDMauGnVxqleGIUMWfrdGXrtydxAGPVFON4x24mNxrl35qs4scaKUyj7tyK7cUjmcN+3fzPELMVw1R5/itu2g29e3deTsaBMAOS/q0imIM72hSrUWWBk0YqXZxkMIZxH19sAbFfIYhYAH/ZcLZxSt1tpzmR6hNwJssQrWyoZnrw9BiaqYP6wgW9CoWRuI1DcqpR0fuL6Ecxfc1uP2+Py25xBCOFMsai20cdOGbflcStd/daj0oC0ENdeKaECHiPasqC2bSap98DutvwcPcQYf5oVtaFjQh+FFaK42xXHp0Q+dMvyLz+C/+tt/mo9urtnvL1lr5fntOz6u7/jDcsEPngb+y3/rr/O9dOS9gJZPKOvIV/OJj68/5f/4375o7419mI/2QHM9IG3QhicNrf5oS+0/o0MdyXHvPY6qD8wajxCk87X64f9rnxKi9kYB+9CJ6a6o+qMfECYRtkyL7fFSF6J7c2Aj1fp12K/T84+36+Qcz0GA8ICInjPHtoiI/phb9hT9BpI3qpYHB+v21M4xE8EIZx3dw9dXowelts9orZuxILfNSxVWdQaPXb/VXu/krZDa+oD6bT1+ugz84ersohAtsPPMD0KlaBOgv5fKLRN31bmThamceCHSBOvHDHXh/ZBY4wR5ZSwrjDsu6x17gyeSWRZl9cr7kJhUCFVwX3k9O9lb+XgKLfzx07FdZ69NOA0XDNm5D5kihdsSuEg7bgS+E0qj3dOeN15Z0aaHLZUxJkJQKhWpGaWV9y41MGvlIgYu6pGxJnYu7NmTrXASwWIbOG4AUWVMzk1WjsGJbhQTshr42KIMxBuFSqBIYmEie+XkRpDA3iMWM6MPBAWxgTVCJrQhyjK3LOyzcKGBYplXNSNDZGZi9sJlNQSjhMDBCi+HPbUKJwrBhTjsscEYrJDGCLHypFbmkpsMRAYqJ55lJ3oiubMbCuNgLKvz3gaIgS9sx+gnJCgHc7yAD8KXnohuPMO5BAKFGzUODu6ZT0Yj1/fImPjIF2bztvkVY6mRgnBv8FYSQ23p82Y7RJRdyVQJvDdnXxayB6Id2XVxuoyVZJWjV7QG7kppn+viRHcOx4XruPIdSxy0MGcYfWAaAtcj3K73fH4feHo98gMSWWb+zHjg9cEYdhOf245Dcf5pqax14nqKuO6QINzfH8laeDplDCHIgAP7JCQUlSYTWbzwIheKGpcE5lIoJfJ6VI6iJAJPcNQu2nAtI18F5Y1lsg+EasQ0cFsTrrd8N0aePL5PfwPHL8xw5XB2MG1Lmp8plwgCg1eqK0jsNGAhaCCYNVeXtBJT62hNjAkFKhXEHg1t3rU8seVRlYKleA5hFGl0R4oDtbTOu+DNLSXdDVi3cMjaFmSnow+xL1LuLa22Iw/Fm9vLY/cN1gdNDHDWO4l5S+b2VrTseKPzHqERC8auL8axD23ekZfqRgoB6YOqStMtbYngu06l7XrRbxta+/f2bK6oLfG84OeBMoQ2zAaRhlo9cixG1eacrN7yjtTYiWC1nYDXFUJC4wDV+Lt//ft89uw5w6SgrZrnB9+54X/5z/8G/9M//ikfX49c2IHd9QUxZ2K4YorCy3nPe0v8hf/99/ltu0a0dhpVcNOH99hbAK2KURyG7rzb+u1EK3hspdDASHMgjZvOK7ThwaX34tHceNaUZ12bt6E17Y8WTtF2gQBBGtwcOwrari1rKFunr1v+V6elxTti1qi+2lGqzbHn3a2ocE59f5xz9tig8NhQsPUtrtAp4jaoxR7Z4f1aBbCt0Fu0lS/jiASyC1RQcUpHoXKPZ/B+TqIVq60kV4Ki3rovXSBI6oOjNZejw/Fc+vztO0yUNe6Za2aRTIqCVMEtUFNmXANPw5HZE8LA3SBQMvtiXMXCFTD4LZcZikSKNqfdweDTnXOtJw4rnIrxdE0cQ9OoXmnhioKOI0Eqs0zcroWvl4EYFt75BLKgMTBoYJKCmpPvnTdJeTMFRo0UMnsZSRT2qrwMmadx5Rgjb3Jo16ULB4GFlsv2NsNLvWbGOJbKVciMdkLkkiSFe6scNSFiWJm4DQWVzODCJZVfZuQ+rHzpLadvEEU8c+PORehdeYNAaULzWhoCv6bKWIUaQ6O5a8X6vW9eI6aVqANTHRjtjk9pQc1jPnGjuaXkz8JtbaGiH0lFzTm6MptyH4zlfiaoMqmhwVitBVLWmgjieIIThdt6hdUB88oTO5CBJ9NCKROrNTQn6cIVM5Pcst8FTmXmXdhzXGCuJy6YKCpEHRnDyuhHTg67qnisoMrz8YJXJ0fGgXRqjsolCfM8s8rAc5RjdZYS2VUlX4yspwPH9S2jBKZjq7DJoeJWeGqlaYiDE2vh8vKCL2fjfqnMCPVUubzMXHngq2WFJXIRAwe745/OTykygc7kmDhoQFk5MeBLZYgTwU6UMnCymZAi340TWHOJz2VhmqazuWutkcyEijOsmVAX7u2EDyOLZCYxbqytrafUQA+xK6rNLEFwvaA4eFZEK5UjU0280ZFjefONfs5/IYYrD224kvN/nW57wHWAzb0m551woKeuB+0ZQ11bc3axbdTNQ0XOWU/jjnfX4DiO5J5cXmslaGgp5bnCllqNkGI6O982pOmcf7XpS7YhqPfmhbDpdOjPqT+X6FibPoAmhvTq1ChQe+I3LQJg9HgWRleFvcdzr54g5/BUkQc9y9nB9ydQwMfoxPaabNqjDbGyII3C8xbbsH3/tmi3nK/2c2IQ1foCq4gapu01s/6e/Svf+yV+9uorfv+08Pf+ylN+aZ8ZEhAb0hjGBCI8fwn/8b/8PapUjscjo0YaUFdJMfHiZuJyqfy3f/fX+df/+x82aoOmxdL+PrWT7YjWNrDyIQel2nbKj+sOPnDeidA9hGeKbHPQbRqo/09W1/l39/dJGsWLcY692ITyZ4QNOdO17i1DLJ91cPLB77G2KUVcOrLWf+5PZF2l3kqwmQce9GN9OHPOERqPn297DrHTjpuerNPmW1xEVfA2PENL9xDpGkmkD1XNgWXuWN2I836t8aCxIH6zEPz/n463y0yIBz6LAzsR7rPw3gaqFlyN54OSlgnXgoXMM2mIzUVK/KlY+Sxmfj7veFrukZh4s1R2bkxkYnbuLfGFCaGOPJ8Kp1x4LbDUyDEM2FwZYmDQymWEec1EUy5jbu/PsnARK1eDchMCX447Pq/OfYF9yTzLtZ2bQB0qr3XgNZFjSWiFT8fMZahoLjwzI+jISRJSnAsCL2PlWDOjjjgnTvmC2Rs6o+KI3KHSzEMHFWY3viwnhEYXShCe2cyOiqmSLVJPzne5Yy+FJE4aC7sQqDg/G69YrOC1Be4u48Iny8qL6UjQgucVklB85jCPxADD0JLDbxEOlvi6jFAqlyxMySir4hWGoJAbF19CCyEOFCQlYggkN9QWzIzJ3qBa+NIn/ujuyPNxx+7iwIUpL1Jlio6sgXUy5hN8TSCyY/VT28CHK97VgsSJUgrvD/BVvUCpmERqfz2eyImlwG5YKVlJ5cC+OjuvrOVE3DdZRzBY3Pjk/cqTVLE48zxE7tfCu5yJJXGStml6q06wREnO53PlRiCOkVSM1xeVVwUOnthdKWuq3GbhDZdYNnZD4k1JXE8jb/LKjSv3Cnl3xUcc+FNXO3Q98XoppFTwEkjVmKLzZOescocMO1JUTqd2b1pKxS8TuUCxARkaUp5QxpARqQyeQCtrmHF3ljWz1iPFB+4nEBPiXHi2cyp3xOGbRdN/IYYrpbnb3OXcy/f4MOvCZIsI3unBtqt3ICMgzdm3WfNDEKwvjKEPPtpjFc7UiTxEF0RtieselehKoecf0TRd2Q3TVqnScrW2xaEtnNYXTfrCp26MtBqUNny0wc273qQNTw+0lnQBezKoQRg3J1zf/asomeaORNoCFh3yloatihTr8qaGILWza48foCNoxkRPDe+Le4P3DO1C7yj6IGqnZVc1vZt1EXtf4Gm/r3TBuAbt+idDxh12vOWjmvnbPxj53l/7VX78dubXPx2Y3XEvCLFV2JwX98T+KmCl9uBCZRzG8zCSi/Hu/kBeMq9t5qMYCf08rJauJ5LzNdBGyQdBdbBGlbQhoU0aH0RWBEU6oHIeyM+5UKEVPoeOWtqGrvYwW0LT0my6Pk0Eb45A1U6Pesu3au95q1o6o04qvSC2VQUVhbAhf/09qaEhRxtV/FiHFfqAZf6QM9bMH2dCkWL1TPHGEM+C/0107tao3I1atpYFivTPYwgN1RxF0aGF921D3pb6765Yba5NCR09q1C8D1oiiISup/x2Hrd2BVU5WQ+4rQdCbP1ycxk5WGWNC2Ou/IsxsvNbljBQS+G9Jd7ngRPwW+WG5HClM9fm3ItQTpVsypUas2V+vuy4pPAp7T56mZtA/VArb+qRiyWxXiofWUGrsErhYkh8fzC+lokvyBxOhah7bvyEI5RQeS2VVCdWh10t7GPkgspRld9bIhdlx6owxcyhJGY3vrcDt5lZlKMP1CWgMTOGO55KgqCU2sXiXaPnOJVAWQs1WNsYIPxYR8QywQKLCRYzv+9XoMqNKFZW0ipcTs4TEu9L5RAiqDMsI+/SwIBxUStxf2hIx+wcJkER1rzH5chUKtNoPOGWk12wROFuNead8els7IeBtJ8J1RiB1/cZ10SiUFlYloXDquR94rjsucvOZ3Hk0zTitTIdT5zGwNu7lnb/domUk5MJHBflQjMXu0Qw4bAKx1NhP96TXdEY2ZPBI2AtrX9emcbIWiqpQgiR1y78mIHRBRsWPlqcr9eWxbePK0GdH55WpEa+KhkPkegj16Jc71YE54W0WpspTVg+UTIM68IxKd+pI2/0xAtW7k8JwbgaJp7XyHFqGquvj5WjGZPH1pxhiWtfGErbxD+NkdPpxCsmBodXo/BdW/ji/ZGn1zeU9UScAzesjHLiqDcsx3vuLm6YYkWrEHxkdljDChlOOVHSHXnNXEnlk7Ajx8BPbxcmveId8MOT8p5MYcQ08W9+g5/zX4jhyrpIPHQqZitk2Zxo517As6i4/Vy1pqXZHFct0VrPrsClf+PoFZeGVAyhFSS3BXhbeB3veqLifbHVB2qllAKhZ2Bt+pSufSneu+02Gm6jefp+PmujWWRzi/EhYvBB3lNHqwgtJFP1YaEr7iRr6d7bYKguLRu0WhOua6d+eEAjtiFpW5CjbBU2dn4hE20wyr3nsGmmHjKe0rl+qFfsWNMBBQ0EaejdgULqb87RhRdf/4j/+t/+S1zvjYudkkLmb/1KYnjygndvvsJDanEZPSusnTQgjXabpulBR1dKq9TwwrPrC/SZ8Ktx5mu9apEE9HBLbWXEGz6nXQNmVbuWqr1u1vsdN91biqkP3W2AACh1SxPXLtq3s0brMXK5zQhWjRhaZcyGcD1+H3ikg2rnplhtdK3A+VyqnPX//fe3/wnWrtMa/Nz9B20gN2uLEMCf3HuZPfpshA/bDrY/NzPC+f3oqNq2+Yi6OUytfx5aL54o51y382Nt5xECtUeAhE5rD0QazijnPsNv4zFIoHjTPwpOinvMK7tixFBJMTIBYe/8sBbSOnJSwMO59NowjtUJxXg17IheiLVt4KIHqJnBB6LCASfXFu4ag1PnFS+CJfhKCmm94GclouaMY0JW57fmxChOCMZ9XTlJ5EKVa585mnIjziEeuTtGvjLDtQ2Hqw+cUuAqn/h4TCxZeTkULgbj58fAGiBKxMrKrTovVAirMMvMzoUkieoJy5Wiwp0l5lApMTBajxYxCFoYFAqFEiP4SPC2IXmrwmpDYyVmwQlUlFQU0RarEJZucgpGWm76xrgwddoaHKs7gsDLuPJZXHnpd+S5EqYBC8LtfiblzHhbOKpxGgIaKuKZN2XHrIkMeIyoBa6Hwo1F7svMWlc0CD+tCT9FUnV8dSQZWowBJ43CZEZZlFUCuTRN4zFDdUWt3RdTnZtrUGG3n1iyojXzBbBWpYggufJ8EuoaOcmJm5D4OGXCGJhnY0jCPE4Ms1IlkE3RAM8kcDU6F0EJNqOlIqPydA/v5oytA+OzhWkYeH1KfFcLppEjcKyVne856YHLq8DFkrmKMEnLOzv4nnd65HjIhFK4SQPPxSm6x9X47UV5u1xzPEUG9b62Nae1bD247xSRxF5Wkirv1cn3E4lACJn9MvF5DdzXictaaD0SLe9vj+IxMMiI1nnLbf7Gjl+I4Wq7+dIXajz2nNx2A14wRpczShC8JUBLANzOIl96399Gc4WN3vC+yIaGcnSAA/cHZ2GIrT8uCZg6rg8i3SBCCY3Cipumq2uuEiB0a7w0t14UOwuH6a69pohqDhEJck7xrmedcUNbatyeyka2tIUviUJoi3DoCJ8JJH9w6smZgvReA2NtG5gCtfT0eXGy2BnpaHofYbUmMhaTrrV5qKDZRN/QFgUN1umyRl+KOJWB01z5d54f+U/+1r/En77+PtN4gZlxWFpRb02K15nL/R6sLdCPUcrzX7eBE87IUa21000Fu1P+m3/vr/Af/s+/T4tQaoOTiCJiFFqtkFUF2oJSvIWy4qBhG4Lp71PTyg1w1phF3dDNhrJthogHBm4LhuhZYVI7ZQjSKdsHEhZir6vZ9G2xD91BmvM02KME/iBIMUpomWVmhmnbUYu3a6+VU9N1cJuTtCFZTrs21GnXfP9MaEe62ml3nSM9m0sECRC8YP1JJhqyVYqTc6Y+ojMFIW2DpEGuRuq/uwotXFdbZtxWR2TdBZw2If639BhCG0R21jWONiNUng+RSyrXZC5jwCUypogPkVXWXq1UKV7wKixDQxez31N94EoCGcFy5mhG9Znr62suD4W71ATfa4BQI7diVK+8ycbTdMevTiufXOz5PE98cXvkqAFRR3Xk5aiU5Z6Pxsh3ppnlJFQt/E694MuyMtaJ78aVP/8kcpyNN6XwNh84ycBXknhbM7u5sOiOa1dmW9inHRdr4avFGRM8kZW9RIoXdDiQUuCnR2UpkVMMVBlYcUQqo0AqTk4FJZFqQX1lpy3CpgptQyvCYs0N2ZBp2p7Sao8SaVEO7e5dGLSlmxcpLAhJG2621oUfWUTlsoWhakK9kH1PGJR3SnOnO1yGIxcF6rIyxcqn4yU5ntBlptTAO2YuwsTNpXA0xfLC4AEdF648ImVmkZHFVqhNBuDVeONGIGGnjOhKygvPpYIPnKaBIQx8sgukFY6p8E4XntpEkZXglXmM5Fp5IpVRAidfKRKoK+11deNC73i+UzQERoEhzrw7wJyd47HwLirz4iyMfLIX/lyc+TxW1tPAPle+Pp64vrhk9pXP9ld8Ut5xMCPLCa87wgS3q5EzXA6VJJW9CIzK21PCk/PzPHAXFKsJSQPXT5UrbfffJM4TBdWErVDjyquSwBMHdwZxIhEXpxCoIXCKcGNPuPKC75VRAhfzwlhnoDJU460XDjT93Td5/EIMV4/DDLcBZYsTANhLazFHe9ZV//fNNbiFSW4hng8alAeBr3Y6I8tD9tCW6zMMSjXOyEJDHnpuCsKchFib1ohe23HOPKL39sXm1AoGSbULnCFGeqnug27s4bly1oVtNKOfFyz/oD9xO6QPZdvsZvSAUHlI5W6p4x156OKnoi1WYCpOTi20bzuiN3djkAqhUTsb1diopw1JbCjSNjhIpwOzw998YvzKs4G/81e/y+V4z3530wLaponLqeuINOAlk3NhHMfza+mPXhd4MAQgD249M6OYcVwqu3Hg43Tsr288FwI/5DW1n9tec++vf+0xAcNWMu3eNVItxf8ktfeKWQ9cDTxkoW3vg50dh/BAiQbXc0K7PDqX7ajSJy/78Dy3wzpctb0v0q+xYnbunHRvvZkSInMvuj4jYb2gumwuwO2zQdP9BSldD9USvR5fKxty6PTB0ltOmW2oLbHPZA0ZTo/oVOgORoelmzcIXR8mXRMoDUWM/ZKrWh92Fd/C40IrRYSgmSJOlUBEeFthlcAqxlwrxZ0qO4a6EGzAojLpPfsYOdUBT+39WXyi1pXZMqNX7i2yEggYthS+oDAdT9xcKDdVWMOBz3YDg1WWqPzYRn5jibz3wM298Zcuha9FWCxymxfiWrgYJ+7Xmd/MzmmFJ8PElRo/UPi163uqr5R5xz9aRlIt7Dzywo1/ITUz0R+fChc7YZ5zQ2vzPU92A7I6eXbGfeBCZlYXtCa+XIWBQsLwU2j6rtjuUVWEInCpkGQ53zvWrG24DK2qRQRWW/q6INQtN08N78GXUZxaMylAKoLpyg5jlAh15WmFZ1rZ1SN44SIs3B1WvFywT8IwVSQ6HpySmznKJcPonPLAsh6YV2FdK0Ns99pIRSzyqZ74KhhzyaQceZVnTjrgZW0uSjNEjackvh+Aeo9fKclG6k75Sa7sVRizYFH56d2BHJV6V8m7CV+FnQtjGniilRVj1cBbK7gELCm704Ef7C7xcSVy4OVVxNPM64MR6iX7YHxJJl1NfH6beZVGtBZelWt+tEQQZ5eUXxZjuEx8lYzDmrClUOwa8pEY93x3CHxhwpc5c4oJL9qHXuWlTiy7ldcifDw+5Ul9gwytf9VwLlVRLVy6Mk6Br46Z1+K8OQgSC6Xc8xeurtlH4d2amUPFRVgRTIVpnQnuyH0mkLlb4W1ZERH2CnNJmGdW/+dwuPIOJT1okKxnN7XdemllgjSVR1ujWjFp2/mJ+0O0QRC0d+zpluHTF5raw0etfxjd2u5MurMMoPaBLgYl14pqRGrrunKvoI1qE2/2VCOCgnSxjlbrH/72IfIzItHpPaWjFF1fI0b1wIaERAKQMdWOkny4SBstFJA+ULgoIQZKrWiIuFcqLfdDQ0u4R4VLUw7augzVOethVKTnf7UcMOBMB20C/Y2eRZyY+pDYsSsRcFP+o1//Ds93laukRA24rVhoi7ukHoBaWkUFwLIsjIztXMwhdudkDDD2IdZpw89WIeQwDQMnnOlqIIq32oKOQuVuIJAe8oprd5mkPrBaB5Y3HLH23bkzeuCkQjTQmDDfhuCNOu7P21vF0TbUb9kzTb/Wz1cf4g0cOb/O7bp6CCTdDBbt/diG644+bYGv50T2Fgy79P7LsUeubzSzdCRL++sk2v6MCEJhK+rVjgLKluiBnbOvgvTcLmlizzb0BkycIPW8+ch1K4/uWVzyQA2GEPo11zZHLS6ioXNbxU+LXPn2aq7uautKHatjKaBmJHGqDKBrS7kvAhpY15W76hzsvqVfm2JWuEP7YB8gBl7UwqdhZIorLwfjmCszgTdLRmyg7uD1qYJWBhlwG0i1ZVB9EgLPg5FR5kvhJzlxi1BQvrvbMcXKYjt+Ypm5NInAlz3k1Mz4w1mZxktGr3ycCkpl0pXPl8StCsEiH43GEAs1JjwX3pTM7cE5SqIW5atDYU7KsWaGoEyUhuaXiZiNGgxso5wyEppDTidh7DvtKlA8MNcW2hs9Iyg1QKnt3hVy2xAVVdQqTmDUAmYsOpBOAUF4WmZ2OyOYUJd7DiUzBjh4JOz2XNSWK3hXhaMFjrVgorwqA4s5S+336tzWopoDY5756DrCMvDVcmQuhZRGahEGSVwF5aVkduOMF2U249m0R0qFWLhbEl8fVmQYqDVyGYSnCe5y4Ysj7AlczBUfIoMY494J6mR3ZhP2MXGtgpTCXQ7cn4QfL4HfKjS3nsPlmx0nE9Zq3AyVnUfK7PyNX878eTFe6MQrTezCDGHmIiV02EMYKMuKHVduhoHLWnkvkRgvyFb56piZh5Ew7kmzcYehNbKq8/NDRgTGtPK2/pTv7xIv4oitB/7gHpYx8Mer8qYsZG10b+73TsvNjf077+94GiInLSyLMMvCClTGppc2I5kBQiFiEhlWx2LC69rvUd+syeYXYrjaHHQFuo13y1HSFsTZd9TUB61LCKGlZ3ekIKica2dMH3Yv+ANlFx4Vmom0dGFpbNf537YakQ0xiljXIz18vVGMbWhKtln0++L4qAewjxbtfDuN6BpRGmWzBu/VOELsUJRJo2hid7KdLf/n16oPgdvrII5b7notIXTiOHc0QVsmAdVbJUtMLa6gVs5i5kG2bkfti35lMuGgDwv7YwRQt566ThsNwfhff+dL/s5ffsGQ9pSceeMLz3fjB5qjx72GMcZ2bu5ghlsfMFTwXM6djdZV1arKNCZide6/uud//I0fk1NCLTXdWUcNV+nuOQfzFVFl6jU4W7RHsIpGJf+JTKpN49QGAmvU71l7178vSNcRtRJW6+Gd4xYmypa51r7fNq3bo9+TUuf9e/RHi/ZovyB6R3s2B+dG+XqjMlscw6NKoUcAUEMbH1Cu7XOCG4UeptuB0vjosvJtcHwk/leX82AZ1VumUP/+7ZxD/1O9DeQahO4R7TpEzrq5fYWsfh4sw7d4uHoxdJ1mCrswKwAAIABJREFUFt6PYEy8zQ3V3IWZSw9cxZa6rq78s9UJvsMIzaEbhDnnhtgj1HziVVZ+FoTIjrnOTDZxHZwf7OFJPGJlQibjJjgXQwQymYnfL4Xfez+zhj3qAVcjuHIKlQtGvrxf2QchsbKKsnTFw40lYqqoBkKI3OYW5fHWI4NG3qlzlQZuDWwxXpUBmYUkGcuFkAK5OJeDgGTuTSmlcLcMrdw3ZAZJZK8MKLtyS4qRdQ2E6EgRDqH1hO41kLSyuHLy5hrLVllC5ZqBfS5oGLmd14ZWdUYj0GvPJLcN1nrHWO+4ic2yn+uBxROrBErYsx8ihYh74M5gFpiXSpUCRUie26Z2Xcgm7DNc64krg3E88cnlxKjGuvspX4eX+HLHnsC7UHhdZ669ITUeBgoGIbKY86P7laqZukCJO+q6AkawiezCblB+SY21KnEKHJaVuirvtbBX5/kenMxdTvzksHJp9/zabmCnK1+PieP6nvswcLcUPn1e+NFRqASuhomvTcm7CDFwcVF4Ple+ON7zybRjHNo96YYjh8OJm2HHcYI3q3HnM5adP6Zt6FaUQz7wvTRwXBt6LV55UpUslRsJ3JfC1xL5ozcLR3ckOVGgHiOuK+7aZECqILltTrt+dhbhx97sVpVKKAapGcqSOjEm6FV1k9dWnRccpxCCUdTY/fMaIlq1LSzQJa8iZ2HvpiK3bixrGUx27oRD+gDlQhAlB2fyLYXdHyIdpKMKPT09acBwSm27d3HOq1XtiJZ4o/vk/Lo7UVtWTfu+hmqIQ5EW5GZmbadvULtuZksHHzbaxL1X1igDLdhvOyLS9SmdHdP6ELBK0w6ph140bGekoQVlVqo+CJvdGqpiar13TxsjExy8u/2k9AW1oYVJtHXcdaSveutClE6hWUdZBoltEPbA794VfvjmwPXU6MndEFlrKwnFGn1Rq1GBQZUUYn9+fl5mvTYd1jnlvlZOeWGXtiFNcM989tkT/rOPP+Yf/OY/4IfTJ+d8KkITC2+DWS4b/frQydgGtjZUR3lERwYh4d1Z2px9Eh4QvI2ZdW+EaENxKlsAhPcBj40KO1PP/d/PQ7KcHZyaIltunXZDhZVOcfbPQFYHWoZYcs5DfAmtC5Oeuzb1WJLi9lD7I80t6rS/sxkk7AFtArrGTs6PrW4dzW3mAgl9SK/1waFobUjfaOozBeu9ucBpqKAIZpXa0bxwruz59tKCP8oR7TEm9QQhNq3blRjLCj/1wqdx4fsm/HIU/uqLwKHAD3PiN06Rg2nrruyqObEIUQgYas4QmvHlnoHfOGYkDsSY+U4K/EXTRoO5EeQ9f45LPn0u/FEu3K2tQipgvBVrPXn7hY+HiUsrDGnmQiuoE9OIMfKze+cPy0B14aSJakIKgvvA246CXl33XEJTDhWW6OxxngxK9sCSAtEmbsuROCqTZsgDubuWmx612eyvknNaMiaBuVSmqlhsw9FVSHwaAu8OR1YRLktkSMapJuZ84pemwl4LN6r8dIE3ssO9VZGZGfMwcKhP+VIb4xFsJOLso+Bh4NYFpXBhLbahysDlJJjvKTZTlyO/UjK7KeHlRE6GVuV+OfHz0wW/8b5StXDhl+zTkeADp2BUm4hUFk1Ugf268qncEcrAUhsTMNQ9eqEcj0depJVdFN6e3nOZEtiI1cAgFU6G1Qz1xDTtuK3C+3cty/A6BCIrP1kv+Z26hxj41XDgUCOLJ96NK1/fZ/5sPCDjxMDCJQmLkd97/ZZ7LnEdeJEyQznBLvHzRfnxsrCoombkAKGciB4YvTI6nEz5vCzc6MKhZF5cCH5YsfSEQ1qZSuG2LFDgZTTuiLwUGLIQUiH4gVwKlcC9NHxJc8SjMobUqvC8sNiJ6sZkgqfQndYnzBUtR/Z55joZT7bN9iBoXYgR9sOOKXyz96RfiOFqo+y0azlaWa1RN6s6Dw6tjR7SrrGyR0OZ+IYaOerKKkYyPTv8NOhDyjotr6eJj63Rfw7bTX8bdbbC3Mcb7b7eAQ+Ih2mjanJ//NoHweRC6Xb8piXzjnhtcQbtgc6IlMRH9nnaqi1NSA9tsVZpz29Lp2/n2fvyWjYBW8KQyoP1XrqTTtoDsZEz2guZ1bt+baOaRM6W6N2jAubtNQqi7MbAJxPc6MpP3sz8ypOJ/ZRYijEXZ1xXvKN2SyktiFVaPpNvupshNtQqG1jtKcpteI7oGSmBjsRUR+TE3/9P/xr/7n/3Wxymi6YFku547GL0adiiN7bror1GwzA0ASqPXG6qjMWZ+wB+lsLBuUbn8bEhUNvXXdpAdn7dz+f8SEfGA6L0UBz9oMXbENHtMLPzeVhoi/GGaI1q1Fzx2hoIsjennmpL3nbrlJ03bcFmiKBrUR5HbQQ+1BluSFZozVOtlNofrolzLtamHXx0/vrI/flYMyj28CJsTtxv6zFpEziUbuTRqrgbr0URRhKV9/PEH0TneCoMJyH0NPVFQtezgXVdpvHI1AMEi0DA5UitCa8JK5f8aDnwOhj1BM93F+yicCPGKxM+XybyYMS1OQ0tTnxZ4fXtBftes+VhZD/sqQiT3xETzERO0DdcA4NUjmJM4YrJVp5MxujOasAgRCu8WiK7EW448X6d+FfjkZcXC39wgOcDfPci8Q+Pwh/Pyvt1S+X2FhKthg6RY233dhWBUriViC2Vn6ozUDCPHCjUsnJVDUvC+5owhdWdMcLlciKnSCAwW8S9sx4FjvMBcgsYXUKl1veICFdB0WHEqByXE5NndvmWX3oy4URETyyHW8bLZ7z76j3vpDJI4kbuGWIlufAc5evTkfsEv7aLZEs8HY1TGZnXyn3NvNLAIpfUfMf3h5G4O/L2BM8m5TaPHJaF/XSBKAx15QKYYpN77EImpURlRi1yS2GxwOpH3CJPp8rbsrR77hp4NjrT4CRWjiXyB7d7jqeZj9PQwl7Hle+7YGmm1Dtu645bHRiqMklm3CWGmtjtCmVxxhRQz9h+QHKl5oyIc8jCRbpk1cLl5YBLgWNzBz4VYdoVjlT+7BC5zidChF098NlNYJeU1YRlcd6cjDA25/3z0WAyVHbMJVCsUj0QOBBLafdoi/x8Ub4YIiuFHy3G16tyZ0rVZ2gwXpxmLqLwb3yDn/NfiOEKlZ4t5aQQWd1I9iDqrf0mvS2e1iaGZn3vg8/jG/7QHX2htlyebeDRoOdE7W0HriItOwpg21R3aqUFLvoGI7Rv6cPeNuxs1vPNoahbKOjWg2hteElOs8Rqj5zoafSFDaVri3H0LihnC2psUOeq2tliIW6ZS9JfN7fmpPSu5+qr2KKtCxHz81C3RGc06cr57Wk3sbvgxHP6ehvMSmnusUUaArf9mKmwU2HnxpNUmDRSDb66W7isDUExlLk4MThjaNlR0WChUGP7fa3SoCEdTjvPYJC9Qq+rWdeVLRtqrYVszv1iTKr8F//an+Hv/aMvW/ghj1CwbWBUPaefbw5M2+qMeudi6MNTcwzKmS5tCGRgtULoRcf1bGjoLtP+vUkD2doZnE0HNERnc+79yfT0lqPl5/PdjtgvROndl03vZqAwWENIzYQQEjKAuzGWdp16F1OJWHveW8zJRk+zIaD1IeJkG7S6qUP1gYpcpXaXqZClxZ9s4asPQbVNh7PFZVgfYl0brb8lw3MeRO2DmqBv23Gfu4awE63Z6/kamaqQUuBlXdgHZ4pKqoX3VXgdAk+ksFhl0sjlHr6eA4eufaqtfKajpIa6MIypNS1Y5kKVP6OF7z+9Jq/w22XmH4aCrJGUK6tPRFswtR5krKg4h9TiM6iBZWl6J02XjBq4dHg6Be5OM8iO2xAxKS0vKyqLC08m5+s1cZJAsIFfGgoeA7cGcgG/aVeUeyhZKCVS7w2VinrhaRBigNVhrkJZjJ07H1VjGCJ3zJyqc7fCGCK1FFidT+I93x8ct8LrGimL9fDgzHubKeZc1cxf/ihxe7tyErhgx2yFUZ0fz1e8Or7l5W5iEeXGJ5YBPEzk+68B0BN8FTIiiZ99sXAxwf3BGcPAcnjPWCKDOGnf6mTqvOJE/pkr91m4kAvujsLit/zk3USYDtwdlHsPfHw5kPMdQ4Cfr0ee6Y6gR6ZkPJtgHPbkurQmBVGCjmTNlFL4qe2o94HDMHKZArtCo4pjYrUTL6LwKaVJP/ZK7W5onTMvB+d7Lwtm8Lamdo9aKxqEdT7xLu25KCto4tn4DmpkkMwwBfbZWCOE6Oy6I3gR43ZQXIVP0sB7ObCehHFSniWjqrHzBZWRMrY1K2fj69PClyfld8rA/3UwsihLcXLdUeKePY5pgmWhvKtILlhURCIiihdjmK4ZZ0FqRSWxlkz1sUWSDIVLIseyUnLh/lHG4Td1/EIMV9tueMsImmjuwKB61qUAHfmRTrM1jdWWfWXdSdUfEGi0S7WHsNDN3fQnnVyPv6ZtW38erjaaUM/UZNOYlP4Q0fUDVOMcA7E9nkoHnx4QOPeHhclr6Sni/Tw2Ebt9yP8GaSiOdw1PwZqrS/Vc1XJG3qTb9sUIDjl0DY1DcsNVzs+n/bY2MW1p4Gd61ippGMibK2wbrgSSwxADRmW/u6Qc3zHFp2Rx3hyX3ucXmFblegyUFFp4Q+h6qGqk0ENOc3mg5+jOwFrOAujzUGSOlxYm+rO3Jw6W+CfvM6Jt0KbTeYE2cFbrgvGgZ5q0JfA/6Pbgw6HnAwSGFrw4TSM551blEvR8jsMwnPsWt/PcwL3g3Un66Ppq6GDX3oVNt9QHu22+8faDttGY25C41ezE9vdE11ZZuyOk9OF1N/Sg2CB8eH32P7V/9JvGcfuidxSzXUPuTrQHhERbJDu6fSb6VbshW+1abO+v901KQB45bLfB7ttMCgKeaQTv9p5pu5eZMQcnW+bHFMbVEJ/4KAmH0Gh1qlJD4E0QvswDQsG9ElNsZgtvtHYz2wRyKWhSYlQWJv4Zkd8tC//+zcDfDEf+qj3ni/qWcHL+oQdOSw8v7vpLlXq+B0pnDkJR7j1z7wsXHkhMWNpT8omYB65iYCoFHWC2gfW08KyuvIuBIVUmEtkWAiMmKyE5A4qOyv26cNKBG62E0jpRR3Umg1098lkN1OvEu1lwMpoD7zC+JwuST6DGVXDwys9tgIPynYsj+9i0pVYT//fbwBtP3KZLfv7K+F6svEiRdz5Q1pWVwo3NlMsbPvZ7rj5+yauvZ75/mfjRu1ue1IU1Zv7cIPwTa5HMF0OE4zvQPftByfcrz6+MaMKrRbgYYHc5sNjCn5oqthR8cFxO5KJ898pJa2B8kri9v4PkqGSePReOquz8PffHwhggDFfcLYU0OFMKiCXWxYhxzxoK67tAvTyx3i1ceCSGwDQFrMzI2AqUvwbe28xNCew1ggv7fUP0B824OGE5sgR4dSd8HvZcSWUfnIjy/9y94j94ckMJA799P/L6NPB5Nna2coqZoU6YnzjYjhojO4k4mcs1IuOJcjtw27sSW3TRyE6OjCa8U2GUPdSVFcN0JNrAECs7BYa1V94ZWVemIZLWROjDUdZCkJGlOJ9J4Y1nhiHzF8bI68Mtxxh5KteUYvyIiqnzsiS+CMdv9GP+CzFcbTvoJXjrp5OWA2RWiVHPtE6llf8Km7C9L8Y4QUNfbOysQxF46GTrg5rEZnsuvZJkO7wjTalrYpqI21oj+KZXcaeqEE3OGVptYW7hkbEjatCS01NH3RKtxkSC9gizh2PU9pVg7XFqpwtCkFZPoH4WYbYxqJF25ziKDmq07PoWCeFdxD+ItmBMOP9bqBsl+igSgk1P1d+PHusQVM+UD0CfwRBvv3upjeb76i7zq08uyRSOp4Y+/vh+Ia/C1Q5sn5D9nmkaetGrAcYqgRAKqRRUhFEDMaWW8WJOiLHBugIyJEJU4jrC2/eA8j/84x/xm8eJ1N/v0iMiqjsqgV7z1wNZ9UFM3uMVqvHha6BK3fRatKZ7DYGa285u9ZYRs3hFQqNLtVOY1h8nbDq40EJCRZv/87wtkhb0KI+uLX/0GuvWYbiZDHiEcrkTvAXchuogxhTDGSmr8vB8ah/maxNGYVY7zevn0ud+5SPnkvBOx7MNfsIQ2gIHzenq7sg5QqN9prZJyazgIs2BSxvAoRVHf7ChsfBIw/jtO572SqfskdVqH4YaSh7751v8krU24e/nZgxrYPXEi2Hlo1QptuN1ueMuBKIVCk7USPY2eJsZYUhsJebtMN67ID7x96sS42eILxA/IdIceXEQzEILdtZ2r+X/Je9NYm3Z0vyu37eaaHZzutu8d1+fXTUmk5KNAYMsYQkZCiRPEBITRjXwnAEwQSDBlAkDmCAxgUFNmHjAwEJ46LIMVeUqU1kuZ9bL5r37bn+a3USzmo/BWrHPeVkSEvIbZJEhXd3T7BM7dsSKWN/6f/8mF86jSiiLA0nFliUZQoZGAtN0pLGGbZcJ00z0Fo1Kk0deOOhFCbNBg2HmQLKWkCNtbthkRTvFhmKDEO8GsiSi7LgSxU4Rr7CKmfWqYZ0j35aZIY5ELNq6qipv+Gouz89xdAx3I3SeP9m1+Bx4YzJ2VjYucpVHNHtWBn69X+P9W3Iz8pO3GyQPGEb+lSawy5a764EzF7gxgZQjd+05No38FOW37Mi2HZhCZvVkA+aO22OmvbS8SQHvHP9qn9m0hmkoAc1Nu+Iuzzy6gDAZJAfUZKRpiNxy3oG4kUjk9g76zpGt5aL33A4JiSNN23GTG54PHqfCZS+MxwFnoO/h9iiMCH8+J7Z2oo2FiD9ZR+dXTDIQp4bUwjoqe+e5iZGf33hmt8LpBOKYIzgPP/A7rkfPIWTeTBOOZ/zul5krPLc5MJmJx9mSJbFVzxyU3BTjVhctnoD6CSuROCWcnTnTVBHuzLaZ6OyyGJvYiGUkoTnjOs8m70gpselahIHWGkYdeT0lppSxzkBrSElJ0XBnDE5gihmTPSlkPp8zKXvGIJD37LWI2roAt3Hk4hu2h/mlKK5yldJvRImukLVPPCljToR2u6y7a+bZyQgzl4tkbSFOPhAFlkkpphPSEOoks/BolgIpn+oHqZMSNNafbA2WB5Yxtpoufn3SW5bjC7KBFldtTAkxLQTj+4t3et+6es22cpy0TICm0otddSZKS+dSFgn/Pb/FWkvSxQ37HoEp+7s/F4nSZoNfOPZ6HlxFclK+j8JREZoTcnFvbFr2YYqqxRcES5PybhAmDbzdKxOZbh/59JGjswFrS0tzqFYGOZef9d5VX6lid+FcuY5u0xdU5ETELsrBzXbLs2NGQsKJO7VQF+QyLQ7/tWW3tO6WCf7h98t1UCmokamkRp8NzpeVv32wH80Us0NKuHfxcLpHs0K1DckqqFRFqwiLM/CCigmciPfL9Thdswfebc1iZlqvf2nRgXUW5F6R94vX9KSqPRHqC+qgphRXD5IYSWm5HtWhn2rBocWpP9vFt6Fgm2FBZ2u8hlYV4L3SdjkX93yyxXYCOCFfv6rbPitrMqs0sTGGICODRmDLIDc4WRHtwCoknHi+72beswNnNkHTMTEhYeRoWvax4ScmsQ+JowMRVxYZtiypjAWprWKrQxGXSEHf07gqqH4cCSZhTUs0BkyJAEMVjyU3sTxfs0WtkKMlEbCmZNQZk/Hi2FqLIxMQdsdAcMrGdrTBEP1IyolODHcxY2PhPeXxwCwNvDvyxEC2me8ws/UTn161NMc9N03PRi1vUyIdZmKKvOCCO9swiWXKMITIMcCYXEGwZ+UYhTONbILnnfP4MeIcBNZ8p33BZ5uRd0fD/33TcCuP8TLyob+lbxw2JZ5nyzArj/07emkxeWDjZvbxhg/XPXPqOB4Hdp1BvTDczWxsou3Kc/lKlfNWeIHn9z/3PPmop5MLvs01n11Y3jnh1XHL27nB5g2qIzEGnm3OmIaJzhl+fki82xl679n4iWMIXHqHc4lpmnjaTqy6nv18Rg6R1Hg+8sqV3yPdqqj05g5r4LF/h+SWKd0W4vd54vMbYXQWmY/sjeUyT/QoZ13GoezV8+oIQxJsvqNNsLaerd/BbmZ9PvJx22AZ6NpzGt8hcWQcEgHLZAI3E8yT0muHv8xYnbF2YuuEa93w+SFxO5bEj59Ot5zb9/mnqqQ8c0yZVTRsdUv0ho9mCMORtTd8kSz9DLjAPpmiaDZHjBj+it/xZXQ0zvEoCfsU2Igl6kAY11x0ET9ndtHxYzxzyGQN/2+37f/n7ZeiuCrQc/naIeRCUMHbqgqkFA2iprrvlt9bajBtRX+MaOEyVQTDVi4CvkDcc0x47zF5cfuuRpn5ISFUC2JROTJqoDEOTaU4KwqsXNtu94WM1pXnvbEppZdX8wShwP5aiyMLJ5POZCgEZIpDva/HUewVysPOPJhAPYLaUhxGC6TCdyFribYxQrZFfZfqROgrG+wehVoM0wR0iRcqk6QzgqUUmMZItSMwSBYm8ilzt/CFDPuQmbKUWI6cUOMQD1+9PbB2jrUdeNQKbeuxJpMzxBRBMyHCHDLeWVx2WB1ZN4a+7+/J3gqoFjWhCDEXdeVf/eQ9fvjTRE5TQfOWIoj7YO4SQq04Y0vshUKUjOO+aK8XB7gvAsSYE7p5so+oQgY1pc34sIjJi/eVFBJ4WooaSuG9tHFzrl5cYokm400Zw/fFX+UxmTKOpBbRQTNOUmkmiWBycccvvmeCybEeS1Xv1araVlNUI1pkySKopiop11OhTsonyxIvxQzRu4rIVdR3WUEsyFc0VOSuWI6oyWVBsdwbtahKokXZWM/ZzH0M0K/i5r2nEcsjTTxrdjjxiFqSzNxlTxMnnvqIaxMpC30fuZRM4y0xHjDSsEP50vbs9iPX0fOuSTRjRG1BPcUVD3xRTp5mopay9tOyTrEBtKQbODGkEJElqN2OWGsLupzK+EYNxjpESiGdU6KzHsWQjWGnM5dBWLcW7RsudSTEgFdDCg1P3UQzHvjOPLLZGHKceLLOrDXgVpmumbG5BSkZgLvpwE9ix5dzi3qLzY6RxG0846CRJhtaDJMqc+pLa9EeQRRZWc5zSx8ybPb8DbngZ95wpTMXZ5FuEH547Vk1Ld+7EO6Ga1ZdpEsTF9sNNpeWnXGe/V5ZrSbGuWez8rxvhWme2dqRjx87Xg8TV4/W3DGx2q7JYWafGp5Pwo/DGZ3z9O/d0eaGPt/wB6Nw5z6jSwOtBBItUSP75Nj6K/5ZShBXbHNg6g1dGGk1cwg9x2B4O2We2Dvm2fA8PWV9uONJ+4Y3uiHuO+78EUdDI8qHfeLC3zEYhzUdX7wrJq423vDk8pxf+2DDF2+L+vKZRN50kdupZXANfQq8y4nQWJKFS9nQbx192LG3PTfa8PbY8XwU7qYrXjnLZ1bZNiuerjJjGnHR4GPLO0m8tZ40CWPsEVOU6sk0OA2kleVtCPjmEV+FDGIx4tm0EZ8iwwwuWz5PgtAQ55Fz50kNTKFI/Y95YBwMSUb+4M4z+YAxmUfxyIU4dnail4bvuJlpZ4gY5gSdmel05o3pvtH7/JeiuHLLsrxuqU5atjbBslQu1KKwk+ISLrkUR7kiFaoFkYq6OIonjC0kX1Vl1VhinBFTMI4lONdae/KhiieV3QKdlQnBLogP+jVuzTLB5uqdYrjnn6gqnfUnFZipBNHyh+aEFmQp8mm4nxRzknu0RPXeC8woRkvrsiAgufCiQsBbhzNaj6VaTSwFwCmQbuHL3J9zrRM8mNNnM7XN+FCpp0ZYqSPUwkxqJMugymGOeJR165AY6XG0HWw7Q2Ohre3KEAJiyuQtYplzIoSZbd+wyoHNds1q05dxEOOp5UVt3eUpkELgx19d87/+5A5vhGD8fVH0YNKWer6Fynmrbc02VzXnQ4jzYaGsimBxroQb31s45K+dj4dqwaXtV6lG1ZJDyaeZrZhqiomI5ErzLphbaQuW1y2K2HtVYSm6uqz0TUdTC7FY6zCHFlGHWW5lofHmVMyILYCfwRNTIhtb2pKLQz1UE1tOyFW2sRjTVg6bUsQIuoyPFE/HekK7amH50BNNT8Xr18fR8ne/qpti2KlBxfFyhNYpaGSTDM/szFnXMafMEcdNcKRgeWEC+9QVMQhKzq5ytyzGgBwjRpuSMkBGYiiIoolI6hETC4qlhhwTrhbvg5aYlRxrcT8bkIQGRyQjLgBdQdFNYJw8GyOMBjR7jE5czSWT8P10xMqG9+w1W3fkURaadS50iDyRw0huS0CxlQEva7RtiGw5MPNn+5a7feDW9GQrpFhiv14nLRYPKWGyZeUjxjimPHJAeZQG/vazLWfdgTwdeTOsONqZab/HNBOX5sBhvOPTNtKfbxlI/N7+gifuHU9bT5JbLvqB42TI63Nu58yq2XKYRobDDnLPYR+RJHgH0cwcJot1hkiid3CYHINODIPhSMfF6pL39B3vMZPDNdr2SIAjyvut8Jl5iTCxGwzSrTFOWNmASTs0t6Quczt2mPmGQXpcVDq5w4WJR0ZxMfGkb+j7n9FyToiBLbfc+ZExgGGiWXXcpYnboaFtZ7wc6Nwlz98dOfj3SHu4vRuRaLlJRx51DYSW1sysXcsx9ly2gRThz2MmmZb+KAjr8sxqPVemCH+etXt+QGK0PcF2fLXPGNMgNrPzGZ9mWiJJLb5p2DZK9A06HtFsuQvCqJn3Z4NrR9ykZGPJR8XbSJaIIozJ8CpmokaGeMBLS8xHPvAbzsRxayZeBst1smgyZJv4UlflxpsDyWZ0hmwTYJjyTBMTWRq28/SN3ue/FMWV0Zp2vvBWBECJpQmCJ4EqcWmn1IkwVZPRVNm+rpJpRYrizJkSHyIxl5aXZry1BQ0ToYQ3UfdV2zK5FHX3AcklFFcqD8VTCdnmgc9W9ZsprcJ84uGU4GVFrFSkY8HFSuwCFNuARgSqm/WixBLhvoWHsqRR22xOXl0iwlpKS9A6i2ppS4nUB6S1rFwhY5s8kM0W6xIRwQdLNAMu9yQtdgm2trhCigTX0eQI5JOaAbhIAAAgAElEQVRhaTGLLEXXOlqCATQQVHidLGcO5ii0AjnPDDkzH0Y6admNmdZnjDfFcdg07IeRIQWsWOw4c7nZsF31YA00hauUc0bDPT/OdA2t9/z1b13xHw49//OfvizE+LzYyVLjbgTNoSgerQUBX1si2ZZCTaUoT2cyXbK1gFoKtMJ1y1IMaW0VDyj3yi4LJ/8qm92pvbgUYlZs8XVi8SMrFhoA5WgVXcrcU+B38SryUtuZOKwmeu/qQiMjrvDxCsKqRU3rioUJtVCWRfFY0b65oks8KGpOhaQtXEVvy9hZWs/UNmWoCCVS2oIqBSWU6v+l2ZFTUbUGwFgpfmV1LFpNNZC9+B4Z7n3DfhW3MJbIlneUltouOZw16BwJbY8/JhprmJNwnRzDnJjEYowS6zUwZBbLlBBCQZmIOK3WGgqkSiHIheOoufQBDBDjIu6pB5VSud5KWYjVFkmKSsPAlWtIneM83xFpuBJDlMQ4J17FhAnwk+zJrcHsV2xlQ+zKxBhyosmP8MZALDYBZz5zrnDMM0dtySgheYJ2zETUCU0STFKcy5hQ8kEl5VII+iOboKwSNH3H7z2/5RgMd9HwxA00Zk/fWlxuUTzWRCYsx7sjwa74yJdidJ9mrAHvLultoneGxjiiHXm/aZmjI+vIMAnOWyIT8yhs2wQSSWwY7ZrDzYG5WeGGkbWBu/ErXBro+551nnnsldgFfr5zPH83sYln7GzH6OD29ZHns0dt4jJZNjLx0Zknzm94eQx0duSzJ5esbCKoQfGkOBHzQJvXOJdoesdmJTzt16zSgZc3QjTCm6PgXeZ6lxjcBkPCrXuu0g1eL3nceXbjkavc8ny0tKbDNI+Z9wGTI1PoeGxG3u9XmBTYGOEmwiZHDpqRrkVyZtOseX595EVQWjdgosd7i5smOoSYhI7CQXvJjhezktMbvtWuGX3DYzeTDgM/0oFu2JYOh4K2mTx60IBmZVZhXa1HvPVonIjRMoWBRMJnyzObcS7gxRJ0RpbOgKX4AbqZMSopZra9ReaJvg0nC5lvavvlKK6MIeu9J87p5wsiVSdNd0JfKoojpUg6IRAVPWhtIRYvE9fC5TmhA7X98ZD6oUtxVTlORkpRc19clalwcexGzNf4TMsz6uu8mb/IsbrnutxbOUht1ZjaFio8qHsEY5nURQRZ2jyL51c9ttNr85KFZ7AYhjiXxPrmKc20I6WGxMDsM5cq7FrFhI4zfcPdaPlwHXnRvk8/3qGmqQdfzvvKCE9a5dWoNYrCEMShkrmN4LKw9bGqPi0+1DZcSkwp8uZQ0EMjwu54xFhorCNRCkFjSnahcf4kDBARcMU2wTa+8AqY2Xaezt3gJdGYwidLVIf+yhczpa9ashPrODPcc39SYyFlerWIXXLySmGVtUTkpMrFWtqrVgt6aqSgdovCUqu+YhEAmKrCW8Cbh8pEqLE2mnGaa4RN/bzoqbW2qAWddbX4Ly77BW1d0DSDc8VQUUxZQJQPV1qPi8u66j1SVcbovYJPqiGoqW32xc5hua1OrU8qtypbYiqB34VumEvsVB3aIVdlK7m03JfIn6X41W/2IfaXbUvVRNUTiwsxA0EdrxXsZGpskamczAljS9h1koyoRdSARFRdEQ+InoooMYZYo7k8FokGJxMpl4ZxlHTicBarmOU5kmrTN4IuT6dSwM/G8SJnOM7YXNKPj94WM15T7D01lGeWZQDgmGbyVEyJrcBsI0MGn2GcA4covAVmPMYoGjtWVnAm4SLEOTKFgJOGZs44axhMYjNdc9F6Puw2dNwSnEd1JLeFNP5iSFgDLWBDx7pNrGzHMU/s6IoaDkvrApMaOukYjnfETulRUlaCF9IEP88TrTVY47geMqvOMs0dOWfe5gDpjEMO7IY929bjYiSMiS9ujnQIn152iApvWPN7n1uebC5wIRJiw4+tMg4zbYw8vrD82vmOrQejDSl6konso/KtbcMBx81xYFw15Dmh6YhpLePck82ESR6NnhevAzfdjB1a5iR0JpCzYQgRR3EhT7ZEx5xrw1VjWB0TwXXMjXIepmKoGvd0zKzblqARDEQCCUuvEde1HFImxkibM84Y3BzobMtnLrLxLb4JOKcEPJqPvJs2TBxQ4DxaYnX2f0WHP86sfOb9VceHecU+zdzNM+scORjDh33gOE/kXKLe1lbw1uDzNdlbkkTGwRIlghZT0b4xXORAahWfIrMmjGtpZF/i7FIGZzEONDVgPVP4/2FxVdR4pVgRuZcoJ5MXwKZyThaCbPlZS7FjWIqwe0uH2v8h4ysvhdOjQ3EpIwjWOJIWZVdZaZe4E5WCCJSJ0db3KxMFVTrv6i5Vl0zAiu6wqAqrbDln4hKvU4uo+qkffJbKeTBARbykegfdN66qdD6HglIs2XJaTTe1kJBbs4QKl4DSxyHwO99fsWmU63HL//H8Df/B957h3Yp3N6/57//5O35ru+K//lu/hnOGeZ757/7ha5599xn/y48ONEGZdM+///F7/PFXL3gdOnorxasoFu8lL5aPJNGajCVijaUx0IjUtprwdhdZtZFhKgiI2CLZbowhG6ExlpvDTO8b+r4piAv3KJAAzBHJGY9DV8J/9L3H/LUPH/Gf/IPnWJPQhctTXeuzsVgRjBaEMp9sEGo4c4FgynWriNKpAKH4SbXVaiOeimLL4oydKFYFoy0ol9Q2rWotNvLX84mz3oc6h1QmwIFCAr4ntoP1pcjyLNmInFrgIVfvKo1kIGHRXBCwORe+oUMKwiWl+bgQ4kVMvVf0pKgtY7kW/rWISovrfz3uuXLXyK5yzgr5zGU9WZIopcD1FP6D1rO0tN495iTK+NV1uCqbLujhIlJJkJMiZLLNoBGXMo33jMqJ75bVIhJAlwD5iGhC1BSkOwMpE+t9kMxUL2K5mJoNaosfnc2pFMZanoVL7NbpefNg3JpfUFGJZFIsvCvJJUi5cBIjGssCVzQjYkjqQBJNKiuPVNFxo4XLWR555b4YQkZMwqGscXzQlVzCJs48aiIHOWC9IcXMIdySvfCBH8E1XB8zrRE+XBWvrpu85lVyzDFxPUeswmadUDK7rDQ50dqWxswE53Dec72bmcj4ZOhyZDe3HFDOveKbR/zx21ecVQpC51cYO6MhIHbNcRrZzMq6VX5w1jK44nd1OxvOGseT88Dd8ZbHHlZ5Yp2V1CWcuyIfd+TkeT1YLpoR3xgaY7nY9hyHgGuUnDwyKttOGPIM0tH5jPg1cwLfKf+SbwnDgXFjmWLAxCMbu8Vv4Dh12DxzFwPbRlmvemS8I2iHWs8YE+2mYwgJaSDMLdZExBqMttwMkXei5G3H7XGm79foMLELics2cfSCZyTNwj4MSHSk48S0cpyHYpOgoeFgj0hKxARqPVf2LTMNPvas/MiUExc5ctE6tpKAGeM9T1eeaYxMJnGcBUJil2BlIq0IB2Y8QoyBgzF0Ca7J7O4MH/WGMSbM0XEIhlvbkk1kzokrdQwqHJxnjJm/9Q3e578UxZU397ZOIpxsAbSiQ+XBb0+Fy0NPKr3nAJ8mkgUtuJfZ3yM7Aif+VAnvLZ5IviJNhf9SX0+ZpwvvaXmPGjtxwtOXNl6dmMoeTkHQagw+KYtRUX7I8+F+N+UYawuQe+L56bPq/f7KQ6l83+RqIFiRlajF8sFqptXM3/m45TcuDM1VwwXCv/vrn7HpemKc+R9fCM57/rN/7THGw/nZlhQj//nf3vLDF3f87o8CYiNXCv/WB+/4ro28OQR+fye8xNI5oU2ZxiU0HjiMjrY3hfAq5sQXG2PkGIq0N6VE5x1bG7hsO5zGQgyq7awQAs1gsI2vg6FkqZUPX/8TUGvYPOr5WG/5m0/W/ONXE9nM9RouRXgtUOXrhZMhVeXm/fD/xfN+QrdKNYSvg0orETNrKRyzNfeBxKr1vcrq0NY2arFGKAT0hiV7sEyADYuirx6bK2T5mDNZipM/QLRai34h1vZvzBlbA79ztRyJZCKUFHgEakzKMqZP51B/ccIUYv1ZrgVtElfbm/cE9a+NTbSgwA93lB7cnxW9M3ZRzi6v+fo98Ku2WZdLu0sdyUTUGhzKmQ184g2DevZa2n2ZGfDEXPijXQo0JB61gScNdGpwjEy0JC0Kr94pm2zBeXYJvgjKn4WI6oqUyqLodI2cwY0zyYM1AbQn64xPLTtvcDJj0v0iB+pCcinMsxAobd/GlpSHnGt8UoiFVShSxm9WjHU0QYnMzNZzaZQgRcWsOWLSiEN45pXDNNBaz1U3oiawTWu+CB6JMwTYBMOP5oT34KXlcY64dmBlM+1h4t940hKZWNuewzHxo8FyTNCZieha3k2WI44mQB8S17PDTJFsE48bx8G3rMcdQscq3fD9tkHbkTkmPnLvuHjW4JoNcwz89GcHvnwzcm56Zg48ah7x0/0O363ZD3seW6FvE+v1mlU8QgNt1+DjyAsMRma89zgvzEG5iw1jOnK5dlzaTPYjftXy4q0ypUt6k3AWJI089gVF2p4rqVc68VzHSNA1bwdhIy3O7elsostPuR5uaFUJTphVEZ1Ra/hibsFDOsRCep96BoWzVjkOhvfOLCuxpCFh5wO3RB7ZzMpYMsKH5w3H40xi5mf7QO9b3ncTl3ZHUsebqcVZy7bLvL0J3FhhHw2exCGN1Qg7MsYZH+GLDG9RZk08MWACjG4iz4Ers8L6zJQtE7DRBiRwZSw3zYpeMllmutZwM1q8bRmY2Dcdl9pwg8dJ5HlKBcCZA35R+n9D2y9FcZVNrT1KL44kRTVYOeyF77K8uBZUmbLKX9CusmktUCryVb2icuVCOTG15SdElEimU0OXhdkUzwtfVVB2cX2QgiIJWoJptSBj2abqZ1Qn3VR4DwYqRL+YPxZfogTlIWK+Pnmb6theJseiJIuiOC1r/yxgqhlmCQku/kG5rk4XkeNSzJX3zzgrJLH8Ty8zn1wp/+ajFecXwjAojQfjWv7uv/6U34lX5Bw563pyiogUS4zPrjydNVg2/Me/0cB84OlFwyHumfeeMxt5geERlpUeOXMFuwspVaWdYlPEm9Jim0OgcR4TRp5sep6de7a95/1HjxASw3EmYBmS4KaJNiu28RUtFOgaWIjSFdU6Hg683GX+6eu3ZLuGal/gLAXhqR4/yZRW2xJ/o1UokHKqEUqCTWVMGABnsSFVfzQ5iQ4KKlaKFasZI6XFjNS2MhWpkuJETy3CjBjUJEQtkVh5R8tqosbzGMWngm7FmMkWkmYmKTmZQZQ2c0LQHIITUxGlYuxnSLA4si/FOAVRI5elxCLYcFpUlFEKaR7AZEugtClVBM1gtbb3lBN/zOfMYErRKloKLJOVJAZ1D9C/hYNW21AWxUpGH6ZG/wpurjiJUcJLM64+v+6AnyThvRYuw0h2GcmGMxtpTUbikVU1tLV5osuWpMrObpmGHXSO20loguO1CCTDz4+Oa42UR/1cbGGMAQlscJxp4NFmZmsdW1HO+oHDPjC7zFEMXxwtyTmOGpmoSPQyhiheeK4+s3NMoIWqESvNw2qN+KmfXdKEs56VJJ46YY6Bx6agbbNkzqQ4r7+cB0aBNM+MtLQ4ehtZpZneGVaUin2FwTnByMxtsFgcL3cj+wz/55sL5hDIjWAmxyM/s04R7woSfMaBDzXReqVxHr9OzBnuRs/Ph32503Kkk3fczMJKAzpm3r8U/uR6Q37d8/Qi0gPjpHx0UULOe87wcstvf/sR03zLFA1z7IkmMYc7us2GOSjDYca6I88+6Lg7GqYxM6eW737a8+rNQEIYpo4Xu9fEvKXdOtqYedodeTUow3HkdSocVBc97Thz1TasHwvPug6NynvzwBwnJCtDTqzPb3gahHk48uhRx24OvNy1rCx8zx35fDRcPPIF+WoSOif2E7gW9tOA05amiRz0jm1a83oUmknQFPjJjcFaRw80MXFrd0yHx1wLqLfMd3fspSOZlhgtrpvRuVjBODMWdE4DLguP/UxuGx4nJQAbSeReuBvAtSXTcZ8NMdwrj2/8iv2QSGlgh+cwCliPaZRGoGlXBM3cpsjGOVoEKwkvggMiiW9y+6UorlopHKl58a0STj5PcI8qLJulZPkt9JJTvl593cJLMxV2lgeI1MOtq4q9WHkLXSUww0PH7oWLc4+GVKcIXJ2sjTGofXDsxpxcq9WWo1KTaZKectuW/efafll4VHOdTBeTz8URvSj+KhqHYGvbp3yfT3yyh20gFVip4fkhcz2MmM5hsiGEQLdqaZuGGITDUBAjb5qK6hnSNKCaedxEVlHxZz0vv7rhbUps255XY+bCWjBKzMKdq8rNmEpMjipnTTVmdYbrIXHWK9//9ClPLjY4A41R8jzSrHr63qLjQEYq2XDGzBNN05Sx4CwLC3o5d957PnvS8t/+e9/lv/j7f8Y7vz5lEi64SrHJqEWTFGGEqepRZ4uakpzJy74VQow0slgsLFmUnNAbqQVDuX51nDxQwj0ci4WvlnBZiFJijoBT9t/yebJZFgwZ7xzUuKFWhFETnRomySfOlakIGWS8LWpVS7GfEIoYYkEaHqK5p+M2RZUbVXEVbaixjSd0qzFlIZJqm1MftIdEhKZyrnKRe9yfA3svgCjvWc5hVoMR7l1AfkU3iyDOFB5oLqHwWRWrljl5XoWZZ23Plon3moTvM293yhsv3E4zZEsfPXEWRiyJmZwcMnsGErly/ogRaxN9TiCOM+0Z5ZZOHCF3XMnEeTPzqBEskbVPDMnRdJ4zmxmnyN41/DwrQ8y1c1Dup7KUKv5zBvAiJBLBCo668MgZq1KMLc3MmXEEETZ2JuRAmgcyhuvoiSEzC7xTQWIoMc3GcGTijEzQzG4/0viSI/r2+JbWX3BQg0ZlYw0hC11IdFlZGc+FvCOtlWdti2xmboPjZ28Hvth7fvNqx7evPG9udvTdEw7phpGn7I+vaO3EX/GJy80btO0hKMZ4LraR8ajYVnjvPJBSwPSFN6mq7N85rJux3qFRORx23M0JbwNib2ml5fLC8W5/QKwjaOYfvXacrRpcjrQuQch8/vINHz7eMM/C+ebIJneovyvdim4LuuOxEaAvE4g3xNuBcGz52buZf/zzDcEbDlNi4wfOmp52njlbN9y9styNmbdxjXme2DghyESIE9gVKXieMzCgrBrPmGZSNKQ0Y1rLfprojOFuMjz1gb02xPnIM++4bDLDOKNOOUpLDhFrbxFtMVE4eGjmCWGk9UqXM+rB9RtEJp72kThndklQHCKBtWuYUmQkMduO9y8tMhUG2KVtma1yNw842dBq4hgVZ8B5w1vbAjNuGnBNQzcZDpppTCDMPcYmnCouJW5FmH+hq/Qvuv1SFFePrbBtIi+ScgzufiJbfKzsvXS7xILUh/7iJVT9de7bgRXBWMwqgQovADCbTHsijOpCxwJncbm4JKelTfPAa+nUMpIasSJlYoL7STVpQTmsLWTutBCYja0U3xqNUgulXPkQ7cJNkXt3+aUTo1II0/Lg90kMTayu7gKpzmSpTmQGMFh+ozmgg+XHX428d/EtUhzw66bs0BhSnGm8K6njYyaEwGw7/tnzF6hdoWbm58fMyo9kZ4qqLs8cTEOXB5JpybTcAr+5hXW74k9fHXmvEcaUCcaT48jrUfikWdFaR+uLOaY3hc+mqcTMNM7Rt5Zxnghl1iYOI+uqIEyqWO8gJoglmkd1hkNmkxOvM4hYkhbZuVEl6eLUfx87tJxf1Yy15eEs3Fs5FLJ5GRdLi6xeecRUl30preCTfYGYGnlzz6Urhd3SBlQailLTmWrSWarmE/fJO0OuGQSGMumqZlZSbA66XAaqdRaTqZyWRAkqV2wxZcMkRSQSK3Jm4eRhZeuYn6vPV4MDMtaWR4HLoFhGydhqcqKSa3FaCvggilUBB30yBFMI8RlXLS9qC2kRoNTWvqmocn6oBPkV3PJcx4jRkrRQHf4/9JlP3EwSy3EauTXCmCMXNx3JZVzc85tkLlrL3ja8OJb2Vk4wNMIuKms15FxiT6TJfMtlzh1crna8OTgu3ZHOOHoXaZ2n6wNbSUzScH3wfDEp/2RY8WYIzL4jh5nkHTYqajmZKJusJAsx1+xJhFVraTIQI94ZvDM0MfJsZVhJ4vWcIRtug+CNMJoix0lG8GroKnlfxND5icfW0mUY7US2PQNg4sBwVB67LTDSamJtJ0wSmmxpugnmjqdXB15frzAWfN7Td5a7oHz/wuNXwsXKs9aR9soydy94OvSY5kdMj1dImvFzZtYe+pnWWm5vAq92V4jccryBbtMheOztkYt2JOdM/2TF/i6z9S2fv7nl2x96VrMysmY49lgPx8PAs88M4GA48J0nK0b3jq7rSMd32OT48mcb3qYjx70QncDkuDrvaS8y5wyYcc3t4Yw9e/o0keaGHV2N9lpx0d0ix8ht6nnUnTPnyJd3La8Oga7LpPlA363RdsVd3tOmhBUYxqHwKv2eM/VoPLJx27IAtZ4vU+IuzfzAO55IgzQTn4SJg+lQhTkG1tZgjeGRT6gaNCWC3bNWy0f9ip8Oe4Y5MJqeG2O5wDPZzM3cc5yhayJxtjQJrB+4xjFoIgeHY+b53DKpxaM87RKtjXzctqQkkJTVumPVJIZx5m+0kTez8jIXOoWxitHM9eC5sw12OHJOy+wFjQeSfLNs0F+K4urjJiIm8Z63/GGKTJU0KA8yAZf8tiiFRPwQas6mNFlOru61JSfunmcFnDykei2GkuZBsfTQnwoetDYeoF0P+V7uNAHWv6NM3K4anZpK0lwOUmsLqUwycooSWTL+FsRLKglckL/gn4WU4g3KJCi+yv/NvTu5VjjeZUOUyPqs5bzLfHx2xi4eeLLe1sDgDDkX3hD3Acnb7ZY/f/ma3/2zHa7ZcjMLf3jt2RiLtTPWJa40cekLIftH40AyPb++En7jQhmy8O0Lzw8+WDMOgR9eB14Zx9oHfvz2wG9ceboxIjny9GKL2HKtFs8oVS1O+lM45Saeol+sJadUDFXrP+ccj7jld37rMf/ND6t8fHEUr04bZkER9eteWMv/xXn96z+7d+y/j2eqo+tr4+FeFXqf8Vh2UFuYv8DlshSSuanWCUG+7vy/IJOmZrupJhbDWjVSY5BKyLRqpq1moNmaah1RC3ItxaQsfEFtSuiyVMpTzihF8Re1BEiXYtBipBDQtSpPE7awq07y2ooQZ0OQItawWvMuF+TX3jupncwf0q+ut9XDzThhhWXKAZuUKInGWt6FzLvksGHmSZt41rVceuWj+Ip2W7g4KURsJ6yD44POIBLpKFmFyVqcesg71HWcN6WsbtyMaotzhiH05K7jVjMvdpnbt2v+LMOgHTqX0F0RIalDp0RjLESwkjG57C+KokawqS4QXUFR92OkNY4Ow/t54DtnDZMIKc2oJt7rLTsVxiGWxV1wDCZjc+C7jwzDbDDhgGs9ZlS2fWSfOz5eHTnOmah7Gr9GGkX9K+ywYnKGccqM3mPpmObApU/c3QmPLlrOeuVmGFh1M+djRyMj+6lnmDJnvfBmt2bdTXw1JMQ/wjZrxuOAzJA6i307lNitEHly9ppszlmtD6jc0psMLhFywrce2+y4eN9Bvuazc1c8DdcNfcj0xnB8teds+wgmz5/+8E/47gdn7KaXXD6+gmHETBte3Axop/xfX+z49BNHfmloWuUPrzc8PWRa12J0pLUH/uBF4vFK6P3Ik+7I5y87rt7bMO8Tu5B4FWf+6C18uhV0Vt6uezbR8jLC+nbEEpiiRXvPY0lsXMsX08DxsMZay2sGzshc5sy7OdGuPFs/czMm9iahh4Z3Q8B4Q2cTxliOUTlKw9oe0fnAqusxoSUyc2sT7XpL6+BdmmFQLteJEBxIYA6WL98IR2/oZMLuDY05sDZbRJU7zWyS4RiEF9Hy05xIh0vmZqYNgRnlwh7ZBGEjpf3e6cwqKl+lNdcx0JgV6ICPA8YIQx4xyZCzo/8L4XT/YtsvRXH1Vsrg7TTwa53huSbeJoM9qajuJzSngtgqZTaK5lKAYCBXlKA4iBdPmEbug2nv/XsUL1JbKWXLcm8ymQyYyrB36H14dD2UYuNQ5NFRJrJdIdmQwkj0ykY6JlPYMQvvxGlR1kUxtS1SUKil8sv1fV0uqq0kpR1a073uEa2lrVSgl8J3EaHBlJDL6oGEy/RR0HcHtt/uOOqMXpcA1HNzRtKSoK4oISYwUtWCI5tmxet0ydYGjtJwmwP/6N2AOsffXK/ZPMnMIXDetfwgKskZvriDfRCyeDom9seJR6uWf3vbsA+RN9Oan90EWlcy6+6GxPBmh7HwaOVZNQ7EFL8fLec8pkRrCxJDSJCpE7ygtijzDGDWaz546njyR58z9VfsUrVbsBmLI2qs7T85cZRgqc9qQV2gGXItwJbrYnRp2RbekEn3Pa2liBGpNmTCKVoEW35va3FsaxHWyBJtU0jrxZuqoKtZFStazVlrCxpzMosFuRdWqC4HgDGC18LH0mxIIuTssEaYqypxEagFzUUsmEvrOggg1YKhwHGn14q1oNBWlk1eWt+nmJv7FnRRQhrKrbjw1Oo5XO5dd1+kNt/wKvEv03bRNuzSTM4GyQVhzTmj1pPTgLGet6nl5WHgQE+bn2EPE212IBPrd5HLNvOB9TRd4jjDLFv++a1DA2x9y5VPvAueV3nFxERq1vRhjwtHRAJR1pg8FAsS51knxXU9DQdEhDEHUk6IdKyqZ2BjjrRty3X0TNNE9i2qSm8q+d4ZHLeIu+RFSox3ntkkwLFKM4+2FuY9H/QNrR3pNSPJoW1Eo+EMi9147Jy4aTMxeZ7YiTQMXB8i71865jxyFmHTeL6cAlfnHjsXhOTq8pa7febz6zOerSN/9pMv+OxyxWvZ4NuOuHvBJx/2hLm4zx+45bO+YUg9jz5qyGOPmhtoEjEZ2qce5olx2tN1T8DNEF+TVhOoKygITfEAACAASURBVB05q/g7ZXcduNutef5Tx7c+TjxeGfCWr3448eTZOfM8s7rYEndfEcOaz55ucE1Pbzwct/zohfD6MNHScPHI8WvvPWXcDTz1M1/YLe/ZkR9Pnk848DQZHl0F/voHludHwRvHG13xre8I3t7x6XsN053hLsBPh4z2K+L8is0xEHTD+wrOOta94+3NwJh6tBFeDTvW3rM2ga7ruDwaztZKp4Ffzx1v9cAudFznxH5ytKKYWLzsopmZjkey8/zLZx2tNTSbntsD+O2e22PDy13iMHlupiNd9hzkwI9fbghOeWINYxqZTOaR6fhysuQY2Ypl1ls+bj1NMOz0iNWGlcB2cGg4cmktXwjsk+GL0RYfw5zYNPCeXbOykY/kGskrxniHGMdMaW17LWHzvYUX+s0GN8svg+fMv/M//EPt6mrYajEHDaJIbe8lvVe3QPk9gOaA4E7eU7+INjktSNdpW1CCRf30oMe6fOm1ZhMu7aAFcTjxfRZeV5lY/u6nnn/y6pq/9sGG773/iL/3xz/nf7+1WNPWHZd4mxP/RKu1TapoWX3fpOV1Leb0PVAn2eLJtZDaT20kMSefpaQ19FUyopCt0qvhe/7ImsxffdrzyeMNzy5L5ENMwjQVM0PnDNaWTENV5ZiUP/rzd/zmtz/mv/rf/pjd2WM+axLf8/B4m+hzITe+f76ia8sx/P6rgZ32nMnEdy/OeX6z4/GmY9sZOmc5DIHdYc/3P3nC2aplCJkYEm3nmKZA4yytLwrEhDAnQTSwbhvEQGs9rvHg7Mmok1RiW272R+Z55u5u4h+8nPl7X1bXcI21+L5HALM8BFDyCclKdRUuJySK05g6vcb8RVn6aWjV67DE3yyIllaSpK3V2kPF60Mu1KngqwWbW4jr1VH9FFm5HNuCvrEgoFW4UblccWkPVzd0Ub42nnMuvlXpwXstRdVyDKoJb4vC8xf5inCPloalyEJP1illzJYd2tN7ppNq0Sj8/f/yt79ZksNfku0//e2/o+dYJiPMOZXngbrCqclLIoCvbdwZKM+M8n06iXSymkJgk6aMmVzG2rIOXFt41rW4eICYeLY6sJUN67WlWwuaE8Og5N4Spsz1beR22vMm9Wi23HqHTS0SEmJHBu1L5FKaERxi6thwuURvWU+yAY1Cj6cnkaVkc3qbOReLzSNnVjB54sO+5aspcdYquT0jDAdyiEw501nLZ2cdn++OfNIXf7phzCgJNZb31xMpCsfJcTi85tmHj9nfRmQlXJnXvJpbPrk65273hk23AYnEKdJs1uCOJJtBTaEZ5Jk8RIw7Ayz0jtdfDKzWHcObyNl6xrTgTGZSz7u3Ral3ez3z2afnOHcDayhW9115Jr3oOKaO/e7IetMwBcvV1Q3n68QXz8/xs+H9TxXGCJ3w4qUyjJHH5y3vbgMfPB4wq550KLYZM5Evrw3H0HKH5aAdx3FmlZQnW888BHwnbGXi56PnJnV0MaLdjnYUfNtirWUcRyyRQzYYY3E54bzhcrtBQ2BIgcNkGSRzzJaIsFHlqsucdZEOxzAMDFYZpkSMDqeeEc/bY2K0ib1smec9TgX//5D3JjG2bFma1rd2Y83pvLl+m9dHZAQZKSITCpGJRK8CSoWQihpUDYoxE0YwBDFiwBAhMUYqIQRTmJUKIZVSIKGiUHVZUdmQGREvIl57G3c/fs6xZneLwbbj974sgQTEIJTPJu8dv+5+7Jib2V621v9/f9sQNNOIZV0GvLQ4mXm2ajjqhk+Pe7xWo9esHsRhbOTLSTAlskGZtaFoTU+ZbMYWh5CqKcdSpQjO4mJBjGJNIYqSaTGlrgGGjDMFL56iCZMSyRgKrt6DY8T2a/6zv/93fmn3pF+JzlVrLSlnVIRsDeb8RC919LE1hTHXihTqUMIitHgmKY/jPZYcNqdCpODRKrA804gtkBzJLcnz71jUz0f0XIzVhtk5xnm5mZXCEttM8Ceep8wPb274V7/3IeM40ruZ/+BfeAo/Gvjdrz3aT+xGz51q5TCpkuUcvVPfryyLbyP2G4u6N+eVrv6Jii7Ebq3sL7G20ucfC8l68zVSWUR1wUx8mtf0NvLeSXmymtk3loJynGY0zqz6Ho3VJdPaGlVz6YV/67feQ3Lgv/yLH/Pf/+iOp5vnXLhbwHHpDe9d9PSt8GRtiKYhBvj9g+XSeWwJPNt1fH03coor2g7aOPHnfv0j1rZgjdCVwikJOXvujgfef7LDOcecM6qFkjPeVa3QHBONWmhTdb2JrwV1mIi50DeecQrMTcsPVoGtjpy0o3EtqWTSuZuiiyPTLJo68Wiu41ExSyG/dMbKmfKub7tFjrPmTx9jXgpn5MBSDGtFp0vJC2i0srZCWdyksmTxqUO1YCRRgx1c7cQaxYl9GysjBbfATqE6YEWqk68Wi/UciGqwZWEiGSEVi7dLoYdZdICV5u8sSCpYwFM5Q/CI/EKMocMSSr3+jFs0j+ci6XxNsARgn3lsxpJyWByL4HK9CZblmmqcr2Nd4bEz+G3cvPeVDJ5qIVSsoMv96uo0sG1bxA08xEIwdYzceOE0J9RbRjIXQZitJarFUcfhZRGZm1KdzEeFPznNSHK44viTZGisJzxk0qLKzHmmMUrIrhL35ZpiWywzIYGbTly2DtWGztYxYeMcJhaimdlaw27r2e/v+GB3wbFYVg6+GgasOOg2zPd7PIatVS4aw62BclDuu8TaFJriKXM130i/onUnbtyez9+85AdPnzJr5vTqZ+yaZ2x2K24fDnT2kls70XeWcdzwsL/l2e6Sh9NLmpsdzSkwhci6eYLpDTpN+F3L7T18/pXljb/hSgtzCry/9TxbGXD3PDzccfP0BdeXLdbC+jda2I+gKz77aeRiE3h6E8AIT7/fo+2RdNdw+vErLp515NMJe+nYXCS6dMv7657cR+yThvBp5g9+JFytBl5Phh//8YpnV9C+Guldw9N2orE9zy7hH/z4ms36Hrtds3EnetZEZ/DpxPXK8UN/pOsafv6l56shcKtrfLjnM4EmJ7b9AZmVvux4oyfScWazviBYw86fMLOH3KEuIHlgDgUTRzonvLeF2ULOkX2M3N5ZPk2OzezJJ8PGr1itM7EY9rMwqaJNITlhyh2tLTjbVg2qKdhZcaLcNS1MES0dXwwZ7Cu6ZsubUNCkzKYme3ht0VgoNjLSkLyyKpEG6J3inGEzCUedEIS1NRyApktMwSFqCSoUMhuZ8LYl54zD0xrLXAK995ziTLYQSgYLJv9yO1e/EsXVeex21i7BN4nWGVh5ZVxcTcbUcVlwy3hhWRz6pjqkInUBC9KwotB7IaVETpapq4h8oDqgtLKQ3OPCcd4WDOKZyF4UZw2LbJeP1PIf/nNr1iZizJrL7YbGCcU6/uqvZX73qwe+XyI/RXBN94hTsKLfKKLkPJh8JLQvL/XcdciPHQjVCtmzC+fK+joWAh5jWGQpyrRI7XIZoRWLNg1vxoEhFbpDpSgbLTRzwQt0zrLpW+gc275hmgNTyszF8ue/c8VxvCUtWraVN3gLfddC48nzzMfPrnD2hHEdM4XwMPJPf3DJPM98PkSG7Hk4nDDrlpWHtm05TANhOlTGjVbH4ruat5RrEWBUGVKgGRxt22K8UOZATErKyv50JKSCCYHdRU9nA0EdhWU0tRSe56PrlviVqGl50qkQRZEa2wIgztfz0r51DZ73S985P6wIvAN7PZ8/2Z41VjyGeZdSqrvKGBoiWIhqFlZULaQQraBUs4R/nzte5wgieNRHIbXLl3PGSj1/nK975pZRYsrf7PoaY8g54uzbS/9tN+t87hROmmneTUsw5tGzax/zMauY/h2A1eI4rKPHemzfdsSCZpwI8i0urABu4oQzgm88kYI1I70o3lls27ArysEoWztxNIZDsoTiEecpqdD4rl7/BqYUUWqXXGUCDKY4xDoazWxt5mrbUFC+nCvJvz4+LOeV85SsrBuhsS0N4MpAWJYG55Qt96zXF+QcaYxFtXZSrclEHGC5vNzxs9Mew5oHIgdxaCx8ICPbVpA80YSJRlreX6+Ia3iKQX0glwdgABIlwQdXK/7wK+W9qycc9xN23fHixfvcPQgyB/rdJW+OP0fMikjH02tls7GUcuLJzYY53rNaNczTPb943SPmlr55D+8tw1z4+L2n/NbVAHGE9o704Li9vcY3W4a557MvE7v1ibYrtKUjTJn9y1s+/HgFGilsMU0PhxY9vcK5yGa35vVd5PLag1gMK25vDV034yelDx1DTFzeXNCXA7/5XcN8Er48WVadx7DmkCzH+wN9t+M77xVM2TKYiT95dQ1qGTSxay/ZzPAHJ2Ga4Kob+Whb+DVVttcrUs4cjlCy5U4jhzSx6Q3DVEADmuE+tfSrljJnQspcqyFNkaPJ2LTh61xoonKbEyvtGe3M1jaMEfYy89kpsw4tqpkiEZoOP0+kqNheSWkEPNY5hrkwFGGgIHN1TYuvo0zJl+zjaXHDC31ZYsqYqs6tUYrJXEiNeSulMKmp8GpXqnlCLNZBbwwxm8q8MpnO2gqnSY6BWjxlnVC1NALgMA7K0uEP8Mj5+2VtvxLFlbeCMUJc5CxmCZ4tWkNKrdTsqc5milW01HDhlsJaArciZOsZTeLaWkZNvFDF8EAyG65y5IP2QCMd/1sqvLIv6NIDyXqQQkeh5JbsFUl50ess7rxiiC7Q0ZJSwhfLqRi+7NYMb0701w1dDqixFGmJMVFS5j//YYsxW9QlfvQm8Dc+HcjOEW2P2oRGUFs7Ay5airWP+YrwdgxpzuJsKYvkS3BFFn6Q8gi6srWAPI+nsqmoAWTioXR8PiX2R89tU3gvGz63M09PA+ud5+PNis1a2JXAU4HTONc0+DkxBSWEGWs8a1sZOWJhtarByvuHmbbtmaeRi7VhCIHPvjrwybMtRQrvbR0f3fTEGIkxkJMQpWEOUwWKti3WZkLWyu5SsKaO71KqoytvhSnlGnGTI8Z5YoaUa0GWEE4xIe2K/+pv3zHaFqtKUipQdSlonSpZFCOuFiBaHmGequUbhSy5OqPkMRLmLH5XkIZGI2Eplir+oWqj4mP8UNVyJa2OKqh4DVm0+HEZtYl1eGQJaDaVJWUXnZcY/NIxNZhH9qaIPHZS6z69RYIYs3xWaoGu7psd0WpX9VUW/6fGnyLvmAdYRtki5MeS8TzarOL5IPX4oYtb1wim2NoZO49tFVrvCKWSt6s79NstbF8ZcL7gTcDjEK2xLxIK3nQcy0x2jtexr7DamFi11e20Fs8oEXFgSeANgybWMiGsyBrxLiMmErNUUW/ImN7ySRPR1hFzoqXwKlvmpEgLJickVr1q4y0vWkMnNa9wzCsShikLM8BYI8ZG73AqvJdONL6CN2Gu+k0tjCbwvDuBNJzmTErCpj2hw8gYHaf2Dc/6Hdb0i5vVsJ8hPMxLl6IwGGU4PIAzHA+JyQn54cjlZkNjtxgbWV+sKHKFDQMlFh5uB2jWXKzAu5FnT7Y4K0wp8HQHuFs+/9wzJMscP2HbBO7u7vAGvvvdG+7vb/n0jafpLzl+XkUF79/03O8zq/6IdYU8PWDEYbxBjx1mY7hYZ+JpxfgAjsD1xvP5V5nr5ytKExmi5RTf8JAz+mqHbWc2eILxHE6Rdee42b3gq9evOLo1n59myuRozcjTyzVraxmDksvERhMfXTWEbEkZ7kLmy9cwqyGkxLaH/aysfaHzwrPWMecIruU4Jh7GiKYjx8nwU+Pp5oJNPWojzgpRDG0Gq4Exe44+PjZB2s4zx8TaCa0XcrTcFo9ByZNlspZ1TpicuW4NL5gR07HPgjSK1xlvasbfs1SnOG3JTEborWfQRIwjRVrmlLFiCMYwpLzwLwunnMjW89RF2tIgLuCLcNElrPUcYqYhE7xjS9U0dsYzlEgWT9CAR+nFIM6zLwUt5v/xuv1/u/1KFFePN3T3tpMkxuDEQZ2yA4l20b+8MIatKbR+4j56OqtMZWTMHV0T+H4+VIganj8YBl7KilxWfOiO/MVtR88v+IlZ8feGQpMtexGyM1haLAeMtQycrfJKzxakdlWiMzgSPsM/HpTf3jaMtxOtN9xcCXHpFGx6Vzlc0vMvX838+Q92PIyBv/XlxN/9WvidDy74m/sTJijJZzrMkvfF4+IGZ+I7jzY0J4Zi3qViLwueFIyTxxiXlVZhe4MjEfliUC6NYR+VQxnxOAbX0QXD1/uBf+XXPmLVzZUtE5XXt/ekIsxzIksip8yoyqZf4bEMg7JZt1hb6cBd1+GM0NjAd56scQrjMHLabtHjiRAC7WrNwxiZY+UwzUmpA4ozS6r+fR9Ds41jmGa61tI0DTXiJUGIHMZAyXXEEuaMEcfvfXpP2xmmVA0PbskSLFK1VHXMe6aKC6Jvzzdr3CMbqrpTF63VopY6k9BFDGmJGMmqYFwtxKkByu86C2tRVMnV9c+naF6+x/qlU1sef68uwub6uo6k4xJvhFYDxjnfT/5UfXI2AbxLmD932b5ZRP2TP2feKTDPhWHd2+UMK9/M4lSplAW/oChY3LpFwFrDY9bdOTUgZ9SAqiHn8yD827s9sTNWLJc503eB7Dpen+BLdaSSsaWDklmZet7tjOcogU6haxOrErnsWl7vA23b8+L4CtY97daiw0y76RhOyjEkkmR2Rklm4q60vCLgVZAQKF4wNqPa164iUq34Ufn5aSYLbIDe9hSNxMaSQqqwWoEYhFyUX0SHaEOyGXKhUeHGV+TD7dhyN+65cZmrqytKmbh72ON6R+cu+el9oFFBvCOqME0zXzjhxXrNT74eeZjg/esdP394yVW3ZUoF5kjWwub9B6ZDy+1LQ9eMHI+RV2Pk+e4JLmde3k7MuuKruxmDwYnhvhTUJNZdS98YpNxyvy+892TLxhRO84TxG35w0zKXEbksSPSM6UjyyhdfB3JYc3El3OwUXKZIAKuY7BETaLuWKRjG05GPP9jx5UPk7t7TryJPushVf8ntvKIx93RtwU4TwWRa3WIm4YPrC0S+5AdPL5F2zd2r18CeVg2vSgsYZlXeDHWke9QVfatIHvig3zDlGqZdSmGvji+OFZl9FS9RvycRGUJDbxswmZ1NlOIJrtCL0DQGOUUGEU5SuO4Cl9KSnRDCzGAMgiWmSFSHMYn328gnFw02nsgx8ZOpalSDbVhpprGpOtxNJaf3vqXFkG1mnwXXtug81wfipYgrCmMRkoVUJlrnaIxjnmdWztAbjzUZm2vH87XJ5KSoJuaqACU7R5kTqoY5F8YCUhJIbRTspb6XZPkn7o3/f7dfieKqkUqtlrpKkR4x9PUJ3GvNqypi8JqZZOSJ76uuxFg+lAnXOF7lAVVlYzcMsZK4f30lvJ5GflwsLm3RkLi0lo9c4uOLQk6RrFAIzJr5P0bHhbV8YCOlFK4uhL/5+ZrcCrbtaOKJf3u75h/tZ/7g9Vi1UsdX/Ou/9d1lUaqjkBgLuRRWrWHWQmd7nuxa/nKf+Tc/UG6PB57nwM+j4R8+ePauIUugU1vzD5eF30h1gcnZoWXqmEnKeZxzPiOqitWaZRFeui1Zm+XYZeZFZDxbi9HAbNZ8Z1P4jUvB2yNRHfvTyLZxvLjq+cOXExed4lzPqrEMUXjYHzmaUO2r1CLJoJQSwVsu1w0rD3dDbbP+4vWek/Zcdjt240DvDTFmSsnkpRD1zYKUyKWKrLNSjEFzhByZY+U5aRlxztUsNSyZQpwDpSgpZn7jg6f8U+8/8F//fmKfBSVjxJKKoEv4NaUwK7Tl7L+rDsFz0WIXin7lOwlu6RBFfRsfI5THMeI5KzLLkklZvwUnFVHgLIuYfjmjnXvURdWQ3coMgjqCNHoOhM6PeZFFy2MuohG7dLhqMXUeqZ6P5XmMLWVxXZrCXDKdNHUs6Wonri11ZBxKeYx9qHVddUq+W2SdGVhn6ntUFpAkj5wubBW4n0X6upyZqlrP16QkZ7CNw+bKU/u2bq5bIXkmOcNghD98GDllw8eNsKFQ2oGSLcUVNj4R50zvBEPgsnU1sw6HvdyQSkHbDcwbhjnQuY793YliI6Kw0sSgjoTju0a5LB6jeYFfCjS1C5pyRbGIK8QCL7YNc0xMmjnmCdM4Sp5pcovTgimOfobBjZhB2a662n3OBSOez46GqzYzx5nG90yhcNpH5mI55ZaPu6d8dRwIsRC15aKz3D8caV3LtYNhnli1jkYnNu6eF+bIiyc1T+/+aNkH4e/9vEdcppSBi6bHmDUR5Wf3kTkqxnW4OHDVRbxXZtPgbeL5szUrPZFSwW97cp54tX/DF2FHt/qcte9Jmyfc3p+4urjEd0eevncNOnFz3UMeQITpqHSrC+zTAGNkvi10Owc50/QN/cVEmA98+FGDscLpLrDqnjEcM1ftRM6Wo3O8jjPXm2sufcFeFBgCv/8nPU8vEyfdA5Z5KpjGsLJTHSOuV3x9EL68H5ly4PMoGFtgvydJQ0gZL4aL1nJpPFFnkpkYZ4cxDWTF5iNP1z22RO5noTjDZScYZi6eWmaz5uuHCSupdkCtcOmUOVu228x8Et6o8oZ6fX96C4MR3vcdFy6S6HmYBmTTsSmF1UZprYXg2UdlyHAbS5U05BlJStR6T1u1HoyncwVjC68OLSUr3Vpoi3CbDcdckHCOEVPa4oiaoXiiJlKBPA3MalEsRxHAI1InXyVWSUxapEdnbegva/uVcAv+e3/9b39jJ/Sdp+8KxHxrnW+08NzCm5xprWUuho0so5iUF62WxRolmsCLpHhbuDeGU4LLEHm+FkJRnIeNNTTWorm2PbEFoiW72jtaLXEealom7/n5beLXL5SHMfJrVxdcNMqTXYtrGooGRCxRYZgS98eBbdcwxkBjLBfrjlgyt8f6fSEVUlKavuHTQ+R/fQ33pqGovmVwlbeOwrotnQ19i/1/dzPLdyhvdWwAVnJd5JYjHa2hAXYS+I9+cLW4BRNtJ1z1axpvuJsyP/r5G3wOXG061BrGITCrYS61eFVV3tu2tI3HOcOqqTyyOCf2U+AQoRHHHAYwjs4Jzhi6zpNTWmJoCm3bIkWXERocp4lN39f9pv7Os+MvZ126QlWk7RtLTgI6YE3P13PDf/EPRpw5ohjS4oxjibKZqcXV2zBmHn831OImlbw46XjkjZ0p+udz01QGAfCWP3Z2dQK01uEWLpBgFwH84uZbzAiRusiJnrtDlYTtH/WA9esxn6+Bpeh5B8R57nbVF0vXbGFjNbmgRijnNIFlzGmyVjCr8DbLU9+6bd/NkmP5m+THZ55v6iPPP5dVH7/+riPyMcx8MZycgbz/03/8F76V6qv/8a/8a0qE5AzH0vNxO7JZdWySY+hgDIVAA2SmEPhqjDxYi82etkSc1q7A0y4jQH9KWPNQCdtW6hOY69lPVXReoiegrNuWkBMOReNU3Vy24cpLlRAAxxIJBXad4zRWEfE0BbCGw6ysTMGlmb7vGbKylUwomV3XYbVwLNSYGG8JqTobz0idlGHdLufAEJAuPiINrrzStNWUsbps6NTw5Zs7hoc7Zt3gt9cMk2JswcRSgaYlctHWDFPxFaL55uWe3eWWvo1sXWIMCXxDzootgSdPV9yfZpp+hy0OdQWi4jRidgemeY0rhsMDxOJ4/qJHBIb7V2hpEIXN86cw7wnjiSwzfrNBdGK8tVizothC0cB2dw0mkkOsSB3xFVkRB1JUNtcXfPmT12wve1JsKbLhNN9ic8uQEhvrUDeRTGGehD/+/A5pnzAbixbDHGfaBWVj8sikLfM8E8ShKjRiFxPNSCfg3URHxWjs5YKubRimE74Z6YLw4mnDFHb84nCCItgcOSRLcQ6P4aaduWgNWhy5S6TJEVIkhcyMsFkVWjFs+8KwHxG/Qa3l8+hIh8R9TnTOENRy3QTmXGikQQSm4pA44H1LSgF1hjkqWQ33xTKV+hAgaukFoBDVMGus59YiKTFFsa66us9raNZEyPLYoT9lHjOL22V9d0XBWf6T3/tHf7bcgsVYsAGbah5er0qyVYDuxBOlhiu2JIpruNNAsT0HFLHCK9Ow0ogzjsFq1b8U6GzLa2ZabWlj5LmZmNXw2ajg1liT+GIWrPX06nlulBZl6AJtMsx64hQavBNsTuxs4rdvHA+HgaebHSmeOPkV6eHEqg30jSGmCbUNxykSY+RBldYKU56xkxBixpRCCDNXl1sehsDh8MDH3Ybf2TX87mEC2yEmL+G58ohbMFQLez1p6oKXzISNOxpjGHLAYznZUJEOWnA4RjvSBEdoPS7VcGNnLK7MHKXjb/3iyD//JLJqWvp2A0CeMzEVOjK26RAM71+saK865qXa+4e/eFNHqe2KtU8IhuNYMFJYrVa0pWBNYp4zHlMDgEsNYtZSavtZoLOeWg4oqYAYS+cbfElcXawZQq7k+KSkXLVXXgx939eCRZUUBjbr+rpL9/yn/2zL//xp5n+/a2hk4kOb+COzQTXRmgp09bJkpanSWFtDbRcNW+NNLbb1m6JuOFviy2PRAVRtkfVkrWR0qdZESnkLF62C+eputaUukEVrXIw7m0OXt4uLmF2pgE/O6IYzGfVdGVUpi4e16q9yrg8ZpRSik8ei7d1Gp7p3BPLL78nUxbB21OwjBiQbS0oJZ5rKRrPnjprBanqrFVxGn/W8q3tfQ6M95/BoFvH+u/v/bdteuR4cZAukkR9Hh9tnhgzmdEZ1zECiMULX9axiwLVa3f55IlthsDWXbd9aBtlixdHkRJcKjUasUbbGsW0Ku94iSdn3HjSTxo5jTJRYGAQahRBgEAEJvNkPqBGOBYpafAnsXGBjVwziSFroGzjNI0LHbZ5IU2TXr4nesLIjPYabXmmbQIwWy0zfW45j4ebpmtOcGQ7CZAxN37E/QJgTF2PEry2tb7h670O+fHWLm75Gabn2DXaVyLlBU8IYsI1nu7tlHnewyzj7ijEK4ONUaAAAIABJREFUTW4odoPhDsuGzfaaz768Zbe1+Hyk3TowDXFqeX1MbMsz1kSyDGxXhW6zg3gPpseYG8QF+rUDuSWSaG5czZa1PejERoTY3WOaLba7IL75nDAlxsOam+0FOUCzccS9wTaW/cuRZB1ffT3TSOE0jTx5usFzYttFXNtQJPJ6v6ORwPdurogyQxEkF2JXY9eG0wPGGK7Wmc1uxZtTYi4VG2TLhDATSs+QrzjFCdsIW+cYQgL1aGnw3cz+CDCy9TO73RYTDGGceZ0tY0i8npXXwdJ0gfk2kjWx8zWeZhbh9s5zNBlzJ9yYa8IhQ1PoJLJpW6ZJccYx58BPJ8eFcRhNRIWRhDGeHCLeOq5dlULcTxGbDd25u+6UIVVdmCPTZSXmSOtWZOq9qCRqR70oSAXWmpKwrprCWlGiekQglISRhiiB/GdTc1VTsFe2jimiWFptKDYSTKJdiooeIZAZMXiT2Gi1y+dicGqZneBEmEx9eMulIXlHZwL32bMR8B6iUawk4tzzYJXJWExWvkxwOe1ppsIrv+GmtDxzgZKFlkDOhlMpRHFISkwpsop7rndrxlQX0BCVIZyIWVCtEEanBuMaxpjJWtGlrrMMcQaEpmkIaeYHFy1/cIq8WayjhgViSeVbOeria3jrwGq5oLUj/+5V5qvc8r8cRvql7QkQZcIYS24suziSXdX6WBOIuee6BD6dI91dxz/zVLg7DIgIrYX7/UBZZut912J8gzGFXg3ee37wAu6GxMP9He5yBy4jEojZsT8cGUJY4KuZpq3LeCkZsFgrNH5ZrM8YA1GMKdUFmBKxdRxjZp7j2+5IzlXLI3AYRkqpIdW79Zp156se6UI4TJm/9L0b/p000bUXpOPAP54d/8PPqqYqmXpcjTvzqBYh+9kQcO4kvdOtgbedqUf33uICDLZQctWzLDjOpfOzdJrORc2iq8quAj07Yxd22jerjUe21nkcuBRPf7pbqap47986/YRHwGzdz6VrtPz72fX37oV/Zrc5a5BcqEHlefmshVJY3iNjrTy2P2tYz8LXouoBH7ttLGWUPg4IH3WEbw/qt3P7zU11902qOCwpCyOZN6nyn3JqgIJ1Up/icyQaR8iRFUrbeEZRnFaMTW6hC6BiOenM5FpmFXJUxhx5WTw6OdZmRES4MBGTaxD6xdrSTDOf60gSRy+gpaM1AysstjMUMzFkpQ89vQ1c9ItBpSiXuy2OyFPryf3ExW6mbwSSYfIzbZppVk/YjzNf3baMs6fQcR8tx9lAhCgJOT7gcdysBGkm7g5K59aMs3J9c0U5PqPrP2PjHcYWsiwoES8Um7FdhznOvP/hDtuu+fr2c56s12QJHA4rdk8T0gdePBea1Rb2GW0KR0nEkrl4blg9F5Ae98XI/CajckKudzAYWgbu704UaVkD3jbobUSvj6Syp6QWcxdAWoJRXp1O5LxBA9wmz08+G/nw+QW7ILx585qn18LF1ZpVtyN0DzRi8Vcr5ofEZ68KRTpGD5+shVxmvro/kaRnY3sareDmWKqusdkt8gqqm9S6yNqtmOeZCUXSioJlJ5HZKVoahpKxBOzKMcXC5Fbsp4ENExuTCIdb3l9d8EexVBedsZRkSEyMB8faVeVqjlofujIYG3jfK3MRYlJM0+NNrKHfcwSj5BLZYDDWU5JyL0tUUq7w4lXb06RCDMpKCi8u13wWAvO0rCXJ8OAUmyy99xQzE72FkIiqDNURBRRKruBikVKjxEoVNbdC/do70o5kLen/hmH4/3X7lSiu/iWdubg0fHEszBHumxZjlR7HS8m0ue6m2kRbPK22XJjIWibm0JDzxOBXzLbBAKtlNOLMTG8toViiKK9xlM4iGYoRGmewzFwGz52feLBCCR03qWClMK0txzlxHOsi6Y+BlhrP4sJISoEYhC/2mWeXHat1gxclxkRImdZ6SizEktGpVMF0zqgYihQa0aqT6DpCzHTuyF/90PLf/vTAsVtzoZldl/miuBpdYiLtbIhiKZ2hicrJw19ohF2jbGRgdpG/e3hOLAM+GLrmgr+yueV7L9ak2PHXPw3cSqLINZ/Yn/Fv3Kzo1ysOMXMKGVOEh+PMdrtlCA+MYQbg5e3Eqmm43ja0y/redQ19iuh2xcMc+fEfv+aHn1wDaVnQIS9dFasZxOCcw1mDF1e7P+IYY2broOkbXIxMIhiBnAvjlBYu1FvApxQhlgzWVDq9UbyDYa4X4Ha7xZgT4zhy8WRFDAl5suHPjRN/42cnJrdBS3prGjAsKAx9LGJYxoS6iLPbZRx3FtvLudgi0ZqCLwXXOFKpuA2Xa/ZkKAnrLG7RZikWW2RBZkgNSkYpi8uPBRNSbO36+LOQvlRyezy3uqnAznOHypjqzjNakSGZVDEIVPRB+1iTlaXz9fbmUsrbMWt1ydb7kyn1PYxVWKz/8HYMeOZ8Sa11yTktcVD1iwWFVLBC7XQtx/RPi/G/bdvtMh71psJeixYclt/YnI9zRnKqx2825FIYdcRYi5OISQ13QwEP85ToTe3+h3nkmXEcYiCbwjYbBoW5zGiemXIDLjKrcCwTnRPuYsFYxZeWHCPBCE4OPCxj+jB7nhjP968MXRM5HmdKTmzXHsiEcMtmteU0HWlaw5wyURwXFwYbt4Su5800MQ8tjbnjyntceWBKiWc3a+Jh5mItrK4VUgWmsrJg16TTK8RUPlzezBgzImugXKII0zThJKHFs/8C1ATaWUFHVqbjlGH9YcF3mdPkWJFpzBXcdtw+TFxtO7wNbDcCJyF/PcMIJW9YXwZKNsiUocyMhyPbC0uQO2iuKDJiniSSTDSXHWwa+LolvRww6TUfv7di/MWKZB+4WXt2vmOeBtoOhlb4+o3gbweebyKrbUMokR/90YFR1mi5JJs9EgM/nh0rE1hbuOwfCKlhsJtHCUNOE1YsWMMUaxbl9XbFaTzSWuXaKc3OkHIghMITsfhmpnEtX9xmnMAnlzsO48CNb9mstrycJuao/OxU3dDXZsTrir0PxGxBhZkVkjNFCr0Tdl194M45E6PlKJEUA1Yari0kWwhlUTOUEZdnSuMrGy9V0p8RwcWZgYq3sU44nMY65ZCK1VGnXBlDkoigzLGuA01XcLlKXbLOFLUkt8R6GWVlHTOJosJsFE9B8EQBpUJwvf5yb0y/EsXVuhMO+yMfNJb2ukU14H0NIr2cHT9eqswsjt4knthSTxpdMbbCF6GKdZuaK0M5M7JMx5QSXRV54GSJ/7CCVyXQIPnIQ2dYp8z3vWP1vOcnt68wdGwmeMWWfm250RFP4vV+RA08zIG29SQTyTmwDg1jHik5Ps52o8komXJe+EymMcK2yzwMJ05qKcYynmY6EZxzZBX+2vccp1D4+3utIL9c8xPXNFx2hjcl4NXzw2ZmvU40OXAwhds3kQ86wyi3fGYtD23HenzJ+sWatilctobfvLS8HgLffzrzCTdImykl8v7aULQlTBOxZO4e9sQY2bSW28OI67Z0TvCi9H1PSolhGMg5440llIm2bXl9nLnq2yoyX7Q5VipnxMlZ81SwC1cg1JkSlxcXqGYaA62DkA3DnB/n6efuS81ArDdcKUrrPd4vmYQGvLccj0emaaJ3Dbt2ha4qsmHd9fz7v574b346o8YhuQrZz9MyMW/z8PpSK4ZzJ6YSsmsUj6LM9lyt1Kiac5HjTAU7OVs7jM5UB6I/x70YqYaHs/tzuaCrTk7BVPOGXfbkjFnD1w7ZmYpecZGgOT3u8xnOKfKWCF8eAzfPT2WLrkrP/19Dxs0i4gfQoo9g0T/tNuSd12/5WPn8D99wK6KKlbfawMcjZr7d1dVlC6oZMRmRgvXVoOFywDeZU4RkLCZkihNcMohv8EnxK6VMmZuNQ3RC6GhQDuPEZAqmBDa2ILLilDO5gE1KL46mV17NhaumwRUIRGypActbnbGNXXRPa9pmZhUVJwe2q4zoijDO7LqO3jtijOikXFxeccy3vP/+BV/sZz6bNpzuG+TNERcjV5cDH734iJQPHKQlxcLtWPjgRri4OcFmDU2gqHJ4aPHeI+WCT3/6JaZ5Qr8yfDYmTpOwdVf0PvHd91bI/JowXjH5Wy6fCN2lYRx3HBSutmvWncM0mbJvWNsAMlK0gW0AvWOXCqoOSS1cZiiWMd+x+sjircBrg7m8gBTJD0c2H+7ARhp3AR9XWUGaCi528PQCPh2INpISeLuBcUd/c2S623EYZmZ9jnavOB4S/eoaJ3ccT46fvHEc72qWrFiDp5DZ04nB6gbvAilkdruONjWICXQykkvGNsr1kwvSfE+7XuHLAdtFDvctD0s3vHEenzN2o8xzIhCIs+N+jnSNw5jAcXiJaxvMXAvJ71713B9HWgtZLE+3FlNmDrEwh8ztqSGWgVIKAx1JEg+lYz0dWDcdtnVIjtBY8BHNdXraJsNoKp7Fe0+0Qq+G6A1aLFB1VasYiKU+hGZZ7q2p4KwlxLwYj+o1dNU2HDVSiuLRiuChoxiHMVWPZYlIjnjjSBh6qdeXFlk0vZ5YFHG/3HLoV6K4+uPR8AE1pmTTgBpDKAXnEk+L5Vgyq0Y5zXBhhOcyEUpk7T03zvDBWhj3ht/DErNFJWMMtDkiRshaDf9tDKQMxbUkA+/zmn9xrbQbQ+8NzhZiKvxat0NjIlrh/3x94jAKs2RoW15cr7A5M7UdgcIqWLrecpxnQtFH91jjDH6ZAVstiHGIKE4zu76tgtAh8/J+ZNs05BbCmwPvXa5ZW8+b6cgPfW3/7g/wO11ETOZZ6/g7+8ILHfn+VvAmVTSD9mx3ntlEPk6Zu7KixIkf9pmXxxOrbceVgz6+5GOr7GLmAWFlOp5sCu3lGjcXynbLlDJvDgMBw7PG8N4n7zOn+lnmXGhifOxoeLHEqdBbw/eebxeUQSKqYQrzMhYsgEXNWd/ULKM5w8pXN1oqmb6x4C1xzPRdizJynCIpL+Rx6n9DURpSxTNIYbvqak6Ur4LRzhls33A8RT69e8N169ntdmia+e7Vhr80tfx3b4644YRzQpZM0R4Xb5ldg0kFpz2zUVQiiKPJdVQYreLU0Ui9yANSL1RVjPMYqbRhxEAqqKOiFHLtkPlFP+WMxSpEt3CpzpqqXJbi5e2YzxhTHaTWYpfxXtbayUqlIiceC55llCdLMeeXB4639ZE8jjzPAv0zfuJRhyVLkbl0y8w58iktRdQSu3TWZFnrami00bqf+VxMnXVY9bM1soxMzTti+W/h9mxVC2glIqZgEUQnnGT2twNtSlz2LdFDMQm3c+zSijELX942xEl5Mw8kaWhN5NINvNEtKdYOZGvAEln7ltYJfRMQbXkY9rzXgyHztAir1Zo1iZsnkf3osK7gDOQ4clLhTVIke16deqbcMwHmvqNQ0SUBGF4nxvAE3/d83DYE3bAzD4h1lJI4PnT83ukBEaXzPVocqVnze69mzBtDTJ7eF7K2xFRQo9h2z3debGh94emThifHkSkEbCPMo2eYJ1K3IrlCnG+4ewApExduhqZAbzFOGR4mmhcbTPGw6jDJw+EeNi12q+AL7B6g28D1iLOCNAZKD6sGxkKJFxzmFn0z0bmWHAvup4Gmdbh2QtcejrfQN/jbhDeXxPtMaWemk0ek8PTFFfn0NV+8nCiqXN1seXNaYorkgafNhk3XMYcBMSfWmx6fE85E5jkwq8eEe2wP69Ji/WkpznvE/oyLixvyfCAEy3xUkJFnFw7vLUEHNBhiqrzA1hj8Wnlf90tgPWT1qM4onjAnUn7FxcqRk6GEwuFQsKZlLsIpFpwX1pIotqMZA7EI0cyI9MQA6jJ2DSsxmByhs8wlEWw1SLi1R5KiOVOsMBfHaZ4Q47BFsdbQmWrIyqaQtJAEiiYunICaep6KY8oDl94hUuHIpQhBCqnEykGkMCk4bGWpUeUMxlgykV4t2eSK0vmz2LlS63j5oMzzROstu5WSY66hu/HAD3p4M1t23oNGjkXojCMUIBXQwnrV8okYXg8jRYVNKFgTyClDEQY1PNgG03gaFU6NMOYnfB5f8nEozMYQY+AUM8OcuO47UkpcNBnNGWtbYoxV2N513J8GjEJW4WEWYswMIWKWsZEzgsOgAs0Sa5JSYts47k8jF/0adOLJpuPhYcCYhmKEXzycSCmRcGhIYISPvONFF2k7S5bMbwP7Y2Dd7+p7uZZYIk0nuAR/kg2hwArlZrPh1fGO8tlX/OD5NVIN8zycBryxHE8jv/H+dzBFeJnu69xZG2JWxjkxiOBy4v7wwDQ6zPWGVVMIMXEcE6koJddTNqVEzrl29NLb0VEsdfxlZXG+DRPat5XybJtKyA0BUxq896hTQgyP+qRSajCstbYGxkoNHbamkuVjjDRtX3UH3kOuWiXnHMkUVBzjnJjGkaiWD+3XfDRPvHQ9vcIT29PrxB/6C3aaOLpE1MJzM3MMFowwmQq0dQhJ0iPJ3YnCUlCzZPi1xj3qsnyuxVGUKijPS0B4WjLizjl+74rljTEYKe9Q4fMiEIcz4ay1b12DXvQR1SBnjdV5n5btXa2W8NYRWIujpau23FvMMoosy7ls9ZxosHSEybVL9Y7Yv/5c7eJVIGpt7df9ry1/o281WefP+23cnq4TkxRciIhJSO4RseQM8qLHIVjjCbMwxYmXe8/+cM8cVxgtdL7lPdNhp0BIiinwzClIwG49vrQ82f5f5L1Jj21Zmqb1rH435xzrrt/rXfTZUSKRQEICAfUDmAHiJzBlQjNlyJQJgjFiypQpjBAiSVTKKjIqs6ISj4wM99vYNbPT7Gb1DNY2c48iB0g4qsiKNbm6fq05fs7ea3/r+973eSd2O4XTlVCaiD0myWmWPCwFGQzRe1ZRiNPIcT4xpsqUAl84jdaBQ7Ggez6eK85lFI6lLghhkEUwyMLeGdzYSNepCAb5gSwEMRicbgkTow44aQkh81QCoNnbDqUzSgRy3FHkSn9rqTkj08KyqG3c7BmvDEMSLcNwXJujmyalsGIFoSlJ4FdNtZLzY8PahKzRHyJXQqJsAfcAQXG5F1B7nlZBincctEdJxeh+wNvHIzV6lgc43PS8uv2AKpn9Z3vS6UR/d0t8n5FOwN5Q7p5Qg2ut5MsI1mMOEibBcKigNOf3Z/bScLe7BhE5Pl0Y7QAikFLiyzc933x9wnSenetZT4InX7FdBa8ZdpkpRT55DWqIMCswB6iCsO7JfkLdWvoCvXTUx0LOAlTGKqgy4rCQI0vM2GqpqmdNmUF27RlJItRHdNcT5A1WFGRcERkSllAlolzY1Y46ZM6hg6rxNROTIamIkorBFWrNLDMcdaVEUBZK2Tr4xhBCk0QgMyUXUo5o3RzfWloqK0U2ZmRKqgWIK0uusMgGEc1JULKHYvAFqqiQbdOUatqBWcomp8BQpUHV1PiEpbQusVAIXZG1Ioms5vu9z38riqsf1jPsFVoLLqlyfghMVTP7gHOVXdW8HjWayLEqHp8uHOm5HSoLllwlH4XiU79ybQQpe2RpNuUiBEIZVt2BlohYWQq4y8ynY+RKVt7PCTsHhAFTEgcrWU1lnT1WWmo4cXV7y7uHI72F4zI111dSPEZPKZKEwJeAEhohBEuoSDxKGYIo5M2O7FTiEjO1zvRKEBBYq8mxfY1KCikVMVRCiRRhGfUFpToMElMrnYOx60iisM6BrttExjnTG8VPrwXm4rnqArve4DAICb96mskhbCgAXpxkP//6A6+vB24OB4JPfPX+gYdp5XSO/OyTz6l5Yu96pJbkUIkuoa1miZXHOaCqIG6W67E3XFZPEg03IKUkxtaepTS9WqGSphbbIVXlZmfodetmzN63P0Nk8ZmSM7m0FneqBWskFckSM2X125hNsOSI2px/lzWSUkLpSlo9sx75i7/6hk9u9wwy8g9OKzdKIMkUqXinI39cIv+uVGh1ZPGKd4vkLfCv956HdeTPO8Fns6ASKEguVTBr0zpR22rFjnjRQyUJIEl1i1LKG8yUNqUTSr5osmp+7gpto7giqVW2a0Jpas1tsreN9xp3SyIQJHjpVIlnIKt4Lm6fhfjPiIZvGVbPf6pavh0/wktn6RmCop5Hi1sXLNfW2aoSdI54Uag50RO5cMNNhWDyC35CAVRaix9BEYV/dtT4u7R++XVolPo8U4th3CVKKZy9Is2Rq4Oj5gkhBOvR8+oAtzYir95j1BXWKs5z5mkNWFW4PezZ5anxPxEYA8fQcTl5PJFQKkpa9lLzRpz4bKeRZUEpCaInxHt+dNsePk4pFp15d1L0MlPCiRvdZA1JVA4KooS4eJTSdMazhkqlIKoj63aNaCUwMhMyLDGhpWQcFTcS1jVSdMbZnsuy0M5hmsdzppZMV+Bf+dc+4+0393w4FSQaYwYOn0eYPeQB0lM7FLgOlEUqSV96KBcIZ0q+IoaOXz0svF0GYqjk2nARo8lAgZS4uup4/+5IjhnRTaSoEaW9h4+r5/6ba0YxcXw/Y01Pd1pJwbOPGpUV0liyuCDXSpp3mOraDGz0+HPrtI83GnrNIDPgGFYFi6fWzGVxhPTIF3/UE84Lx8cjSRTu3nyK9x7RuTY1GSWns2R+qDjZDA+to9w3feTbSCyKUiSlJIzuyf5CTTPX17fUWgnrgrYdxcDHd5IlVlxfEc7hvaJjYDpHluz5EBwZR9t3BaEIrswtOZ/ZJUNJK0oZKpaqIQhHioml7JpER15QQNWw5ohWFuU6Fu9ZSyIngZSGEBJKGnyKKK0QvpLkczdeNPOX7RGpkIvYNrCMqLKlmMjGRgybS6eIisotVO5ZjpJrhFqZRKCgMQJCVShhSNukoFaJ+J7Re78VxdUa2+ZSkiGEdYvxiNwMA51pwMXLxqaINXGxFkelVMepwiAybkkM1jN0A8cZvGjakaYHSlyXgJaOogoPIbJSeIyFKVRGFg7W4FLlXApVWexc2OkdmEzn7vjm6QNogZQWbRWP68xHf0Elg1ISIxICSy4N7lgFKCqQkaIQS0VrzWXWdArom3OikqlKk1JCSolPrfI/LgvycM3J9HziVxKSJRaca+W18JHOOBZRuMweaSTEjNYdOyu4FguqJogTqRYmD6Z6rBSklFG2te2VavDAWnvO08I8BfwaCSGyZPj6/onP3+zICVKMzVlxSqSUWFdPrxyneSJWQWc0p2lGyDbm+y4XqpRCZzRSVHyIWNe+Nq0gGJFDjxCV07QQSmUJmRS3PEbVRn5SSvQmgBSivWfZR6J0zEt7GOWcG9eqFMZdj1CO0+mEUoqvPzzyaj/w4/0diZXLNBGAH5aenw0FXMTVASMFF3/k95JmMJVPbmf6s2VWgVFIehm5j/BVEkS9FS2bS1CyuTqFevYdAi3aA77VKz0zyJzUlPytlipu3R69Cc+10NTSgLCKbz2FanPmPcND63c6U/BdDNbzCOrZ4bet7375b4LUXgqvZ5RC2nRgcisk1dZtEwi0rPxEC/7+Tw/cjZXoLf/1zx8x1WwHCl5mkmKLfNb1OTfxd3P9+LPCzjiqtJQsSaXQ9Qo/CerSzAkfg21QT+PwOROKxJ8bykOIhTl4jN2R1sLpEhDCYFUi5oqUiRBmOmNJqYAI1LrSd45QLPtBEKpAJUNKiakI1qIIHjotqUrzNJ3o1EilR6oMIqFyprMGhGDsDao2488npjGGqsjENNCLE0e9sDMDB2vxYebaRkQN6H7g/hTYawPc81gre7NshgpLNStaa5YP/wi7BMzQMbgDMWTmrzQ5TqQ4g5QcBsfD14VpfkfJmixn9ocOuexaiO9SOOiFZTmjxEBVhVQM65yxViFr5v2Hj+y6SiyKT1xi/1km50hUHTIHTuuJ3W6HYiCvBScr2XZI4xGmkBPodaTkGZMWSCvrxaJNRluHWwfyEvBPK2ZJSHPNRZxJWtCbHUJplnUlPkJdelAVreB+XshUXBCsLGjZhOT70bF6gXEVmRLFF9YaEDIT/Q5UZj0bdrcXjOkY94JIpa6S7pM7RPKkKLn5wrLzFmMc7+7PrFkix1tW5i3UOzLkraudE2/PC/fZscoBdS4UZZFxI1DWSpaJhrtuubVVOHRskU0ltlQKM1dmCiorjHbElBDKUXJCqo4qElm166DllCpKVWQkWSQQZRtjtq5miglt2mFxqKId+iQIXV9yCFUBSUQ5S581tVjSBghPZUYrSSmCVdaX/e37Wr8VxVVNFZzjcY18PHlupeD27op5XdFKkwo8nmeunUXvRnKa0WXB7jRvasZI+PRaU7Csy8rOjSzzmZISykpyrtgaKGFCVMEoJMIMTLYjZcnOnVh9ZamVUDzqlAjSYEQh5cwpQEIiCVxKokuSXlT+jc+/5C/vP/LJuOOSIu/PHqMNcQqkWnGi8UjcYLlyml3n6EXAC0XIhSJH5jhRakRlQSmJrrOUsPDp/oqvMwwqU2vmfqo4mXAh0vc9lzVwv3hSLY35pDpiKeSlufuuR03NimkRvLvMLU4IwcGZ1lXKCVMVNRSsUfjpxFE4QgiEqrg/NtCd+PQaP0We1gmjO0yR+ArLsuJjZo0r8RnqWS1KKWxKdFIiOoMPBY2k1MJ1ZxBC8DTNvOpHsiyUKrmcVwZr6K1ros4cOKbaPj+lqLkiRcLHSrUWisAqgdOOUgpPp3kbNbUbrQhJSpnwdNn0Si3fLyTBr48BVU4IOn7fQJCGd49nwm5gTCu9O/BheaAiialxwnYo7sSErI0N80hiJxWvlycmC3iJN5Jke8y88jgYHB3QUAjJR6pUCK2+M+prhYagaZrKM3BzK8LKiyPvubBhK9VbZ+B5QCe2MaPaukHP3Sj9DG581lWpVnBlnkd93wrPn7X2z3yP/NLlap1N8xyLsxVVxRr+UD/w9YMjmiYQRbaopCIyf+g8P4+2nRzhxWWYnzt4qvCb5dzv1nr7XiEMJKeZJ4/LBu8T1mVkaF3e0SWUlJhQcFIxHgxCdAgSy7LwSb3gw5f5AAAgAElEQVRhsoX5XHllJCEEOq24LC0apu8kNa/sR4dSA7VW1py4UDgvILPBUzFV0ps9p6cJJS1rvrDXPUo289BcLELNSNFRtUDIpvvTIiNVAwJLJ3DKEuSZ/V2GJXL9Y0l/4yEsoNamY5oyrBN9t6K1QZQbhskTyoFlWSjWUaqlnCIhQwmRsgSsFgQvUUqi1U3TXwrBh1Nm2BvG/SucjciYMGNCf1ZBPLEfDNQHUK/g9IBfIrbvSIshrAqpPSIJtHFoI9rrdAXZG8wo4QzdqoGVpT7R/7RSsqaeAyXNqN87oP/DApcTZS7I+Hvwv/01XTUsT5567RlGg/j6jFwKnC8gZ3bHWyieGo4Nm9APVDOw9oL9+xmhFqJuHXC5L4TuyKAPyDWS8xOH6xtyTIQniTKOh1PBWItSiaIq+rXm7cMOROT8wRDiSNWV868yWezIxdPrCnhmvzBojcbizg+sKbCaHaRIV7vtsJoRypGExiFwuoXLRNOkCwaJ0Ya2m9UND9MOY1IrZpnQuskG7krrXAsSIba9TStBluvLgbEoRayKEDLawZo8lISVikBGiIqkBTZLLFRIppCFwqRGkhdas8u0CYLesabarl+RCGuho5KVJNSMUoYxV9bvmdD+W1FcLRUePh7xVdBlTdKSD08XrJJcVo8QgsO4w+dIucx8LsGoHr9mnAKLoiIJNQIS6S+4ulK7HUtoYyslJb10/E3MpMvMfmc59DM/u9LU1BNte7D8ya8nPqwWp84Mnd14TIYf3AzU4umN5noY0SIiSuZY9sjVcxh77o8rp3mhFEmugqgaTDOJFjJ8bSVR9Ci/UJBcu0InLLlIHrKnCpjXlrtkTOEHUlFy4dRpHtdERwvhPPmZXOs2+tLUUnhYp+aiyJVhGAg1EIXkyRcwI8syIUtlTnVzLCqcERjZui6Pa20A1FrJOfFmvyPVQsiFKQjWLAg50WWBjYkSIkuEk498fnu1OQcT1uiGuFCNqh4ISAHD4FCqEYNfDxZrE6VYqIqTD+SnmZs+M60rUpkWSvsSXi1wzrDOnnmeX7IM2++QtAjKJiIPMbJulm5Rm3twDZ7LGtHGcp4nfDWUHPk6KmIORC05XxYWUfl4fsR2DusvBFF4OmVicIQMp3XiKRiyFZydxe4H8jSRTUQaSxWCYDUuCXqxsMSAo2OKnmw0WtoX/IOQ3xY3z1ysSuWfHZc9d5yeq5GXEd3mLzRa/oY4/FlblUt90VY1ZlrLBjTiNzO0vjNppGzf+yw9iBuvKm8F2jMF7I/3ij9mx1eHzC/OgkUmfnUM/MGN5jJ7QhpwIr/8nufXpOuLV/H/7dbwL+SaPBxMYQyR66uONU98fr2DqnCuZy2JqNyGrVia+7hUjNKIfGHnNcskmM7g+ozPgqdsSL7SaUtMBZkTvXU8xYwIkl3Xo2VgjyTHxG7nGQV0/UiMnnvrqBri2jF7idKKoBI9MPQHXCepMrR7y2eu+sqnn15Ta2WZI7vDgfXieXobiMkTTq8INxPDIDFf/Izy9YnCwnSshOmay9aJOS6QQ0XmzKCOjWY+JHb2homKlQNLseAmnAgM+omrccR1AozAc8K9HmCvYRq3MeEISlGXhfB4wOWOOo6UfmIuhdFPmB6C09gbAQ4IK3X8gEg7+OFA+KnD/kgTyxljX9EnS5If0d8IpB0Q/3ig/OkT8r8svOt3vHIOLgfS8AdouVLf3eL+srIgMKoyP0ZqHei04O3xEXe8cDs46psvyH0iTPet+3QrmOrEVTYYpUjhA+NuQOgKhxE198R3Z2SpRJmQzvHTTzLCOkqIrctI4fMbhxQacgKVWP1mJZag1ECOmZIaADbXM1r1VKEQaWANnnkKSNMKfWtGJGdSWqA4kL7hbYShJEVRBXRzI/pQyDljrd3u+4lgChKF1WYDM2uESMgdFFQ708kOAKkSPjRDT+mgqkIuhiwqMtn2jN+0m0uRZCqxOJawkCvEIljyVqQ9O7JjizUrqR1qT1Lgc8UJjdgOpUE1E933uX4riqtPDhbXSZxQfO0j928nrNUkY7FdTwmJeTpjnGlC9nQh1OY4E0Zz8R6lBE6JFgwrDVPW1DAhjUGKlvf3g17z67RiXI9NCbNK0i7yj84XyqU92O6urznXD3x6dQMlMfQGJwt7XbHGYrVkNJWQNdlmOlMpayFRGDrL4zE2Ha8UpNzgnz5nOq3407eRTmVejZbz2lqavra4CaFki5MpkErl41LoREFZj5wlXpo2CguJGGNjSOVMER6FINYNaKlBryspd6zGoHRmepqouekezn5G+4i52bO71vSdat2qIqgbRE1qGI0hicL5fCZrzVwiVii6qx1WtQ360yvHIRuS91zvRpYQiTGA1qQUcdo0uy8CpQTee7quY06SyyWgZIPF5aK4LDO/DJU1Bj69Hhnss4QbjDbkHL+lj1vLZWk5g8AGt1SsS8NgLJuwvjOWaVlJtRAz+LyQcmN57Qk8pY6uRESJzJNEaEUukTGsnKPmqUCMgbc+0gsBJePpsX3PMDiEP6PtwA91ZTSVJ58YquSXYcZUgU6Bi5ZkJxmsJZTvRBL9Rs1UNxSE+NbW99Ki3jpYz0SFbbBXRdlE5U3vJNS3t7IQYgOtbgWVMbBpENU26svPP7B+Oyp8DloWZcNBiEbQrxtaRJT2uufTI/8Ljq5aBhf5/Hak+Au/Og48XTz/V4a9gCC3z+dlJCk2tMP/t/3i7/p6XAJrHBBWsj4tGCn5+sMFYwxrOKIzXCnJUw0o2Yw0RkucNhzXFSENShdqyGilWHMTrrsq6KyiHzUlV4pxWJrLU8kzOzESRSUnQU6OyWm8b/dMpwVFVT75xNDbyrjrUXvBehGEckTgeHw6o3Pg0BleHQxpeiSlRIlwnJ8o7BmsoKg7kpyYf13x5sL4sSLtDUZfYeLCogfW6W+Yp8AxXeNPfwnqGq933EpQbmQMiZoTr64e+PFdQipF3kfUl5+CvMB5B6rgfAfhRLrP1FgwT5CfVpRTlHXFjlegF3IOCKfojAGhWXXBfJzJ5xVxrZA/+4Rq7xBfJ/imUsoJ/iFcdh03roA9oPcj+VrA0SPeVMJ/+vuY+xNvvvo18/tEf/0XqNNnEB39Txx5vtBXz/pWcvUv3TGXRM2WH1/9PcgTBOCS4OsFN/ZgBLlqzvc98bKilGLfv0Evb3h7/0AOER9OeLXDSY3KlXROyKfKPM+43cB0iXR6RIgJ5wxSDfhYECw450i1STrCKum7BYtn1690fYfaCfIcCGVC24zfzDHTVOhdgtwkChGNHXbEWChCcZwv6NqkD8ZWiM01XHLGqA6RBV4rHmNFCItqElpyjBQpKFlSZSWnlsqSZCHUphMkBarSrNGi8C+d+SgqJmmEjBQ5E6sCkZEKFOk3XM5adSiRWdeEsYYcVm6251vMBSk1I/UF4fR9rd+K4soZzXTyvA2J9+cVqQ05VaSorPPCaJsOSgnJWhIGR6YgteCUIlNoQbBlCzNOMlJiC5B1ujIaMNrx67DyY92hnOIiKlOYWRfFaxylW7gaB+5uLG/VHdM0YW0HSI5z4OIrN71mdJrjPHE9DJxCpAcma4khUzX0tmdOAdMZpB2RsuKs5MNxIqJRseIHha+Vv/owcdVtHZ7UYl1KrcwSZJVN2EeliEpKgUtqfc4cMsUokpAIPZLCitUaZGVvHYMu/OLtI1pZrjpHyYGUZHOSRY0wmnMFETXvP67I2lyOjXhsqLTom2dG0ZLaBjznwGl5x2e3N6SUmTdCesqCOUxAK/COc9OPxRSaO7BULvOEVhI1BT48zSy16YqklIRYWFJAYLCyMvax5evZ1ukKMRMiLL6BFdewUdaJLzcbQPCVWMuL5irGhZTbSLCxx5qgvpYVn0GXBUFh1xv82v6fT0Xw17OguNYSX8YOzgujBTc6pjqQc0Xcf8S5ysCCVR0iF167Sqywy5FfFUHQFmlbdiW00NIWE7N1l3J+cevVb9s8TX+4Oeo20+W3qIZt3FeE2ACtYLWilLzppLbTl6jU2mz+lAbgY9ODllrRG7T0OZamjQpbAZSfxfEbq8rIthv2OXOnFGOFSww8pszoEsvUgoU7UziGhtRYqqSrhVjLt7BWJDtV6UImuO93I/u7tP7wplnkpRYUaVimilKGnY/MXQMaKrPQA10JuCuHUJlaQO71pkHMTCViMqxJ4NdnBMhC8Il+6Nl1J5wGpTWus0h/IUioCvLakWIb7VzWwKvOUWNAxC0LL2WYezoX0SkDM12nEYNBFo10CUzBRgVYTu+OjFcP+EUhSNy9GhBvKuj20CO9bQiSUdBX0Qr4lCF9Q7Uda1BcwiOHwzXuxrB8fKC3lkxHMA1KHC5w5Q2IV1DWlhEVPFUr5MfMhw8T45zxJSHPCzdfvIax8PRXE/MUMdFiXcLvHCkl+jFyM35C+FA4/vI9r95ouNGEd4VOWsrHgZvyinL5hvL4a9R5JN6fSMoxC8f1buS0V1j/Q856Jc+KKBN1zPj1hNYa219RTw/w5YqYDFoL2H8DLoPIrMtMd9fBpSMukYd15vXdAbvrWybtoEBnxp3jvEworbgxAtsPHG6uWUtBlYHH+w8Nw7FbURfB/vYPCf4tt7dtD0b35EtAuieE3XF61Bxuev7Bnyf+euoZLtdc3U98swpKkY38r5qoPCLBp+3qlcRYUatCCkEWhUCHsWb7PEGbPSIHpBQYbZBEpOmxOZCqZEmRWiRSaBIVJSudkfgqKTUh8AzFMdWZu91IQnJtV4wAoiZVSYpHoi5c6oAWkqJLy6WtjWWVqFBaDz5nj0iSRGMjIhTv4+a9lgpBwZZn2uH3t34riqtBVmYElyXwydAhRKOb340Dg04Y4FwrVoEQA794/IhJlSgaaPJZOJ1S07h0unDtNLeHEUEmRs8UEiE5PCvr7orXfuL3R8d9lqhu5bXZs9bI108n/upj5sYZOipTKIQEH08LH/OBL2NCqsqyztyvF37v9Studx2/fDpTjhmfEjevX1E600R8y5F9lch9x7u5jWtiqhTlmItH+dz4TKIySkNQ8OglcuwgeZxyzEo1O76U7P3KdS+wKTLT85gygwI/7lq8wLowGsG/+tmOD5fWRfvDK8WffYTJB0pJ3K+V8yroThPOSv7oszuuO1Da8vDwwPuljTGVal0tnyL7/Z5bLVFYPp5m4NvYlFqayFxu4ulcW1HUqTb2mlMiF8mSCvM8I23PafEvrKVUGjdJlxVnFVfTjKoCGZt9QynVNBlVUmplSq3AWNb2789du+ALa45NVi4EMSZSbkWKUmB1xdQMSSCs4LZrFnUjK2uOnIPia7/y+e41y/xEHh2D6igyIqrl9HRBiRN9FChdeDoFLqGdlDoDTgusKjzmiqgdCsWu7xpDSm7jue9ANo2WLZg7l5fOVMkRrTVSiFYcbV8vRSuESi1Np/Vco20CeLuxtsomcJc8hyhvfamt5d06UFCfi6varPxsTkdRSiM+0wTopRTE9uLWFPESfDCcfSvKVBa86gSvD5rPnSRejZwfIkdR22hSfNuN++neYpd7qshczrv/H3aSvxvrEluYeM6CNQX8YnFOM5eE9KBUwKV2jUx6z8VXkm/Zncq1+y7W69bNDRNVSaSOSDpKiZRa+Hg/sRtaeoHVGSFn5lSRpBa9cpUY5AhecHcQpDAhTACjkFKQxRWoQJwS3diTYkTbQkk9XEm4OkDtoL8CCoe/15N0ZL9qUIqpQtdratJIFZE5UmJEGtOcWaUSfcDUiBA9fXyir03InIuk/+xHpNTyYlVL2aU/ZPBbR7tXpBjJtiLWiP1E8vpNgmDZxQJjpPgLQmr2/1ZmyB7bX5PjypXT7cBRU3v+fnjHfn5F/nhCP1hseU/4WmA/OxA/vcd8eYccDsyXM93R09UTnfiSdPkTbv4oQ7mju3lF/Sf37GuGUcDdDfzkQHp7RP+vhvcf7nm9ALmwmM/of3pLvix09sDj10coCa06uv4GYSqy29FNFcYE6SPCXtirwv7ujlIfWM9vUXZiWAuyM/SfpVZMiAVuXsPlK3BQLksj/H+o7H94B+JTkCNpmfjF24HoBG4Pa/B8pCD1QKqJY9XUXJtrmRZZRlWAbLmnKkKpyKpANpSIVBWjFGutZLGx77LASEVOlZpNy8qVklwgZrZQe0lXJEuV6CI5aLByJKlMKIVpDaxRY1Sld5p5WZlCTyhtIgQwF9Eyb2vbB7MQ5GfIsWgInZLFNqrsXriJKPutY/p7psOI3wZL9H/z3/2PtZTGxldKsWaJj21jV0o1K3GFmBJZaAQFp9r81LmBaYs9ITe20ZwSuhSWCiklDIV+t+d0bicAZKUrhU+6BEoTYuab88K+H7BK8+E88XjyXI8dcYsGWaqkH3tMmrnrBL0yPMaFW9ex5sIxVt49BqqAT24Hun1HlxJOCE6nE8ckSRhqSBz2higU784LtQis0hitsSWycxLd9ajeIVIgCIlQLThVKUWfWz4hG6l2ioJLycjdHtM7OE3skmc0kQ9e8TerZ580j/OEkerlIa6E5NBZRE3c7TT7TnMzdLyfZ379cWGJFa1btU/M1BzRuoUQl6pYUybXJjgXORGz4NBLrsaemNtrNQIWn18E07k2ls8SIqkWNI5KItEKD2MUvWqMYisyt71kHEdOIYNQhFRZgwdtIGaWlEAoqBlVE292HSEninR0yvC0TFhrmX3FSomxjbUlUficeX7up1RQ3cjkI9dXhlspOdxdIXPkyknenyZ++WEi6x1hXUArsg9Ia3BWY63mm7X93YqGHMhJkEIgLx5tDNkqpGsB1ao0sbiLkZ/dXJHzwj/0gq4qQg0NIfIdwOfz+tvu1WfeVtqKrBcy+/b93/2eZ8E7gMitmAq1EfabLmuLp9k+sLJ1tSgVSiX7lV4ZUlihSqxRWC0wWrKT8MM7yQ+toNqeX344cVxWnrLhZ3eSH3QH/uzthfNaeNiYZf/Vf/Lv/U6Kr/6Hf//fqVYqrGmHkrFkgk4M2oCQXETFlUh2mvMp0fU946hxOmPKjHGOy5Sw+QO9uca4oR2CPPzy4jl0QxubKI8WAT1csR8En785tPvSNtamkI2zpVxHRFNERul2LwrZXFSWjNSieeob0bY5FGrLectCkIiUKhBYUi0INFmUNvJ5iVbaDBu5XWuoVizKCspoSskYGZElokpAEahxQWy8Q0rZIg8EpExxGbl6kJDyiqZCGCk2I7UGYSE5cHD8P/+Sqy++INRMTCvycqGKPV3XAxXpNJiBKJvjr1SPXANYyyIj/c0thQvS7ZjPC8OXB+I3M1Jq5MlTl4SUjpp2iNsOljPVQ14lJVdsX0hzQH/5ivjzXzH3ik4b5nlFCo3qK/2dRxlD+CrwzTQTcYzmiiUqxn7Azx5tPG9u7jj5R25ej7CsHB8TKlseYyCWwO3tLbsM57Tnq/snzsIScoDcUXMznIQsEbJ1x43Y9o7azEE5CeqmQ1KxHdRiMwxSRANtSvttJqxW3QuDkJTbvp5aEdMrRSkXrnoDSmN9xFrLyXtSSlgHwRdkaPvMXCvnnLhSCr0hjS41btiGdu902uBzIm4HP4XZ0ELtEN8pg5YNWiqlJoSZpPuN/RehqgZ6FtDYjLLVDts++Z/9H3/2ve1JvxWdq19MilH1BLFlw2lBMRpJc1HlFKGANYVpmVBCoqRl3txxIRdKiUgFOawYJTn5jDUDsRayGXi6RAoR1/VIDEv1/LpYcoYQM1OpKHvFmYjYV+4OI9oO2NQ6S5e08HGOqLrj11Ohm2eCNcjjhR6LMpLD4cDgBLKu5HPiGFfmIpCyY04JlEM5Q1QSes2XekfuVENMmIbhTwUKHikrMaz0VVGVJM4zwlqehGCtBqsEpRasydyMV6SSKGGmkxB3Pf/43CHWmR9/8hkxFuYngxWKh/v7LTomcDx7ipS8XzM/+Oya1Ss+Rrh9vUeLwtPkscKRyHx4urB4z2G3Z/UTQVau+h0pJY6+kIXmMifu1wmtLePYk3OkqoHRtQ7YJdKEkEVSlKBzkmHcc71rJwnjLMvjCV2abu59tnz46gO7fceXr/ec58g4OiYfQLRxx9PlwloNne758JRabp9N1ORRtWBTxhlwnaIp3wtCZGoqrVkjWyGRw4WhJp4eOz52YKfCHDJdXdFKEKrhSkXmlEgpspD5gTugdOYgPacpkJVgugSMtSwpIKdANhrvA0ZZKM1ppUQLXfVK8uffvOMn1x3/8tDxT08Td8KQauFSBeT6He3VbxZXUsoX5AeAfgZzlu0hpvXfSkF/+Rmb7uu7BRewUZtbILqotQEGpUBKgeosSSmq3DFNM0PKCKGRIhNS5puj5ckorvSF2yuLShLtFE8fAz/6aeTp8YH36tD4V/Gf/6Hun9f60RX0tiU2pATTcuQSdtQYUDWCM6xOshOwf20wIWCKZxCS87wQ55mTNQTxQ/J85rOSqBRq6rgdDE9+Yt8N3IXI2En2esLmxLVfYOeAAnoEeWo4iGLRaaWoNtZRtUJNaKmQ1VBTAmRzytWKFpBjxQuxTQNa2LqUnlgiUurWljDNSi+lJIvN5JLXNoZGknPiSs3YMxijuaQWf5XnSIqeXu8pH8/chwvjOLbnAQWjDCoZfn4OjFlyuHrT4Jim0I8Say2Zgu0XWOHqB2/IlyfYa8bDQP20J6kT4lePlJJI6jP0j3rqGqmHPfKxJx1XRDihOkXyR/Q+UuxfYw876tdPmCTgsAMzE8RC0plRH6Cc4N88IKJF/uIRdZ9h1YiTJf+FgKCx1vNkFdoeWL1Cec3937QufhAJ5QJGWM414bo9T2kC1ROj4cPHgKiCXx0FNTiK6kipSUKoA+9+FYCeuTyyF5mf3FQQA7JGPh0la/zI0wm+et+yP4PcI6Inp4y1CYEmbXvJFzd71nXlWANFCpzRRAQ2Zw66yUAEiSgjpdek1Tdpzq4wFsfoCqe1sPjI46mSRI8PiXmDp06xopUjbxOQpBM9Ci+giIZnshSQ6iVTVUooKTGiUNo2/EiMTDjG7grFAiKwVxKRM1kKjGp4iFxauSMBoRy1SPKYGqy2tEbM97l+KzpX/8V//z/VRvLOvJKN9XMwkjeDxHSW++PEL58y0Wd651Ai43SlM5ogFJ8OHQeZsL3k1ajRYuUULGjDZU1MeeZd3hPXyE86id07/sk3Z15d7fj504WiE7b0TRhOJdf6Mo55Xs+6lOfHlfzOfwdaVIj4fxa9L/b1bVRVSjsN6Koom8OhKkk1Ztu45PMP3qz5G3E7N9F23IKERSqUEEkhY2pFm6Zhchg6mamiCfF7BUsVHHNCUrlRurVjlWKNAaUM7+fGb3LCY1V7T9t7sVHOg2c/9nyxc/zT9yfOSTQwaI5No2Us85roraG3gi9uOn45lRazUist2rGJ7o2QmJoRubTOSckU1SHVBnMriuRXao4crGWplWuteLckZh+wCnbGcJxmul6TSkMmfDoMfEwLg9BkrUmp4L3nD97smBMYUSBlRgXHLAg+cSmQC2jXYaXg/QwuHila8mXnqDXzmCVTiJTUNpx102+18SPc9B1pTXz56pb//fEjZmmfz8EJvtgrboaBvzin9p7KFhoTS36BiZY1cWsqX+w7bk3mT7++EG3HuolJk2i/V1MJpYlFn+NxjDEgDZRKKr5ZmWnOwFT1VjjlbTwoUJWmVtwYMM+EdkemEwJJQgtJryWqJtbcBr2+VrKQ5LRyIy1fPUSeVOagHdcatIZeSnwMDFpzPWrispIyLS7DKUYSxxmOVWNlI+L8t//5f/A72bn6q//4367QrOq5eAQKpzTkmYfVkUpmzfC670n4pv0rCVFHukFgVk81ip1rBHRRFaV6ettT7cJdb8hd4+4praGrQADVN9eVlAip2x5Du0YQ6vnJBcK0wp52GCkCcqqsWZBzK4ya5lHicyGGghAGTfpWQ7h1rUQpLwkOlcjOwC49MD0ekVmy7w6ULRxeOUupgSQl3egoqRViyYPpLI2wW2D1kEboAkkdkaVBdIUBbjqoCey+8UuygFVCL+HBU8OCrwvd1Q5MBKvhxxXGAf/+iHud4S3wTaHwESkK/rrD2QInIAV8BHcuUDpqiYiuso4R/+lPuXp/BV9FqIKyBoowUBO5FgpN/lCyItc2pooyU4tCFEHaBnC1KEpJm5zAkHOlVsg0rpZQ8uVZVMq3qQcVeJ52ldpQDnmLzKpFN45TmRiWNv047AdieYecOn7y+3s8A/4yk9SFcbhiP2bW+QzLgLJXnC6PFFU5nhX3p8IpSk65UmWkrzC6Hj97zqlFyyy6ggdZ1OZaFu1a29gsNVWENkgCWhVqMiidWnzWJinpjW6yjlobHFW0OJwgTOuIiopSFaJAWKBWitIYIQm0Dr7VjecmazPt+FxZhHzZRxMCWRsm5z/6n/8F61wVUcklcyMTt51myg5HYK0G4Vf22vEHbwpPsYXzimz4tAetJe+z5mFV5GXhx692fLgEujIRjSbnzHlNnLzjyibu9jes+QG/CP7+55o/ub9gSofJmiDaDd5EwSDEplHZoj/YxL1C/SaP6MXctY0pxSaqf+44CNWEcprmhii00WdBUkZLSRVRKmpzUv1tSwiBNJpSK11uyuSqJGhBdIk+Fna5EKvGGc+lOI7eY91IWBtjZC8G7ucHznXC2IGlVDpRcNpTS2YqjrM0yAiDKPRdh0/NaTeYgWUOHFPi6mpHPC5c76748PhIArq4cHCaNWXmeeUvU+TVbuThOKERXNaFV+OBSWRyhTlLVIUltxb+YALOGFLKTNOEE9ABjz5jrebrc8LWBUW7ydY1oHXHOmdQimThGBZ6p+ml4ZGA0YZL7vnl20eu+o5u12F6hRPw8WMiJYkVHmcMwa+IYeCaE1U5ipJ89At7ncnBojeXohQC2bWIhV53+BDopOT6zY6fDYVR7RCyORVve0mqmmUJvDaZt6lyJRWPIaC1JOMKPbwAACAASURBVG67oLSG+2nhB/vGEhqs4kMR1K2I1hWGKnG6cgmBddNWGa0pOVPDmS9uXvHryyZ4L02T9WkvCCFSq2NZGzeHXLhUiRAaIwq1toJ2QpFEZZQCy0qxI3uZUdKwxMLZR1KRXHVt3PLJtWWcZx5ixA5jcx+lxFok1ML0tGCFoubyf5P37kySZVl23rf3Oede93hkVmX1Y6ZBYIZPiFSoQ4FOjQoFGiWKFPkL+Ctg/A9UaBSo0giBxodRIGEkAJsZzHRPd9cjMyPC/d7z2JvCPtcjsgGj1DSUobytraoyI9yvu597zt5rrb0WVxJpc168gF0ZFohY0p9kXQXAV//ue4qE2fjnwALYxHmXfs6fzgZs9I2cBRsrEI7SpRQkzRB7gtZdUsIt3YYi3J3v0yCnMFAOE8gLiLEAohYxRj5d+j3sV0SNiHGq2LjgaQUJbaXiqBgPacWtoUNiwyMGK2rWoOMNhiWsDcKfH6BHgsUYLKXQcT4vHxjffIO7s/vscACfVLaIcRFnPb8PNPbOqGZ4H4h2zEvIveyOQjiiDypWF+x3014AIzNICNqAPICB3H2g+BM8GRSNoPTWoFxZ7xx+kyE3/Be/Qa8nejmzthW/RuOnllmHM9aK9AWtA78snJ4Gp79p0D7FCHCPzzn3CqJIy7i2EOb7QL2EftIGRsRVZVM0K66dboZgaK/s85jOEjl4ahq0mCaajtA2meFSEIzhlSyFZEqaRdrQKyiIFfaHgovxO++4/ZJ03vn060HOL6gq3+QHvn/aKB/gtCTSKuz5BT8rv/0Xzg+28x98Uyjrid/85om/6pWFzK+W79nknl+d7/H9E0sm/B5b4skTn3qlLMbdJpA6VVfuNTHKYN8UH0q7h3sS36fO9uycJfFdbfzyDtq+8q0Zw42v1PElczEhlTPX3LgbFc9xDi3qrCYMVfbRQ6/aBRFYfHA3LU7cnQ0NTfP4N3Ba8K6HRiejnEpAfd2Fj73ysQ4ez5nrPtiujtxFJf+7q3NKiUUaf5YG2+OJf/KbZ75eF773e+qnZ77+cMfjeuJvP18YQxj991zNuU8v/C+fG99V4asTbNszdyXje+V+XdgaNDWKwKckLAYLwkmFz22wkOjpS7rGCI1KsownJevURs3OouIkVzQJZQyGVGQUVlEGg+KDzWOEPjEQSZgHdXWIgoMmnpoaBC9K1oUrncuEVmUsaE5wugNr3Hkje2ajcrcsqCXcoWiM0e4ouHAVKCinpFgyum+sGl3Sh9T41c8e+T/++tfUzXhcYOxP/OpxIefMz++V50vjVz/7ih+eKr993ni6PPPN/SlQnOKcz4kHSXz3eePhvJI00BMz4/0C18tnvlof+EziyYSlNP7t+0TrjU+umD7w8nJl5IIPOBfFNCw2FlGWNbMNuD5fuYqQqLTW+c6UT9Z56DvWKo93C49Z+H2H4RnbYspu+/xEq8ZIlROJQuK7zcmLoBbavK036ggzzQVlVeUpG/my82uHv/j4zNMw/vx0Yu+CeqOOzueqWGu8ZGdBkRbZh12c0RrNnF+/nGjdeG7OyMQkjhnuylk2frYWflFW/vbzzg9lJe2NooltLNjlyt9fjd6U38sZaTvL/sKHJfHL5cL/VJ3imd1DBwMhfE8pcrvUB3tvPCc45YXHp84P7pxypbniA/Ip8bcvgyIXvjmv3K3gefDvvB/89XcRpXLtShNBhjDoXEhcag00bBUuQ8HhxUH9p1tcbb/4JZt3ksMjM5RceugYx5x+9YWUQxu5LAvnPGitkXPcU1mdND3O2l4nSgy9D9bq1NEZeSOTYQjuwtVjDwrzWZvFzR0ARYO+s7ETIduRN3daDVElieOLohQ8eXgVdUOGcOpOceg18TIG6oJaNCRmYe6rwLCGbYfZbRxkQfUdgzE2pVWO9UH3GZslMzpMhJw6dycL5E2f8DnRnGSQtEXRNwZeHekaOYRnBW/AAPsWrYl2fw224cOAXxa0ODxV2v/ziXx2dj1TlpXSc8StJEfS1HuqkEygX/GcoHa8FiRPWsk80D9pWJ4DJgmYUSsni0BuMyMbOEaTyNkDw9woUjAxRi4sUws5cFx8xms5OmAfhvvUyeURiQpegIpPJkZTfP/xgceZZFMOYLlTxxrWCDXOst9jJEv8079W1As7hk3WwQzcF/7ZtZC98Je+8ve/ek9rzl/2K5tB2Tf+vfeP/P7ieN/4+PyJX737ir/3yxOffzB+uzzzy1Ph55b5fX/hu8uZu/HCN4+Ftjxyced87bw7V9bced8T3Tun3LjXxNIF98G47rzkMykN7spOH8oQpyusWWl7hNEvI6Z/2hr+h9euoPe39blKxzQkD3/Mx4+iuOp1oEviuQt/YzsnFYZCMcXTyvdXJzGwXPh03WN6bCyc6aENWjq9nnjZnX2rfB6Du+XMx++dB7kidC4t4bpyscEPe+YuOWW/okuZFv2VrxJ83ypDF87WWVR4qXDyRJZGBk5F6SNjFl9UxLMkTsM5kzkv8H17wUYhASNNGm8Q+pXFOQ14UQBjr2FzsLdKuX9ktxExIX0efhqb792IjnabQj6zGQjcQlT+fj2z+WA3R5rHtaZMvZOwU3nqYYXgISx3FfDwwKkW6Hjv8J0OHnYjr4qqcL3sVIV/0b/lm/t7vpJOHytLyTyuEdZ87TDyiV9/9x2j3OEpk4jD2N35sw9fcdk2zDrkzHPdaKWQh9K7MbpTq1Lzxp+sie+uV/7yKfHtZefruxQu42kFLVy2OkXbOg3pnHPRyJL0zvu7QntpVAbLeqb3TrPopFuRCc3Hll7tGKJz1JVLEt5p5v35jto3Pu2JfnUg4dnpOUEPHeBFwKzzQOb3G/ywVch3vE9Ok2d+9z1UXfn5o/LhtPDzx8xvfnimS6Kq4/tAFKwLa8osXmm98EymjQ5tDndcO+k9PH0yflufuV8zpQm7G33fuTsXnpuz+YLsDZPPGIm/3U/oZeP/GkaWhb1fSGvhfrqtb3MS1Mw4aQ59TR8kN1Yp/D4V/s+//oGUT2iChzZ4TBKTWzv8hTt/JsLvv+tcXHmk4O2Fl7GCGLvtJEsYgbD15ys7C6t3uihj1H8te82P4VHkBVGh+0Ltn8hpwX0gWTiflZxj0nhdV5w2tXRheWAuN3mBeY/4I8BMsD5YknI6KzmfEd/IRRAtkwrUcNZVhRKU4qOUiC+xjHMlt4jk8UmT1NHQESxAbxPluk6UtB3DE+HZ18ho3lHN9DaNZ3uae5XRe0NFaL0hHnYt4gXR2CcyHoXU9EESEjqcww2uSUXdaMNJAuk8GOdroHZ0ZOlI2aD9HWTfsMsaVFC94vKCFSOXgqxOsXtgg28f4fcVdOAjs+oKe3gMyi7YWtGU8FNQlBRgCWNO1oYUhZbxHXwvSAVeFtgNaYbXDG5IG5h2cEGrge+oZCzb9BmbSLYoSJ+GwrOXbgUIT8PRFnqDMRrmGcTZkBClW3xnwyLeJWUhS2iGQ5YxWP2el+eNCrS2U3nHmFTs6MpII4pAjLqs1F1IusWEtToqobH7WDvb5Xvea+FvPr3QR2WVR7J0ytX4v7ff8fjwgPjgT7/+BVmF7379RCmFsy3onvi1voDAr5bvY/BoNNrTzn1Z6NI45cG2nXA3nl46rsIpGSyJHzZDyiN/IpnmyqWlEOgP49KEkQcvlhiWWY+19hKf9YtXXD5yX1Y+biHzuC8Fa39cE9Efhebqv/pH/4MDXM3wJCyE7mo9HKNlTk1JQczZNCZQ1vn7psG/ptkNrBKLzMs6NyEJzQ2KCa8blNeboPfwdPI39aaqsmg4oY8ZBWI4aEFtoxPTdxlBbNKE2pGcOJHYe8MnInTtfT7/MnP2Qg6gBDKVNd1y5SLyZXyh8Tq0ySkdHZ+94d1fF8WhBbu0TpYY0Qe41BYQuTg6nN7ahOBDQ6Gq7G6hBZhPsphRksSYrXdMlM2UxzWFr9honNeFYpUlJ8jwtDttJE6aGdpj2tCVbuGFdM4L+zB269xr6Mw+tUpyKOrT8yt0G8KgqLENZUiEBAPkKdbeTUgirDnNIm9mGWrQsp14nmZBvZrOsqpL6JhyFGjhN5UYOOeU2PcdmzSLeEzgVR+knLnWnWVZ0G483p35kBtXz3y8biQfeCroCH1USomc4JSM2oRNPDy7LMKWA7GI1znW+tDIoOx9TJPUwcOklus+uKbQuy1kZIraw7ph8HLd0GWaTs7i8935xPNeb1q/VWMd9+GxPj3oIxHhucmbeB7HLSaCliXCTpe08Gm7UFK4Ld/njnTj2SJ+Zc1HdM8rKhV0xfEfr55aAP/tf/2f/iThq+t/95+5iOCWcLlQ0gn3qY07aL8xvdlm+HU+HDUmigPMadfQNbrpLRBbLNYIgHlj1Pm9NmH0aM58xCEuc5pPHGxkJBtCIRHFr40IAlbVCAhWDYuQlFCpMyUhIphiOMTnhGBcr1o8hzNwb3grtNbodWqKPET9MUmoBLo0TWcT6HBSPpaJI+RA9t1AOmVqIbV0WDr5dEy4FrCpt0rODLWc/iUdGtCmklk9LjQLlB7SrhT+UEdmpwRkE0bLM9ZJDu1Qbog2yCucR7yWd+gJrOHmUDM8LchLwaogNT6XY4ryOD/iWjvimeDxCIkAA0ZG1edgzogiWYLuJL1KUaIjV6DTB7TWOJ1OfPysvGxXRMK/qpSE65lvv9v58JB5WHc+P0OVzN2yYu3Kekp8vEBOK3v7zNfv/4SPn/6Gu/tI8DgJ9O60tqDe6eugjhOWc+T6dfhcW/g1tvvYty87y12mtJDibCm+Q68v5LTwm6qsd2Dd+e7TnArUzN7CUNS8UfKJSx2M3NCm9GUJZ/gBuwrJMos6poV9xHd5Oix6RDEPajWlqC3ybrgn/sv/9X//N0tzdZ0bv01Tr300SMqVKSQ3kJToc3JLJPKIbvloZpSspJEQzVxwSEoaO6XEBJ7gqI35YcbNMcikHIWCa4RQqs6iw6O73gmhp3tDVFGUIYMhGUcoKD4MmTlLw4VThUsa2OwwUXAtiCoyKiUrjTBQWyQKg64h9HOPzcknwjBmwWW3sfoJEU8qFclfFFiHi/m9QCdTJ+SeS5iELiRSUcrd+RYWXUfA2UUSQxSvsenpEhvZlhZyOtFH5zFnrL2QMpDv2KyzpxMFY6lCKid6uuA2GKkg/UpT5ZQisX1rnUocuN/2fQpgFQz2NNGoYlQfjJ6hG1k8RLUWE0cmCRKsqdNq5doXVBO6xkHUOnx9UpoFsmY5fKEKKbpjbahGtlTDpmYjkVN4Y22L3A61ZRaw6omGcfdwptZKK0rbX/ibLajek5ywtjNq45wXNBnDB1t3fqgJH1BFUTUWSbeDKonR+8A0RJfiSusj3IrHIPngKZ/R1OluWIdlcHM03sdAzVhtsKQT1oyVjKUooj61Oo11p7Oxa2z2MhAVlqnhA+EuBXpyFGw2p2uGQ5eOjcp5icOUsfFUCQ0ag3XNjMNv/tAdAmjEsAK0/Cq8/TE0df+6Hn7ZI+tTFlx2wCjScR1Ii9w8nRoadwmK2AdChtRxNILhlzR1Uo54Qru+Fmh6FFvKchfN5lFE4w52wuwTNtf5UYy3GjYi1izG7UeiWsX2QdaIL8mzOUUSKb8WQ2MMcglD1JT9FrSuecycyYFqRkriotdZaAsiig1ACgmhW5t6wzkA5IpoWIZkdUBxA3NFxiwWu0JdGHsUKeKHZnagMlFSMayXGUSd8TQCVYongzaLltMI01YVpITH05Cd5DJjqgaiBcYesg8SDJAGXhXxmK701ZC1IcXh644/Nrgq+sMJPt2TbMAVhEjW0N4i2tyFMffgsHWJ18Q65IWcLmhylkUJKG2AVZCIkPE0EA3EPRchZRje+PqrM48t0VGGHY1QRT8Iw+HZC7Y47hufh+EIz7sy3NnqRueel8/PpPSej59ecD1B0wBEzpl27eQnYzk7ngZZjd0Tay7YKOw53sfy7huuz9/x7M6lORdPnNaVvQm9GmqDl0/KmgprUnS9Tq+9E1kdM2Hvg3t7xscJycqCR8KGDMw6LXfWvGCjYyXOgZ4mKEBonhmDvQ9M4Pr/Q5v3oyiukBx8MoJVixiQ7iRPmEBTomvwzAAWnQ7VU49kyTFGhOROl3NF6JpjGkyV5oKmHjfT1C0ND3EcmYCpiQmXMcaMDyG6HwASvTspSURLzADdqwafLi7hrq2dZwhfGEB0ZQe6XxEToMQkNFEwdGnhnD3CR6aJg0FKE5fzeJ0u4fW1j+jM1Es4LEsPGF1CO2Qtfv4yXcHV4vMQVXxCwwuDPjhaX9QbRZRdlb1u83B0ik+/7/FMtQhE3np9PRw9uuNCZ4jyIgPvz3SHroqNSiJhHtAtPth47cKTKjocWeZGysC84x5UxZpjk23E9KLL3GwOqwASkk+sKmEGKj0MDFF2E5wwRfQWuXnXWWgmX/DuuAlmc7w8eQhxRVGLDlpFaJN+dWJys+5GzieSO92Fe1WaD9wGkgRJyi4j0Doctej0TWLSyU3Z3FA7kKoxO+CBL9DrNE7NiZhlUdBG7RPZUKUXpZvhOGmJ7X4f4L6HNk+EPkXCMs1gRxwB4D1ilmZxsyUJPY1HEyDTAaKOcO0PR/n455UGPrMIBVhh+E6alg6HQPQYeB0OosLu43Xadggi/3KG4k/p8fmf/CY+j1Zo9j1JV06FCLT2MJGtI8yLTablBhETYpSJeG1oyoR+rqFSgFjLY+y4R4LD2885q968zEgxsJA0GjObgxIpCZBpV2bUVGewoUPQNJHjiThjFRFnvQu095RWkit7H2ErkVLoSRelzHzQGJ8QzstcJKoILaYVx2BcN6z2qYc50GNleKMRRaeqBqqlRvLYR2wGnKcjHmrKZ8QHNvWoqgnJsX+JKEIH6bGgxXA1vOxIGfFzSTAKlIpng68EL5eQfHiFXeAloW1AL/glkF4f87O8JkwXQJHlEt1tVviFwDffRU7fFp5hes20Z6H0BcZAltC1pdGwXsLigEG1jvgd1jtXOhDxLb1nmIzNdhGqRcZf80CphuU57PJAGzGp6B4pJi7PSFLGHsMR3QueBu6J0WFLE8WUCiqUDVTPgQixMsTwl2eyC00K+8egJbfuLFLYxHAGyU+cTpmXj99Ck/Azs4TrTqsXCk4pmSErMi4MV7osiBWSDlLJ+P5MTollUVQeGKLhWZiAbqjDKplHF5qHl5dDTCj3HVFh80YbsacfbFZPf/w96UdRXP2P//if4nPzFRGWNOkDC+2V+qR7ZqGhrpATprFpLxPBMp1TNM3ILuyTZpISi8569NC3IlXffJgW1NxBFd5y1nSiXJOmE5EIxJRBNhgS+W02D5Cj2y8aI+/dl/lS2ywIoruwHB2BzUO2pIgmGSVgyruDZvRjY1DEw9ohApED2VoIHRVyndd2XHf8fiYKTp90kKuwiLHIymHxIFrJKWHdScsyiwsLfQbAHNln0og2ryunQydwhHTGtXWbB60Eyp1SCLTNOqZCmlhGlx7QMRlToQyDpBghqrVpFwCRn2jzvTOh3DSL6yoFGZ0kRraBKnym4lYm4vllTE6eh45ruiF+QnQ3wwe6FsZMbJf5M5k4hMZ8j2nC+Ud3aUx6hfVGUQ8FscPCQ2/rp/d+M/vsJm9sPjwmsQBNUVgOf0Ul3978iXntc6p1zOmvY0n3P9A0NedmiRHPd6CkdnsvQe/IDdFk/j3zuz2yuvqRQXkbopgJCfPgPmY9XI4JtjEFuYJ3j3zDn3BxZdf4boY3kIK1waUaRQZko6tP5HRA3+gjMUaiI3QPA9csIfKNRwY6qkfRfBT0k0Kc4/mNfhM2mxiwI4SuS8RIGrEwyDMyCpoavYXLv0tMfgWtsrFIomTjVBS/NFKtNIHxMu8zddxrmJLuztYGzyKkOQ14yCFULYoOs5h6lM4iibWA5Hnf9opKwa2jK7j3sDXJCmpIamFKqk7rocdUGbgJmYza4NY50BEJyiy63IqngeVOWmD83U/kk0AFbc7Y7rHdQ1byKSOeSA0oOZrLplztE0nPLCUhFjqoaVgYVKcYXNfQzXVh9Ir6PZghTcKQ1RrFDxrRYCjuFUuOFCOroylR3PDxCbcFFWU0ZxuZPrWgdeSYIhwZkyt9FPYRqH/sJwM3DdmEOM0MGQ9h2+A9Gh8LZGvMpsn6cmNgDIk802Z0zzCLEpXQ6oExbEe9UBK4hI7zIa+IKKqd9wrlJJT8gSGZWjeubfCiI6KbivJcVywJd5K5qjE2R6QBJ3odkcph8JIjK/PUE2qRaejTRihdo8J+TjEY5tU5nVY2e6a0JdgQPGKj+hu0/Y/0+FEUV3/9m++pNljyseFOh+gS2UDSY9PIhy7qOCTL3Nznn7XpKJsTNw8f4F+yOBjEIZQltEaqitHI88BLKX3hcK23QiVuUlWNaRpNJAmoPXljKOQDIp8dVE7HBMks0nxy+de4MrVEtUHzowiK177Maz18r0Tj+g6n2jT5/jrfY/f472X+/rGxZoKaDz+Q0D8NNbpeb59HSYrIoGTBXir4UUTNQ5X4DMqke+yYiiHgdNUMKnNjFopO+s76TZNRREnH2tXQPJUcr5M96A3XAQ7rwY2b4ObhA+YRp+Lu2BbFjA9jeMC7rTvP+8AMrqPTR9BbvXf2qoFQqmNDqT6o5jHUMzeQI0InQNIZPGpGmYfRmBYc+3h7gL1qiG5FMDJ1Rsc/fbbR9kVBIfDF2hoTTVKJdaD+OjGYpmBV5bUQO7R1/uZ+iK/k/8M4FL64f25/9saodOhrEXZ7T39wA43jjhpGmnR6chgcDYa9Xud8HxD3oTq36bCf6uPDfWhNTCLvMslgjI6nQrWYGHt5vnDZ45DbmmE43ePzVkkYFZuNX/ZEys6YtOChvQqEUfAjIkH6LQ5pJbSLiQuajBQqQEa5hA9Tdlw2vJcbcnl0pYIjY9CBra0goHpCrM+Q9oQS2qM0wKwh6Zh6HujUv7oFUpZGn3pQQzXQbsXDouFwhKfh0hgzuUM0UNCkoflxDdrRVEmloXkNGlW2uOgxUTwxWGaBmXdkceScSYtiWkk/nDAM7iqqK+neSV83OAmkKywL5Cusd3Gj1Mz58g181+FjC1uHlwW6h/2DA60HFbuXCNFu97htSFthZGwQ9Kwp/ZiynA1P3EuN0ZXOsVfcMXqizsEmRHFX9uZ0qbg53QqimTbRK5v71nCLYg5hqDBGsBsDvxVg7v6mMWSafEZjNETxET6QdbyezcOjOHYXaknIfqDYSqvC73VE2LgVNgvPRecJ0hLsihboHWEl7Ua1FLI17xQUkaCkd4+AZbWCtY5mYXRnV+ie6DhlGm9Xi/1fJOoIY3C5GsMX3Bb6vKfegil/zMePorh6bp+QJXOtUzek8U9tsZgOI0VlagZmdy1db88R0xGxCSx9uQlCgZsW5HZIlBSTNfNwFAlUJM2D8RBtujuWohDQAeoDybPgc2EpBZOnuGbLIVCfi000364dXnUo7UAPPFAc90n/jHEr+g737ZTS7aKHaWjM9NAlwcn1pn8Ysz09pheT2HRiDw3GcuTFJQ3/l4lkhQD8KBLG7UDtvYMGwlTm5prnud1n8STMTZFYzGnqxHKJjWFNUSRpMkrKt6DmIsayLGwe7/HcQZPTUhjG1fk+NM9wY5/F1fzeR5fQ5U0Tw213msHndgUSeRM+945p0BUdoRObisVEQwRTz6JlEyNJvgUpH4WBSPzerUj31zy+wycovYmoERF0Zvs1jsKfKTbuXxRTR1H3NqLGJ53tEiimDmfk2ACNSZ0eIuaZDXhQAYWjCPfX3Cxeu7HjGo/Xy29E568ICDe0UVSjgybOhS8f83pFwqdNZhEsAcMfb3xMK5Ex31vQ2W+Y9p/oQ+8XRI2cEu6d5jHJF6qS+B7P7+9v9zIyZgGcSOkCuscCEaALyB50U5K54KauKnUYJ/AaRUrvWE+IVmTJtHaJhvVscDoFRca70BKVASNB3qG9n681oHd8iuVrrXh94eWitGqcxyCnhbwMTCZN10pc3wlKSdi7C+lBqVMT2Guj/LAE8q4OCezeoWS8bhGTMjJkYXvsnNZ79DKQzyv54x1skGuZizSE6tYKsqQorHyl94pqeHmZ9UCezNB0s4IGzShRtAbbsYbMQ0ZMKh/tTHQe8fm7wqhYEzBBW8Fbnvf2bE7GipImknUHVqkNejtFQ23RIJpLIEoSjIekMhGk+O5sxP+rxXDPGIGOhQzGqCY4CffwEpNeGRJN97FfDSEmCgc3M1OTQRsXRMpt2GoMx5PMwPvCmBSjq9AZiEnYRPQSRhqWQ84iHRsJv+qx+zHQQD9V8c2ATpUeU6CWMWkTRavzqBu8aCDzEN9To3OaMps9Cd4cZWDeoJVgNFD66EFb9oT3nX1MDXJvdFpkpnrGJVF7x5NOcX802j7+uNOCP4riCjG81cmBxjoUEYYdm/hEFyxu6qOzfnvoiQitB3V2JYTSMtGiw9PjBvvNzeHqHiO/k+ZpzNdtdfoBcctJcg2Ha59C8qRxzUfxBDUoOE8hJvYp2DxEpmEvc4NaVRXvHosLYISHScNJfR6wo93Ga1VicmW0EKba3tlVb8XaOJAqD+uFZEIpSvbgoZtEQL22Ce0eTs0S2XB95vRpinypEDXHTVXnWX0UBklhqOJv0bGhDEkkBq0LGcEPUWuK4ZxTTqyq7ALDKnrQUJrImunbNtG1EFS3LfQn5rOomvqhrXdaDffhvQ1qM9owRm0Mh5cO19ZhCPsIFG+Y3jIMm/ukS1+7w671VtzAazES3mXcOptbITTX5OhHpMJEEI8i9826jD+Y1NhBNx9FzzHOLrOOdqZWK2D+PmboaDAV6DHg0I8KZYZlzwmmYzz+oI8PFAnvk7Kd7/kNBP4WuYIohnp/mzhw8HwxPRnu21EoD/cbKusHyjqLbDeDSWUfzzsJmp/04y++b0HBWkFTQbxNyuQYP3l9iIww+DRIeYPZhYtdT/yaGwAAIABJREFUSWmJQ96UXASjze987o8kNF3BZGa/NTRVhMzyIuCF3Qz9dMeeX27rxOnk6bWn/cRin2JxlhR+LVPQlHPH6+B9ucdzorAQY3AFbIki5DxA39NGY/RB/vYBflfIefpp6SOxhu9AhaHG8m2CJPT8iaKK2JlhnfP3A8Y9onsUUedn7L6HTU854fIpYqDSA4hhuoO0CEZXoT5Wst7DKWFjoJ+I93PJsC+wNfRliUKyxvVLecG8oWUWrOMcf3+dKE8DtfDes12RrvRjGl0aaTgvPfNCp++RHIFFZutxltc+wBdas5ugMZDsaE6aTn8pE9oQduthxzDTHoyEp4LtQelt1ujSQ0dF+BrapMyEEQUGAxuzUe73iCQ2ucR+PgrNJ/o+G05INCJuq88BCusJNJAydIIeMzg5e5pNZqeP+PegDeFigyKJbd+pPth6o4uzpNhLn8crKLHXGhP+Fg3/pcwp/l6iuCpL/Kw5jRyIW0uUJZp9d+cyEvfv77CmqGQeS2Ef17hPFuXl8kJPg/THra1+HMVVrUGCHZv5OLSO8+/tzd8BgWTMUffXh4TFgr8eajeNCY4gtzPiNZh2HgrmpMNxfY6LH/7CMb8RnYohYb44p0PUHEtxaBYtE8rtNDNU8kQJonhq/TgE8xyxNoaNm56FQxsFU8g8PwM7BO1y078c2W/HJKEQ00R4mFu6EaOrvbIT92uSEEGnlNgtAjtlQgjbvIF8GKIwJiJYPYqrpCGut6nFkfk8aRZHJgIWJn5LZmqjUnD0YrfPts9uMcKfneE6P6NOHw1RidBnydS6z2k5Y/hEfVKmjco2YfO9hni2m9348310RvRTVHGaDIbHZKCT6R5ajObGwGO4QZl0WuingIOZDvpOuTmm3x7mqApD58j7rUCZ39xEtY7fOew//pC2Ez1Qr2gadNplBColqHsYbh412nF98/UOiu3oFcfkXuVGCdl8HmKz1teJvdsjfVk4Hv9+oFtjXiMzQN2STK1W6LJ8Ct9l0oI3BEs1tA5TF3a86h/Ucj+5x9NHQUURnCwbmgyzgRgx3ZUSvVfMDm1QxkWmT9XGook+Mj4LZj1iajwHEu4DTWMOLhwLZoAtMco/H3aYdHJFbGZRSkUor1SiVXRKDTxNLV7LEwVaSCKoeuif2pjX4oQRyhS+YxP9TLfMyzTtc9wTSRNIA5zkgzyNTt3uQ5MFqC6M0UjE9bhUxN+BdHzcAReQJfahtITJpobX06JKs5gU3LxjNHzckZc9GIsegepgdHmZgnmZVijvQ+SfXqA13GLvtqVFZNfpnvqyoXkhW2X0Rz5fhH1A7XfUF+PZE3s/UYdSxx1tSigGgtAY7qwYKS3s3UgeAdjmgpkwqtBEeBKhqpNkwYZQTWCGf0sTLFfclW5K0XtqVVrbA52ftiqtddrYYCibvOon2+g84TRT1CvWOpYCWRM9c/FKd6Oc7yhDeNmfwwJmhiLfrTBaJ7HgSelWaQ53y8rTPgGEHkzFmjKrZiiJ6oCGiacM5dorBuwt1vY+QhvWe0dIjNYxayxl0pDbx9igl4VVT5SSuOgzVo3UEyqF5d3KczNUlDuD3338zHIO/VxhZT1r6LTXP2459KMorg7fkOO2z3/IG2i6iWuDFiQmA63OzWWmr2scTnJDGV4pDTO/+ZIo40YBQZxb9saDJwD4V0ThOHREhCzKaBEw7VOjgzuDQfdpbgeMvsWhPjezZHGdofsJjQ0QeiUCRpYJi4ZeZ9J2h8fXmDUAMfl2XNNp0oQqh6ah4wLb/GySRKHXMLIch+KgzWIM4KwLbZqZWvc5uDPQqb1y65RSQo/pnUUVa3ukz7vNIincv3sXwNm1kslkFZonNEEmTb8pp3VnWRacRs7hfXMgabWHUawUYW/7K3VrRusxgq1Z2S8XhoFrZtsquwR60moN1FMXvHe6jYmu+ISZ4ztKkicn/0oDHtDw8ZrjoMzG65o8riUOmmNNvhZNb+lDt6Be3WYZdIiM/RiFnr/DiIEGmWiqhyrB3Znl2x/cNHrTsACMec+4ttv1xbXm6Fznz1mfzyP5FYXTYz1NKmfKa47BhQORCuqa0Bn6nJi9da3hTSe8sUgJMvZGFaa5lg9U76f6+Ef/feWRzEif+Tt/8p6v8zOnc0GlkNawQFg0MQbIsqAS+hLEGXIOBF2D1hERNAvmDRtXIvy9MEYhHVFdN4FeR+UMhAmlptObPTAsB3xkVAueZsPriTEzRFOfHnw9oynE0TIMm7YymiDxZj8jz71YEQ/avYhhJiQJR/guBeM30D+Q0xnvjTFPJaUjMlDKlEkE+hDDRYX1DH/1z+Dv/nlhtBJU9JymtNmiJsvsI6wRti1oQrV70AupLQyFZpXeooD89hIh5dmF1mKYoLYLLSUgcbVMS4ntBR5bgcfCy3ZHbVfyKqheaJ7Zh9O8s3BH5TPL6T1bq+RyT21PnHLCp1h7643OC0kX3qWdzWaotykf1gek7TzVGhPA54Wvq6FSaItgSUjd2R2ECM0e0uh1o1PjLCLQb3dHSma4kZPSy8I5RdO8mUGB7bozcuYhn1AxvtLCc/1MHglZwy5m605blR0jTbPrixlld3qC1jreK6e7B37YBkVWlmWhTu+0a1q4fn6mufD49VcoCyorTTce3r1H6aQe2al2vbC1CndKTit5KeScubYKZCwrJb2jtU+c8j2a3rIOQaX6aLz88FuedUdkwTT2KlXl8ul7tv057g3942LqPwoT0fwP/guHN0XMPNT7YdZ2bAC3QzDNIsG/EKMJBXlzSPg00DwOuqNgGvi/8oM8DtSgJGe3PQ/Rw7jy0ETd9ErMKb7j8JojzUx0iRlyaurIsFtHKIe2ab52mlw604tG3G4H/hcolX+J4uFjCvKnGepEDI6fOc1NrKmQemPN4RXzlha66bYOrc4bcZ+7I3lSmzOep4hwKgt9bJSkQTvkyL7CnFTStKcIy4mSFE1h5HkqgqQSAwUp6MlSoostSxQ7dyVu4j4LBpnj4i+XnZRPPG8bqsrzpeIonpVt75PmE56vleHQJg1odojRoY1BSTINW18nMoUyO/cwNzyy2g5DTOFVpH0Tequ+aiv+FWvJpjj57Rp+FbZ/OQ16oKnHOpI/+D0/iqfj5w/NlR9U3Jzc5DhQX4NJ4ZWyu12fv04gHpNbb5uIt2vvNq140Ixq817QKJgOhcVEjXlDoYq/WU/TsFIn/t7/8X/zk1Rf/cf/4T/0p75TWmXbKie5kjKsOU3didH3ONB/yLDgfD2p1tOyBnpZ4x6zAUsRajVKjqZJ/EzOmTFeGF2QZOxYhKjbyskdTx1T8LGQ0oKUOFxM3rFrovkJc+Xar/T+ma9P93gVrq3ycD4moC+MFo3Xvu/cnVeWVdj3GUa9rrzsid7hjhH5lQmu1ytfL8oSsyiTJVB2PNCY6Xc3vFNS5pQLY5qFXvyCOmz1hZwS2u4o7xZKDfeil7GxrMo9C6srn0ejtBr2FnYl5xM6EXPJQqudl2k+LAnO877P5ngfLGtmpMTz5UJxieB5jQGCNoTP9crI8Fju+djjPjhLaHVXc+hEtnofZCV8zEz4WGIyU9NgNaGo0HrnORcqTusLWZSrdUrfeZHBOznxuRjn5T3NhU/bhpQw1Uxyj4ohrVKnt1xJidF3hmb8tNKfLlxLh2FoN2pvFD2F315KbKNRljPCHmuFoKDbcHbrLGVl33deXl7Y+0voasvCEOdxecei8LQ9k1LiYU2BiiUo5X1QhBYeae+XM5/bxtO+sV07skRDt8hgjDA8XR3MwuLnvN7T2jYbs0FZF566cC8vqJ3Zx2CrG2Vx3IXr3qKQ1wTZMFnpk6lQT7w/rdS6TV127OOnJSr6/+2v/vkfbU/6USBXN2pk/rfN7XrJ0e0cyLZ7CG1V5VXcPn8j/n4WUZJmp5On6JgvkKqSjk4o3Q6Sw2oBETSl0EKI38TEojl0M2K3TqC1hsnrRB8ELxxTXzmiDMxoY7DmNaZQ5uscU4F6GIemmR04PYd82hmMqTNTOXyzHLNxK/BkeBRivBaHSRWDm/8Q0smaWU6FhIMoy5JD7N0763q+6WOO3w8Ieb72NB02N5JEzMC+71ACgSvuaFdaiowyvOEjkLpqDUsZqY27ZWVIQtzmdxAIVW07qsq+j+neHJ2yWEDTYrC1MTW5lUQjl3vWU4zuXvYWcPgYcUNr+Lgw4s/31uaKUrISOgUP64HXYql/UaxEPSGojGlNkL8oONzHF8Jx7NXBPxAluxXmoeuaBY+FaeLtdQ/E60ZZz3th0sGSJ4UtMi03wtpAxkQVp+mnShSgQxPJ3hRKk7Kz3mbBKG/ewyzwfUTExzCShZ4uPoM+NXCv15gkLFKyZJzXXDiY9J+AcIxnS4TTHjewEPrKwR9Q+j+th46PnEcjaSavCfIH+nii9Y600Bsui/KQ3jPGC3dLRiwFmjU1mlUTozkv1jhdNnpZWPqJh6Xyw3XHfePaK/f3Z8668vz0hPuCl437fA8vhpVE7o0hjUimGKT8fSQLtMHFOmwNOyW+vzxx3S9hwvv5TMfZGLRS6G1jJOH8nJChdGvIkjhZ+A+1ifp2D68sdfirXnh/vmfvRk7gtXN+fKSacHdaWMsJxfh+XKAOzu8eGNdBk4XEgHxG15WC0KpTt2cWFT5poj1fyWthNWMM5dzP7K5cykofnZ9L4aV3hjekCdveSWWlvhjmz4gI39XP3F92JCun9T2anbuHe859Yd8z/SH0lQ/5jqen33Ivg9oUPxkPmikUfs0ek3H+gNPhZSAl8V0dbN99YlmVxB27RsSQe2doZdVMs4+0vqG+MErG24D8wteSaPo57sHk6BWeN0ezUFNEIH3vO4+ckAK1C09tm1rQTPadqxtluYt71AQfoe3L0SfTcU5ZWXyNfZmNpTufNPz4vn44M1zQlhGPxInr/j09Jf50gc+pc7bMvghrB9t/S22NLRmPlnh6ca4q3LGS3594nwIAuLSMto7mQskreVl56nvonpclQILWON/fk5+feTj9jL5V3i0rz+vK4sLmgw/vT/Tm4D327pzIxNSkzLP6nMM+qG6VfWw8deG03P9R7/MfRXF1PPqbDhjisAh/jUOMPoDXTvoPH0f3313JyxIeFg5uI55jHho+43MO4WfoeYhiyD0KGtdJWS0zUDSq+yMOofZKXjKL5NvUmbtTZnefU0wskhLLWtB5qFWPMR9hBNvJmLKraZinho1BzlM7MbuKMQ4kTL5A3Wx+NuLxefXjM8uJYT671cGqTvXII1NV0oXpoBxweU6vKF+tNRCfiTgk0tRYxUG+LEqvleKRgRY6hzB8LaUwep/RL8KiCz6cu/PdjdZdE9jYZ8xLJ2V9FcunxN05wmTFlEvbX6OJxClLRBRdr1dQpZSCz64mJl0MQxg2bsG24dI/6HNEPXnQWm8RpxtCOI6Cq4Xodd74zOmng+JykxuaCeDSo/CfNLINvvieXsXlk96eEw7Hmr2hrbOQ0VmAjWuM7WvJUzg644+Sx/h+DW2XehSzzfubcgeOqcYbKjuL89DFxP8Kc61IpB/oQYUeDcC8F3VORopOraC/rpn5YhMFfHNfvdWdvZmq/KIw/Yk9nvwBz3AdjfXuDsZG9zseTwsjCS+toe1rPulA5MyLODKeWIFv8gOtNX5zNRYtlPLIWBqWCt+1xmdPfPgqUgTen86IDnJzcokBEvUzdwrp3T0ynGWJaKeqkTUpqqSkyBi8S4F4V7PwdeqJReEuCZYUZ2XvnZRXPuiKPp5Z25XFV3xU0rJw31f+eX1hd+eUjNXg/d0DDyOMoz/1C7ae0DWh/TPSnWtzpKw824WfSWbJhfxSOaeFbRhP10ZOnbML5wTp7kTL73n59AM/y41clOfL96Q6GItCGjy2wt/zhVOK/X9ZFnS7kE93jCJ0Kh+98u22kXPm35Iz9V4Z60KtF8be+Pf/5Gf87XWQn/+KbRMe786MnknyQF8SDylz/+HnfLy+UPOCauXnXbBkbFtnP99FKkf9yNe/+CW1XrC+k/rOsNjbVs2INa5AHYVdBlKvdIzVV67jGs7ontjqxlcPj1jeufadZRTUnD95eEdrjZ/98ldcu/OhdzJOTifquvD5Wukv30XznJThna/e/4y69bAFGkbyztauqArv7/8u5/OZ/fkj4RQfFHZZr3wlxrUOtocce/t6xzuHwULPQB2c9A4ZjW/yIKnyUo07LVQs0LAJbjAG3H1D08E2BNuF6itXUZb14YZcXXqCUniuRk1l2imFwezmg9USrmEOmk/zfJ7ymD6igd32aKZPX614u/D1crp5Tv6xHj8KWjD9g//8RgveRtWnjsWk4M4c/ff5Aae5accGffhSvbVfOKbA3h7aB4VzaD4Oas/MbvTaQeO9Fjbl9lzwqpXK8zVL0jk1dVA4X47uQxRHvb2hYaZOKuiw+AyOa/eJQLj7F4f3W6G+mZFzxmf0w0ELfvFebepnkoSPiRj0wcMpTEy3a0QDLcvCkiI8fp9IRRINZCrHzerpKPAGQxxpMcWXJJCMMi0X8pIpKoyJRJ0mqrKuC+dlRQ6rB2+cSjm8DfHeWXOhzQJSZBptSiCB5seEg/D5+RI+Omll27YoSfLKXqOw0tOJbrDXwWXqoWTmSNnwKLKO4n0wbQ9iSvA48N/SYXnStN1jukmmSWwkArwWTzp//i1l+Ha93Gg/q18UHDrmmp629TrX0eElVeZ6HDqnBufUqvFaHB66Q1UNV2Iz1unDdSC2hwGr+hzSmFqJ4zrN7Ka9ul3DzHI8XuNYu2894I5i6niOWOOH4W685zTXtOYykTGgDdr//NOkBf+T/+gfeh5O1xzZlyMantYDmWUt3OXE4hVvyu/6ha/WB37YK1+ps6tDHTysJ6oPrk8feTJlV2EZoKcTmcjO1Nq5imG1YSqsywPITt2dnBJdFU1jHjoSNBLRSAGU6xMXeWbbNmyv7A4ffvbnkJeQOWQnS4zw12rcnxV62AkE1RWamcf7d3w4v6fuz6TlPlD//coiCVsS7/oTG0pOhXNZkJJhq1wYpN7IojzVjdrH/0veu/3IluX5XZ9133vHJTPPOXWquqdn2uNhxjMjAZZGggePx/YfwCNIwBjEn2L5/zACJARCPPAAQozEEw+WPTJGyEhmsHumL9VV1edkZmRE7L3XnYe1duSpEbz5waoKqdWVJzMjI2Jf1nd9f98L5yRYhW4budzOydG0KJwaW7q7LJmjO5IkZJORQZOJyJqoNZL1HSVGJqe4+kDJsLeQcy+YnhzeX6GINpa8XAkysqwzkChWsc9gnaFECSohhy8YlcBLh6oQUtu05lKJ8Yy0B+53D9TyQvGRq+jJ0WScnnBEjFi5FsOSMt/MVyZrW5RLgSAlpa5knxC1oFR7/6JG9gWOB4MTlacMHolWI/dv33F6voBtxyguZ8bjO1yMZDcQKJhSENLivW/jzpSQRSNqZUlnLAZpxmZ2qBXtdq0VIEhwmixqay1RBZEEBym5lDaaQxuSluQQGfqUJ2tNkq2KrYZE2qYAphEKQrSGAllfo12CgqEIUoqc/At7UTkyMDwcGbRhEJaFDOvCZAXGJ6JVVGkJIVEGy1A1i19ZwozQBSWHmwRJKcja8b/90//luzUW/P8aEbQbeHufVgI1U3roZy6ZWsvNobWBmW1Utj1uxcSfAMhWZfKpJmb772+DzC1cNOfwLYGy7XTiFvi7CaL1VojLa79ffycI8YnGphf9IEqvelGfvIZC7QyJlOZbr6dVwDWgpJTC59QKo03LydJ9FNocLg2IGWNaaKmSlFTYjSPjMBBjZL8Tt7GotgJywRZJSpEqJNZJVr8SlUAVAaIVS4QQOO6mBnpKbiMfrfuosLFdWqUu1G5Cxm3EqEQDZUa7pvPZQKa1aKkYJnPTO63rymDHFhwXW/HwvISm74gBpS0YxWAsuSpSTjg3cFoWLvOMMhalW1r6eb00qJJ1y3fp40CDajcSragp37RHOWWEtIhayV1QXzozKemOSV6dqUDLtaoV2dnQJPLNpdeOx2uMSKVQlGhz7pu5sDNi23N2YX3qAniRG+NZc89k6T+fKagO0mspqJxRQLoxYf1aMup2XQklvgWqbteQ+P/Xj317w9DH7LxuCj699rYR6EbBUz9hvVqqwG3x/j4+vj6f+fj8xN3hiJSSg3Hs0Ag+IsSesM58s1xZQuVSI5d1YbIWieYbZQiiUqygngOjttQ0AolYL4wo9uczUmouEk7LlUkP7MaJ3yiKRc+kVDiOmfv9gSAiMYAP7Vj5cejaxECMkbudxprPSLvEHDOTmkh1QVqQ88o7t6MAMWZeXGVvWhiwqEAK1MERnWGsgfX8Ne93lvP8KyZZ2U2K8zrz57NmWT+w0xOxKr6WMyq2HDBnLNfoWy9irCyiNVJMAkxpn0UtlexhXpe2eYiRbEd+NT8x2IlJCg7mnt89FE4r/AWWi38k58rjmhjcEVTAxNzNO4ofFkNRisOQmOuV+x8dyevMi7znq8sKbs/bmlpHaLUk4flYLEEmcprRUrNTjrlEsHuyucOkR/x6YZosy5pRBB6iZLcfcELwJRopdvwwVhbr+FxPPNeVGCsLgrdJ4cYBdleMKLwdJk4hci2GvYBgJU/nmeOgcfaAD1dUWHk3Dex1kzVcVIV0IlTDD8XEVVdchI/LM9nuKDWgNIhcEDrztu6Qsjnl1yxRCHTOFCWIY8D0icG+akL0PCCZlEE7Qc6NTbJWorRBldw6LLUmymZ6QLR4D2EUaINzEyYWfG4dg6YHTose1p1zRXHE90aBSGqa4Ry4KxnlNGehYW8YQyakghGAL0iT0IPGyAmpLKVGLE0DKN2R4aaA/lfz+NeCuRJ/+MdVStnaheHG2jQx8qu4eMvjyT0JXJoOdHrBp1SvYKv9XhtbxdCLiV0DFtb0kM6++Dbg1OtOuouqdOuqEAJjDOsyo425pU1vidqtB7CJxJt+aboBruZI2Bru1e1vfQr4SmmjtBsL0BepnAJSakpJSGFvgE32pOO/vDja7rjbFjitu4uwf57KGDQwKEtMAUG6AR8jBTkl5uCZpgltzat2TErWFBmqxKd4+zyyDwzDQEqJ0VpstwSnFLg/jJ2Z0+gqsNbycn5kMJZBK6xzDMNALfkGvJxz7KeBeZ6JsWuuUJ15aaPLp5cT79694zQvlCKZBse8esCypEQolRALCYGPkRBbAfS6NEaudHYnlVdx+sZ4aq1vX2/jy+24bJ/jxhgChLSdLz3NPnV9Wmd0ZP+5UtqY95ZS/olYvObc0rZLAfGpnuuTc6Sn0jdA//r9mjPKGHKISPUK0LeHVbqxA/362R6lM3W6ts8ihCb23ZiqtqnR32KRNxH+DYj1f729rm2k2PWD24h6Y3FV3frxuL1OIcT3lrn6G7/116v3HlJGyIxHYaXm6hcO04CscHc4Aoo1r4Qw47rl3daMMFAjKKNbfpu0mHJpkb5JYmwmLZnn4HHDPZJCToFCwa8vFFNRcsAgMUIjZaEGT90PKB+5rgspZaQ1TVKhFQ/SEUXFSkUlsgSB05lLKtTUSqWNFuzvLNNauDcj35weubvboYrGyMSgLX72nF0mzrFd58oyuYlricQq2sa3eEajQVpCAXLioiClgk2RJCxrCQxCkVGsYsWiyTGRdG9/wOArvL//UUu3jx6oFCk4+0RKK1pozv6E9pnp7i0ZwSA0JV/Zq4o9vqf4BZ8MT/EJp4BS2bkBJe8YdKSIgTVltDVUH5llYa8NpMxTumKtRfX+0poLWQlMreiaWJVj9lfuhgGkbWM6rXF9I5dzRFNxSTIUTxAeNz2AGW+bdyuBFNHKYVQiygResNiR7FdiuaJXjTGZpWrOCu5yZj8eSPPKuUR2e4vNE86ELgQ3PBXDIJprNcbEtazs9ECVhhgWzrXplmKpzfmoJCZVZt8S8dXoEDmxpowwrsW6iIgQBpl7nqEQyJqYi8WTST4h7cgoKiGn7v5szLsshQSYXgsXmPGqhXfH5BmlQ4ZAlCCwzSu6rdFSYVULoYYW86CtoYaE7BqzXBSIxD/5yZ/+K7sn/WsBrvTf+k9rAwzfJtI+cb83gPBJwKOUsuWECIFSrzvrmw2zFFQfkdQubLfK3kYk8KqfgVfB+9ZXl4X4FoiJW/xC/9uygvce7fRtwcs5o2iMkNYaXxIy9xHTpzqpTwBjSp+wZzkjt4Uu+sbOkGk6+2+PcLT+ZCzTF7atE04phapNcD+6BlJTLYxGI2Njz7SzN4A3yKY1k9YwzzO7w57n52f2bqSUwkJmryxrbMyRpjEyqW4uG8Gge+q91Tc3mKoV1xfqJt4U7JxlXhZ2ux2HcWRZFtzQ9R5CfQsgmP6e3DjdHEhKKT6ezvg1o6wipMx1zSw5E2NGm4HL6ls22pZmLDTruqKMZZ5ndAeVG5AqubNMn7AvOcZGTW6Aon+v1AJK3XSBGyhXGyjvzyXqK9DagFHOuSVltwPaj/sGSF7HxrefoQGZG9hSEt3Hl7p2J2SHLMqaro/qz9udecb2WqLaBfn0zsOsmonhW0wv3wJXr2zVa3K/lBK1jTzztyNUtlG0VG2TJGnOy811tI0ynWxau/Sn/+B7Ca7+6K/8fv0QZ3ZVIUTGGEeNiZXW1aeraK5aDC/zSs6BaWzX4rtpxBNIsfKgDYmmO/x4XbhUj8wSIyoGQ62FSzgRqmRwhrfGcs2FUUmen08cpl0Pa8w8xcRRWO7cyBID0+j4mBb2emQp7Xz61emptVjUyoOGwTrUMGGovLEtlkYbGmhMkvvPP8NfZr4+n4k5MFmHrJVcA4MaeTAjgwVdIGjBw+CoSvLyfOFCYKiWL+4GdqKyOsPj00xIiUkL3uzveZzPFJX5cNJEq1nWFWvadbHTlqUUzgXujWJOGi+aPCAU1xoh8kqVIzuh8GXm+eWKVol9DuzHgWz27T4nIqfTl/xo2vFtFpuwAAAgAElEQVRwmEhr4EFZ/tFT4WORiGEg9I3MZB3SSLIPTUMEt/VG+CtZCYQW3Ncd0gfiUDgMhsVnhmHA14JMLQx5zBmvNCf/iFYGGwXoARESJyLHYUTVwugGTs8XgixktePN2IrandZclxd+Nl8R5g07lXg3HQjLM084hDOUkBCDoXqNT2ecc+SkkBJElZxLYEQwGclcJLJridUkifOKoSJLZq81S00NINeK0e21PV+uWCeRpZUy1yKJvQJOC0XyC8pIVK5IZVhDwjpJkW0DO/RxpquQhbwRLKUmnFAoI9Baso9N4xto968hi1tlWa2VcxYIGTuO0MS09o2CIImKUA4o/Mn/839+t8CV+Zv/Sa1aIvpiXXu7+7bAsO3o6SnXsTR3U9eIDMPEuiwthrM24V5z5DXApGkOtw0A0WtWckpIpb61y7bKNgvotkhJ1UcaXYNVweiB2v9W5lVforVGdN3QVhmgrCOE0PUB+bbgTm7gdL20lGWg5kTiVQi/aXi0VVsAdtPIdEF06dbkLaJBiY2hajsgKRrLElNb4N7udqzryji6/vyvNSmDdZALZmhhoYtfSdlDfdWyOedahlgXI4/WoTrA05s+SLfA0JzbDH03dpuvBCtbu/00TQhR+2fVGKoUWrSCUQ0IKqM5HA784mc/xzlHKk2AapRmTYXVe4qQkGGJATMceH45M+wmnk5XQqr4mHDjyOkygxLEVG8szbqGmwD/xgRtuVGfhHM2N029AZ4NAENESHcDJ20svOXvdCAU2t9I/fzdANVWjErveqy9LmfbFEhVkMLe2E94BVebZupVfN8qk7TWiNJYXrkxXzeXYgPyqZ8numdN3TLjeN2s3Fgz3UpQ6a9rE6inzta9dkZum5pXhuvT57697v6aC83NdHP3/ul/8b0EV//m+x/VIjRO1tY15wac6tlGdeYaM2LNZAlOWkajiDQtURQtiX1NMEqBlFOz/peCqwKlKztjWEJmZxQHKTFSUFWBaggytxG4lEzG9HNUEQNcc8DXipMTJ1b2VTPVzK8PGT0c+J9++Q2n+cIq2j1qqILd8T1ZVkZtSbbwWRIkExHFobXmbRaMTiGNIITAVBU1Z4RY+OG7d1yuC89rRJrE5/bIT9OZGto9alQGg+aqIjlE9lHzS5M5nRcO+4mrX8laULNFOENNKyEVlNGU5Eg1ULTG1cqH6wlXKsYq5rRyHCY+m44cSAxujysLURV+HM+8G+9QujAMO9a88uuD4teU4Hr12M8Nl8sJG1vn48fo+Z07GJPhZ0vBC0uSA4f5hceaudcQRGQcNNdFcdAT38QzVkQOzhKL48N55pR3PFZH1ZmdNJzSSo2JJBWuaNYUuQrBXAOzMC2PsEh8TszScTKWHDxRVI4eHgbPj61kJwNVGV58YVCGo1n5TBoEFu0z0YKfM4EZd5wgSFSJqOFAWpsk4iQdRqzE0EA2Coo06Bypqm2Ugk7s0MwSbBXUvm6OpW3Crzkw5JbL5VNEyspRO845I2W7z8aiSLQqskK/nxVNcpkQK05pSla9rqxQcqvhUQi0ksTUmMkcC9oJQkpYrRFVsmyejMUjreMqKlOuqL4ZKEBG8Q/++T/7boEr+4f/QV9vuqi337gLr3147dGTvtOn4aGFjMDYlha8gQEhWrqs1q0VPZWMFLazTx0Y9XiDJixvNy+rJTlHrGq2zNIDR2sHZKTYdpo3zUs7CdLaipB112RtlTsbSNuccVq3cY3oQnGnmmg91xYgqeiREv3/7TDcMo6Am/BeyS1zqFdR3PK1WnRAqoVhGJiUQQiB90sDKilgrUX3VO4YY8vOSfnmeFRKsawXZG5amnFqzNEa2+9ui7zu7yuS+o4noKvAWcmyLCgBwzAgKK1PUJsuPrUcj0diXBsjVduuLXfDgtMNiBYB5/OZ2nfnoxuQyvLh6RGfcgOYWjH7QhaSmBNLbBokHyNhjbhpR/SBLCRrCH3k2vRoa0y391s6M5lrA8kpzH003cFX6g453ehjsmxUD7E5/7LEWnsbp4oOXlVncWpcb4BISUMufYTbz22thpsTr2TZHHlALa8gZhuNf2pq2Kz5GzjbwJM1Q68MaoCvFdymm0HjdjrVemNwbyNlpSBmsiy3jUMbfW45aLUxuD2pnbJ1fnbmqveZtaTxhOgbmFbBkm8O1/yP/vPvJbj6gx/8G7UKDbppqnRpzsFRaN5IxefjwE4BRnEKkd1gyGsb2aypUFShJoXHU+XIZ6qNsKUzxLhCMq0EmszHx6+ZnGWnNKFkZBUM2jIag1WVmANJW7x8S1ICtX4khzNJDiAEO6v56S++4WINZpw4XS+U7u4d7dsmuwCyD0QyRh2I/gNa236+CbJfSUMTsbuasVK1sXSOjKPj4+Ujd+49byQwWp50u4f9/uEd5AuYHQuZH0nVDBt9vJ9LQcm2sfyBGagxcBWVgzBQQmtjqBVyYtyP7EthnCyXVfEhL+yKwpjEU8i8VTPXpBlq4MHd88tQiLGgnaLmAb+cyTsF18Ik4Nk5DqbFGeziGZFhf2e4RsnppXK28E1YiCbyeZD8H7/4OSc54cpMdobRmDZOCwsHIXGDQsbMeXogM/K0zFxVQYfMWAR3KqNkYK2W31SSL3aNqTEFXJVYLbiUghaVf+EXTNlx8hcm6/izD7/CS0dQcJWZ+8GxroG3dmIQhR/qHm2E5DDCXRXsdCHGBpBCTkSpkCqjqiBXCFkidCV3Y0xJmdgaUXvoTbv2URopBF5Uhh6yJKpGiMwsMjIriijtvoZB5YqXrb/wJrGQkYzGkxkRzDWRpaFWiS/t6wlHqQmk5Dl6DkajkmSWpZWDF82pZIgZZxVZaUTO1BrRCNYMVRn+5CffMXDl/vbfrdvioJQiRo9S5tV9JAWyj8WybIuAkeqWw7TpY2LstF//3VIb+7Lpt2AbqdkOwF61LaL/Thsz9hOjuwYBlHFQGmBJoeUmKaVuKchbubBQnRnoVvxUe+WMX5ClYgeHUC3ErP172x0oab+lcymyOyRj7uGi3dnYgQG0saSxlpwSOQa0NVAqOQacG8g5tUj/JeAmfVv8jTGE1d8+D1GbS007QyVSsmR07TUKNCjBGjw1vo4dh/2OgdLSm6XiPF8RsnIYd4R15XA44JylhIXNpTk6y/4wYbXher2yXC989tnDbRQreyzE3d2BnDNPLyeoDRw755jngBmHlr6uNOH0Qi4Sr1pa/mWNvFxnlLPk1D7/JUR8arU2W69jTZ2N0uYGVGoHyXT3iFZtVCn7+HZzfTbdW/vMci3oW41Hd6durtEOEPX2PbUVuZZvRzx0pizRRpBav7plmwlAIqyGNVK1vDGDwM11qpRCbuGkUhNCAFFa/toWMkovX+0M3RZCup3vn15LG0MZU9NzbX9TIkgxYsehs1jlds6q2vLD2nvMCK0bQ7GVh0v52s+Y+ubnH/+X30tw9Xd++2/UIhO2NpfqV1KgU0XFRLaKmgrOaUTyqHFkvj5zLI53+x2+b6ou68K7/Z4YMjkXYoy83R24JM9dn1ln20DvSxZIIqm0aAWqbvVXtTIKwaIrVihkyE1eIGpjp/zMzlkuKSFKZCdaxc00TSyltjopmnvYGYnICdsL45PQ1LKitOTtdOR6vRJyZI6ZQY2t4slULrlgqsNpg0grUrT2yURlEppgBLnrWks484UaeCZzTgqPgHzBaMclFIrV7KsipgWKwwkDzCTRwi+liggzcl0ybiwEX1lry9ZT1uFyYi8Ma608+4KWFVMSi269c1IJltgmJqsU7K1FlG1TW1krWKOpIlFSJYdrM+IYwZ2YWZfWnnEvKz5nzOBYZCatC2M2OFlAtYiKY9V8qJFcFGqdeffunp+cL4QMAzsKih0W7yxaGQZ/5cSFEtoxEzlxcAM1eN7ZHU8i4mPAF/hYVg76noubmACxtnHgHBfmNVGVYUxr01Iai2fBCHmTuOQS0INBh7aGFgFUzXPxkBRuGNr9oBayD1gRyWYih4hyltEHZilRshCrxFGowtwyAg2WkFMrnlYttkb1e53GgDbdKQ+qRIgZoyAbS0gR2adUSQtUAS0LQajmVDbgqqbk3FtBBjAKKTVaa/7il//8uwWuhr/doxg69V1URUnDoE3T4qjWmaVU050IWUk+IPXr+KTWSumMlhA9ZVdLpLDEtHSA1R1Z27glx5vYN6aC0hpSt4WqpntRSvRogwy5BYg6qamiuwI3zUppwu2YfWMObGO3qjA3JkPVJkINqdVPbGDwFggqGnVOrbdx5WAsoeabhifGzDC2HBspJXZwLNcZU2UDdkBJkd1uj5SCJSwczYBP820h1lpju75NKcWyLLy5uydEzxfHA784vTANA09PHxmHPSU3R52Un+i6StNXlNJt5CWjRCXMK1Irdrsdwa9IEg/3b3l+fubzhweeT4/tdVvL+zdvUKqN/KCBxcPhQM6VdV3JfubNm/d8ePqAMYZ5ificWoWL0izzBTfseb5c0dZRpeGyes7rzLo0AJyFxMmBIgXBL+2YbUxglQTv0caQwoI0hpK38Wsbg+lPzAbbI6eV1p3YAvj4VLskWp5aDQ28Ct1GozlGhFI3Zik3pwGCvjGo9HiNeDsuMUakNRASVmlCzagtgFRs6epbbEV/ff242qGJatfusAylF1N35kr1MeF2POHVCWhU0/2tfm1i+a02anMeshkyXlliIxp4ukU/lELt+jOrm0FC6M7Clcbq5T/9r76X4OqvvPm9aiaNlEeCAklkpyxZtjqRs6zoBLIni3tlqH5lUH0MbkxPaQ9YO6DV0MOWmwRgcm3zWHTbUF1jJOIZ8wh1weiBcwiQMuYwYEOhpIhQEl0qSwpUo5AlUsSElc1mH6Nqx5mAzqpnJLXg2aUbJJxp561WAqKgKoUZGiM/CwPrBY3Fs5J1xlbbNiSltmiWHBmEIQsINNAicqbUFStG/GQYctcH4QiydQmmHqeismjhm0JRxMrqMwd7bO5mmbGDYomBHCMqRbIS5Bh4e/cG7z3OF6SMvNNHzP3AO/3EW6HZa1AmMHrDWCRfuwERE1YPHMvKiYjV94z1ypoFzkPskoLntLJUzSorl6z5UAMuwSoGrgT2w8DjOhPQlFh5SgsyBfTdgeNc2A2GY1mR1rLEQtSCumQenefX6oElnvmmeFxp97t7Y9glwaQlP5DNmZfsgAkFZyH6tk4uteBExXZ5jc6h5Yt5y70U1HjmajSzbpopYVr8jdGVXRIsZM7S4EsCMvuqEGiuquCq5LLMCDfyoVgeiOyq4CQSjzXyub5jErCmCvoC2rH2jagLrfXkQgI7MkR4yY2EeKsERWhQEELh3ln8uqLGhGSPzhWv+kQiG1SFF3nFC8m+VPZIVqkIPetyFJpUy83l/z/8yz/7boEr80d/vMmDb0Dp0wwhbrqXzQWo+9hvC2zMbTcdfAc8va4m+E90SZ9U2HTReY5zizyorQohhID6RM9ijMEMFq0c1+WC1aYxOLnc2K1tTJZraaI9N95AzLIsbdzYtRPQ0XyMaKdJ8wqyOQE3JkH1kWPtr7eWchsnts+FG+PVmDPVnRcKv64MpjNUOdxYiRACRTaK36FY/ErNBWcU42A5L2uPVijsdjvm+dKPg+D+cGRd1xv4M0ojdXtO7z33054qEilGdruRl5cXpmmkxjYS2O/3PD0+8utf/BCdAjVmhnd7Pn71DQ8Pb3k5P3KYRq5h5fM377uwvZJzSyDWWvOzn/+cL774gmWJXdD+Asoyr1d2uzuEssTUIxeKYo6eEFtsxsUH7DByXfztmCrThPt2mG7AKeZ2LDfQugnGcwodZLmusatdb9TiGyJbee6ro1XKNqKUvLJDJYWuuVLUlKC7YTC6awt7Rlk/zlt22429Tbk1GWy9fVKhaMYH0c+BTd/U3k9nYelZV1JSUroBpU8forOR7fzS1NwrgEyP9civ+V2N5WsjyRR71pd4/V4re+xuSSkoa0CYdr2W3l9Zaxsr1n/633wvwdWvff47VRlNntuCoUdHyAlZJfPlhJ1GbHLUSZJCRIsCxkAu+LggACsMgxoQUrKI2B2nTTN6kLZt4rpEwHTJwOP60hgqoxnIUDV6NMznCzEEjJPspjc9eqlFMYxKsPXseO85jBOIdh3dO0tMHlUVMld2g6EaxbKs7LPA2sTBjcgSmdXIJUVe4toEzwEmayiicLAFVwQ/ljuClaxB4NWZIruONUb244QTkSlpLtETACsHbMq4NHNNM4dxx0VqdkVjdLtXiLTgysLHoDlOCSveMOuVj6cri34P8xmRI04XlIavhIZquc4nzuVAMILHdCHVitd3cE2gJUI6LuEZUTOXKLCx4jlzp/bofr8+qgYujzJirMLkQkAyCMcPRkvMmSgTL5fKX5RHbChoV/ithzvuY8FNY3NAl4BSkkNsmljVq1yUagasKwsiF0YtGMeBMEeWArkmjmXo696CNiNhaferCwWnBE42Q1gIqWX99Uw6Iw1egpaVU23VRVVIcglcisX4yjBYJiWZY8S3UEKWIomiRQZRFVU1bVStlUEqriSMsDyuC5FWhSVocoFLlqAVzlrO1aNDZk2ZtQp2xhDizKQP6C5HmGviwzqTgN30QJivzbGcE7Fq5jSzc0PLKlQSx8DZL6zVYzZzW2cXqx6RQvAXH37+3QJX8m/9cd3sltuiso08gFvX1AaORM8S2vKKyhqQQ6NHGzDZgj5N1zM1B0HugGMbTwxa9QP/6goM6TU5vJSCs6YJ50pisg5fM0a9hnsCnV2TLWyuj4dqD9ospZdl9uRvrVtwoLWNJUuiPVcIjVkwQ1vEU95qVGrX8Wxjm/b5jN1pZ7ubopCpuXCY2kgtl9j1TV1DtGbczrYaBSmIPjANFqMl6xKa2Htso9VpmljXlVgqu6klq3vvcVJydzyy+BaX4JeVVDK73Q6RCvNywVrLMLgmTi+ReZ55uLtvOU0W3t7d89WXv+Tt3T3GNGehSHBdF7bgVucM43jgej3x8eNHRGf/pJTsdjuOuz1rykileXmZOT7cc77MlFK4XmfMsGdZEy95QQnDzjjmEIkdAMVNh9RjFNZ1RX4yht7YlyZgj1hr8aEzpKLpxkpti1ZKrYw0047VxvrEGDGilxkLsZkOcb3DStZ2zNXQDAh6yzXrjNlfjmVoICZTSo8pqT37Sr4aE0opt3LkrZuyyhbItukTyZ9kTonXrLPtEUPC9MqdrbVm29zc2KgtJ6s2gf+Wm7UF24aubUMKBmWI5TXsN8Z4u8bTP/x+Ctr/3b/6e3XvRna912wUGrEGptGQxBUVCztrKShUam0LY+qjYmPJFZ7igi7wLCK7JKhuxKZMojIjMUhGZ/HxkaInqhTYqHgpBScLIkPwbdScdxZTFOJlwU7t2ARrmFPgoTgWASZ5Bl0Z7I5YI0MpWCTn6HnsWkmUxKuBMVe00MhUWIRnrxUiRozJiGRJfuZZK75ZKkIpfnNSnNbIqRTeisiiRp7Wiu8aP2k0p/OXBDPyg+GIr80dXGvFMCDiE8JY7mLgxQnUOlAHhV8WSvXowaHlgcA3fJ48Xgxcyp4HF3lr4VjgqyXxg93Ay+VrDuN7/s4xMD3IJtLOd/xguPJytVy95lfLI2fl+HJOFD3wizWwT2fea8t+2rGTnh+9G3mQKwd95ByeSEJwFJCTJeUrh8MOXxJ5jlRlbptmpODRax4/nqmjZUiBD3WgMvNO7TjsWn+eioVg2nW5w1LETPQapRxSFkpcqUqB62GwRRGrwG/GqdyuVaQgIQn9ujwnj64KoRWqB3AqJbiowlQsolQCCUSTQGipUBpiHZijJ9fK7CN7qZnFjj0XtNzxdVkYNcQrBJ1xVfJxnRndG7RJ6AKYJrtYS+ol8KZ1oqaC7sTLFjQdsa0vE4PIheASxySIUpFUgZA4KdlCdUsmyGaSGEohy8KoDKFCKq183BhDSJn/7uf/4rsFrtwf/XHFKPLaaPBt5KGUxjmHEA3A1L67vuGaftPeROJKbBUoIK25pWvHHDFmaDooVUhp04pUQFGLaFU0ncXYspBSrw1oTsWt860J4FNPRA/zcvu6jR6bk8p2u3mhaXOM0mTfGCQpG7pWSqPtRNGSHHsYaQ9ebAGjlRQFWgt8bMBT2UrwBWtMFxEL0rq0agut0G6HqoWhj2C2kdvTy1MDLkJ2pqtATs0+3wGb6SGtwrTewf1+z2m+MPTFVvX3PkeP65qsZVl4OBz7wt12tEtYKH5m3B0gZQ7TyLt3725ifW0ky/mZNWX240SqCXwky+5g62LG4TCSFs/5OpOKbGXRKfF4vnINawPMVfB4OjOOI0oalDLMa8C4ASkUS2j5NhmJtQPLslCV7rU54QakE4LqY9vxixYwujGItRT04HrUhiKsHjXoroELXXPRx3spIWtqWjXoBdHchO30nLJtJC2rI6aF1DcPUlSKbCxFYyw3rUF3h27J7KbHVNR2Q6padr2CvG0gcm4CzqTA1hYNUmkslu6axS3LS6SC6MXZorwyZwApR5AS3YXt9ROtVdvsdCC4OWQVpNVDPweR7XvEBFrDtnn6x//19xJc/Wf/1h9UJSSyNi3ouSqkKpyXyv6oyZeFi8goPSBCY6Duhu0eqMlC47NHCkeQYMJKFBKXDcJqLnlt4bUiY4yCUDDjQIiJFYclk+MCSLzOGJ8Ru7Ex6f0hlSEpUIvggkLJSMgRXTXVCEJaQRokYETbmEQUTmkCiRDndk4uVxCGuSas0Igyo0TG2j3klVEPDIPg3gycnp8xg+GzYaKmiEiNRU60++Aa/O3+KTp7+ma0PBTBz3LgJy+ez8Yd0vpW3zI3GcBnzrLOgsUqbGnVK6faglLf3z/w5csjTzLzls94+/gluMqvVOR37x/4HQ2HO827veCLneI3JwhxQZfC9SVytkfK4pFCcRWtqotrgvHIQRY++Ba2nLgyCUdOFes0L6EwLwuf70ey0NTatZNasayZJTQz0JdL5TNdyEnymCJGCGQG5xzk2LSPulClQpdMDE2vazSUqqBLOZorVCJyF5XjUQguWaOEJtemYXLO8TLPt2qxGDNSi9fIlyqIQrAkgYBbnYwRULWkLImExEtDQLJLnq+lRsTMYCqTtZS18JhX9koiRGUplosqDF23PIkMKSGFwivd9VvNLHPXHeoxlx5dMlKVZPYrIhcW0dgvkyqpEzRZKKC0jR8ShcLJRJKClyKbDrYUstD8jz/9ro0F//A/qphXxir5xgZQmvpfdcfbJi63dmhjNx+6DqmFUErdRnVO6Y58m2it0eICZSy5pBYkai0tar+FjJoOVjaGLOZ0E/EKIW6jxprjTcDbTuw2kpFlEwfLPrKstxGTcbaVEYt2o9jiBgQGHxZkrtjdeIswEEKQgu/jw12DBjeH46umTAiBFJqaVoy0bRdawOnWURdjvNW6dFMhWtkeqJmpKUJJjIcj9/f3LMtCCIHDMCGE4MOHDzwcjkgJ8zyTJY3VOl8xxuAGw+Vy4TjtO2BrYnunJL6sGN1qIBTt331oHYCn04kvPnvHmlZUFRyMIZTMNLSxnI/NPah1cxaeloVCc/C17sLW5ZhiIQO/+OVHjscjS01M44EYAsJY1rV01jNx6XU/tVb8Fp6f4ieOOoHZT5TYgvN8F+/nnDBCkjsVLUVt4XPakEIACdZach8jKu3gL6X6i839Wgp2S9DvuTgt/6zVKEFLuhZ9BNnGjX3HFtrrKZ193UwZdXNOhXZDFNLefm6rRRK5O/6UpPRQvrKG2+dRpKDmgtR9FJlyj44oN10Y9ERlKaGLlkvuWq0tRf6WnLJVLnXH61bj1MHYBq7CP/x+aq7+7d/465WUGayiasmytvNlkaAzIBKxKAoLNWVCzZTUmZo+4jvsH27GnZD7vUlV6KNaZTQxeoQzqGRJKUKcqdohQqKMAhE1UXlEKhQF13DhbrijFIGWleJjG3tPjvXlmaOUTMaBHhhUZM4L8zyzH9qmRteRu7FV6fzY3qFlr/AKC1pAUgMHIXman3gxlXqVHI9HRqO5zDMfVeAYJAdjWZYzTiseFZyWipQr6Ro5vH3L0R1Ry5nfGhxVTDynwKIrd/OZkzXM0XIeNHk5o4XlfL1QheIcA3r3QA5Xnkvm8/0bfAnE3MZ4O3XkM5H4+//OwBfml3w8PWOGkfU68/Z4z2oS5rxiZCIFh3WVKDOXZ4d1nv294LIE/Bw5DG+YfeaSF5SEuEaqm5DrwsrEqBTxZWHhDsaEqxWyIRuQfma1hoyh+CtL2vMn/9fX/PZuz/BWscq2ob4zlqMZWop/7EJzZdFGMlQJwvOUJCpm3uodTzkBiVVV8ip4M+xYS2FRLZpHWMl1TZii8DGitOSpeI7RYVxoVVxJUJVG6MLiIwpNlQkVFecaCcoxyWagWWzkfTIses/s4bJccINisIZ1iUxKsqpIypWpFpSCWDO1SNZaCVXhtEOm0hyFot/3a+u7TVpSe4D4RVmupSCzQJa2br9IgSqRWDNDNURRmWti0AonJCJVVlVZU2UiEaXjf/7J//3dAlf6b/7HtWmp2tcpdtt77kJho7pWoOf2SEcl4tc2KqupYMeR4D3WNYYhh4Dd7dsThoiaHGyBjd1do/sIrgm1Y89m2uGvc2NvSsLR7fd9gWmxDc0lNijDnNeb464JTfuiGpveoaq2mArRrKa1JJYYGMyAc47aIyByaRqmYWyveVmuzXUlFVVoVF/QYgJtJCJHQq5AaGtgd6Dp2opY6bEHk3KwsR9aMF/PDMOAc+Y1GT4mSndwCSWZr2tjAuVrevw0Tczzym63Yxja6Ovp40cGa3n//n2LdOgGhLvjnsPk+Oqrb9rYqe8yX85nag4thPBwRNkGaNewcD/tmU9njp+/QctWv2DcyOPjIyHmbuOPrYE9eMbdkQ8fHrksMz/+zd/gMgcuMWGRpJJ5OV0I2Ha8ejBr6fop0VkppdoY1K8rmK7vbcwAACAASURBVOYWkbKJ3MctpV5ZpNHIrmXKOZNjK0feHw4s8xldG5jedEvNXNHPzV7Y3ZoeWoVQEx/3HbiSXSDeTtVSMiVECB6UgtrGQqIKpFav7QSlRVUoFFk0o0QphZoq2pibm7XWTAqB1yqajDbuZk7YkuuBm2axpNzHf/JVP8UniexIIDXXo5S3TC2RE6KbPhrDRQNZqZVaoyQy19u4Mf/v//33Elz9tc9/WFUBKTXKWQaaC1nnZlyoZCwVQe+D66x5jBFhOtNaaXlpNDd1Ss2JNmqLzAlhNFpJhiwYpmao2SXFqATSJ7LQGJ2oVmOEhJwwpWB1phTJ6IZmec+ZWQgua8Ymw8OdYi8UT4unuMYk76TBK0mpAT9nfv7yxJv7He8wVK1RNaGkodgdbl7xIuBqQtk7rtEz5pbIrZRiEYIUV/aHgedLZskVaSTfXM780I3sneLke+CwtKhcWaXkcRWcZcbnSpCFUeyJJqOT5+X8AbRhEQdqChxNwkjHWWamxTPI1jbhvackRxZXrDlQgEOAX9mEK3t+l8KvDYK/9+/f408LVIX3J4QcmLPgmBLnxfPn58rDzvDg7vnw5Qt6NDzlzDV6fikK4Vr4yfmKzZKvgiVWyXvheWcFyox8iPBGJD5/uMM6wV+cr2hh0QJ+sQj+2eMLP9WatJxxofDwcMeD9yxkcq5ctOLHciKXmcU5lhz5Khm0dMjqqSHhpWjTlhoJpsXoHKsiFCiDI9bMmiJThMQVpRw+rjgreBNW/urdZxQkP9gdcNFjrEQnSRgkInoetWaSgodUedaZvW8bUqUhK0GKlbVIVI6ARKhCrqp1wNZWau+TaNo6JEI15tYLBdqiez/p2iU+sVQSlQDkUgkURjGgcqSIQpWGQQkuybPUwj2apCQf4oy0IzobRPH8rz/9l98tcGX+6D/81otIyb/qRPpoAy2gGISUDPsdOSfKlggt2+LplwWp9U38Ta+c0VaTVo+2fbTTd9QhrC1xXGti7DZy1RYEqZrwU4tGy286YFkq1+uV/e7Yxo6mLchStxTwFJpIdb8/dF0LncFqfYElZUJpdmOAIrdj2fVktbEp07hvaelTd4jESIkRWQq1V9egLTG3DCatXi3xenAI0QX3gDCNOTFGYvrir7RAa92E3dbeRPo+Bs5LE7CrTyz/jZUTDMPAOI5cLhfmdWEcHMVH3r17d9OApeg5jJbT+dKA2dAWc1lhGgxPz888HI7sd46XlxdqrVz9ytu3nyFL5udf/oz3b96y+Mh+vyeHpgP76vTIm/c/YDmdSaWxNx+eHjnuJk6XBZSlSJoebfYI5XDO8Xi5sCwL3MTUAu89u92hgSvvqf0AW9ucmGVdG0uj2v+UeHXUGaXJtS0GMbdzovZQ0K1VQHQdX+3htMHnBiY7UMzJ9xNKtOcWpeuRRGMSehq9zwEVC0K1GJIaIsoYjDBkUW/hs8TUkuPFK0vWXnD+VpwCNVMKtyiIxqzp26h6A1A3jVUX16cYUVukSUmtf0WYmztyG+dSCqVnfwkMolZqbSPQVJrrKNfmiK3/5PsJrv7u7/9eDRVUrjg7EWs3kwiJsoocV5SQOOfw/gWtBrSQLCnguk5O1cKuA62ltgy/pTTxMNW265uK4oo1mugNmNhHz4m9FlAVoxQ8hYISBlUCKytv7IQ2BV8U35wSqzGUBN+kwkDk6jPXeeZ+57iXilVbVt2YGKMzh909a3zhrrZi4L1O1Jw4DhMfronBFL48XcAojoNmDSMLHhlnDsOegRltHD9dLT7F1h2nm27m4zJTQ8XsHNd1gSqJNWPUQC2SpAdquUDWrd6nBH774cgXo0ZXz/UlM+eZZypVGe6F5ddHx+pf+OZyZjBHPpRn/trdA9ek+WVs2tQdlq/yjK6aXGbSWrC1MhfP++B5Gd7yvF54/+6ej+cFqx2ItuEspXCKkSVHpmFE5DYCuxt3XP3KKgo7UxEenJIYa7GmbVTfSAmxIgfH15dnJmlYlOBSKqsPLD1XcA0eXZtr91e1Un2LMoj9XiClJGqBSD03smy1Xi3A86wL7+2RtSZM2RzyiSQ1OsN+aBs6UysMBl0V1xQIXb6QAJsqtiY+Hwd+827HEK84PTGYJheIyXNJBqlamHKTTCiWonCiFTULIXhJmaM2PFOZOjMVFShEy2OUmioErmYSDRAqKklUkpDMofBYPEnvWltBLsSaOSjBzip8j3EwRTB3p6TJBasF/+2ff8c0V+IP/r0qjKH2/CYpuj6lZEq+oIQDq6mzB6VveRhS2RZ/ENuoLqwrwrQbupSV2me4BdBSoCToYWxJrp8sHKUUdLeV6w7IjLM99yh2ZqnFB/jUFgojmmW96KZPumVPzWt/XkmMHq0be7J2nZcSTYNVkmdyA6uPlJSQzkIX0xezQ6cW05D64htCs8WXKqHkG1NhdXOQaNoYsgq6yL2LiHMr2cyiBaC63utXUhuNWmu5nF4AmHaWWjIxbY63zJv7e54fnxgGy1ZPpHvWWFhXfuOzL/izL3/awFut3N3vbmDX0MaJdrcjhMD7z94gUuFyufD8/MwPvniPEILDOPD8/MjbuwM/+/obhDP4ZeX9m7d89fLEu+nQGEat2e3v+earX4GWyA5QX+JKDJXFrxg7oKUFIbimxDTtOT1fiGkl5DamCCk1dqg0nVqRAgnExSOtIvt40wuVzgSJ+v+S9+a+tmVbmtdvdqvZzWluE81ryMqmSiRCYEBKiEIISAQIDyetkkoqCxMDj38AISFVYQMmCAxwkRDCRBSoJEAYQJayifdevLg3zj3N3nutNduBMeY+N9LmSaRebCfi6Nx7z9l7rTXnmGN83+/TwiC2BFTsFfrp9eEc/fia5Yd3UDac/2x8sMa/apSuRQ3wmuu3lX5PlwTzjrZGpJsnUlINmYhQqFrApvpXXKF5W3VcaD9rpawd0EmeagiDM69k/+t4MOcMrfaQ7y52L1X/X3pGYQeeXjvKr8UU0HpXSgx4o2yv4DTKKmctIK/ohr8a7txI//DHqbn6+3/8z0kxlhQvgCfW+uoIjlvmZjcz9XWtAbUajCTFkIyqL51swL7ez5kUGyejXdl2rjwYy5MUdtXwfj+SouMweBZpfZMR1jUz7zw1X9gFgw07/vThkdYaPzvcMHpYonCcHC1WklO+WSSxs4FmB5qHuUZagmGE2+ORJRcGO/Fn5ydgx7IsBDeQaBxzZLy9I5czNhZ+ftswW8HvRgYfCIMwG8c/+jbyITZ8bqrd8hOLqRyL5ZwLixFizpRhpFp43iKGwO+bxu/OwmYd8aL5c++C5ZRgZaQ54euDJ6bGYirpsjIFT/KBt0YIOfH2yy/45vzCsBam/Q1SInMQ5th4KBecnYipMDnL7xxHpnHg+bLy7hj4x59OPJXCP/XuPb96edJg7S3x9uYdqayYKnxbInPuxTOZlcpkBs4pE43qm0y8AnwF+uc/jSNrTqxxoRWD2c88b4knOzA2w69tZN8ct75yZx2pFTwqtZj9wKUmtqxSliZX/A8EMdjSOHkFdZ6McDEDYkZajVgCE5l3wN1uT/GGqSZ1hPfkkWIGkmmUpXCZBs7bRto2/uW3k+JJraVkSxscc95e+ZAnC4fsaK6xNtPNWWCa4cUIs1Md8F4U4XKxlkvNNCx7pzKb1GBrTbMvTcBWWGrm4AcoibMDW4UlQBbDoQnRKE6ihc4BbJpz+F//5Z/9dhVX7m//iVzjYV6hhM6Ra9KMItvYTheNK/EBrMY2mJa7NZxOpe6jHBzOqThv2zakNQavuivrAiWvuon0PDjgVW/VqhY4hq7BajoqCcOOEALnky4+x+OtalOMU81Ni6SUGMbD65gx56g6FWCY5lekgRiNBBitf+VsGTvgnXCJmp21NekifVG9VhhV42U1JPjqJlMHbMH78CrOrrVS4obxnt08dl1YIF7Orxvc8fC5KKU7vKZp4uZ44Pz8wv39PQ+PL8RlZXfckfKKBkk3Bqcog91uZlkWvrx/q53Amph3ASnqcJyc4/b2FtOLse8fP3G3P3J7s+N0OrGbtICdxx3LcubNbubD4zPjFHj/9h3LtnJzOCIGPn36RFw3xHnu37zjtFz4xS8/8O7dO779+EHb1ilxiQk/7ci1qEMwV/a7G7Z4IZfPjhPn/Ct7ygWPqQ0n2oZ2aNRSyRnb77Xcsmam4ZCWwThoDTd4HdVZRSpooSGYLgAV6TFOV5jnDxAj10LjhyO6KgWyct6ETFvP2kG7ugmvTC1z7UxddVBWgafY7mbUMNdWMzUlCF67SGnTcWPpGYa10vkNXOfyzo+fyfFGQbg/dBda4fXeuaJQxJrXTMFWItYEXjMJ+zP9w7XGOUf+h//Vj7K4+g//pX9WliJgBj59+sQYPLtppBih5UYYroHtI8VVDiawbBcYhBoDdqwMzYGtRBFs0wDuWgVrHLuxpz40eEwLRybYe2pqlF7w1wxzsFjrSWnBtplL3LDTjsvlGesngrF4n7TrOgYYPXlL7Jribb46jsrGmmZens/sJ89sZ3LayKUwzzNZoGwrzwg2HHiJinh42RaCiXxzLjw2oY037Fh0Iy2WaZq4K5mTD0g7UxukGjgZQ6FxFs2HuxGPp/JuNMzWMnrPcQqca0JKRWLkJ7dHllSZxPLFXrtpj6cXXPaYnWM/BI7OsdSFsTWOw8Tj5cTx8EbTC1oihCMlb/i6cpwm3g6Wzd/A6ZdszfPm3Z6nTQ/apY/ZPz09c9xPvB8r0QQOu4HLaaHWzFYdrS6Mdo8Lhg1HTkrMn+eZaBLnzTHbjVtvWcsO61dlHzKSYmNrlcNhzwOVl+dFo41y4f7mFlOaugA7jsgYxesM9k7xHGOhoEiMgxuYwoDpkpKLTPyfyfLLtHC0dwzmxNvRQb5gasObgVGUuv5chQ1DGm/5xfYdc56w08Db2hBxBNcoPrN1yUF0niEawpYRo8VNFU3+2Buhei0sN3vQjv4wkKqhNaFJxjg1CSUaa1b8hGuwhoHmIJ8XtqqHusH3JJWqlHa6ZhsTWJpyJ+m1AE0xO//Xw28ZiuH4r/89aa2x5JU5DHjrsE4XFMmCuM8OkRR1ZDj2uBTbidRhGLR7s274UbsKtuuOrgWON7rpXLP+rLVcLirOvrrq5snz9PTEMO5fx3m5VaTqaMqgjKkhuE7sVkFwiUlF9UbHbVSjYxuBJSkfRscrG7EI72/vecwred1UexUzBaFZ9xrjA2BrpPRTRowRalFdmTF4DL5VxoMWJ8rL6g943HonQ7sjqSlkT7Lqqe6Pd+RSuGwr437HPM+M40hwjuAUevn9y0UF+YM6voYrBiIMrOvK2jMBa3+Am7G0lnh7vAfg7ubAft7RcmK/3xNjZDeFTuB3PD4+dsGzbg6pF5S//zs/47zl1yKynjb+7OHXfPH2S4ZhZqWntS+R+zdvOK2Jl5cXvvzya57OCy+nE3d3d5zWTd97KqTSqM53gKrh+6eTcqDlM2YgpX4Ne7GjQcuDOvB8Q5qj5JVqPLZlvJs0ZLQ1Uuldq15E+XGgph8YCrpY/FoUX0d3V0MGFGgW565FU9eFjT2upgcxD1gSDXeNWSorBk9tajl+jaR57WD1JAEjmFiQ6UhtmVJXgh+p9XPw8jWiJvTrYREdF3feVSmqzSPo82RaR0XgEasBqqWbKACaaFC17SNSU5t2VLs4Nf+v/82Psrj69//on5GMw3T8SqyNafAYaZwvkXEKiNWxkhHYSmLvD6xlZQ6WmAZyuzC5wIe4cOv0YCVYFl5TiDQ1oqpkACrTPHB6Ud2kd3CKQltXhsGS14Sxlt3o2TvL9ylTrKJfovRuZW7kMOBGz1CE2hJmDCyraFi7eJ62C9nPNKfrYN0StmmU0hIF67QwH8Kk6RM2IQM8RcOxG3o2Ue6eNQqzfDtPfLUPeJO4H0cGLGPVqCgRwdOYhsAuWGYCdXBMrXHJEUnCpUVGUxmN4f7NDlPB5MpqgAzHm4FxCNiYuDwvDDNYUwh+h/cebxqDNYwOgkA2C1uspFNmOmhM2rY49ruO1OnO3SFY4qWw2Im1OgKFKMKArhneZRqGLc5EK7RmScYQV8diNz4+RWZZeIyWMhp+71Yfl93kCH7m66qA7NIazo0k9GfXrSDjjlwatTusc82s68qfLaoRbsFjx8Dv3O35OR6a4O2FUIQt9+zdJiQHmcx+d0MyCl31p4FoHCsV0yxPpvHty8J7J9QqzC7gJGPcQGwZM0yaHRgS3gxMeHLeqMXih4Bn1YLPCNUJITkWKYwFSss0cXg3a3GF07BxyTQbmMQqO9JZqgFbmh5ujcGi63jzHmPbKwNQmmVzRiO+ulZ6Q7EU/+D//ovf2Jrkf1P/0P+X1zWPzWOoWyJ2W6idd4QpU7akG4EY9ocDNW9ITVgDrVSOw043eyv4Lqr2XhcrFefqyM9Oe6QjE3wX6XrvO1YgdcGydiWurr28nRVxMMwsl4153jOOMyltxFSYpwnrFeiYc6bkVd+LVU3M5CdMLbSWyXnDGkNKmQ8Pv2YaRs0GLBq9orPwgkGt6z/sbLSmLI5wOOopdb3QrCPvBi4lERpY18OorSXs9+x2O06PT+z3N4RWcMYSghY+c/CscePo30B3Ih3HwPPjE4sx7KaJu4N2ptanC/Nhz/FGC6SbmyMxbnz9xXt2ux0vz8+96we1RUoX+X//lFWHVhKxFT5+euB3f/5TGo2Xxye2TQvL0/MLIWi36k//9E85n+8wYSKdLozjyNN64SdffMXp8qKjsNPCm3fvebY6sl1eTpja+OYXf4ZY1Ss8PnwgZi0YTtuZ3eGGeXfD8vLEh5cnwrhjKxnfC+srK0q1eEpwHqaJ0sfHsY8dRCp+milbJIwD5865Imcl+vvxdUStRH/zyjZrrdGy/nkTevFkRv2eNDCa0t5KUdC6MeQYsZ0fNY4jsRacWA18thZjg4qFadguBLU4jLF4ozmKtVbM6LFN0w2kFnyYAIcPyuq63u/OOVJJuOA107IVmtHf39mAd03pyDEiom5CkYwxlrJmKAVCUP6V8TinxaVYS6kFYyvW/FVO3I/tdc6GTRJTdUDVDlYWdljC/sDpckHGgFRDwnDehGwW5mmP3bQLONaZhyoIB06jstqC2QGFD1FFutsKhzCyJf2sp6q6pKeHhbU2JbuXxp4jzs4axbQ1SttR8gnnHJuNpFgwRghjQFKmro1cz1TjiM9njYIyDY8j1JFqNuYKkUijEBoMVkn9wsr72fFubgSz443ZYWVjfOOYjefWC7LeIiRFrxjB1A1M5s1+h03CMDrCuOd8yaRwoVbYniw38wFTF6QJx9Gw94ZpOGD3jdYcUg60DLVtHOaJnzrV806DsK3P3OwPtLuvyXHh7vAITXj49MTd3ZHDPHLeFh2h7d/jwjMlHXEy8vzhhdvbA98+feI47vnfv3lmxTD6iMmFhMJUoz8zzwNuGGiDo7qRIUb8uDGWZ3bHPQMW2Qu1Hog3hpdyQzOWmldseMvT04nL8wcGu1HuR9wgjDbgBsP7yWMkYpNhNCu1ZtZywDtYnJDbnn+6zqRWGGQgNaGJpZSN3AyXpAaVEBxbzvgAM41BoMUVV4QmjWojQ27MzpGKMBv4yc7RmmAHATZdyyT2w9eCGKGIw5CwbaVYg5kCmFV1gsMAKIRZBoOvYIKhOk81DlsNMIA1HLAs2VLE0VoljCMtZ6RBDRZs79IbIXtHwNFQM1BB10XToPQ4M2kVI0JLv9mz3l+LztXtv/Z35RxXze3rDjtrLb4aYjsjZgSjYr1SEiVG/DhSU8T1ypzW8IN2qYZB9SnbtkGtTIdbrrTr3W7Hup61GMCpK815hkk3rnmYdVGJF4ytLJdEmEaCVY5RLJkggrEjuRa2ZeHNmzcs24W0boTxapFXV9gwTDpC6ZRqbxw1F7A9188Ix+ORy1qYEJ7jGWxgnvcqAi9FI3+60636wPFwz2m5MGNIdeFgDUvPJgxDZ1TNe3JaSWljP0zUYtkkY8rKbrcjOMtsHJeamXYzALVoJE8BdkHf3yWf8NWzPxxYXp4x3uH9wOVyYW8dS9E2tncO55Uf1WpmNI6X8zMiwtdffcmSIul04vb2lvubI4+Pjzw8fOR4PDLNg4ZK95Pr87JwPB45zBPH473mDD4+8vTwjJsGlmXj9vYWK43vH5/4+c9/zqfHF56WM3e37zidzjw8P7HfH7XblwU3BJbcXgGsqWRaT0PHONwQFH1hHCUmpv2uu7MGbFMashQF3E3eqmBdINPI0nBNMwAtjZxWrBu0ODENaZ0W3Swpp+4ClFeXnf3Bf69awFewp1gkZehFnNnPSK6fR3B5e2VQXQnydKAjIgzD+NqpbaLuwSaNad4rJT5uVK+YhrolHVM7r67O2kWv9ppH2MOfO7sL392mSTuc16gfTGe85RU37PB9EbM9sNlhSa2S/+cfJ6H9D7/6PYk0DmYi5RV8wjRHEY/zrUsc9kguIAU3DgxALQY/670ybZHQ0x0mq4iS3U5dnG+LoWA4iWNnDdHo2lMlE9rAc43czo5zbdxjuWHSOJCYOe4ybvLcDzPPz88MfiIcBnyKmGIxNpIukd3BcWoBO3h244ArmWgKx6Ymn+esoNHZGIqFeR4pWySVghuOEF+Q6JCxMjqn3hExpNKYzICxjVwqBxuoYcWZHfN8AdMIwWJpzGVhKbNyBrdICIYDA6lVNhQvgBVG1zEsboc3mbwZPI3jMBB8ZLEjNWU+/KrwfLJs1VG3X/PVz0b2o+Xu/pZLXRj2Mze3I19PC6u3fPftE2OduX8r0HZM5plqj7R2JriZFD3FGxhXbGlM3pMWQRipacfjU+KlJtJLZLAju13EuBtyzpzbjMRMbpU8TIxWaHLhxhRSKaQFUjMgI7XBsDsQ6xPeDBAtF69BxrsoxKHgTEBCpEbLmhMpO5q1mM6AquKQHl7fguKITAFPRWxgK4Um6pyvXUgfQsCVpq7DqxzBiI605bMMwJgrgLh37MVRGpqnZQqxOMRALaLTpZJIP3AgV+O5Rm0NJpCCApSLOLKobhaA3knPXjAiBBmITcPqGxp316zXMaKtDO2KmBHEFmox/P0/+y0bC87/xt+TdF50Rp/zK2vKGqGIwYUA1hDPG85/toQrjt8werrgdlVnTdBNKm65Cz5j13GF1+BiYwypJs3K6mC1awip1Iqb9trqzsqM2bYL025HLJlxy4w3M34YSKUHQKK4iFSyvg8jnM9nhlEXNnMVIAvI6GkdtmQ6uHMDnIcdBhsmoOe+NeG8rVSnN3O6XBjCSDYaZ0MxFCnc3d1pVmH//NYYmadAFoWDTs7xuDzy9njfacrgUoV5eOWK7QZdrNO6MblAounnOzh+/fCRr95/yXcfPxKcZbfbUZcFO6koPp4XKsI8z+ybOhb9Xket+93M6dMTJUfu7+85Hvd8+vSJr/ZHjDHM86g6kM4Ay0viw4cPPLeVFBv73cDj4yP74w1ZGm/v36lLao2M045ihDDMPDw+Ap797sB3Dx+5vblnt9vx3YdvWWMkDLvPo+BppJWN0sCFCZHAtiyqDwuelhO7/Z5t7cJ3I11ED8FbLlaLqykJfh6pMbKVjHMeSqI5dWM1cQzBUGMkW8VSXF/XAupK7q+1vnZ0PicVGCTX10BwAawY2jWFoEdAVVFBtHFDN3HouHOyelAB5Q/VqgiH0XnWbcFMHkkC0jBB8RwmF4xkroHbIrnr+Ro5JZzvv0vLna01vzoMpV3BDmBMxbkRMaY/d2q+sKI28PIj1Vz9J//W70vJMDMwjHqdc2qcmyC2Uos6swYnDKO6/qy/oZVnTAgKkAS2eCKEkWHwPD2u7OYDrcE86npnydze6PXPsbBl8PkTu8Mt2Qg5C7SRlyWyP1Te3+75dBa+/5R5f6d/5idHz5oDLW2krfDSzriihddpPfHzn/8BD99/zzgZzmfPIPDTv3HgwzdPhLHwaTHs5wHvva4pKXE/jxi3EYLFuz3iTow+sN9PPK8bg0yMTkHISxLmncVKJKXIuKk8YxkKZX3WYPRkccXiRJj3A9TIWSZcgyE4wgCSE5PPhAGoEDraJgRLKroHDn6vuhwn4DfwavbAVgiDumGdHl6a6JppLzcUc8KaCduArOkNrXpcaaSYKXUkZyEx4OzU3edWGdipm6rMRScYZiKXhhmPbFF1mJ8ulWH0JJQmLhJwXjACl1hoYhmdp5TcOYGFNet6sPOK6jB4Mk0zHI2n5J5GIZBao1bBWwemMogl2891QSkJpE9nmsYmlX4Q9Biad6y9O+rkM2AYNJBexGB8B3mKQsCbAVPB9lGiRsr/VT2qiGjWr+FVx0a1NFd7aoQjtc+SC5oqXa+Si2YaSKBJUTyMXLl8vE4krGhQ+fWA+x//+a9+u4qr4x//XakNGj0MOepijtXNIxIJ7obl8sQwjWQRpp7zV85nKNqh4vbIUBopJhXrJsHMHtu7EljPm9sb1jVivGOeR2JqnC8XRh+IThh9oMaNIUw060nrE7e3tzw9PWl+XHC0mjmMe87nM6E1ijMYW2iXi3adBk+0I3Y3YrsLqFZDSSthnMhiMXl9ZS9571+hpGY3YOiRKN4jrWt1akOCwzvzGuqcUsKPE5MLxKwaq6sDMG3d1ThoxuHQsxNNLRjnGeaBQQwXKUjUQGDvPc+fHtntJ1IsHMZAtMJh3rFJ5X530BFSVq3Q8+mJaZoI3nOYAmB5enrib/7B77Jtm44qsDxdTqw1s+86t3memaYJWuHjy5PGxGyRm/fv1QJ8XrhcLnz905/w8PF7xnni+fmZf+JnP8cYw4enF0breXk585I3fnr3jm+++8Dd8cDa4LIu5NLw04Q0/Qwua6Q2y+6gWjqaMOOJtXDOG8Og+jmaUHLEDZNGQtBPNgb8MNPyouLH1MBHLHt9SAOvmiuNq+nXzXqd9bdGLTo2C+NI6x0KoVPPr2Oy0hlvtn9tPHQdr3vitwAAIABJREFUX9mWnl3ZaKUwzvMrXLJ1llgQS7KCyQUx9jXtYBpGvQ9G10eF7bWgMz1eyohqbK4sLVcjdV1wxyOIe+2K2WvEjm2Im/H9dzW1YTpuRKhgQYzH19Lv59C7Mqozy//ov/1RFlf/4N/8PTEEbgbPbj8y3lecDayXFyYHwc9YI9weZ8ruxPbc2Jqy1oiuSxqE83mlFsNuBGkea8EPFXwX9LIjDxabLqSt0tqMCxd2covZVdUvbi8YP+FsxkWjcVh1weYjed1w40RLK5lC9hBWPYS21si1UUxiZw5YV/BDoLgTYXwLq8UPGVMGgm2INCYXKNYwIp0wP2Bdo7VFXa4IQQqtTYx2Vl0NhVhXzGAwzRBy6ZzBI2yFp4vHuQtvJ4c3mmfnaWSbqRamMPTw54IPgh8qtYATZUJNk8ej04YwFuxU1DzibDeLqB5SRA8ujA55hf8CL3u2UyQn+PDNC2nJ5CQYJvzBMO8sb94m5vlIXQqXc0Ss45Qd6yLUqqLri3QHczb4bClToVSVmowo0iccVbwdi6fUDc8MfsRQlAPZs1hTPtOcTkxqG9lKotRBtUlSupSgZ6c2Q2qGcdJ81CYZaRqfVG17fc6dHVhKIVXPGOxr3uiTZGy2uA7btq1yjesSEQZTwXlyKSA9DcI4qjQSWlxdiy1Xf/iU6LoWBaoBI50X2cxfKa4KonmCIv33F7rkUMsx8dQW+9rVA+UNYPs1t0aLzn7Q/Y/+8Te/XcWV/xf/RHwYEdPjMzq23nqDDB6z6QctRWGJdhheO04FhUCGEBhsBTthuwC31opp2gWLMXI7er4/r+x3t5y3BW8au/0tWMtyPmtnaDkhreDmPfjA9nLm7v6eYCBhqLmQy6YhzttGXs5M847RGC6XC9ObtwRreHx5BsC4z9quVjQNyQ4zdMgk9CiQvpc6py4x14GnKUYtphCcGP377rM42gaL5IKRRkmJeb9/7eqBtmRzzp8hpzlxuLkFB6HBZgWTlCVz2ha2krk73uL9wGQNl5Y5zjuO046PD99zPr8g1jFNE+l85ng8gjV8+faOj9/8mi+++IJzWfn06RM/+cnXPRtxYw4DOel1vL3ZcT6fqdI0OqdUjSAwWlyflxOXy4XnVrg7HPn9r37G999/z3lbeXx85P7Lr5n9ACFw+fTMX/zyG45v37PGhf1eC+FcGvd3Nzg7YJxhSQVpSvg1xrBdFobDQWNm1qjxOS6wbCdy2nryulV9UK0cj3tl2lo1BuRswEYdD2e9lophCKqZ6iNoTNUFMVfVWXU2W23m1XH3w6gZ6dqnWotqra6R5q1B7SdpUa1UrcrOEhFk216L6ubAmYLYgVYaYRzJaflBEac4D2uvrXxotTINI6VAqcI0z6RcenfKYE2gtqogVOnsMwRbE60z2qzNNOsoseCc0HLBvJpK5HMEUEd1bP/Tf/mjLK7+l//gXxFlDDlq3DhvF4ZhYN55cm6vY9wUCzGtYPW61qr6rDd3M9MuUKrGuyxrwdjWOXfKGlP3qcHYSlsL+2lmKQu7OejBRrxqVIhsa0U2g+TCuesgDzc77ZC1tXccG+sFTudn9uOBc1w5HtX129rEMFWGsCdzzY5Ux7Drm1atFSdg8DhvsOYaJ/aDODCjaQJSdBM0uSJk3cgpvSi6Zs41BgvzpB2oeewst7YgFNKm6984jlqUBWF0YE3D9SgpmsFQXp85yoYJe+1U2QquIYNG7jivbmCNj+rtLxFaSdh487ljWwKlVExziK+IgF8CWzIsCKVotyirGpMSdax2NYboO9WDjGBIWUjG4DD4vkZohuhArgXpj3OkQdXCO9akDt7mkNI/5yt2xVlqMTrIMwEJmZhMzzc1r1DhKjpaM8aQ3bWDrV2oa7hzE0sNE5dUiRt4mhZ9De7vBuVQRqhFu0MFwZjAKvU1bkc5e7qeDD3lZGs9axBlWFYDuaqWOBeF6rYhQCpkY6ipMk8Dz1IZqhZqpdVX5p+v+v5dq3ij0OaCGs62Ls9pWe/T/+ybX/52FVfDv/DviLbywPXxVxalOae8UEXw04TpSe2vp3tnwY+EMGJbJG6JcRgRpy6Bz61JjxcLJmGcx5uRUhdyKdzMB5aWMVmdaa0LhK3vm5dTGOc8jyzLws3hlqfnT9zdvmEcRx63BS+G9we1Mpdq1Ap8Vl1XmA4UGmI85JXYwBnYSmY/HDltzxymmcvlgtvNn10TKAIgWGFZFqXL54i/SnHCiGuQRMeNLWamaWIzDScgvZPVrGMcR/Llwm634+c//YLvfv1rxmnGCOQYiUVty2vO7MNITisOw6U29vs9TTLn85l80SLEjiMxRt4djrxsC6YJt29uiYvaWj99+Jb55sCbmxuO+5HdMPH9+VkLx1QoVWn0palz6Iu7O+7v7ylWvz6/PPHVV1+xnC98+/ETX715w1YSNzc3XC4Xht2ed+/esayVp5dnEnrq+/r+C57OJ5Y18+nTJ25v77DWcrqcWZaFnKr+G8tJR7XjpMDQGBVlIMA4a54gAt4xWBWcx1ZedQiqG1IA3yX2PLY+ZnDGkqrQYsQNA94Z4poxg2Gynma1AyVdB6NhzA2xvmMWVKguJaKgWY9pGUOhBdW2iQ0Y0c6T6R3Oy/JMwxJ6dE5ZTvqMYDHOEZx2Q6Wq5suI3u/ee2oxGFrXRlSMDYQwd5SI6FjBGK3rcu8SGwNOmWqpdW1F0UXSm/o62r/SxAF8d+9cMQ/yf/x3P8ri6r//d/95GccZYy2n00lP3GWjNYs1CcNAJHed3IUh3DKMurnNcye39+CIGCO5FoZRNSv7aabmSKuWGjd2O3UpezF8/PDCADRvmY5aWOMVwjjQqAVyWlkuK8MYmIOlZEOtwjAMjLPibFqPAKtSQLx2ekyltYHY79smYCxID9oN1wLc6WbmRTu6WIPkiMXw7V9+y9dff4U1vRBrrrttM5iCK4ZplO6CDngneGuQziKcfFBorhVSx9cYHHXU4N9ZGsFBJWOdQHFYqmogHTRZwYL1Am4DmXSxRtRSa4qeSKMDucaJFKhaeFGg1QEjWSO6mlCLI9dAc4WYPE08tjpO5fpMRS2WOt5kcjOXmDSrrxmWJlQ/dR1vxXuguVcpiTGWZdXwZ7FaiPrWFGzcHFKuqSa9kBkd0lSInnOllEbtXaFkelyRZKwJGCufD30i2ObJ4qgSsR0NU5pm8sWkI8zRg22FhmHAkmujho54oQvIrZBaQ6TiiqO6hOBRtrBQ+ygRIAtUC61oAbp1x1+kYc2AVOVBGmkY42jOEE3BZ0ezaAZwqxRsL751snA2Oo2Yeo/Dob/jf/rtb1nnyv7Rvy3OOaRGak14O3UMj8cHDblVjYd+AK1q5iDSwAUGp8neJgyaKxgTOSXwIz4EpqDzZqkFejaXNIfxDRML/jCzLYoHkFTwfkC6Xb42bbHf7JT2fVkXLknwtcebBKctyhBozjC1yk0Y+ZQXpmliWRaM3ZFfTty8ec/ldGb00ESwkgm7Nwzesy0rL2ljHiekad7SPM+qRaqVYh273qVY1xUbHK7Bcn5Uu7vvrU0JGAHbm6NX3Yy36iiabm7YjxPLcuaw2+OMIdXM4Dxpi3x6+gSSscOAMaFjJbSwuLm5e4UdWmt5d9zz7cNHShXe7GfOqfD27VvMtpLPC89kJu/Z24AXw0s7MQelxx+PR6ZJAasPDw88PDzws6++1uDQp0+cTifyGrm5u+dwPOKmgZt5z+3tLc/LWb9fHJd14Ztf/JI3t3c8pcr5cuHNmz2/+tWv2H/xE7ZtwxoF6R2mUfVwqapDtMcgzfOsIu5c2aKOFAE8BrGVGCO26wWmQd2Al2Uj2Ebt5PMrvsFbR0GjesIwkEvEDztAT1PkjWBGijOvjLMfLmDQNQNSgP75l4gBnKjhQHD6fRHtfYsoMNQ6lYx4T00bZp6RrBZ7wfRiS1lbkhRp4oMWUkMweq+aSjUDrRqcKcqKW1f9ezhKTVhR52rrVVPuiQo0BYpeExI+i/I7VqSPvqv8uIur//Hf+yPxfqBk/fysA9MK4+wp/ZCH7RmkZs8WF7ZtASzeVayZSGmjZU1MqFVHLpiItMY8BGoVDnvVZl2RIM6q+NcIuN4JMM7x6eVMjSv7g3Z5rBmotjA6y3yrSAipE4VN182u/0tZR2wVQ6uGJlfECDRnMKZnVAKmM/LmDrWtsZB7EkRKCYdhdDNQAAulMo+W4HQEZrpO0DvdZEspDFYBtRjVCI7OE4IHU1QwLcqi8+ga1mxmsF3sQ8HUAW8FXCIEA8FgvaWfoJT/5tCvXf970iAHqP25bWBLL7QySBm6XtFhstCKIzcNTcYEovICiLnz7ejaStH1I8VMrAqFFjGKWWi67qemheR1KlGrkvlLqxSx7JxDciHSFJNgAk20K1W76NtU7ZjVns1qjCPTn9fQr03t17XY/nXiFbiNIM2T7Wd3dRWVGih3sTJPgSrCU428c3vitdnRCyNDIzUtolyxxNq0k+Z7Jmsz1K6xeuVCNp1qmA5N1jzT8LrOrNVQaiWVzIJwsobQtZ2CIQtk0Z9Fm1hFZUjXtdd0jdn/8Bssrv5aoBju7r4i5YWUApUM8wFPn5kaQ7MVyRtNKkMYSCVCKZj5pi8QM14ahobUSosXsCpALE3BnFIKBE+wnnG05KzJ20mK5riVyG7csZRKyydylT6uqziriexPU9CFZ4242Xcom8PNOy2Wulj6CcFXR1oLO3vkZb0gVPL2gg2OYd5RSqI1T5TGkjbc6NlNR2ZvkQViq1zSxjBOFGm4yZGXheBHdqPDWs+WIuFwizdwc/eeuL5weXnGOsvucMeyLLimIse8naFZZuegVQ7TxG4aewr6RowL1Qh+dNzv3/Hu3TteLi+KQjhfmAeLCyN5qUDj5uaGpTTe3L3n48fvqMaznz3bcoGa2N/u+dkw8PHjR17WJ6abA756bt7ecF5OFFd4OSt49eV8orbKw8sn3BB4eDlzd3dHnCK/XBbeDp6Rxv/25/8PT58eeHv3nv3ull989y3TWjm8ueFwd2R7WfjiyzuMMXz19j1P5wj7Gx7OJ0I1xHVhOZ/YH284fX/CjIEtasFinT6orkWcVEyYNOy2KmhR6CeePn4zw4AJgaE742pT4KjgYFvRJrnHuEZLKy3scH7EX92t1WD8QCuFYIXau6yGroMyGr4ttanLL3hyNfpzjcE3p5on37tBFsYKmzNUqfhxj7WOOnUIatIg7etmdtjtX/ELGMOWBXGe2iyuGgZjlRy+RUY74DEUST1VUBmkrelJV9lehWA1TLUajyCIVLjqH+0A3hDGEWrkr8Oh7v+v1+UUmWdLvHTmmVMDycu28eX7N5zPZ9azgMmEm8juMLM7zLhZNYHSDNQdadHNYVkq1jaonkbl08czwVhM0SLYkpEaWFpmjRf2+z1z147GZWUcB45vjkxDJJmry2vA+UKrGWM84rI2cJrFmZFSV6xVZ2lLDdMaDmhidA3e4muuKOJpLWKMsEmhpcA4GYxpWDcwOnVoD8OAN40mmdA6VEqEwTtqzQryJXQNUIBQsFScsxh8T+GoSPNU0yg1Y6pns5HZG4xUnBtwxmCdMsCcNMwrtNVCaYjXUGt2BbERnGCu/LnmKS5h64C1g47pnUB1Omrt+kzbD2tV9FC1lazhyMAWhRwdLmzATCwrzgSiaRhjO0xYuFTLEAolFcIQECk4O7GlhnEJkwdEAlkUwxNbQZyo2Nw6EEutQnCjGnWKQcR1V3IleO3cexEd+YvBVEuJqXeutMi8po+UpoaVZhq2WaopuCrUVqhtpLqI3VCHnrFMYji5pD+XRuwda2saJjh8HWEoWGnUUjTVxIKJGhZ/dU5n0ZHnpaTPonREAcnXWC9rVMtVdQqw7x0p22vkYCtNDEcTsH7DiqUi6oAErGvdlfibe/21KK5KSZSihHBjDKG7i1LV7osxhuBsJ0kX7u7esK4rsWzURj+lex1m2wGZbqAWpmBIqOOumopsBWctPgpt8BTTGF1gN4w8bZHnpydsGGg0drujzuvDpK7F7YStghtnzq2Rk6gmoQovD89Me7WhD6UqMG2AsQhuUn1SdZabuzc8vZxYloV5CDjraDTV+CQ9SX6fIsbpgzFMo4Yq56wdCWM0X9AJtjXsMLK3Bm8ay8OvMWFgnmfu7+85L4q22PkdDw8P3P709yjbyt55Xl5eVIA9BnISZhHujjcEcaTdnhKUTzQax+Xxmbfv3jFPnpeHZ4baSEXPFefH79kPEz/7yRc453j+/hPTNCH7HbU2fvHtr7i5uSG7xv39W5bziuTCT9//tANTI9F6vnrzjrSsvH17z5oTu/CRv/iLP+ef/Bt/k7svv+ZSKvM884d3XxBr4aFESlYBdW5V9WAfvueX3/2KL95/zfF4y5mNT9uqWIow8ZQ3vv7yp6zrqjqxKfN4OXFzc4MftcNYaqE2yKVQ89pP3bq4lBj1a2tpYvDTHgFyUlCpnkwFMQ43e8Q1hq0SnQpqD7MS9nNqhDCSRe/tBsTSKemtATpObGjXQXoMkrWq/yoxqW4qX5DpgKyNYdqRtke2NTHcf0lJq0ZcWEOLidoixiVyLHhnkJyITYs41Rm+YL3TQq41JAzkXPDzgVwq0S84DC1V1ZZYS8u62ToXcNOk7qmwYxC1PLu+IbpdIJWM8wNiBUkFw8hndOmP73V1RdugWlFnJ2pMxHXlm1XF/7WsGAZ4WgAVZmcWWhu6s8swHDpioV/Headu2Nu7ie2lcFkWBjE4l5HcGOfC4SBMU+n0dcPUduouridasq8d2FZq57MZqlMOYU0VaxxrvoCp2kFtDecLVoRaPN5YjIi6ZoERh7WB6CvOQymGYdKue64KXUZJOySJYCFthWosTlQfY5vD+c85nbWqdga0rrGts92kkXOjlqZ55914YcWQY2EYHSIWgqOZikd9IzV2WG8WCAACu6wIsjIhpugPAu2EmaCtlKKhw00qtgIJdbzZoLMsb6A5vGmMzpCi6uK8d0odtwZHAxmJIuz8zIUXzDgwINgSWeMAbtL4tNQ7adZS4o7sFBytjuHK5tRlXrcFcGp0RLvpIo0FOJgZqUITobaGdRNb1UmFpi1YkptBPLlFEI2jU3SRKgKqEVrpwc/WUfBEA+s2qNRkTXgDwVgamaKVHSGpoaWVhvhKk6z3UdMJy2xAnKUCo1NWnzFquMFpYVSrdKyIINaSyZhgkGZ1efYjrRaM9WAaFZBqqeaq8ys0wuei7dpcR2Hhv8nXX4ux4PSv/h2JJWFLVaH0mmhek9onsbRr8gc9YPb8HTkn3PiW7A07o0RpGQZubm5YzxfEGpwLDPOO9fGp29Q9W8mMaLjy6GbCoNZXPw5clkg28VVfYjFs5QKjw+a+oQDzfo8zlsGDRXVSjkgzllwV5X9Zs9p9OxNqt5+oW6J0V8VuCMpIatr5MCUyjiMuWF76iLIgDNaRto152GkL1NcegaDXbTET8zxR15Uw6ajLe8/YNWND12y9e/cOYwzff/gWP+xpLeHcyLYmolkIg+Fgjrz4xlsct4eJX3/7ATePSIqUUniKmXHwxJKYD7d44dUZktYTDdjv9wyt6QhyP/dcq5Hn7QVpRb8/DIqp6DyzeLooo2u0fPjVr/j6D/4Wd/sjX7098u2vP2KNYV1Xllr5w5/8Dk9J9SiX05nWGvfjgb+8PPHp8ZnbuzcE7zGlsZWFUhxfvfuC87by7fMZUxqyPpBrxdtb9scDLniS96zrihOnmr+aCM5RRDs9Qx//Gq85cOW8EEZFXUy7I7YaxrJwtk7FoQglZ40OSglxk663i4ZE2zAwWMNUDRIcLzUr/8zp6ddXfY+lR0KJCKYlyjVdwHeuVXcnetGfW40wTtPr6MSKttOLu4YxA63T1zsCIuf66kwVKoM1xFWxJ9eRJ0AT2xdzdUVu9PGKDAoHRU/GAvq1C1pECBgfcJ1WL9J1Zj/SseB/8cd/S4qo1skHw2CFXDacqRgXmGaLC4bchNEPtAHkCgLdBaR5bt/NPPziW9alMM6OQMAysL/b03zCFNiNA9RGmMAPsDrNoPTBMgyOrUZSbBrcLtLlCrqe3YyC1MRAo2TXtYlCxWm4dO2HyBY0gisLSylMkycl1e8ZYxicpayV/U2g2olaI6FVqrFITiqWz0ryrzRMaVQKpmUG43Cok9KaSsBjfVYhdFIg7TFofJfxHicG64RtzRg79DH9iPNB9aq+EoKFa0dkE2wrfHx+4vbmPbuhsm0LtW1MoWDNjDcoo9DNuAZm7tDg2rQjS9KRYcuUi8HIAScjsSRsrmSBLWW2BFsyIJaHrbK1TEkTVioV7TSJiKZcGJANMEpwd8ETY4+bchawbLHSXCAVMF3/9DmwfVGDU1VB9+QDa9xo1RCt0fxJZ8A4Uk0MYohoXJImQHRRfTdJmOwpRnBONGi+asqCtSqOr+YH+IM+ZhudVVkF0GyPzuoCdmfV1SldaiFd75SFnhssVPmcAqFYBqM6wx+sGCJCMroPliKExiu2QR3UlWZHjWMz0mFLAI4+kSfZShBD69T8//yX3/12aa7c3/4TcePw6oZY12dtN3qrYwU3q7C6O+R07JA5jLOS2XNUncnWT/25MMwTkYzxE6aL7ey6EHPCjHrC28+OXA1+GBn8nlQqtWmki2mR5eVEy0XFgG6gxsh+10+LOWKkaeaXCIQAbsS4CWkNNwzsx0HdDLWSlpX9MJGkkpcF1zsLzgXVVomyYC7nB0qlj1MMBAfGYPt7d0GLsdHqopibwxjhconsDweom75XIzw9PnLz5kuNu7m/p5TC+bwQLx8AOB5ucS5wc3OH9/Dp6YHf/fInPCwJKxkzaMjzw/9L3pv0SpZu53nP1+4mIk6ezKzMqnvrkhRJi5QHggBKGhiSGwgwJBGayv4zHntuA4blH2DQFizYgruBB4YBD2wDtikYhATpmpdk1a0mu9NExG6+bnmwvnOKnmsg3KpJVWZV5YlzIvbe63vX+z7v/QM5Z4I1nB/vefvZG1JtHKfuKXOWz1+/5N3Hj1qb4pz2Y20rL1++pFb1OoxRvU9ff/MNx+MRenLu7vGOcRw5RFX/3rx8RUuZZb/y9qc/46uvvlKqfjF8yAu3N6/Zto1me0LzcOQUJsQI337/QREEqXB8eeCyVba7e8Rb1lT54u1brh/vGI4zU5ipCOflSux9bI/72nEBR4LR/qpnhlMqhNF2qq9Rs/q2YYeZJr3AuH/WakrqhZrUb6WsGTCbxoI14r4jDgXuWUfA4js/xg36s8r9pIZp3cvRC5DFKtQ2pWdvYAiBnFRpq0X9C8PQ+7Xc8Dyk1bzjnZpVSynP3ijXCrVmxIKzjvp0Y2ut4yD6r50ahV0plLpTq0OKqmUYVeog452npP7wMV6DKH/Ok1X+8L//UQ5X/9nf+h0xPhIsDKPra7hGqYaSKtYVjDM4P3B5KLjZEbsX5tXtC25fHsjlDhthiCcezxkjZ+oeeBkHprdHHj888un9I8vDmSqex/OGfzETlhUfDGaviBeGntCqtTI4LS5qreFD5nSYOb5qDAftVXVDwIaRanTAKFkoLVBbwZrAWjZK7b2SXbnaa6MVQ5XC0Wsd2IBgjdZQeR8xNSlvae1hpQZ5vfZDIvgmRKvVYEP0tGq5LjshWq3VrDrMlW3HB8HZ+JxIpWVy1dJ67zQwNYS+6mrCGCzIhrGZaRzZ8s44WZhGsEnVqeBAIm1ZsVMB72HJql5JRAq6/swWyqQrxuhpaeCyZnCV6waXh42cG3u2rLsDu+o6Dcs8RFUNqyiMuAXiYMgt00wC8ZiiKYaSd3K//nMDG9S/ed2TDjdVup0gYGPA1AYDpL31a9OxVUi16UrOGXwDR8ZVoVkhi3lO80nTyhmHVs1YE3varymiQxqtumf/k+nPK48hS0O6xQZrumG9IE2HRBGh9XBAMfV5uGr8UKgsIohXZak1/3ygb62p8tn/UoYXukEQ5Wbl6ihStDKHH/TyJ25Wg+7N0l//p3/2K5YWvPmb/5601ijnM/7lLaVpSq9uSr9m+4SdXkD02PVMEYs4BzZipBJaJZeCNWrwvKyrgkdbL631oRtuwc4zrQrBB3IuuOCoJWGaMMQDxTWi/eENXDbtHvR+ZH28cIgz8cWRVPa+i3aULXF+9xXDeKS0yiE67h8+wekAW2Y+Hsnow21wT9N700TkvrCsVxi1o4oo+AKlOnxZKDYQjy+Qjm4wxpC2DW+1W9C4H4CTLgT2piecWB0yOooHMrSaGV1gu2hqcDeZFy9eaIGyVazE6fYFLlUuaUOsYVs1SDBOuqKiK0ilJuZ5ZjDCti68ev2WUgpfvHrF9Xrlen7EDRFpjePxyMPHd5zvPhDfvOK3f/O3aHeP3N3dkeeB8TATxegNqVTGMPHqzSseHh6wzvHtt9+y7htTHPjitQ6KX758yU048M8+fKfKSxO+ffeez16/4WG5UCz87tufcX9Z+eM//mPOLfXy4iNvPv+cx3XlcDiQlofnqqQqmtgyj/q+Xp0yw1JPx/npgOKfMrFBramnrUZScPjrwvH2BcuayeWCC0ewhpoTkhJs1+7Tmmil4E3FBq/MqlaRUgnBMY63pGbYm64bJ2nsNVMZGceRtm20wTO7wLIlTrPnen5k3RZdY7u5D0SGva6M0dJKoTWh5qJS/K54hTAMzKcjueqwUw3EIuwOBRoGh0fI267rXhG8gfN6BeMIe4/dD1HXvJ3ZVtCb/SgDNa9sJgEetvX5FGpCoP3h//CjHK7+q9//i2KIZOlKhMnYJhTbkDxSWmXNBSdwbZltEbbOJSrZ4MSSWXHmJbUtHGzG1kCzhqEZ0pTJtRDqhPWJVnVFVnEEaxATcHklRDWMezdRQiGI8CJ6rEu0mjuociaXBed1AJTm8FbV8VEmkq2c04pUaGakFmFbFsZ5xrLQ7izeW/xLh7UNyRFTDLWrFrYKqRRFCWz1AAAgAElEQVRtUkDn82i1gmr2CsssaWc8WNrm4VT6/e6EyztDRzksxeOtEORMMJZwLHgqwU860HmPj5m8qFE754p1I/MA+74iLeBs4RCiJhNNw+A1zm8rVlaqVbTNMAyk5co8WW4/a9ipQjGQA60ErDek1dNy47wachU+nR9YOdDaiNREbboWrEG7ccmBZjxSVh0wrH9GCNEHnNYhy6kPxIoish1LYamiIZytaS8jQDbSuae9x7RDQ4MxGGMR41htwZTG6Aw2wWZUAfJ9y5J7wi7RKEYp6pjGiCP34ae0ingLpesB9IYVCt5a1Yw6BkfKD60PpTdLmCYsPJHW0/Nw5cVRe+oPAFOQ5hFRdM/TiGTo/YHGYTot3vquuLeGQzE8atBvz8PU/rwVUIP9f/THv2KEdvd7f0tarbqrFksYlfhs40SzkPfEGAeoC1YsMQ6sqdGkIHnXPS96uj4cDrqzlwZoxLX16ponWvXduusw5TzL9YxxYJyj5QpBScLiLfv1yuF0VAm56NeYp4DPrbNOwFFpFrZlZTy+YGmZyUCyBhM826cH3bO3yna5wOEG5z2mFk2VGcfxeOT6+EG/Zzfz4nTimi7U3MjLzng8qbkQum8n4bxHSsWHXhMgaBJoUAp9jJH97p5Sd4gD86DASSPqn2D8QUq2Sdex15p44UfWdeXxcsY2XXGOxxv2fcc3XRPV7j+YXpzIaeeGTmnvuILbFzdsJZOWlePxyDefvuf17Yl9z6TrSto2bm5uePXmFdu+c/f+A59//obyeMFMM+Ogw97nn71RhMKeGE8H/KCvbVuu2g24aQmtNZ4tF77+5S/5jS9/Rtoza0n8yT//uV5kx5MOBtIj5NazPD5y+8WXisBwDt/VHiSwLupzoVbsqPR4UsZ6q1T+ZXkehm3LhDgTwsDeCnXX/9eIxUYlni/nM348UnKGfSFOunrBO6Y4UUrCxYCpBaqlYMhXxUXgAyZ6Qj/lJVOYkpAoGDeQjZKpn7sRbUdzbFd8nNU/lhbsPGuNT4M9KZ9LDOD0lPl0A6rB4Myk3hnQHs+gn3ULGGnsteFjxKyr9h1uj90XoenW0Qel4M8eyQlnIiWDDb3cuiMZ5J/8jz/K4eq//Hu/KYaJnArVAugDyxZVo4sRct6xTTBZV11PikDOOwnL6Ae9tsVRTYHiaK1gnMX7SMwCsSBsHELQkvfJY61j3SuEQAiGnFbiYLmZAoNdcaMqFnE8dI+UPnhrsZwX9cj4ntJaC9AaywItV3L1tGCxTYgmchgFPwSaJIZBcQ3UwmAHfO+DTWtiuXZOXNFy5NpX4bVW4uCUSmQjXjYCYz8IbByswfZgkTEQnahX1XncsBO9msxzXTiNI8EJtvsoXdhpZJyf1UjUV9y4hFSPCQIERS84gdKorT37vlzzkLoSYhqmWigWWoSi38N+bVxbZbko1iXXRJVGzQOXvHMMA/MwdsuKcN0zSwpUA5fr3lsNYudgueeDea69bF4y1kUMgcHp+q1uaqmRrgZpKh3KU5LTAknIpSDiSNbimqE6w24qoapKlaVhraqPT60p1kWSESXLe0+okGrHNNBhnwjBNMXZuICpGSdNB1P6IAX99Rv21iu10ISsqvBW16Ot0VpRrpb0JgMiwq5rQujJ49L5WxZpWhm3mErpBnXrwFbB9xJ7q0k5AKRX86iPy/IPvvoVI7Sbv/LvCM4p72Vo2BRo3sBeMHFEpKi5zTtMa9glacQ87LTwhhqUYk0szCiID8CEUb0deesrtESsYLeVS14BS5wVlpebYVsWgnNkbziazgUyGh3da8VFZWoZgW058+azV7z/dH2OuMcXt1gKdq3sTc2qowvksrE1mK3ncr1jGAa8U/hkaZWy74TDwNA813UlnGbqnmiy4lG0hHFaTUNQFMDQ9+Hj/JqHbWG2sKdETZU8OKgXjqmxmEpOGVMtdhrwcdKEV8tcr1e9sYy6khyCfo3joCDS+09fs18fKQyMpxMmRMrjA9lo0vLw8g3Bz7ydI9u2cS56+rw9HNj6ANVaY981yh9sIJcVqYWPy5nfufmMw+0N//y777iNE1/89A37vivXy1l++e4jX375JbXtDAUScLlcOB2OfPr0CaxjiBP+5oblemVfVrZto3Y+S2oGEyddR97fcTuPfMwbDK+VVF+T1oWcbjF1Q2pj3SqTF3ZjYc+Mx5ekPeN9I1C4GjWNmrRCLYgJGJfAeGpKGOuQfaebnRiHkdIsreMrZN/1oTFPtKQt7z5Y8vme4Ef2tisnys/Pp7w4OGqLvdIhQze5P8XeEYF0wTilR+M8NNej5A7rHIOxbDmpApoz2IANCjx1zmFp2HHW4ceqf8K5bmQVeU70YDSyrg7kgsdiQqAIanrvN0RjDD70c+Wfo75ba2nXFUxD/un/8qMcrv7Rv/uXxFpNfZaycUn6vsQxEqJDnH3+WUovQ69FlWqA1BTNZGqh5oh0mvVTkso4cLZBsEy20Zo+qMYxQlITdF7bs2dFRIgcsU4Ife1cnA4Ck/NgtHPS9Y5IXCE6CF4HdGP1PluN09BKMNw4AVNo4hisx1ghpZVcPamqmiIGpBrtscQwR70n5qYreuPg2E02cdCEqms80+E9Fmz/s/rKCdsfokV/rcq+7au9gPOFvSmd28uOa2h9lVw4AokLLg1Yr+r9GD9j3xo56Z+35IxzgQMNT2UahXlYMETut8yWJlIOes0j7EkTe6DXQQNKXRF6As9cVVCwWslWO0evPfdxWsVwecPoXL9nKPG+dT7VblUBtF08yBRqdRQB01Vp7EI1Dqr6IK8iOsQ03Q6JgVDc8wGu4HqYxlGdI1WARnSeQsb2LJzvNVwFfd1D08PAE6eqWfPDe5O1saI5ASwJSAV8940uVTmJpUHdhNWJAkGldQ+W015fYyjdB+q6orWj30Ofr3U7lIIOisagi8Ci6A70s2eM0UCEtc+//w+++u5Xa7h68W/++6KR7oL4kdL0AXscAmIDVdSz1MyIp2J8YPTasv7uu5+DP+BDwLpAzYXqbKf2jj+QzPuOFoDBY3ediEspzwXL6/WqsV5jCJOmaCgV2wrZQN0rbtCvMQ1eqyJKJq0LwQZqXrA2UHzEdRZQKYVSNkgVEwLGj30vvmG9B9Oj9d6xZ/XaBOeYguN8viC1YUJ89uAcrN78Lnum5Yy3DSOCH47kWphPLyEVjFl5+PSeKcysqYA3mkJ0migcoxq3b29v+fqrrzgcDgpBLYWyrfow9APzEJFaGMeR6Xgip4XglLAuUvn1X/8ZeV1YloWH8yO/9mu/xvv375jnmZvTSbuu9pVaKz95+xO2/cKyLxynmRfjCVMbd48f+fruPW9vX5FS4tXhpH40gffv33M4HBhe3tCS/kz/73/yh5SiqdHz45XDlz9h33SN9pPXb5BSOZ/P3N094F1kvnnBujwiceR0eIE4VQ7f/fIrhlGhobvV9GYMDpuuXBt4MRSTQSxjGNnSrtgOAOsJ0RMOJ0pu1KSE/5L0lGdy90dFj3ED5Ouzdwug1KINN85jbaNWg7ORNk60nHB0hVGsrvKkKpm/CN4Z6D2ZvtfsNAHjPaSCi09r3ELZNtwwUFvADkGj58bQOkdJ2TSGWnbcU2+ENc9dgs99h/tGPGjVz1PEuFhD3hO0jHHx2fj+5OFxqPnfh9AfFP3mXz3Y+qNVrv7g3/4tiXFmJSMUTANbI2J3vDO6/sm6esNYhKK1Sr1yZK9qCjbWUlL8AWshFcyOrQEZLM00ogwYSUwuc94atep7EI2+2d7p+6WDBwy9MWIzSsUuWZNh6pnphb5NMDUjdkKsgV7lFOpG9BPFr0RmTYhJg1K1I9Hrg7IZjxPP+XLhs9evoWXWXQ3zeijqjD8yo8kKRG0rYZwIdkdaRKQyOE/rwNFgO8dJNoIcSE98J2dYSyIaxzBBbRslaVF8qAJ1oXjDOFd8uyXEkXRZ8MPCUHQbsFwqt1HXgRd7paSBwWjn4xjviCePtMD9pXJdI8sSyU1RKlu2tKrXfUpN72uxKpcMyFUHquZ7717nXWW6LeF6wQ8HmnTifm24YDGmMYr25C679scar5+ZyyqYYCnNcOxKW247qRmWEvuQnCm9uNhXYTWNZBSY7WulGk+yChw1ksmiXsm8J5wfML0aJ3eOYq25K4haDm9tJ8Jbg5juscSAH7CtApZQKmvwz1sZEs/sv9CUXE/tjEps56epQr91wv/+hFzA0ZwO2q7fZ6vJiLe4op5VZzT84Ex8rnCSJ49rv3f9x3/6K+a5Cn/t74iYCzVnrLvVU35qNCscjscuP1ewjrbcgw9qXB9uMPsdgSMpL4TaKIODeGI0lVk2dhvJWeX2ZpoasEtjHEf2facaTxwnXNvYSmGcTrpW84ExetK2skmBXQcfs28qiecM4jCyqM/hcAATOMSRsl9pfsIYIViHDRGzatdedro6S0kBjdJBlsaODIeZ/fpI3a8Y7xFvCaX12oJImGeqjfhSwWzaIM+E1CsuRH2ImUIxjpT1a5dWGQ4zbAvTMHIaPdvdI3dNfTSP9/c4YxmGgZtXrymlcL2743aY2WxijpGLVPYizJ1WP44j1/OF0Vu2bePFi1vmeeYQIiUV3DTx/fff4yVT687nX7zh/LjifORnL2+5Lxuvbo6823Z+/qe/4M2Lk55YzxcO8w3FwuPDHW9ffwZNuP/wkXmeOd7+hF98/Wd88eaW1CrnfeXF6RV52bhcLlC0wuW3Pv8Z7/YLX//ZLxhfvmV2gYfrQto/gfX4eMQ5x3x8gZEGNXG+PJD3HT8c8KcZ10au24OaVq2FlBmOM9bOHAbPw+Md4zDTOmelGlUbtnwlDAMld7isaNhCNvUjtaDDkKSG84bgjuSyU+rKIIncE5RCJHjIywVsoNIYjGcfBdOcmjBLoZX1ubHAOAsSsJbn4EMrRREPQ9DjWtbh0JpAq7rmwXSvA90g3ISWdDic55n6lPoLY1dbOsiv9f5Lhl7JkXSN1Cqt7Tg79HVlUqWrOt2jlA1CRP7Z//qjHK7+4d/+XcHans7MtKY/ax2Ezhg30LzVU3/WIVsqII49F0KMtNTAFPUO9QOapWkiyyl+Bu/xoqAfJ43rJsSgKzQplWrBWVViY7RMIXCMhXEWTOdSiRiWYllzQdzA3YOuorZtU1WtSm8mqJgWda325PERsF5bB2YJzDHwmBJ2CLD80KDhBK4UYmg4py0IT4ysoxewjlwF54VoHEJhHHWorC0TxerfLVgjROdpplGqUKth70q2b/r/eNe7a0vD2MrNIZLSxlYq3nhKFrZ+WLPd+/QE9TQdE/T6JnIKBc8Vf6zYani8wJ5H9qT+szUlxAxUY7g/70Q/YKxwXlas71w9Eapx+BpIKC9MQyMVh5CaEFzvwXOiAhxAqZheONxcD6QUA62xWWFruhq00jBiKUbBoA1Berl7qZVcdZhvTdEMT3Sm1GDo4NdNeiUXQXWqPtjYKiT3ZDzXZDv0NaFJHZJq/9zw7ynlqQbHkI2oP85mcq00ibRqe1uEJg03Qx/MhNYMAf1+bf+MZXTYqqUhTtX90onsTUTX7n2IrK1prZBSe6miSl1uCot2Df7g21+xtGD4G39f5nmEPZNSITdtdaef/mscmKJjvyyYITI5NQGmfCHcvMFVYRwjdx8/4mNkENgQwunIJPBwVv/K4HWl5mZdf6z374jDhPVB/V0Y0uVRfTnGkrcFhxB9oErDek9LmpQxIVBbIg5HEo1hL1QcxVRMzeoLW5e+zhw5DPpgerw+9jROT3EFw+l0wq4rRRqpWqxoybR00F1KiXY9MxyP5D0zTgFbLePplmaKckXmkXXfsdMtXD9CTboVksaWdlJJGnm+vWGUp643fdAPg8rxLyaF+UmMvDvf83oc8CiU+HLdOdwcVOG6Xkk9rXc4HKi7JhTvv/ka++olXtTzVatwOk0s18LpdMv9+SP7embNhdMc+fzVW+I88XD/CeccaVkYhwPrdWG/XvjpX/h15jiwXxfevHnDV+8/4seBWITH5UqcI3/2zbcgXitqLETnmceJkjM1Zb789Z/x9S+/URCqG5gPN+ReyTFFixjLdcvkQg9DLKRPD4TTQC4wDapg5prwQ8S3zLZeCHFC2hORWms90rbj4pGaM36AMUZq0Wt1TWeM1QGs7Hv3FUJJWVdkWDV6Br2hHf1ISSt2nqnG4dLKUhPEE5bMMU4KrX1mT3m9h+w7rRXkieCO+gltA6kKPGy14tzQo9AZqaIrRdGItSFAbUjbNAWbFeZoWkGyMnF0ndj9aFbrUaqxygOzHheNdkYCUpwmrMquOAtnMGGk/j//049yuPovfv93BHFITUhWTIVpQkIHkh0oe6FgsUWNuk30ek4SqEXwJjEbNTQDPKEddJWsStNA765EVzMtF2QMSvfPmsBzUrGuYfo1VLqaOY2+VyPtZGOwrkE2vbJGOB6P6n3ZM6YZSgCacFkMWMvYhGvSNaOfwYdGyxupRMg8K6NDQGGOrTF4p+gIP2FlxQYIXj1DBEetOzSPD+r7cc6RWkeKpEawjmAzTgziDNrbaqAjRXwHaHrvMTUhzhIMWFexDpx7xZbPqrJm+5yDcs6xp6pMpjwyTJWRgo9nXrhGvHFQCnW33F1mHlMlV8e+QZFELZbKgZQ2YtT1X6mWvSX2qmk90Lo35zwla8rSDRnTHMkEbAFnU8dt9dqi664evZafE+JGlBFVupcoPBU0Y8gVMI3cLC17DS5FDZ84OyhpvzVs1XXeU2dq6UnjirDtO8E4bND3qvXqKxGhOqO+TGPwTQegTQyl7X34D1SE3KqiHgy0aijPxck/FCg/rcWfwoDPY0pvB9j62lH61y5GPyc6BGqvIlVLqZu32k/YO5lc9ygDePSQ8dRk8gffvfsVG65+72+LQuo27Hig+RFvobkGj48448n1E67dUD2E+bYb0M4YPFOtXJtBfEO2pPA353DjLa0lgtXT+V4q8zyzNSUIY5WIKynDNHHyA9Y6BjIfi2NohpY3tusVosONI1MItFJJzjAMAwcfSOuFIXxGbhdcr0vxPjLPR655pZwXTJhYndH+qbRiRdUiG3W90xqY6KnnhBsz6/0VFwKn04FmA/Mwcm0ald2yNoEP0XP/4T0+BGKMrFs3Z3cDf/QOg+d4OLCvm8rky3t2E9nKRhOHnyZM0mFjCLoSaKKYiFwMFmEIlmmakJJ5uKy8niJ3d3ecTi84Ho8YI+qVyjv39/fMtzcUhPXTJ9guIBU/Tdh4AAovXr3m/ccPeoPLGUPU1d/Nkf3+zKvTCw7DyNff/YKfvXzDnRhW03h8eOg3fCHkxh4Nhxg5Hm/w3nP/eEWwTIeZ6/fvMThMjCzrSjgcyF6I28bL12+4Py/sl08Mw9DRCr16QTRxmpoODuI60bivA43t3Civq7ejVxyEuAFq90PkDdeBpDbO6h8sQL7CfKPDTjgSqvYS5pxpvUTZikd8w1YDUshywrodk88AlN4sQPcMiPNayO1HnDP4Vb+PbAOlJCQ/Kiqi6Qohr/o143yi7But7lgqrVZsHBAbEFE/S8k7xlpN2wK+1n7DbcRhIHUTvesPrez7zwrTsQuuUwcbwziyS9PXbgPWOer/9d/9KIerf/z3fktaNVQJJDGqFOai3pRSqc6x5EIWo3UpVEyzmCrQMuBwxjA5XaHFbKjGkbyuX3erA4cr6tvZMYjXA1VoDtAwULTKI3Jenr0zTyusKOrFM1IxDLRWcL7hnYNenWOJrFU/D9Y0qjSieCWUs+JN5XZ+Se0qgxGtZSGjJn28PhDDk/pRMU0QLGOoOCtE1xDRajEnhbyBdQV6D56gg5oWyRicbQQsWTzGVF0RdeUq9r13qxYnhWYHPBXrhGFwpH3H8FRJZRBRo7SqXoYYHbfHTNoCmI2bm8DsVxg91Mr5bmBJgfMubElYVn1fWhDWXLhxqlzVIvimHXtucsxZ11yD04PVMO+0rLwyaqPmAw+1ct0Tg/Psmw4EpnWcSl/1O6fKTnCGJalfK3bfYymFrbcR1Wa19cNp9c2zub338Zauhj2VJ1f6ANMsqVU8Buuccs56tVVrhtJVIq1r0y4/I7kr+KJF2b2G56mculVtftDVdEczGFSUMIb4xKP6c3eK1hqmD3XZm5467Xw9Y6jdZlHJiA1aRG10wC6AaZGK3geLOJJU5W6J8I++/xVTrsa//vsCaN+RWK1ASCtNJhg19jmNNxi7IeeV6rTSY9szzkZlCDmwPpI2VVHGcURyIsbAZUv4qPJ3SgkpSnw/jNq4fd03Bhex84BsmdwE6wWXKlOc1F/klS776uVMKxUxqvysuWKkISkpQkS0YDjbwLpkalooTXsNp8MN4iI388D9sjDPM2T1pUzDzHVdOG8Lsq7QMsMwKNrh5hXbqqDNaQg8Pp4hDASrnXL7tnG8uVFsAFBzoooleqPmU+vY95V5nrFmVJVqjKQs3D+emSe0EHk8Ya1l3TeqAbsvHMaBZj2HwwExjk8PF4LvtRo+6GssiYeHBw5hIMbIIY788rtvGYzly599TrH6wX7z5nM+fPyO1ze3PF7OiNFU4Kvbo1LrnSOMAw7D6xe3PKRG2nbu77/ns5ev+O7dB968ecMURrZ9p2A4OMe5YzGM9TxcV87nK0fjIFi2vZCM8PHjR2ZgHwIvDifWVFmfUjet4YyqLzlr6XAQXcda7/rpUPrNtytAkrWaY5pJ66om8eMto7OYWtgFmgl4UykpY+OgadTrmTBNSOhVGf3G+LRuIO3gKpiDmuJZwSgTTGGPal4uWe+U1ivvxoaB1iqG2OGhWowq5apcuL7SOJ1Oz+kfh9DqTrY6SAbnwOvqG2efO8dCXwPUroxY94QEUZ+F80p7d01V3dagbYmu3eshJkZCUxglbVOQ4y/+zx/lcPUP/85fliJalHzdC4c+KG9mwxXD9sQgy4KrBh8yrunqb6uuKxEKfG2A6QTtpT7dy3Uw8KIogb0+9Y7m5+HBIwSrnhQvBu9cT6s2YpjJZQVTUXRnA6nUZjqCISmmQ7T3rTXwMWrIqCM9fFNQabXqubJeE5Gtakl0KYnoPLXXkIQQngc8rDAaYRiV9r6KrjXVg6j/Xd/0MDb1gV0LWnDuLDlZnNsBrbuZ4tDXmJHgNiqiKzJnOAyBoXsEx1CIeLCrKsHWEMRRpGmnprXM44GcVw7GMkTDIWZSE0qdWPfGZW1k49hSpmDYqiU8VbQYSy0FsWNXfRrJqPH9+bquvWvQeHAWm4XsFmJU9I1y6hIGZd2JV5ZTcwZf8rPHuFkopTGPCu80Ve8L1z7AbaVivWOvhcuS8WFCbFKFz+rgNDpVL1O/F0r3I9dctBfQddO8MX0NKc8rQFWCKs4bjLfU0i3lRft9QSHdTjpJ30Cp0pOR0ltVYJem6Ifa04hWE7a+9t5DaZRasTaQOqi0hp5q7+iKXQbGYtlC57M1+4x2yEWFhCc/7D/+/lfM0O7/jb8rxhhk1xqHfbnDVmGaDixNaDbqc8YJRjJtS4ituhj2Ai3B+AKGE7EUgoGlGrwV8raBJK1xEI91jnE6kMXg7fBcVGl6KW4InlYzSRyyb5hhREohTA7jInnbiRbmYeR8fqDgiNNEk8xUK0SvKTXj2K5XXKkcj0dGjxYux5nWEksS/HyLazs+DFzPCxwGStrp9DYAxuDIFbxk9pwAiyuCGy1xHjnMt6Rto6yPpJKxg3rGbBxoy0pxwovjCWd1sDzvG94FzF453t6wbBvROz7/7CXn85WUEi9fa72QsZ7vHj4xjZ6b3Hh3XQmnW/zde8I4cnf3ATeO1LUw3hxx5ytvfv03GAd92N7f3/PyxYE4n6jS+Lg98vHjR6JXM/3jh/e8/fxz3n37NaTKm9NLfvu3f5vb0y1//O3XvL9e1P9WMjFGbBPu7+8ZPn/Lr716S9grZor80YdvWe8e+Pz2DZ/SCtaT1pUxKAS1bCuX9crnP/kJ67py2YsOOvuKH0fEwDwqpHbZEyINTMUZqCZCs1pTYX4YhHCRvK5QE1Bww4QhYLYz0izNPNXbBAiO+MStqY2aEkNfTQfbyHYEtMm+ZIXYUvZ+Wmwae55mQhGCGM4t4Ywlp8Tgu4fL2Y562CEEjNE1j3WQ9p1xiOz7roNhzoqS6Jq7mYL+/l6xQQ8ukjeq6TF36zXg0DKIdoA9oR/0yTroytOPlLzheIIXPrlNO2iy9GvNFeZ55vF//29/lMPVf/Jv/QVpCKXCZcsKTWyNC55D82w0NSLjEQ+tejWCN1UbnlhhoWrLSi09ot+HE2lGeUBYWlMfHYA3isEAIAuDrzhb8BjG6LBNH6xPHXmYQquOrTVs94+WUrDBEIswRa1RadWBs9QG3iuyIBXDOHWV2SjEU31guhwqVT06VjJD6GqRUbWslMLRO1wEyZVrDkyDrjj3YhicwXSsylNQSawj2G5sNwVnunLlnOIPasXYgqmNMQZy1gf56A2mK33SDN4Gcq5MwbPmhBVFI7SU+/pMaC0xev3nwe4MBHITlpIw/sCeLVvSoWurliZPYSqllmdrnz1DpemA+qTa5B5aSE2w3rOXwuAD25ae+0+rM5Qiz9e9Mb3qqq+2Wu3VPcYRPAQsUwc2793zmaumgi9bBh/YS3lelQkeh8H67n2q//8ZQQzPaUDzFBQTVcqeUszWelXOXURIIA4r+n51IVzfvwZ7zewls2bfW0n0z6wWBF0vlqoq2WIt1ULIPK8qjbO4zu1yGEwf0KS2H14vP9xqxJpegq04iCdVV0T4z7/5FRuuzF/7u0LVgmRQn1B1hlqFcThQy4rFsDcLaYUQwU84b7FOaJeV2kSJ5lJww8jgnQ4IRqVII/XZA+JaYpyOZCM6AMWIdyMh6IeYfoqoOWGcJw4D0Y+kXAkR1uXC7YsTl8sD+6IKhhkmYjA4M9IEUlbCcLOuV4zogybge8mvpaQd6wMhTkr0vayMJ+0d26EPARcAACAASURBVPed4/FIcJ7rltiyEOaRWjbStjLGF7gx0vYd6k7wA1vNGK9+i3GYuS5nnCgE0nalraQdZwzDdGBNO6kWXt68ZAyW9+8/cnNzw+A8nz59Ig6GL774gvuHC8tyxs8jrVZyM7x6/Tkk7UE7nkaaNRx9pJaVT4/a2fdqOGAlU71nq5mP798pnqFsDMPAdtl4cTjy/d17/vXf/G0GF7i/vydHw3Gaubu7ZxxH/vTdt7x8+ZJQhFevXvH+7rEjAgyHnn78p7/4OTUGjmHi+njl/v4eiZ63b99yva7EcaCJri/3fWcYJoyd2XOiWpitns7StmFCINiJVndayTQB69X74m33d+WndnblRGUrOD/ijfYHGvODQpClPYP8asn4EJ4v5nEc2beFlht4h+vJOjEB5z2uqbKpq5yGmQasQM3th1qap2FcBLK2FPDkn+jXVEu9Uqmqwd30MlYA16tCkIztyIi6L/g+cNasIMPRKlak2t4a0GsmAt3ULwnrhegmBf8V9QxaUQ+YWNtlre6X+Bf/249yuPoP/+pfFIfgRD8HSS4gnoiunEWEnUSRkdwqtsozYbqJpZmAYafV/v4bfYBC0r4/efqx6hZArNHEYfU4V2lioSmywPZTuyn653v3Q9WRiNAojM/Ea33Yeqdk/9EMEJSobUQfyqZ3/tWk6+nDYCl9+NPoe08W9q9DawSbCEZRCadRkKbpPrwwzZbL2eFjRYohWWFqltLZX6E/GBX4aTCosb0YwRhL9Br7N6YSjNYNqQ3A4mPFiVEvlaiPq0khyEDt61HBkJsWqVurabMQDdFUJg9f3Br8cIFquLszbHUkV8jJUkxhz2rqTnsj0dhLJrcJ29eutfsf1fhdSCnj3YgYBX5ahL0Vohhw4Ipjl8pVKql2ZaloOXKxsJeMOBUjqlhNdhrtn7XWQq+6EQqFhlSPe/rMWFV7wPaktBAMlKqk9Sd156kxQpp6xfT+ZtiNrv6sU5p7zUoUzaVoIbyFYESxHNZCcVgKg/cEY1lqBuP1s6rv2vPrT0/KXseNNI2CPLOzRAzWaZLVBoXdPuEV9r66rKKE+oxTLxaia2rU44UE/ut3//I4V/9KFDezPGDGiAvKYFouK+MwIgPsy84QtBZlRKghIN5QUqKuFmKEuoAZIRqsH/FxoohoZYC1BKnksmE7iIzpQHb22XOj07Tjsu1E59nThvWjmhxbwpcHchnwduLy6QrWcXdfaUbAWXAOYxy5OSTowy6GuUu0llosPsykUjStkFd21CuTJZLXDPs9lMLy/aMC/sYTnx7PmE3N/bVttMUi7oR3nrrfY6qnPlFt9wvzMJD2HecC5vqO4+1Lyp4Ih0jKhlwK84uf0LhyAkx1nIaJ63ol3RfevHrDpSbKvjHfnLg9Hqhr5cV4IM4Ku0wpsS8LH7/5mrefveTh/Inj9Dm33vPx/iPH45E5Dhyt4cP5A8uycPPiyLZtbK3Qzh+5MSMP95+wFuaXLxnjiVaE+/WR/+Nf/BFxr5zzxu2rt3xxesnN/Jr9ciGOI3/2J1+xOeHD/R3l4YoJjr/0W/8a79eKuy6YecXe3nI0lWgt77//nvj6JS8PB0rZOBgYppnvHx+gXZBdB+gtHqm18dnhhse8YU3CRwfDgPTy5X3fSW1HLhd8h7DGeMC7hOCRZWWXhjtOtL0itapHKyr6wRphiN0gvz3Q/ExtgVobzDqwFBFIGTwYbwlslDATnT7g8rJjnUPWB5XozQ8PUgBaoyUhhgO5ZoIJapgNei0YBBdjN57rsNPGoGZzW2l7xgaPBKtBhSHi/EBaV7bRE4YTVQQ3ROpygdpIAGFQuX97pADSy1rxBfETAC47/OBJeX02rv4Y/7rkQnSO1ezkoqZlZz3NFnzuQ0INpFaQqKm5+KRsiIY7DZ5mdSCyxkDT3xNxiDwl8XrgQgwiFmkNa6z6iLC0lrEmIk0wPeLujPrmTCnUVgnOPK+uW3/PGur1W7D6q2pxAobCsb+moQMhnSk4b/qDWA8AOsC1zswyFDMiGFLTLkyqJxtLaCBINxw3nDOU6lhzY9tVeJWqD+IYO1bHOKWPO0ergnWNwVlaEyxVk7RGE5ZFwNaGMWr5OE3C8RRxrhGCHoidadjWCE2fE8YWrBX9GUgjpYJF152jP7Anw2O2bFslJPXMuZjxzVHLwGAGBiekooqMi5FSRH/WwWFtP9i3ShXAuD40GrxNeNdIyXAyCT8JkElY9sEiuVC8IeWKsXrwK32w0CFUDeIzBpojYzDe0KhksX1Vqik7DwymEK0Bb8k0nPGIwDUpg6w0obWMtxBtINdMwmmJtwhDDLSe9MwWjBhSqyxi2LvdwIkw1KbBAixivVoGvMWIQotNvy9aZ9WUD11ZdWxZ3//NQqsbg/P4XdW/s/8B66AKfMNbLXA+GE+zlrlfk8qIy/9Sr/N/JZSr27/59wVvKfuqvXleVxNZNk1pdBiorxWxhropN8lF1yXICWuELa0QIxYF3pW1J/9aUOk3XXr6qWFsw7rp+fSPNfjxALWRy8ohBB7Pd8TjW7KNnKLnctGknzQLTdW06Xhiy4maMi5EnEG73XpT/fnxo9YszAculzPjOLEX7aySWrHjxIv5yLLeAyCifrHrsuG8MBnFBtw/fqsq1fEzTqcTBVWoyvKIc47b05FSCp/u3nE83LDayNvjSdeNNIxV9IPHcXf3kRAtr16/ZdkS795/o/Urw8TN8cieK8uy4C3M48RpGnl3eeB2Vm/U8XjUrsd95Xw+M80nchRu5yN3d3esubBfHyF6rp8+0WvP8adbynrh5uaGm5sbRCq3pxv+7E++ItWNOU4cTyfmFyfMknj95jP+3+++5su3P8W5wIeP37IuibJsuBBwOH7ysy953DNzDOAjl7v3VIIS5q0nlUZdNi5l5/O3P8E6x5KvLEtipNGwXJcN07buF7AK32yowmMd4jzTEEhJPQlqPvI6nNiAkQ2x8RkXQs5AwYVAiyOj9ewpP3sEjDG4eCSnDfKqas6ow7YpkK5XaDvDNEGrWB9Yc1NzuFGTi2n6AK1+1E7DpulAEzySM+SECRZndAjUotysr/npmreWYRxJSfEgtWnNhskZxq6ueYvtp0PKju3qmxtHqo262ilLX0tq56bvHRiG2H1g+iAVtg4wnfXh90f/849ywvoP/srvSpZGzZr2M0YUNOmFMcoz4bx2HwqAo5CTYLyjtaRqQ+f2NTzeVD0ANIM411eADcQTnJqVdfz6oUrEY3hsmWYGBqvKf3xa7YjS/KVVmsu6XuyvJVMIYrBiCabhjTCIIQTDyfTEqbfq+eoPrKc0Vi364HN9PYW1tKypNy+e0akSBt1ua+rzKt50oGgwBhG07kQaezIauzc6EJVSMN7hnEEShKhJtDDEvlKEdVX/TbR0A7WCJqOI8uv8kyLs+2HZPN/XPbUXn1cOQbiJhuYM61o5Z9hwlAyYQNqrDiKFvsJMlDogRtderT4phLqmrD1JuXW1RxN0DmmFIegA9JSQzEaoxSPdqJ9LxXQPcqtgnHqLtHuxp0oRaEKT2lWhJyVI1aBg7DMqQf2W+u83K8zNg1gW17BVaH3VqKpqX60Z9WLFCtFAtYGPnZW3A5emnmbFd6hy14x2U4Yq2q2K1m4ZgeJhkqbIG7EUqdTaqL6vBXtSu+TGYCsNrwc7UPJ8UcUvWcFXXaHiKlL0dWe6X1T0Z/PffPjwK7YW/Mt/Q0zovg8xiuMV0QgxHqoWUobxADU9r1gwlZyK7lNtwXVkvvSyY9qAD5HorBpp0Z6rVq+6796L1ngYQzy8UORBg5vjiev1qjC987dKuY4Tbd8Jw5F5nrlcH3WtE2L3AmTtfnORsq1qNhShWL1wZyus64pYQzDQykZKBRlegvcMXZ5sxlIxDHHsJkJdQ8m+Mk0Tj81j65Vp0B4rKfq9pL0otrlV/DhSlxXnBEMi7wXCDGQddJzDNOGnP/2pGr2Hoa+QAq9eveDbX37L4XBgr43BG959+J5Xr7/kdhr48PE9j9cHjrevyFkTaDe3NwzWU7cz1Xim05FvvvlGvczW8vr2FW/evKF8fMdpnPlQzpRSeHPzSmtqfv5z/vrv/VUu28Zv3L7h0/kTbpj49P17cs48msbLXsexXHc+Xh6RwWP9TDkvzLcv+OaXX3MaAuN0wo1H3r9/z+2LE0vK+GHkw/vviS9fMtuBsQmrFPJ2YUuaKHFVTbVj0J97cZkpHNnrzjzPnO8/MQ4H7YFEZ5ScM9743nNpFApqNGrsez/lYCEZIaeENYGSKuP/R96b9Uqybdd531xdRGTupprT3Y68EEVbNGxZJi09yRAgSA96828xbPhH+AfZAmwIlm0Bpg1QehAgm5TYgLqnqW43mRkRq5t+mCt3Xb0TMHFPvZxz6lTtJndkxFxjjvGN4Fh7Nt24ig1NAcAxOkeMWzXPKN5aA4aBnt3h9ickHcwE2zraCjIFo6iXHUlHU0UHyiQ6YVJL+ul8S66W8EKNhTS5ODxUY/0zDL5dMzEl2loIfiKX1SqBouelrkqEeRh2e7LkZag2SObcSFOlVG+0SkBrhdFekP/1//GjHq6cCHtVmlwZSlZB5PvntUcaDzmrdglj/VZoo1QXzEMS6Ih69m5q5q8PV1f/CoN5ZcGhYJ+nNbs11MGmGg+8OJnHsfaOl46qo4yBPqgjO2Vqisfu0/YQryzRM2Fr9inYWgkM8mhFygPPMQYoPzxjAA1P0vrSl+prxgdwYt+DiqknbvitoBJ0gajsudLUMUWDcNrrZD6gyUMYSdYo4F1DxseMorhkHKlazdAvzqg119X9FebrnMNLIwWxKh2n+KYcE+CErXjO1dN6oTdBvIB6crU1ZNFO10LTQM+YGf3KBBv9e33gXewKCAbndBXpE/TMWaGJ4RwKQqsGi702KyiOWjsJ8+MVHTDkUSvk8JylIW2s/ryizRJ6XYyAD+PacraWq85+f3fm9Vuk02sHlGQ1zFRGUnisDeclsHhPzebVMy5etSSneLZtexngNumggopy0crUEt3bcBXwNGdJ0KJWDaYKbfgUe+8ojaSBNjxUZYw01VcbXgNEcWzdox12seaD7uVlhW4NA/C/vHv3mzVcxd//J5YWLCcz+u7GdiJgEQE1U6+LCYeBE51zCNHgZ9VYO6jagNGGnySoqQxu+Ey87bWdt+5C7xzzPHO5XOgjcnuNjpNGCmrdcCnhfCClRBxlmtcLo4r5VSZvD1u00vcL4Xg0g3EDRAhp4VoNMkdHVm/SdM32OV0gHg5I3ahN0f1sD6JmfhecDUVOZnpbcfMN3kXKuDhmb1ySeb4D4HB7R2s7z482qPh6obgZIdrq9fKJ29tbUjKsgtUEdUvBucDXX3/N4/OZKTrw4GTh8fyJaUrM8xHU8fTwjrbvvPriDQGhOTtt3R+tj1G3wrIsLJgZ9FO3M/OXy5EffviBN1+85fsP7zm+uqOuO7fTwuN+4bSfKCpEcXz55ZfopfLv/uRPePPmnp/+9Bd8++9/xe///u/z//zpH/PTN1/w/ccLt8eFv/yzP+dZ1ZKNqqynM2+/+QaKnbJuj4n/98//Ap1vuJsW9t6pXfHTgbZtRoVu1umlVGKGWp6NBxZmvEts2AMmButG823HxUg5PQACfoLe8MtivKsQiVMi4+2N3leoGSf2ueLh1lTavCG1kxZncNs1Q86kOVH3C3K4f1EqdbmHVhEHdDtpl96Q3tArp4px0w7BFsdulKWqxdebmm+GbUOcqa25DGCqiF1zeTeYbe9mkA+jkkWbXZtlN34VI+M9kAy62BpQdxvumRYUJY4VxzVhVP/of/pRDlf//X/yN7SiL3RrN9SU66+IKZzmJepDBTejuR/+IDAW3/XPJGcDWsVWLkHlpXPVdc9JlN4/e7S86/TmcCIkZyd3pTKNAlyPeVa0V/xQyMI4KMZBcaer1aZINW6gRqSeCGGCK4dKrKze185NmMDBJVcmrURnZcBelKZC1cAS2nXBbduEcYXYetMRim0EnKgxiq6rz6E+9WZeyF7baGnypNDp3VJigUaIoN0zhUaaDOLQG0a4vwIssz18tbaBt/EvqrW4xpQ81MLNHDlMG7l49u7YikcVw9kE815dcsH5Tm8B7yOl5c9eoRGo6nTURQSr+RHtqHo6jSDdFBhnQ0zWAc5UBwQqjaLmUdZuaz4nQpNgQziFPvogq45noneU3sxrhFqzYrch5Kocdmzo0u7ZR6WV9Sr2lxCC02vPoWEarnDU2ja8d2gVw314T6oLLuywRKYuXJqFyBqmItVun3vP1awG3qGIDcClGfzTmV8KZ9d4Gs+/Ikrztq7NzZuts1Xjfaki6lGBtVfMU+atCrKPFOoIg/zTd79hypX7T/++OmcpuDpH1AdTsbYzkubPF+KIevt4OzhXFztpaYcakCmivXPMG+u60qcAIXC4+3r4GJ4pdaU5+/u9rKP6JtE384GoU26XA3uzstrONfpunXAxjFN967aOKRdjah2+oOZMPNxCL/Q64s8pMc8zUWDfd3re2E4Po6QyIGP6fklb4CydMd2Ac4Rsxsc8PAycd5iPON+p2wMyoHjVH0ZHnA2ex/k15/Mj2/MzTDPz8Y56/oTz1wTNqMFICfGmOJxOj/gguGFQ9jEZayZ4tCf6embPG9EXYlhYBe7u7lifnyyCva7gJ46v7qwu5+7A+/fv+ebLr+zjryfkdkEfVp6entDdjOblOeMU7n7ymvO37zltz7z+8mv8ZP2O+/PK3VdvOV+eCH7i8Pqe/eGZV9/8nIdvf+D980fubo48ijDvIH2llEJyiYJDfSBEYe0dNlhQWvK0vBLTRK6dvp6Yjkdas5tDnwLy6QPcv7XVszbKVlmGR+98/sg0zxSXkBhx0w0tF3pebYgfLCDSAdkKlGdinMm1kxxkbfTthBselxqtnLl0Q0JoNim9NUgxwlANtjQTy4lCsLVH6/jgPqM37EjMvpuBXcNsRJlWR3K24A8LDDncCM+LfS+XB1unD4YQ4iwRuT7jQsKliZqzqQ+9Q5yhd8uRjPuI956K+cK8S9S14bbd+gpnG+z7GGSf/+if/iiHq//m935Hi35WlZzHqjmkoVqYfKC28R4cHiaRq3JlVO6iVkRug0XCa6Mo1G5dcZMP5rnBCN2rwJ1WXI82XOHxoYJmXJ0oGEzU6pEs2eaagjaqt2GqFUsRdudfyOBzVhhojjdOuY0ZEce+Ge7jPJvStIhVjnW1pJZ3MMdA7Q5HR8UTUiLozjZSkb61FwO03e/1BZhJ7tzMiTKM+HWs02Kw+ywA2sb3aT2ZRRRP4+BsYNiqohQmFwBnytgo9519tCJ2LcaRayOE0TKqhRgCUeDou6EcpNJc4PnSyRLIHWotoBNmiKugCRFPbdYN6JyjjPu/j57SBTcOH13MlG2K+KCri5o5fkh9hfFnsPVya4p3kaaVqmP4rjZMOmfIlrl5PvV91KoFPJm9Bhtw1IbsPta/ioGJq/2EcCMtbYz8cf8YRnmPsNWCXD1i4ui9Wb/iWNMFLH2aNXIOiqtW1zNppXvhUAsijdomnryVSxcwFEy3tW90nop1GnqENEIdu4egldqsY1O8Q52MlK1ycVbj49S2Q077f3CIaYOr9T9+/5sGEf3P/7GatFns4VCMDC3xBkmBXiq0xmE6Ws+UH3vqlsffGaoVAR8iLtoPHrVTTgsZqQYm6wJtPwMB6aOPSqw6xGuzi71WXBgemOHfKhVyMYOvRegbc7pH2Sk5U5udlpz/nOKqtUJ5JMWFrQ41wE2kZSG3jpOI9JV2OeHvv7Qb1tppesK5YLRyyVY8PYYhKSslHHHR/nvPdoM12dsTfaDXnb3uSGmf00elklKg50KaD3RXzeO2XRA32zAbPMvhwM1yMMp2EOqeOWcbQLfLEwDHeebjx3ccD0fOp5PxWJxjub2xh0XeOMbAeS/c3b9FRgDh9PBA7Y3b+zu2beN2MhDntcrl7s1rvvjiC077SgiBP/vzP7eOyGXh22+/5auf/5x3P/zAnu1NHg+Br26teudXv/qOb372S7oWHj492VqsR1qtPD2/I00z+9MPzNMdW9mgQgoH0jxRtRPmieg9W64cj0e2ZtfGzTzx6dMnQlworVF1pz+dkGnCeW+qXldqHyWrQ9Es2VhO03FmXzdrKFXFH8zQLrlC8KQw4aWxA61+Vjp9gCRCj5YeXVB255EQcNqodSRgSiFOky1nRF7WlV3NI6KqSIi4AQg0evv1ZDpKV/NmyRwxgKqOG37TfawJjdBcajOVNXmcS/Rqq8qQAl0c0ZkivO0XA9h6R1GHo9DLRm+B6AXnK6U90f7lH/44h6u//TuailCjY6+V0G3tYqZhR3SevVkPX5FGFIeTbkBJ8aCO4hqMOLnKKMquFkvHwSLhxd/XxEjmSCX12QYuMr05eqgDoChWG9N2gkYrNqdZF95YkRHUhvM2oVSWaA/VONKEyxRZVawfdLH7XxghIh0eseDta/be+IXXz11Ruv/s8fHelF7rTjU/ZGyO3gopGnx08qY8BFEQu+cHL5RWWUKiaoOm3C6wV4eqwznztAH2vYl5j3ACNFQd0Vt1lA10VinUMJ4WmE/LOVMRZ+lMaXRwSmLdG5saeHdKxrwjd9RXcgc0ULt/2ZSoeipGF684GiOxN96jMl63rdoqs6LkaspMlU5vwiSNoNW8RGpG/uImzj2yV5jlkUU63N7gWyY4h2uCS5G9dBxWcu/VVO/u7H2/6GyK0rg3OLV7i3eJx1K5eMfDpbIER3X29aeRNNy8mdpd86hshshoGXVxNIo5+rWWRrB2Cmeq/RY9F+fR1um9MYu3wwcMlda/pAiHqEpx9i8iQuz2Ma/KcEVfMBEGDA2f1T3kpWO1dvhnH37D1oLHv/MPNdy/ZS+wP37ACcNgCxI8aZrsNBICXXd0e7KTc/OQjrgBRpNQoPWXN4Hz8+hkCuyXR/pzJby5e+GGNOxUYi+snZL28abS3gkxEibz4DQtsAvUlZASnUb0N9TomGNCurJLR4Zita/PI77u2beKRFPC0mJqTNs/EeKB7ieT3o/W6+f2b4n+Ffv+wZbmr7+CyzNTPBjSIdzi9iem6Hl8fBy1JQrrBgnSdEOgU/eOHpIVbQ6oaq87bXum+MhXb3/Ceatcto1DtsLmcGfA1Hk54G6ObI+P9OcLIVo3497NR9T2FaVwf/+atRW0wZs3b3jeV16/fs3j+wcO9wutdG7uXxG083A5cfTBOFXRBtbbN3d8+vSJb45G3L+9vaW1xr//9leUUrjknZ/85Cc8P14s+n1YkCmyPz0YdHS+Zds28nZhSgu63AKdXW1Aujw888VPv+GrQ+Dj4xMfHi4ck2f7+Imuwt4XXPB0Ve7uHe+++5643FByJoZoipK0EWIQ8w1dLnZ31QIo3hlUlujR3vF+snoNtUJwme7Hg9NWJ70Uu3a9eSRIN7bqLg1pEFqhaIX5S0RPeG+sLpWKholazdtSrlHx/GQn9jq8C9dI/UgdzdHZ4I9BUHFjhVc88fiKEhJOhnfLXLeg1l9HuxagKkJC2gnA0mdbQVIaVVHDyL/Z/5duL1H1AUnJmKHbikuKl0YLSgr3rP/qf/5RDlf/3d/6pW5uDOF9IDjUhpHnCnNMbF7xxVZmUTxztAeQdUcGpBccZt6+3sNcH+qOKGlAY0sptJjIXTk4SGJhe6XRqpCx1VJ3BenGUJIqRBWSE4p0pNrDKbvA0scKi0IjEBCiG+GKKsShwHs1BUiGgjsFg/P662oYsa+hO9ww76Pm35E4amqyMQX9CO90EVwxXpcMFSo5j+v1ZWiL0tha4eA9CdgbzJPQejC4vTSupIraG3M0H1ZF8T3QfDEj/9Xk7s2ztDd94YwlZ+tGL42bATJurbGHSu72Z/cKvQvLFLisSsb4gsHPrGVF1O6lJXdbP6qpT4qt19J1DUmnO4/4ON5r0L2BNKfq2LZM6IkeO0uw4ejgMjVPFJl4PmeenFoFjQYrho7mn1MBrZ49VKSXgSNwL37RUhq128HPe08ZX1MqSp3AN2ceJyqFRlWHSBxBiGy+qIHIcE05OltfxrFSvNLdA0LxEJo9t/3IE+1iw6ehI+xX7+bFylx9Zlcg6eefz8tQ3K4qbx8BAE/RjgO8ChuNdq3REfN0/Z8ffsMI7csf/BO1yKat/cxHAOqBbQPsNOYxJYLg8MuMUqAIfT+bTyQYhd17bxyla7eZmln5kDxNAtv5YqnAlk2l2DbEjcJctYl2P11shTEleyC2iouRPuRD8ZCi4GocvXbKMnn2tbKfLkxpGCbTzGVbSd6xn57wh6OpSXiieNbxJp6ksa4rOIfzAa0FzRlZZhIgYZTgajHPDs4MxpKtnkK9rWiCs6/XWyUDpeIPszFMcjZjuyq3d3espxM1b9y8+tI8btLIJ0tU+hBIaaLlws3NEbzj8vABaqcGO1UQPId55nTZIK8c3rwh58ziIyHC7WxJx0+PD4QA+3nHt8qGw8dAk852foK98earLzne3gzaeOBYBVmOfP/uV1zef+KSdw73Nkzd3b/lh/ff8uU3X+Nc4PT4RPQBauF4e48SeHp64tyHP6XsZr7eni39F2ameSY6Tyk7PgiXczZyunhqKUzz/GKWrJcL0/1bW7UNwCejaiSqGY/dddiJEZwnOZPp3Qhq1P2MnwIi5sULYVCC24qbjvTqYCAKtOzYRjnx+njk/fv39OGZqn0iJGOygHJIRj1X1RfDKnRyr4QYqS7go3GxQMEF83PUETuulXS8pe4Z54yn1TqoYdZhy5DCAI6aAV6yktLMvmd8Soh2ausMXDj0jG+frY9miBmDn/f4tNC7o/+b//VHOVz9t3/zP1bvPWv09tCmInRit2LZK9cqqIBXAo7QNyCQe0NSAPWEsQIMo96jD8+QdzOhKT0K0gs6etzmbg+03juTE2OVhfjCYcu9x3545AAAIABJREFU0a6AWBeGd0bw6gxWOR4Vr0dfpqPiRChpYByaUbmjOKsiU2XqWEtBCFAbuwhBHLfBPH9deKl96b1ZFU9dkK72NcUGrYATYhWCKyTvqN2bQut3VumkdiDGSJdR75QSre1klzjtDRcKJ13Yc+WbLqhX1upJXlmq4mNi6hd2t/PKC28nG1gDK2hgubFgkc8Ot6ysZ0FC5G99mXg9Z9xNoDw7/vKhs56t9LltgSqF79uFIK9ZL/C4O3ysaDfj9a5mNF+rsaTWoVpXHBQhOs+55pGuUzqgfWwxyOSuiLc3moh5n5RO66Zit56RpmgPqBfmbjggS6mKWVuGd89dRxgxgn3rjqzFAKUujJosW7XloRxNozx6ZBKY1NnZk2wDYu9sPdBER8LSIfjBIbMk4+auK1doVbgEJTdHxpSqonYQNfCQt+9zqF96ZebRRu9gRTQZD8tXglacjhuRmGrf8FgTnRj53UFp9t77Fx9+wzxX8x/8Iy3qEOdo+85ye8s8TXz67oNVj8iIICdTlDhX0s0RvKVHDkxspXLJjwPBsOKnQY32HrcY7NLv0XqV+kqYrB5nmiabdJuScyYupiZ5B9u6kkaZaV8vAC8XYK8KLRPGKizc3VPLyoGZeLQy4mVZCMHZmiseiPmZXEzezPVCEIeMSpErgVl0w3VF43HQ4zPz4TV5fH6mA1p26Bln2HpUDTfgUkIGtgK6vVYaSMuMXCtigrDvu32+5zMTwh4b880NKtaWTjOlCh/otbF9fM/bb77itBdqKUQPl165X464vTLd3HDan1kOdwYBPT1yvD/w9u4L80yVwrafkNOZ492XaM+sHx8Jr1+z1Q2nndIbzAfKtjFXwR8mQi2kY2J9eOLN119yc/ea7777DjcdeP9v/5RwnKi18/UvfsbPfvJT/vj7dzz/8IHZw7Zt3MwTy+sv+fAX3+JvD0z+yHFeOGFJUIl3XC4nEOWQbIV2enqEfEG753h3x/npoyX3RgluI0Kr+GQrMJzHjb/rvSefTvjpYGvrDqmvFD/T9hOunxAOQylVY0/tETdjh4aSrX/QdUg3pGmitDCI7dtIczVaXUF3XDzSJZh6WcfgIxHnhoFTFWvTUhuUnCDuFt0fCHWl986yLOw9Wt3EskCfaHlca/FgfXZDlXOTGA5E7aYUk6Psu/WBnh9gUOHFJap0OJ3MjxYTbQ/IosO7sxLTgfyvfpzFzf/DP/iFllJYmXlqF/azYVTUCVvLeJ8IDUJ0xFaZ3IxMjy8mdenOlMlhWVCy+fWaVcksiw0sd+OAEFYbfkozRtEm1s3Wc6VHO9mHcb34IDQCr9RxEwO3oXDKVrOS92op0RRs9bOb2nZVVZ5L5dILtxI5+o6nMgdHEMcyOS5lZQmLMaEm8wVWnxGxftXZmWpRh0JzbJ3uFrxulorLiUsD9Y73oeNK4xJshTprQkuliZmyU294L5A7k1cCQugVDVDkcyIP7CHruhoEN9mKsgU7rFzEE4rn3CdDY/RC9o6GHY58doRutoncVm5QDqMmJpROC43YZjZXCM7u8WEcyHI3ZQeGryhMyDV0MHAHXsG5beAxDojfcd1aSGQANZ1EmgR6t4oe9bA3+x5bqai30NWLHUAtYSi900QQB62bN8l7D1fwKDZ4dHVsuGH4h4awjpVz6o0whrMVQVujBKGXbvVBPXL0/qXuCKA4x96qiU6l0fxkh4agrGWna6QkS312zGvYncdrJWgnOBuOXLMk4hWPMS/eMEvqXtaPRSzwYetzaF1YG6xe2HvFN//SbADwhx9/w4arN//wv9bcjCLde2dfHxE34dM8THOBtpkPK8ZIGUmUls8vXqvluFDWSk+epSdy2CmPHwEPcTFvSHBo25Er38qZVN32HdRkcfWBQUWwBKJzxLtX1HUfax+l7ZfPzCHdsFbe/DllpQ4vRyREukyEEMfwZIY/raaCzTGxj5qT5M3HIJgS0bcnrr0VIUbrkhMhHu9NXg82CMWR4CrXBGVLVpuwnfBhtrVX70zHO3LO1NMD8809PgSEzmGZcBLY15VLNeVwXy+8ef2a0gq3y8z7x3fU7OlP74kuEg4z6/MzTM5WZc1OQH5eOEqAFFjXlcMSWUvmzd09D6eVoJHt9Mh8mLh9dY/uO7e3t/zxn/wJX331FV++fsNWCwHPq7t7shZyzuTnM989feK0rxxvb/nq9hXfv/tIOE68no/86od3iCrbtuGrcvflG27jzCrw7ocf+Mlv/Tan0wkXjpyeP5Gc+ULa5RMyVirRW0ejm2/oOPv50j4PB6VCViinwbdyoB1RxkqkDvU02PBRhqm2m7rTr2qQM/id82OVg1VZSJztQdku1O0RMKYMIRKm44vc3etOdkfSWLP0dSXGaEBE5+xjO4f2/PLn635BWiekSJMZpeOHMtFqxU+GI/HjXmCnXkcfNy4buM1c3VonxmRpnG7ql29qRGkRpuBpY4VyrZZy2tFSDRfhHIJ1gOq//XGiGP7u3/iPFOzhuLRG9J7YDZMgOEJUYxxJZRYIGpj6TvPCUy2sCnv3qLf3do6CNPNrNRTCYhyiYg/budnglGJH/SjWFX1BPqi3Il8diU91EaURW6Wnyf7pbbVsCa2KV0g+vRwqAIIr3G6NHux7CDSSs/44EYgYvLNkazRQVe6c57HbQ3ej2NZgIGzC3vF89juFrEQ1T1KJgVk8pTe6BCI7fnQdBhVSq4goM5a4cwgSLemYx8M0dXtdRKyM+MqUEu8oxRLhzuuAZc6oE25KQaeF1XX2nOkuICkQ9obrZvDWMPxmKKFV7pPjDjeM9Z41d3yz7UIfbR1dPJdsISJVJSmfrSZjeKnZszrzgAVnrxHY+7V0h4s6KPLtpe7F+vo860jFXVWrKBCdMzaW+OE560P9HgzF0e+nqmQXCGONVxU2NZ9SUhtwVJVT63gZ946BNlAfiCWZ8D2GxCIGE9UeiWJYmi5Ywbhg/ZrYx/cSTEXzO8EpczNvoOJIzhN0KJWiJA1UVXagS3gZrq5f967WTpDx7AIJRx4+rH0Mjv/Xh4+/WcNV/Hv/lTYCKd6yr6sRyJsSnZIvF3sgtQZ9eEL2DkuCQSdffGLdPjAH29POCKsKkMAbcVdEqPFI8GCdWx1tdsLw3jMfJi6nM8fjLXlv7E8foBbk9pZDa3A4knMmuEZWoW07N7e3nLZB7C27YSAu76FASNZFqPMtKR7YtgtpcuybrSi9ZMq208Ykp8Th43EDCtmNMTQ63Do2uKVgcrhuQ13wwQYEzP9SmzIfFqJTvI92CmyNm2SAve1ysj7E4ElB2LcLTTP5dCYEe+Az35JuDuatKhsyRZb5FeenZzINaud4c4PDkprLPPwjz2fmN/fU08o0TTy++0A6zOznleXmllrOHA8T0gOHu1v288k6CE/PpqK8umU/XRAH28Mz7tWt/XyXW/72L3+Xb//yz1g/PdFmz2XNTBXWJITkaaWyPz6hh0TzicmZmrQsC31X87P5TsEbB6w18vlsRv6hQFGrXWM+cnz1BedcYHsGiVA25uMt2362AX/doY3/p0qYPTVHwuGIENF9tYfVjWEpem1XOBb4YjTt1kB289BsY7CJEee63eoVdHsG9chyM+qLFLZH8HF4t/zgVUWc9wT68FaJDeXPG3ghHQM578R4Q+sV1Ia/XgpUu9bqiPn7kGg5W4WFRKi2rpa8ojiY30AMMGX73oslyKQF3PZA8fM49Ah+mow+7QJ9P9FKMZi8Kvpn//pHOVz9g9/+m/rrVSITFe+EgHLoMHtldkojoDRK35nV7lVIZMtKdcoxWYdpKTteYYoJj/A8SoGDtOHn6jQ6qXpyaHyhzuyjXSzyjnmOdiK52ppm1yv7x1mJce9UaS88rShKVCW+AEshqVAcxN6YnaUEA8E+v3QWcQiVEIVDleGTFaJmVP0L0ytcE5LO2UpxDOq0QX53gZqV3RVmiVxk7HYYhuYXqGUzOrfLeG+bjANK16uvVqwj0BtioWO9h9dEHIwPqwFPsToVJ0QcOv6cili6UTwqhtcodXQZ+olDb7TQKN3hRynzuX9WzegCwVGLDUHi2gtc1avRiPJAX+ytcmkzTU0RdGLpviYOr9UK2rFDXBthhxfcwPD4+XFfNECpo1HozdOcYRX68K7t3RQqxdl9CEMYAEgDiTbw0a3eS7y3gmgRvFP2pmQnhGqJwV9XxIoaBiKMw0AebK8o/UUdS+O69FjVeBQblFa1daVp8p6swl4d0VkAwzlLfUp1L2Eu76D3bAGn1tic52Lf2YvnqmMq4T//+OGv7J7016L+xrFQcya7E6EVugRSEnoJ+Gliip7L0xMkgxYyBVDFucMLvsDLPXs5od2xiUIzOJwTD2GiSYB1RVOglUZKE85HYwqVRp/Ax8C+ZUtZLZEQbPVzzju8/w5ELJVQK+HmntPjJ5DR5b492qwX7ow+7G0gcvsZLTu9N7YKIOTzM+KF+eaeemngFg63N3YKqsM/0zs+KMfjPc/Pz8zejIE+TfjYqOnAXC9c8omeN7p+trxszxM9BnJV4jITcbx/WFG1xJoLgeaPnLZGa3ZaluWGGCYbHGqllI0UD9Ti8OpYnOfmbqJ1+OHpwvndO5gnQkocliPPeSWo8u5P/wJ3XPjqeEOeYLs8QxK+vp347sMTzx8/AJGlFe5vbhHX+OL2wJvpSN93Pk1HUlH68Z4vX3/Bw9Mjy5t7ni4PnFvjHGX0Zim5X5AeyXlmWY7cL7es68q2FyTBzXFmWwvrMOwTEuyfyJ8MuCppQVRtNRsC6WYx31uprE/fQinIIA/jPNv5Iy4eKLnixCHxFW26A48xzeZuCmNbcU5sIN7OkDeC7nQXQTxePTIZNtaFVwQa9SYacHa3Ya8nQ2z4o62dp8tHzjnagHY4gIWbrHmgNtge6KLkwEvkm+6Q5M3v1BK0jnojeXutlv5TDwKlDOVVBBcTYTmwbxcQkDiNZI69/4yvJchzoddKxdudxAl6eIuj03MG7204a9bN1qc7CDZolcv6/8/N5q/Br6uu4Md6SpqayVYg45DewLJ6VIFYhRXrAXRU7mdPks7WK/RgnXMIte0gjjtRXHTkLpReCeq49YnsO7e7Y4pCoeFFiFw9K9YvaOts69QTERYB7ZZIjni8s4dkwFTYqJ2o9vfEd4IGosKkhSjKTMWPXr+oBnKW1vCqKIoUQZKtxa9NAOOOSgScGh5Ca0O8I4pQesE7eC3gXOEWgWjA3JSsymmrkKbIYQwI2juIULW+XO8hBmg7vXukKVnr4LYZE8sM0qBqg+rkgjG5eiMmK5g+eHgVGpe203UiV0bh+2CUuWrbfrX/FhGmYElPs4M0lGt3pJL7qGrp3dJyThAX6U1p4qlqKo3rheC9JR/FBqPW7WPl4VFuvTONISp3O+y41kaqMmBZxEYRQ0BkhDw6W02csMFpFkFHDMLYjhCb/fs1xdd7f7G4eGeG8qKCumBJxl/7pVoQZxsewIjyqggepx2Hp4qp4tUw4obR6DBJp+BoYsO5pxPEsV5Ly5v5sjYctdSX9OCdS1YXJc5C++KsvGnMuEmM2/ZX+euvhXIVfu/3tbVGyJk+R/q8DAMspGWhhztqzri2vaTWtG7E6IzdUxr0ZGsqEVtXiCf4Su8OH2eUwDE5tu1ECIlCIJ8+GCSxFEQnXDTjt27P9OmVrWJ8x6UJlxa2dbX+I1ViOZNbY56Nhn2pldZ3Ui/0XKh+YV5uiXOk5UL0lhLM+06tmYkO0aKylEecm5iON8gwdyJiiZzVqiFKGIbprbEsC6fzD7jpFW4gGuJ0YJ5nTuedevqBpjs+zrR9ZZoWq6O5Urmv8XznDPJ5vLV1zRRH7U2kPD3TvRBe3RCqUNaNw+tXzK7y8LxzuVz46vaVIRPe/QVLc0z3xrfan1amWTmfV+jKL37yc75/fkBc46fHV5yzcagexKPaKJ8+8eUvf87Tx4/cTbdMMfH2i1f86z/8I97+9s/YVnsQv/vhE8ev33L36pZvf/UrXn31BfW5ELKtVp8vnwzoGmfCnCitEWMiOCPuTw6ynyl5h/1MCBO1N5gjlMo02fCzb4ZqcDESj68ppRCXA/vpBPXEvCxkNblcS4f8hC8XmG5p3eO00WmmLokzQ6444vGGLsZ6kmoDX90tgG21TI0uhXiwxis7cQZ6a0jt6NMPIGkAdWd6X8chYqIriBdczaAT3Z1xHFE/0ddPiNvQ6iAchqpkK0fSARlBEpypuPX8hHcRBsOm1jygkYs9fCeLtF9PquX8yZKGyxtTuC4fh7eqINMBFQ85wzhZ9uBs0PvTf/mjVK7+3s9+qSJCcMIsakNIN9jm5AwiGnUQqEO19Ue32q0sn2tv9gC+dJpXFnV0GpN4qyDpgROdT73yVGDx5oP6WsWqsETYnQEWwZPpnLujDMr7FQsRuCZGrUNShyIJkLiWMQNdOXhv60TtLN647QlL9wVxvKFSxTxNUUZg1gOtUQgkaVZALWZivn48ur4kEHGRUhpT8HQtJvyIo/RCUGFJtmrPIykmXUna8cn4T/bwt4e2VhtwklNaKxjX3F5b1xsSk6lHXpFqnYTqQFzDd0eKZuhuap2P1Ql9V3qwDrymA3HQzT9UhpahTukaWeJmdVMSWEvlQoPyuX7GpYAfK3YVz9oa5x6ZvTWZzM5xbp2DU6QX8IYJKs4Z2Z2AF/tZFbXvOUnDSWSv1wRmHdU7ntwCqmV0ORoe4qrcFbL1UKqBOhlqmiEiBBkKUBjr3q3bFiU5PwYk42QB5HH9Xr+2TIc6yu6dVUE5B94Jcawzo7v+WSh9+MRwuG6rSBUZw1Inu2Cr5dGS0tSuNR1f21mE3JXg7L3kbUkPNP7Zh7865eqvxXAl/9nfV9RMtz4lGEmGWi+2EklHAFy8QbXg82oTvT9Qy2VMwM7WF97jYiCUTlxmSunoulJ9tenbJzv99241IfmZ/PjegIhuguVAOtxSihUm91rRvAHby2BCXKDarjekN9R9Z4qWtuthHqf8mWk2lazWSpoWW0VdTvamdxaF1pJNfXCeECY0Jg5T4vJ8sjdVfUTigePhzehv2wZBfNxk94ykhPoRwQ+JZVmsHq93KII7mrHVwKmVw5TosTGFyOn5mfnmhsfHR+rz2eCfy4HL5TJglpVdd0opJv16T90MYQGVL9++5jgdbbDbLjw8PMB84P54oFXFJ097NDjlKW/02nj88J687RwON7bS8J6bmxt8M9/U4+k9PkaOb74ipcTpdGKaJnozbtf5cuH29pZtqIyX7UwIM71XG4zU8fbtW/ZxOty3jS+++IIP3/+KvTa8egtK1AzO46eJbTMu1c3tgcu5IKMomcFUk3JBfKSPYRbNiDYOx69Hm4B5WcrlmTQdqdvJqkXCxHRcjCfjjKWWc6bWT9AXEA8umepZGz46Wl7t9xDksFg3WLGTXJ9uLOk3akTc8GLJqIjq1VAPEm4genzQwVuzplvR4f3SZutnVfMYXq9tbYSjQXbLvpOWheaUljNB/IspNviJ2gstF8TbUOb8TPSBLe/2Pk3GMStlQ5zgRudbHbgI/Tf/4kc5XP3BL2y4mrDhKuHAKYsqCQOApoEAAbv564BKNmfpsp3EKpAarGNYmLqQvXXbnbW/cKR87YQOJSmxdY5O+S3XOLqId5bgqihntTqWvcOjKptXpDuaqq1a9MpOH17QUTcmznw6NAhigMcUHJ7OUawypql9TQHh2B3q7OF9iErHE7XYEIaArxyrAZ3vxLMPf49T5eAH2yhnlkNi65XgrHsvXa/P3gnCOBhUDj4aomIEnNDGTffsDMxKUBiw1T5gvSIGqzTfj6kpXq18XVxnQQhemaLDIezNBoTzGDxzVZRAxeP7YFqlkcTs9hoVaTw3QfDsqkzV6oDU2+trqzpP7s3gmHROLXDju739nefS7WcTxH5WvXfCQMignq2PQ+BgiVmTiXUXigitG1ure29+Oyp9eLy0ddQ7XAdJ8gJsdc0G9F+/FgZ31PAOaqnkroJTu29N3r+kTWUMsPt4rZoO9piDxTmqwqod7wQ3gl59tE6oNtpQuNpIRrvh1bqGwiKO/ddWnF2tomlTWAOE5qk0BGxdq+aaRhr/+4dPv1nD1fR3/7HmbYNgLCfzU0G9fAvqcMWM7LSNfT0bZ0pssOma0bZZ/1Id4ESJ1j3XJnKu1MnB8xm2lXRzzzTPNtH6wt7NTOhyJsYJuaY4XCW0jKuJvK/Ag/WquYm43LzEYftIirla2PMKoUCtuO5w3lITzjmIw3sjhotAMs4lYrC16N4vtKpIOSNpxGRF0FxJaUY8lB7xwbxVJW9Gz3aKD8GApCKGU7hcCNFM++nWc/ruHT4sppgcZwPnpQWnlojs+cwXX3zB05PhJyQsAxYHed1YVhtiTpKNuTRZ4i06R97PiHds28b+/GwDXFR72Ffg6LhfvuRnP/sZ2+nM7pTTu/fsn57Yys5PvvktoreT2mUoUP1ow/T543sDaC6L1RSdzqwPD7ijXSfBj6qY3khpJq87PiVulpkYI+/XM3f397jnC957PjxdwAXC9MZ+Bv2R5XDD84Ml3eYB+Ly5Ex4/jOH6WrjcKuI8XTpsG+nuFTXv0J8tQRXuiNNE2VaEAJO93vXjmTAlNI2kzjgtTTlT2pPdNCpwPNq6bj5aWu/0A8EHqnajEo8KiaiFtQgh3FD33cjtMdKHyqTdE6QhulmHYE+mzvqxSvejkLk1Sxn2AUlsDa9GktbFFMm8bbgQ6LkRp8mQDEDZLkhWdDLTrQ5TrpeGF0ftjVp2vLPOyuRmejpS8hlQgw+pon/5f/8oh6v/8qe/o+IacWAJHJmgNjyIdxwczLmxhTDWttbzOKtDx2l/Q7g445udw1jLqQ0DXQzZUBgFzAwFSoTkbY03q4FJ61iFXCGLdOUJRWS0QozTv4L569RKin23lbRrSr+CP6UNa4Ig2izdKGIdscNMHbxV0CxupOS49hS28ecKbni8cMJBbHhyzczdr4cHdR9D5xwcrnamEOl5ZQqe4xzZdrsmS+tIF6ooq5EtiDiqKBFIPpiSp0rEsajD+UZcIHasE7BnJhEmMZN2Q1m8sngPbofmERdZW7P+O+2EOLPnzFYtuWtsr24aQjPP0NULBc4QC6rU8fONmOrWXeCsndkFutahtJiaY8lAo4yXbqtPBbxzL+XOex+YhTFAV2zQy70j3q4TA4EI4TooyfD5dhv0QrVBO4l/qVNC+otnMGBqUqG/+KqaqFH31a45r9gQNOaNot0GmrHurZiaF8Wjrn4mWzUZiUUZX7v9vnfCJ22m/HkL8Mxj+O+12NZlXJeGmRAmZz8bufZb4shj1Vlcp3Thn398/E0brv6R5pyhNMJiyo86S1zVnE2ZihHyYPeIrbeiN3J0bYMcHTz0wuG44OPMftrIQQjZYHumDMhg9OzE22S+Fu85Pe8WM9+fLDkRDrjg8GGh5AwukKaJEBI+TVz2C8d5oVyse27vQmkKq8XPvSh1PQP2YHJxYd82wrLgnXFVAOimLORzsXWPgp/N8wKwnj6SfKDlRquWakzTRI+Hl3qMtm9oKeYjimPw6Rc74fkD0OjFCod7ruh8w7SMRvJu0NUribv3Tj9/sBvkcaH2RgqWUAyhsm0bsTtKzmbi7o2g3kz62Peylcyrt29sffnpkeftQlwOvD3YClVLJW877x8/kVLC3Sxc3n/g1VdfcXk+UerGlA60Uqm18s2bL/j2V7/CH2Zev35tJs6mpBB5upxZfeTrN1/g+862N/bQqe8euZyeAAdRIR149faGhw8r0jLiAtLb5woYF6AUfFpQJ4bc6I1wc49IRLGi0W1txqEaitELmmMMTWlI87QNS98MkrAdkyz80Ieq0Dtc/QgujV7MwXUjflaT3KBZD/5Q8AEZTfCaHxB3ROPVIOuRvKFiQQfEOGy9Wmms89GGQXj52C+DfDM1wS3eGL27XTPJW/jhtG9QK94V4ywd37KvBcqODzrM6m5Q5g0s6iShk3kXpa4viApE0H/3Rz/K4eq/+LkNV1O3h3vsfQxGisPTXefUldoF8QpScURSgy52vQQ83Smxfq6HiVciu5UHUjRzZsaP+2XpgsdTpRok1CtuKDVZxkpFPDpwAA5Br14WAO9MRZVRLu2cYWOGJOEQhOuAZ/UksdeXqL4lGY1XlCSO70NQ8XTNeI0ohXk8hANwkIZFl8ws70czAd6qXVoUpqEMRemjc7DTRwBAG0RRU/y6KRgtGkW8i5mYRQX1HboM4CSEYonKY/QsrXJIjskJk2/ciil+i4fcMlMHNyutQ+5iQYEyzOT4kZYUcq/2lu5WxB5CIHcHGqiaX9AuvXfqWA/bwDSGAmf4ikWuNUTdiO3qKeqYnSNrYxIhN1OmtjHYJTX/VLsOOOLYmyUbXVPEJdo4JGUxFT6LDUy9CUGFFoTYTaW7NpFI60ze/wfDlcFOlY4Z7dMw57dhpWhqXrKu3p53YvDYosHM8SJMY9Dro6KGQYNrI/SwoezquBacL3jiMMM7UUu/irEhM/b+Cs4UXJy81OkwUtUARR3/26ffsLSg+72/o1qjxc4PC0E9pVXa+mA1OG7wl6JHYkQ3W3GkxdZdKUa2baNvKz6A1IoLE7hE7gVyYbq/p5VIrRtxssTeRS0aG2OkZAyyqbDc3pBEeNwaqVjybW+VeZ55WrM9CPUBWU+4ZgNe3y+4aWY6GGmdGPECZXQSTsEb2KxVwo09OL33XOqg0p6+xblRFZIrpTrCPEO6o247Lto+/JDGaxEs/ddRBKVezrgQmOLMerngp4PR7CUiUXDjZib7Rn34nnC8pVbrLdTurfJnGtHkQa4PKXLZNmSodHUeabe9wLriFxvwCGKKV7TPUU8rww+Jd2bMVudx0cF5x90uFA/LpXE8Hvnml7/g3Xff8+bVLZorn54eOT2vPL3/yO/+7u+i3q73p33lu+8ifD/VAAAgAElEQVS+Gwt0TwqB5Xjg8vQJ15VMQDvI69fEp40ucDMtrJdPdpKbHcIBzSs+Trw+HDifz4bjmJLhPvoG84RX4z1JH/UgaeAXwoGU7uh1s8F/VCfEaAT06KzKx4VEOL4iX06jy9FBu0BvY9jJpmLuw9jtJnxKtNSQ7RktQlgWq7npnZDMd0g0U3FM7gUkeV4/oiXjY6SVnXj7NXUcPDg/2hAngZACcbKhvjXj3oQQKKOcVqnkdYPcCTHR48Q0TeZPKRvqZ1opuMv3pHhk6wW6s8Skt/cn3lvVD4UYPCWbJ838HWk88Ip5J//4x+m5+oOf/paK60ziSQ28VybxPGmzDmw3hvHxgCmD3h0QW7kB6itHAs5QiIgIc09cpPFMY+8GcGxOeFnmqfGvtHVUOnEklW8l4qWz0VklsrU+Pr/9WI1I44bKAk4FTyeNBBrX32//H3vvHmzbst91fX6/7h5jzrX2Pudyb24SgiFgcUVIUIjykBJBA1oYIqIREKlAJIVapRQUWDwUCKBogSAiRVFFUolJKhgSeVpEEDUaKAiYhIdBkIQkPPIgr/PYe805R3f/fv7x6zHnXGvvfXJvspNzcs74Vu0913j16PHq/vb39+hwcBY1Huf4TIuHmv49o+NrXuKdySOjtihWR1xVqiQf6qc4kzs3hKlMielZijs7NZJC7nAkyLq4UTKIO9PIHxjql9G5qLU5Z/poe3U4Q/vo0AHmkfA2ssBHe5ZE+W6P3IAsine4mZXcK4+z89gnntAiP1YHzeH39ag10iRkzZGCwo/sMRZxlt5CbfMYkFZJ9AZELuhIMtxioqI+THuLSATBDPVJpoz0ilVommnDNpeyXIjiePqnETU4S0ydc9cjO74M36kYKDZu0gQWaTfa8L0SjUmV10my3UA12g+3jLlz0iCqazqXVcGuKUjcqlwBJNPhyxWk+5RtRMVHm+YS0yDFexrmzDRUsjuMIhrThfXOU4QDhroyp7jvIZ92dsOUePQ+1KvxLYkzxDmaGMWFBacZ/PXXX3t3kavyk/4lb4Tdm1YjEeJUYtqaU5Ca0u+ohLKynMI36LiccC0xwago0iu9HqM1MPjABz6Wu9ZYTk/pxxPsHN2/DzuFiWXWcBw3AfOYm2q32/H0tTegn0A6r3zgYzmJMUmhtgMQpqjHmlnqCe13lPmWE4mS9zSpHKpRkpA1cVv2QyaNTvLNuzeopxN5N9MOpwhTto76gnmG7KSyp6Pk3aMxS3nMdO7e8B7h1zclRgBvvhG+UR94/ApHmTgcDsiUOS0VlifkbvR2pKcEOfE4jUzGxycwZZ6ejixPn5B2e1wUOx2ZX/lh0QgtT1DZYRqNUa+V0+kU81IpfMwHP5pDW6iHJ+QE83TDd73+OtUqt7e34YhIfEh3b7wWo/M88WiaeLy/oZnxtC+c+h3H73wNXR0TZSa9/zE308Tdkycsy8KcCu//6PcjC7x57COPVqSlOLz2GnmagBYRU92BTNKEPNqRiGSj1sO/a96/yrI06pj0OMZHFg1grSNtglMmwUcD2+opHNPH53L7+ANAfODmC/V0xLsCFc050iv0JaZb0AnW5ys5GtvdPlIm1EhzkKSE8oOPiL1wZM+m9HnHPAuLGXZsqMcclqJTRLrWI2kGlVvq6QCto3OKyWpzAtvxqCin5QmFzp3vSNKiI/SIbDIz1BttWZCRDPCc9oMFdQM0siDrDlA0zVgxkqXopvuRjIYf4fiuNDlW18Zt+EC0UzSi3/J33pPk6md8fJCrZMJOMxMNqZ1aIghDc+KAYT0m8vbh95eRUHGs8jQl5pZY1M4RZikbN8TsCUfLLBQsHRnWEbSnmGpEnFzBkiNuLLNysygHOpoc7SObJxdytYa1h79hZ06wH073bXTgd3ReSYm9xHefPOLRnMZhJHIUh12+wcx44nVEQQ4HZ58Q4p1vxFRJNwI7VcpiHJJzi7NLTqFROhyGD1H4XS3nfG39nMNKwCMgIw9yUG34CEK8r2NqqoygOZy1k8f0wJP189QsTRylcuc3JDrFGq+rcpBQRHJ3hHLJru/OZI06nMIX7Viv7JPySCK5qsqBGy3MI3t5Lo6b0ocaeagj+31KSDO6ZHSYtZoSc3bmidT0nKtJUihg1TsnH8S1ByG7o9MVTrUP0YJzhvQbjJ0rJwnnr9sRkXdizxsCRWMyblNl7tEXF++cpviu1WAaSmcVoXrHPZzH0+rH5TFJ8jp4iOU4f/gJB2F+okTOxJGhvaxzMY42644hjHRDJdFloXn4v/XayUVAIqLThZh3U4I01iyXaeq8U1y4U2eyxF/9nneZQ/uGDRs2vFfwyR//T3qM5MORV4YDc/N6JjIqTm3Qc5CUc8c5OoiI2Bq+TsNStjoKW9pBDxNZbIjM5d0yRWJeutRXtWbk2zIF6cNSrOBKJkx+KhFZ1s2ZhukvgjNiWpbsnV0aJr0ezsVPLCbMrXbEJeZD3FknaQm/HdWIWrM71Et0vsPkJ3Q8hZqWxwS7phK5qAQmc15xuElC9j7mj43zTYRi0se1C3vwhnvl1pS5TKhUUkocTTksJyQnRCK3khKTSxfyIAM96usaplBZc27FoPdNIm+V9oXH0oeJL2YVca/hoJ/HpM/dMDLqwjJ8rg7qYaoa/kLTIKVJO2UScu9IKrwqiWZH+sqUgXUiatfEcYlkveTwkUJjeh3TIBhZMkcxikQEfR8U4tThSY25/WZCfVxyQhOkluOaxzyoZkFCJYWFYBmDMtPVZzPysJ2ssTCmofMedfYgVKsv3ZGYlaQrTCaR26zHsztqZMzf91CtllxgkHMj5s9cc6pnoCdhtzi7MUXYYSVxfsm87paw5NRhOTiOVBJlJVzuLOb8jdfeZcrVhg0bNmzYsGHDuwX6ve+yYcOGDRs2bNiw4cPFRq42bNiwYcOGDRteIjZytWHDhg0bNmzY8BKxkasNGzZs2LBhw4aXiI1cbdiwYcOGDRs2vERs5GrDhg0bNmzYsOElYiNXGzZs2LBhw4YNLxEbudqwYcOGDRs2bHiJ2MjVhg0bNmzYsGHDS8RGrjZs2LBhw4YNG14iNnK1YcOGDRs2bNjwErGRqw0bNmzYsGHDhpeIjVxt2LBhw4YNGza8RGzkasOGDRs2bNiw4SViI1cbNmzYsGHDhg0vERu52rBhw4YNGzZseInYyNWGDRs2bNiwYcNLxEauNmzYsGHDhg0bXiI2crVhw4YNGzZs2PASsZGrDRs2bNiw4QcZIvIjReSJiKS3uy4bXj42crXhBwwi8nUi8rPe7nps2LDh5UFEvklEfvbbXY8f6nD3v+/uj9y9v+yyReTzReS/eNnljrL/GxH5uyLypoj8bRH5jB+I8/xQx0au3qUYDeAiIh/1YP3XioiLyI/6ga6Du3+iu3/Fyy5XRH65iPyFl13uKPuTROTPish3ioj/QJxjw4YNHxlEJL/dddhwxlPg04BXgV8G/Hci8tPf3iq987CRq3c3vhH4d9cFEfkJwM3bV50fEqjAHwV+xdtdkQ0b3ulYBzpDzfgeEflGEfm5Y9svEpH/+8H+v0ZE/tT4ex7H/X0R+XYR+UMish/bfpaI/EMR+fUi8m3A54nIR4nI/ywir4nId4vIV4qIjv0/TkT+JxH5jlGHX/UWdf58EfmDIvLlwyz3F0XkY0Xk941r+Nsi8pOu9v8NIvINQ6n5WyLyCx5c/18UkT8gIq+PYz/lavtXiMh/JSJ/RUTeEJE/KSLvH9t+1Bjo5qt9f8co700R+XPXg2MR+QwR+WYR+S4R+c0froJ4dZ7PFJF/MK7xPxSRnywif2Pczz/wvZWzwt1/q7v/bXc3d/8q4CuBf+HDPf69go1cvbvxhcC1ZPvLgC+43kFEPnWoWW+MD++zr7atH+WvFJFvEZFvFZFfd7X9s0Xky0TkS0Zj8DUi8s9ebT9//GPfPyoiXzD2/ToR+eev9v3kUY83ReRLR5kflqw9zvOfjobiqYh8roh8zGg83xSRPy8iP+zDKcvd/467fy7wdR/O/hs2bOCnAn8H+CjgdwGfKyIC/Gngx4rIh672/SXAF4+//2vgnwJ+IvBjgB8B/JarfT8WeD/wCcCvBH4t8A+BDwIfA/wmwAfB+tPAXx9lfArwq0XkX3uLOv9C4D8fdT4Bfwn4mrH8ZcDvvdr3G4CfQSg1vw34IhH54Q+u/xvGsb8V+GMrgRr4DODfB3440IDf/xb1+iXAZwIfDUzArwMQkR8P/EHg3xvlvDqu9SPBTwU+BPwi4PcB/xnws4FPBH6hiPzMj7A8Bhn+yWzt5TPYyNW7G38ZeEVEfpyE0+QvBr7owT5PiY//fcCnAv+RiPybD/b5l4mP8l8Ffv2D0dLPB76UaAS/GPgTIlJeUJ9/A/gfx7n+FPAHAERkAv448PmjnD8C/ILnF/FC/NvAzyEa608DvpxofD9IvOcvHMlu2LDh+4Vvdvc/PHyH/gei8/8Yd78D/iRDPR8k658G/tQgX78S+DXu/t3u/ibwO4k2aoUBv9XdT+5+IFTlHw58grtXd/9Kd3eic/+gu/92d1/c/e8Bf/hBWQ/xx939q939SLQ9R3f/gnENXwKclSt3/1J3/5ah1HwJ8HeBn3JV1j8Gft+o05cQRPNTr7Z/obv/P+7+FPjNBJF5kRP757n7/zeu948SxBPg04E/7e5/wd0XgoR+pG4Lv8Pdj+7+54h2/4+4+z92939EqE8/6a0Pfy7+EEFq/+z34dh3NTZy9e7Hql79HOD/Bf7R9UZ3/wp3/5uj4fgbBLF5OIL5be7+1N3/JvB5XJkaga929y9z90qM9nbAT3tBXf6Cu/+Z0YB9IbCqXD8NyMDvHw3UHwP+ykd4nf+9u3/7VUPxVe7+tVeN5/el4diwYcP3jm9b/xiECuDR+P1iLu3FLwH+xNjng4SLwlcPs9RrwP8y1q/4jvH9rvjdwNcDf05E/p6I/Iax/hOAj1vLGWX9JkLdehG+/ervw3OW1/qv5ri/dlX2JxEq1Yp/NEjeim8GPu5q+R882FYeHH+Nb7v6++6qHh93Xc64h9/1gjJehA/7mj8ciMjvJu7FL3xw/RuIDm3DuxtfCPxfwI/mgUkQQER+KiHPfxIhQ8+EEnWNh43DT3jeNnc3EfmH3G9YrvGw4dgNf4OP49kG6h/wkeGlNhwbNmx4KfhfgQ+KyE8kSNavGeu/k/guP3EMiJ6Hex32ULd+LfBrReSTgP9dRP4q0VZ8o7t/6DllfL8gIp9AqGCfAvwld+8i8tcAudrtR4iIXLVfP5JQ5ld8/NXfP5JQ4L7zwfrvDd8K/Nireu2BD3wEx79UiMhvA34u8DPd/Y23qx7vZGzK1bsc7v7NhGP7vw78sefs8sVEQ/Dx7v4qIfPKg30eNg7f8rxtw/fhn3iw/cPBtzIaqBecc8OGDT8EMRTtLyVUp/cTZAt3N4K0/Lci8tEAIvIj3spPSkR+noj8mNFOvA50wnT4V4A3JZzf9yKSJKJ+f/JLuIRbguR9x6jDZxID0Wt8NPCrRKSIyL8D/Djgz1xt/6Ui8uNF5Ab47cCXfR/SL3wZ8Gki8tOHG8Vn82w7/YMCEfmNhAr5s939I1XP3jPYyNV7A78C+FeGzf8hHgPf7e5HEfkpxEfzEL9ZRG5E5BMJZ8svudr2z4nIvzUUqF9NOIf+5Y+wfn+JaCj/YxHJIvLzue/T8IMGCewIFQ8R2YnI/HbUZcOGdwm+mHCc/lJ3b1frfz1h5vvLIvIG8Oe5Umeegw+NfZ4QbcYfdPf/YxCVn0f4J30joQp9DuH0/f2Cu/8t4PeM8307odr/xQe7fdWo23cC/yXw6Q9IxxcS/qTfRrhNfMT+n+7+dcB/QvisfitxD/4x0d6+VIjIzxCRJ2+xy+8kBtlfLxFt+UREftPLrscPdchmKn13QkS+Cfgsd//zD9ZnQpb+0e7+TSLy6UTj8X7g/wS+CXifu/9SiVxY3wj8B8RISYHf6+6/a5T12cQorhPK2NcDv8Ldv+ZhHca+P8bdf+nYtpZd3L2NyMHPIaKGvhxIwNe6++94zrX98lHuv/i8axWRLwK+3t0/eyx/FvCL3X2NXHwC/Fx3/8rnlL3W6xrf7O4/6uG+GzZseG/jYVv0nO1fAXyRu3/OSz7vI+A14EPu/rC92vAOwEauNrwQDwnQc7Z/NleE6SWf+6uAP+Tun/eyy96wYcOGl4EfTHIlIp8G/G+EOfD3EKkVPnlzJn9nYjMLbnhHQER+pkQivywivwz4Z4jooQ0bNmzYEGlvvmX8+xChxm/E6h2KLVpwwzsFP5bI63IL/D3Cb+Fb394qbdiwYcOL4e6fT/hTvWj7z3qJ5/os4LNeVnkbfmCxmQU3bNiwYcOGDRteIjblasOGDRt+ECGf8rsdBHxE0k8jGHV3A3mGPCG7GbcOywl6AxK0IzLtyHQQpZqBGfQO3kASuEHK5PaEaZoAaOaoFjTNqIBrYWkLoopbYyozS4daTyAGmsFG3dxAHFQpHnkPXMDqAtbBFayCaPwtBt3CK2jeQa0g41qniVT29LaQygTece+IGa7O+3aPeHp4intjLnNcnyYkzfSuLDbq3DuooHRu8h6l09qJ4gu4cejGJA3VPaIa5xEQ3eEq7JIiGj4x1Zy6HDmdjux2N9TeEetoUsAxU8yMQz1wu3+MNefOGnPKzCnT+on9bua0HEmaEWtkzWjKNMDIWK9Yb7gqrTnTVOjSKWlHNzAUaEwGnU5HSSKYG906qpnWG26QS0IQzAwXJaWCpgKlMOeM4ORUUI1699bIeWK/f8zd0++ipIk+6uJupFLi/p6OgJHEWKerNwR1WOgkhHo6oHSQRK8nVMOrqLUTWTxeQxUgn1/t6hnJE0k6RRJ3x6ekPKOApgyquCqoxqvTG0s94FcJ7EuZMBdyFupimBvH1sAXOJ7im1E405n5BnIGjUqox72kL5eP0IYL8bJAO4IqtBP+pZ/x0tJbvCPI1W/80M9xkQgPO8PBHniEdYTrTEhq8ZvG7zIKSL4W4ZiCIKwCncjluJY92hKLfVYkgz7O3YXzOT3aGMxgGllKar7slzs4irmfy1tLXcvooyI6VrhYlNnuX6xKHNsfCItJLte+iKPrecZ687juFed6XN2A9X51cVzkvH9ZM6+IwJWi2TUSvazXsra75WGmFhGqOhJdB3auw/3fh1ivMcn9rIEyvnJ7oK7m9eGIISL4qFAd+2cXuoJI1EBHPXuCbJdymlzuuY2dSnt+LddTujtydQ8vdfLzPucygTV113r/r+9jaYLp+lyibK6OuT7f2uD5g+oJD+7ZetyD+ut4Gp4a7vmZ49Zzxfnvb0ke9ZT1mscHtH5HddxHvdpe2tU35EIxqCuX6I6Z81u+7Svfljw9bzskBRmxGsttgWkXDYs79Ia3IB8sh9gXQBTvFZLSaw9yU0qUVxvQkZSY/ClSbpDxQCaJ9k9FMatIKpTplt4bDeNwPKECacqIBdnpDoaNeoK64VnQWhFNLGJR71SCjCWN33aKX3HwTpoyhYSrsLQTakLZ77jZPeZ0eko9dXZaedKc4/GOnCdgQoAsHdGE5wkTmNxQMxZviCfEGk/9SGknHu0mhIT1uEfNldlOdNnTpFBWgudKs85tClIx24muE60o1QWRhLfOZLCogDqqwtScZBVR55U0UdSYtHNy4HTHThW8MWUZ30/FyYg4khzcKQlIytJOzBkm7bhmDEfSTTzidiJJZmlB8JJ3jqeK5BlXaOLsVJmmjInTe0No5M4gVAuOUNsgZiR6bxzunmBm3LU7VJWSd0xlR+0NQ0jTnqROr3dkjU7NEGiNZIa3hqpSTwdynkk50+pCmh6RVOn9hGsi0XC3c7+Ti2BSEFFyypRecZScM2CQLu1/cwsSnEoQv5TR8e4nOomEZ+W0LGRV2mJQ5tGQFSjTvc/sfGyO8tbt3hssx/jWJEO5gXYIkvYS8Y4gVzZmorvuQ91jIHQNHW3P2ve0fP+4tU9og2TlPvZ9C8unSnwM7ZrZNajJSapI9zPXEIn2T+RCqh4iGrFLn7H+deYD/X5/spK1h+gaZa3VWq9x7aBiBCqc+c2oYIJne2A437RrotQFLEEehKIOVipEB7+SGkUu5OwK9cHsWKX7c3t6eYv7f1W1F29/sLw+K5dBBM4EZoxUgHYmCvGeiMSg+vr9uX+OZ1c+4JhjnTyXJXaFloJEvKje6zoHeoq//IpkX3A5aelRs/M7/Zx3RZ9Xzwf7nL+lK6L2zL7nG7MSdrtXnbWMtSgbfz0cQMQLdFkuXSiNc2sjEh3WexbzHMQEPStNQIym+xFkhuOb0TgwRhwyRtoudDMsJ2g+GqQUJAfDW+VkBqmSbaZMe5wTOSmtn0I5cqHbCfFoL6pVXCe8eRA2a5S8I6F0tzO3s9qjreqDFJ4HJwIGkgt+ZthGylN09qmTy83okCvFGt4rJWU0K9knbrwj0jg0x6VwmzqaCikpSztgvVFrB3WoC6UoYpXkJ7Iq3k8c2omUb8g6Ye6c7Ij7QikTJd/i7jidasoimdk7TTIdh5QpVpnEUZ04tgV1o3ZH9Ja8SyQ6t1PGWwvSJDBnSJES73w7iiYMpxlkFdCZhtDaCcmZ/bwjpxTDHVES4H6ENJPzntorU050c0QLc+l060zzK3i/QyRUKXIm4eQ0I6poTlhTujVEQj3qgLlhXlHJuFZUZyRPLMDSnaJGBQoCaYeL4W602um9gjWEeM/S/AhzJ2kipx21V7JMaE705QmS87k9SCnTLKEpOnNrlVImHAlFURNuwlTi/U9FeXp4k5Iyh3ZC18EEoFrw2jFxXBIigpZpqLpg3sFOo6Gfx6DbSNMu6puiXr23aCvTDPVufVVj+SXjHUGufHSKNlp9NTl3/sB5xLx2MOtgfyUWaUhc6uDJLkpT0pByLPQdG8TCzwqXBYkTCbVLhAWhX6lRk8m9Y9a2jrWtG+uNONWKa/JVGrRRqWlc4zIkqLXubbpcO8SDEYHKqgo826uqQc2jnC5D7RukYtwcW8nHufcVTONYdUHbRSFUk7Pap7ZKvOAmiChO3FvnfpnqQstXHXgf92w8uOualw7Lg7duakHU+qjIOuLG4z1YCciqmKkTD8H0nhK0XuEicZ1I1EPG/uNVoLid9z8TZ1PcnZ4cFaENNfMhETurfuOPVTUjD6XULgTvemxgclmPO8niunpaK673yhd3WhLqIP/nl/5aeSvxDeQORTo1Oeb5nmpkOu7v+h55aFj3lDe9LFwTtb6SpxRKrPS4ret6JO6ZEB2NiMVjcWVJcY7sTs/GkoOMuYfyeK2uvudgDbQAFdJ0Gbr3Fqa03oIsLcer76zHi553aM5YbZAnOD6BtA/TIMRHooWJimshZ4c+YaqQJu5aQ8uE4diy8CgFWWi2BDtIu/gdJh9bliBwqqgoKWd6G/Ut60jNQYXsC80qKRWk7BAgyWMWGtY6ZdrHQC1n2ukJKWWmsue4PGXxaGtTfYoJWHof/WRUXqOTqO0UDcx4sWv3UMskcRK468qUHqPmJD3h3TAXHu8nvFba8hRJhVbvKPMrHO+ewG6mY6jcApAwyFCXI/uUSSlDr/h+z+Hp6xwk00+N29uJRwKn41NOrlgu1LqwK1FO9c7SOzlneltQCrtpj0mjeUe8ISQU6BamKk0FkUTryyCLR3Ka0FTQsuNYn1D7dzCX99GtMs+3uCs1Z1RCmalLJeUpzIWioVClglsogAlQvcHSjGPklNE0hzVHM9RDXDPQ2sLa2KhOLN6DjOeZnMq53pOcyAKab9HdTK2HeDfSDjNjN00s1qIdTzC5IGlHa9GBSiqwmipRRApFEy5G7/VsmVkRFuEUAzT3c7s65T1eZmpdyGUfpshU6Hi0UZriPohevh+ReIdgmMKfM3L9fuAdQa5WrB1Z1fvqwpksjeX1hspYsxKZ0qCqkM5D7fht5f7IPl9tvj4n+Fn1WVXkeh5tx+/aKbVyf3kdqdd0+fu6/qsyUsfJV5XFVrVoPeAFdp2Hnfx6frmq7/WB63btMgjR/f3a1ZNfzagtx9EPTV41X1fLSXa/zLWuK0lbSVY6+20Mk5hKcNIH11KT43Kp89RXlS3OsCotD7WOsyn0TA55Szxz7Y17JlxVwXu8PaL2XNVztd6+SHdZyz6rQx7lyuiQagL8WbPis3V9vkK23qNc1x3HdVliuEMMohObZxOW4meyzeWwq3ONqj641mL3Sa3Jaq5eP6A4OEbxIVddK5xd4zzr8SsxrNcVfC8iZWjtMlpWi8ZmdwPHZZAGCb7tHmTM11Fdp5FISfG2YKnEy5aj4UmiPE7Cshxolnjz7mkQoh5kwVyx5QjeEUmcekNyZmY1BTlp2nFcjmdVoEw7alui7zELtY3xfuYMw/+qJqHMe3S8H6IK4tzKhHsP01HKeNpj/Q5vHUvG4oVujRMn0AkHau9h/uwzSiORgY4Uj465NeiQ96/QlgOuwqlGuvIy78hulJQ4tcqhO1lPSO2hGC2vU8oEkqg9U9JCAm7mGfqJkxaO9YC6oWlmtlCS9inBlMBh6R2TmSkpuRS0LWAXn57kHbqTVVFA2h2p7GGp4WfWG916mNA0Ya1hcmCpC6XMYIZoA+8sOZOnwtQf46qo7kEnluWIlIlKQlIheydJ+DKJQy4TU5k5LocgLG5ISqgtaM7DjFk4He8gGd2DyKv4UMIyOsx/ZR0YzbcUDbXR2pFeE3l6FM+6L+S8J6UdJGPpDZUde7/DETp7hI71iichScZUUOlhWrRKTtDw8DfrNpTTCfGKSmKadjTriDuuiTk/AjNMld4bpUykMpHyjGsaw0APs6AUvDdad8IkOcUAwYaJ+yUP+N4R5MqHL2SoABe1wMdofVUDEh6EaBCKujogjU7wNBFqh8fIeDVb5aEyrR1TH428XfncnF1frkbu0i5Sb9MwyTSBUiGl0e6tpkkdPl9rv7N2SNKdyz0AACAASURBVGj4Eq32nK7U7MwtSExDw7y1ijWDIdjKUIbKIq40CWUOQAejOJ9nNZWdX6exfi1mnH/1q0mezuSyjXZSJGzlrqGI+Dg4XxFIEJquRC2Urj6ew0ookhkt+7lXXRUwS/HP3cntsv5MSEbva1eP9YWQy7Wfid8DM7KE3+uFEIUfMD5OKHrxDTjfs2T31LDzPXuGDNxnWYLg5meTsA0Z0yW+3bI+Fwsi0sPRj9RjbFYlTKpdPcrskDogTssX061rqESHOXyhdg1wP6uB6kIrDmNgVrOfVVtY/Qsv7wuADjVArwYBca5gZKHqcfaj0xQqoK7kswXr6sMJ1THSePwNIVn4Id4buLyHhSuGI+9ZscqJnPc06vChGoQqaZjgrMG0BzNUHazSh5oUH/CCeEdyYS+ZzhHJyk4btyiHLuzmHaekcDqF0ITRj0/Ju1Bb+hi1ay4Iwj4lFol2sfc2lKwaatr0KIiXFvCOzjustxhIiJJUzn4zmhJZld4cc5hI2OmID8dvVHl1FsiFg80cD4cY1LYFE2WadvQKOu/h+B2IJTKCq2IC/fQEvIcs0h3KTK3HcODsjTlN7LOwE6HLCfGGyT5MTmYkElkUFedpO9HaQutGkUJtDaTj3ZnnR0yckB7HnTw+pNaNbk943yvvZ2k9/JBSooti1sM/MwtqytIaeX/D6enriDXKNNF9YZ+jYzLCtSDIRyIJNFuohwPT7oZO+M6VIjhhTk5AUaH3SpkmpC9BcsqE0PG+UFRQadS2IBR6q6hHg5HrkZIytZ1YzDAzUlKmeYcPdSmXNTBCQDJdjKI5XtGc49zlFqRA7YgqIp1deURHSPo+emthnuydlHZY92hCr5rVxDB3ehvKv4AomjLaK6VkLAmp7Flqje+kTGRRTIRshjlIyuF/drZndzCnWydJQiZlqU6Ynybor0GOur9MvCPI1QrXUEbU5MH6aIS6CWdvaXim9xXGAG9VI1aH6PRwv7VHvGrh/WqbxPEufu6wi4XaYO4s8zBB+rMd+sM65Q6ncqUCjHO0dBmc2nOO95Vsjbp3giSsfPJh3/Rw+XyL1v2vSACMut/nphefGrt/7GlVS84Vlcv/Ls+eW4J4Pbzvl6OjFpbjOp/xd3qwuJppV8f0NqS29ByH+mtYCp+mVU05K4hvxdoebDMFdzmre8/sfmZ2D3ztHtTlonitql+cqq3vPCOwYFRvJZzngcGqzvp6HYM8JT8vr/U41+tKejsLo8+59nXQUbo8l0iuyu+qlq3EyoMPnsl15nJdcZ2DYLqPIu9R1mcr8l5BKqH2XEXItL6EGqNX+7ThqJbCZJFTxiVMhKoTZkfy/BjTRlYLH6QMpyWhec/JHM0TppWbmx1yeIpJp9uJ1mMgUI9PkDKR6eSUETe0vxEBf72zdCcL7OYdU35MnyakPOJ4eoNjtfBjsoi0a8tTRAruFToUzYgr1itJhJI0zGS2IAi9Vbx2nqZH9NOJH7ZT9jtDZKIuR5o7XjsZQ/uJMs34lemmLuEQXi1RaeSsiB2o3qm2C/VlOP5LmRATcp7DBCYdFWPOGVVQcyCdO/iTnchlisi0arg0mir0E5ixm/YA7IvQeUxtR5JG1F5fDkyp4BpBBuqNpMrj3URvleN+Tz0eeWVOLL2EL29QITKKpT2tG80aboY2o52OSE7Q7uiL46oUc7oJJ2DKmX6cSLnjkllqJ+mCpyBGi1VUd5gpvRvNFkQTXSfqcsAH2dzN4fPVa2e/u6G3ClM4xU/dcVsQd54e7+i9kcSZpkxtPQb241pKvqWboeYxsBJBBmGVckspivc76A3FqfVNrN0N090t0k/DpOlYPwAZc0NGJORt3tNtxrySUgQ1uQquGU3CYhVJGkQPpQu0egzn9npiFuGkgvYFS7t4od6VDu3Dvt/VRgRVNMJqEoSC685fzmRgVZOI1fea66qhELj7ucFaHXAfRjtFHeI3jc7eXcbopo+6RTRYMmUEf0QnYqsidMWyLn0aLgxSdulYSmO8hFf1PRMRZe6Xa5F+UaLiWi9OxNcKwCk7U7s4Pz8kHl1DPTib3FIQv2RxHyV2Gvdp1GTIaSuhM5cgEEM9s2H3US7lQPjk2FgvPrali5UjRzvG6rr10EJmq83sbL5dFS4jt1DWcgs/MLiKEhzPYmrwxh7y6sM1CopT+nn57FPmfiaCK5m5Jp7jtYtR/EqSnmfWu1ZkrkjXNS6LennGyFmx9OFHtp7AUtxDl6hbHyGVSgQcFJfR4QbaCITIEdlAX02ewy/qYQQuXH0Ho/JWWowAhywrfahfOS4wXYUuCnFf1/cxVLhQIf3sfxa/dai9WQzv74im5+2BDSd2PwF9mN9K/I2ApOGXFaaWUKgyLWVEEtO8w5YDUl6hm+HSaZYw9iyeQDNeK2XKLMsByiPevDsOVyqndcBqmFsEej/RZCKnQqJGZBeJeXeLnF5DLLEcj7R8DBNQPXBbJnapY5LChNgO3O5uUAkfHeuCqKNU3CtLPWGqeHiTknBMI3y/+ImbeWZpEcFVl+9kyrcooYZZPzCVOcSonJlIVO/k2UiiHE537BNkLRzqgnuYszpwOj0ha+YuCYUZ3NB+RGXCpDP7kTLfotPM4fCUKc+IC7OGf9Yu7+m9ob1F1GueMTOW1lAVTuzpvbLPN7T6BHdjnveId45WUDKiMWC/Oz1lLnsmr8yP52j3vIeJsjc0ZY61h8+Wnei9MUuCkjFv4UgPZBJWbZjUHFOhmUM/4D7HKKjd4anQOSEpk7SQeYKyA4GDG4mb4RdbEE2UbLiE83f4j4ba6PUuTG/VOC6hdGmZVztDmBP7MXygO2g/jjQPt5jdQQ8/u1pbEND2FFGlyILrju4V0UL1TJIMKNUrCUd3r9LqXfhMEQTQRTGMKc+4JHpruC/oCCwIM2lGVGntGMpWW9A8RTuaZrwtaKuIeahWWmD1F3tJeEe0cNdC1PUo/OxQ7DHKuheq7g9G7PfKi+MSICpnE83D860jbgBd/bNW89p5AC8Ptl86ZLhKH3EdJSaX9AY87Gyv9rkuZ01rEArF/Q7zeWjKhTxypchcHyYXwpMb4ZT+VuXKvZ+zknV+HnpJaXG9vr1QoVpVrPuXf05r8KCcFatat/oJyZnpyFkVE7kKeBjH2Xg2d8W5XYTT2dx4f8f+sL5XEXTna7+qlPiIOGyXHVoZhG6dcfFFz/nhtT24/WeRSLnIPvcqctnvuVGO4x1d37dVFXsRnuvqNFa2EmZ38wjznh5sd/Phkxc38BJ4cL/cgpDdz+bj9duYXIba+1bS4bsf07SPQZsotS2QR/hD2ocy4hK+TKLR6K/RhC64wOl0DLNbG07oqzqQBOmnCKyxRj0CeQfLE8ruBncnlT3Hw10QuN7oouynRxFcARxPjXn3CE+NY11IaY8mSL1h6rQeZh+rDcqMNGefV2m5IHRy2WPaEFFqa2jOYeKqCz4GRNZPmNeIdItLC4JgBuUVln4MR/se6R6OFmacJAe6FmSaYITukzOqjqJMqiynIymNKLzpVTRlukRk3yQw5ZsRpm84hrcjh9MSHXw7kZKiJFIO8pny0GRFzj5nqkJJgrdKFqX2FukSzHijdUrOTMkiJcQ0Y+1AyTNpOMo3O+Jm5DLTrDGlHKQ2x2DyfVoiVcbqn+UGEvdZVShlCid/wEXJ0wR2JGc4VkKpk0SzQu8dtYWmmSlX3I192iEWvk8mYVZ0V+pxYZpvEVUOhycjfUcLHzCc7I5bp5/eDFLoaTjth9rgKdFxtB1Z2kIqmUzCeyWr4B65t8SViiByCuHEjZQjf5eqoDnM1W6VkpzuDRchs5pa9zSHlHYRICCJZUS2itdxbI7zWaR1wAXDOREm6XLzKqfD65cPs7wLlauVJGgS2pWSMZ19o4baolBsRAlqJDizsQ6GWdGBHBe29ntJVrPF/R4wX7XxTuRoUtPIV5WMGPnr2bndzC993+jg7Rx6uK4fKtvonVfn+WXYleYWHUzTiOTTB2kT1IfyZM/vgGyM/n04uAbPvHSnq1JWfCgI96LULh2vqoTjYLk4W6+kJ51Dzc435wyBi/yx9p0SEYClXXZalTPn4tjsY9ta3Lku3OcVzZXJr06fLa61h7yLGK1AHv6jK0n2kYtJPVHLswF2nlYSFf4SdkU09ayOjeUrNcckTJImnFXVVUE8R0RmIbco8zpP2kML9LUZ8Xr7RRIby+Mehw+BgSlVLmTexEChnt8TOf8vw8Rwfb6Hb1NVQYepe1XN1mADGffDxFG7kFhbJarVNNmV4qGgLRp5iMZNwTwH+RoqZdymESTQlYe5tN5LWJYD6ARa0InwV7I7kgyzkk9AG75ZBNEyIGU0T2FeE+hV8ePTCKHqR9CETTsQp6RMSrAsb7Cbdsjdm5xyoqfH5LxDWiW6IGHBmRzcGtA4Pf0uTr0HkZhv2e8iyENJpOHfIznMbCmnc+4++im+tXYii5HTLhz0gCTzUB0SSz3F4MgkohE9QS4jWCvhpog5zRq57JmyxvdjgnfDi+B15FOShFtFZQaMKQmv3uzPUW8pF07LE1IqzKkAQtEoL2flBIgkUprRJHiD5I5yGu/6CU272CYaTtU5jVQFJ+YS6+6WGn5AQBk+F4aCxvejquw0rDNLNTTtKCnR+gnXCS03mB0wCbOieaQrSL2Tp0LSRBuR682drIJKmLNcEs0a1pXmPRSurHTvGGH2agbttCCLhN+eH5iyMNVwJk+Ey9pUbqk9zLopzbiHH5bCcLqP5yzi9Npxi2SuIoanPUWUkg1xp7cTey0clydoiueqUkLRTBkhA5GAFCkRTWyd5gWY8F5DZcwFWmWeX4lBBdEzG+HLhlv4WdWKulCtBXn2hZ1mmp1wdiCRSmSSjGWltiNMN0Syrcb9+O7vP94h5ApgKEwSvh/n9Ve9goqEi8L91Rd1hvsbLkQofl+U42jdZ1W81n991GctayVUa1nXZa9Y16twLzVDWjvvdKmHqJzNSzYcnV/kG7OSMx2ResLq8Be0ZI3YOkcjNrlnAlrVpZW09XE+MyfL1f0GJK9h3edYi+fftIFpdVx/QYe5Pp+HEXLXhMOv9pMo7LzDOrI9+16N45/x6ZKVbF7KjdVXxGOca1XAro+9hir07jz0nXoGD7efHaLG8os8tx8W+4JlOVcc8Ms9elEzcH7/9P5y1fvP+HmP9OyPNza24S999jc7fyPhk9gjgGtEj17T4ysy/Zzynys3v5fgZYwKFdMIyt9Pe+h3dG8cOpTs1HpH2b8yMlh3cklAZ8qKe7hMHLzBNMHSAYfT01DEitCagGbunq4ZqI9QLHLAWYv9zenHI4c8xbpq4D38T2yBeuJQjQ981EeFb1hdSDk0TfMFt3BYh1AfsirqhqHUFkZ4iNNrmbDauEmJ5mNgqWlUYwFvmHe8LXi+QUXIamHSQcl55tAbh+ORR9MeEaW1Tu+GiIVzdxqdrVlEjtEoc5jy8iB6tVdKKhxqJaXMqb6BThPqOaLRlgNJI09TyjdBdPBhUprI6kx5wqaJ5D7a3gw+oapkEbobHaMvJ1jeZF8SUyq82Y68+uh9HE6V7nF+18KhvQlMqDopZ+YkeGv0kWQ0DbJsQPY1p3tkHs9JOLaKSZBN5kLtPZzDV5ebHln6o6k2yBMnW5Ci9L6w3z8Kv2+7I2uYQrs9PbskmAdBpBs6sruLS2RZd0enx8jyBil1st6EuXMuJO+RP0sUoaIiWE5hiaLjvqPSMW8oGcdovVLKDtFCSkBKzPMOTTNVchD4vrDzeF1NODd0tS6hDIpj3um1Id7J7fVwkXCwVGg2FLk1c7UA9fRSP/N3BLlqjD4pPNrDMc0dz/dDu8XPXgnA8KHxkEXNoV1SSMf+Y+ksQowVaURM3U+KbmelI4iI0vPFp8sZTrxcmEJq16rMGnG15voBLEX0FyBXdsMgABoO2msWcb3fMVmK/ByMqI5pqEJ9yDwl6Dtdw6MleZCNZKFkmQ7fNPdBJsKM2iTW58EbfWQzj/OOD3E4qU+je1yjMi8dc+yXfFU8RgM6SJZo1H29pnN+qjWn15Aa81X6+fDPupg5bZXZBM55ylKoYyKXjv9aATL0/Ay52rSyuk6okoIOeZFnMZRDNyBfBKVs8e+cJHFlzh7BCdkiPxguoSroIBMS6s1q9r3kbosowFXh6+nKxMjlXRU4v2SioSiF07kiQ+Ze7zlck8vIQbWquWpgGmY/tyjLdM1pdiFVmUui2jPxHw9+VYhlvEOFHuoZKaIUhy+cyvq9yEh1EQ/YZbwX30sainc9rINXaB3ZvcLj2z31+Aaebmj1dUrqqBYeFwep0cETfibmRjscSEmRbszzxNJamFot3ov4GDK+ZgKc9hFqrjO0Gg/HiBcheYzaXWF5CrK7KGHiIDNMt7x5OHE77cilIDitLSRR8AlwuncSHlnHCXWlSUHdQuWwCr2FQ3l/ipDI7tReSQiSFFu988pEt04TJ3lHVEgpcViOlLSn0Di1CM1XCdOaW2XpFYipVSKNQ6h/zSQIz5jyRzBEZ5JHx1uSQK+IhE8VKZPEKWWPimEeCTp7bygLeaTQyJop2lEXemukIpiFHxop8UiM3e6W1jrzrJgnZm44nBbmpHQJH7pbhWo3iGR2047T4TVMhDIpy+IsfvGdzFLoFqY9aKQ8cawLr5sxS0F7J7mTJCPJWDwsPElASz77HVs7kfMUgoZOvP70CbsyhWnOYzqlrAknlCs3o3uP/gJBWFCd0aQUEnjlZr+PftVPiBQkZSZxnERvhuZQ2nKaab0heaKeKtU7UiudjomS8kTFSNaYc8FJqArGgngC69TaGIk3xqB6jpZQNVRDq5gkkrezWTBZxXund79YSjwcnL37iwfC30e8I8jViocqwfOmA7lGG5GDa8j9RVZ6puD4WdltjoZfrueWec6xIs8ee11XIQjEMz5dYxty6STPMssa8gbPVdnOJjNP921lD37b6ET14YFDXbg2FWow17MAIiK0PJKriVyZwC71xy9Z6z8coWFVgxi/MTK5j5bvr1mTg54JjN8/mzOCEuzKxHVWTzj7tkV6jugfGlfO/GtRq+T1QDp73nWtquVKJFb+/fBazn5hQ0m0cwTjIJpyUSidS7609fGvEZP3lNF792as9yAl16rSeUoduewo4vfahlBCn3OBa//7VpCrk3NRsuo4rlioc3UYKc+uVSL3r/vBN+NXSvB7GpqIoWHB68Jrb47UASmT5g8yyZFijSkJUzJ6e4JrAVWaOpqUu7sjsxjNQUhUnC5GzoXWI0w9pV3Mn+cWp6sV6jFG6BIq2BlpzRB7B+k2mLU4UpRZDevCXXUE40bDz6i1E2WycEovE3QlJQWdSFY51gVXRzWRBSSFsiP98TCxL8gCzTutV3rvuDc0FboprTWmnJiHL1OWhTlHDiQdcwaWNI/lTJqi855SRrwPvyqnpERSBiExPEmoY70hKYe/EgSBqHdMJcd0P7bgqqQ0hX9YVpLGlCyiiqKUkrnN4SuVykiG6ZnaG0kSVZWcEqkIxStOYsqCmXBqlaUfUfYUDXIndmA/J04NFjK6Az2dSHkX5lgpuCq1LpAzS3eMxOPdq9APqKRIieKCG5FMFmEqSqdiEteapj3ztONQ7/B2ZD/vURRbTnRZyHkGq6EChns7duXjO6dX0FQRr2G2lUaTxyR1dpLoEu+ELE+Y80RVoY9gCbwhXrHD03jtekVyATfyHKkiuhSKOrMWnlinVWMWRfp3UNKM00n5MUkWbFl4szWyCSc6VVK8Lyn8FTUVUo7ZVtwFeie1iMTs9GhD0/xsI//9xDuCXM1rTzIubjVn9WdsbveXFR2jtYcMxCNCA84dc2yOdWmoFpqE3p2UBHtOWH+y8KGJlz56jeH+E9MZJKWYxaSW5yquuX4c1MP+zrXJ675vjYyosevnev5brjKCP3DJubqc0WFelBRntN/uXEelxTGrynTlVX5vh6sy1z+vIiEvYfXQ5SpykWsz3X3WKP6go32YQHVU1T3yMk2D1VkOXWb1mROLiE1FyR4qUldIQ2rJa+8/5hyMbL5yVg1Tf/6lXlUJ99jvodM9BLlY09Ktqk5PazqNWH8qqzIa8uh671KPCNbVxtYfsGK18Pd6Zr5GLmTSlPP0SddEtuNnNVF9jeEJVqcNkjrtag5D4DzHbrpKpRG+Wpc6rY7na3QsRIPRExF56nK1d5TX9EohrhKJWVeiKqFO9vewvxUA7TScQqOzR2J6AzkLTz2mivGG+8irtDxlnveQo/Oc5wltneILc8oclwPdHZHKPELSOyeMEr6pa8I1zTBpmA8hHOZbi440T6Fy1e+B8irkQtGC0M7mOQArhSSJXCZUC1l6mPMk07qTMFR3JJ6QU+ZkxnzzmG6N3uOasyZkSvQusDQQUDVsNCJKC78pzVSPOQGk7OmiiOwi27iE714enWRvMSFx4sjNtEcxlt5JaSInxcfEvd0JcqQTzYyicY9rP9FFSZ5GIs4w+SU64kaWEilTXHhSO7dlJvc78u6GG5mZ0o4lGe4dyLimeNdbZzfvSCbspsypOd0aqQrF5iCKkjET7uoJkZlOo/sQEfMN4jGd0NIXrBu7eYe7kttC/f/Je5cmyZFkS+9TewBw94h8VPV03xYKRzjDBX8LfzV/BHcc4XDYl/fR3VWVmRHh7gDMTJULNcA9IrPqjsztRUkXNhH+AGAwGNzUjh49JwaaRUI8uHdkT5eKGBbGzpkSUnCkO+SMqMsqHJKgMSHiJtohBaJkJAQaDev8yBiDm39LorUnVBpmgSkPHRYLDOLzX4yJKQTWAMfDI+f1AmLoqqSUiHFyjbFxwlp1W0yAMBLImBQ3nVblRReaKnmcCCETpWJi5DC6AC7KrNWzRM2rDkWM45CxOLKoQ/VrKczL7GbgrXp15jg5uk70Z+BbtnH/ju1XEVy9rTbTnsqId3GTa+q8vnhHBb62KPEKwQ0h/PrzbXOZmV4B9zNf2nhQG0eqhc0E1fffBTC3Ev67/QDazoHy99tWjn6XYvklOPIm0Gn7/vBaauGXxoR19MtREtlTNa+v8dZ2efVmn2Q3BDF0vOUbaNm3T377/Fvefbfj94lXfh6l9Ca9QRd7Z3xFju73LO739tvHqXf73Svt35/i1TXYHY/uTR9sL7f04Xaf7vlfctenOw/svu1yQ7h2G5wNFQq34CuEXxwyALRerWHN0MRXnpbf4tG11BcSb75706u6XVMQLz7Z2qi9iAJuhQQhCPdglUo/1m88tnqclMvqnBxXtnUS9ZFGFhzBCngKRtULU4YT13JB1L3fskS2soWyPPE4TFRVR06CuCG7Nda2YCpo6QOnVa9I3CsQi5fRdU9BmkJ+8ACwLthJqEY32nU+S7MGClECFmRHMFMaWNeFSKPOV3JORINDGlibMTJALJgIWq5AZcoDGbhcn13RO2S09sAKRagsFXIaEC2kwTk+oitIJHZLHlFjGA+YVt6fjkQraHGV8aqeVEvDgGlzQ2jxcToMfp4UI1PXZtoI6DFEr7CMjWN+pNYFbRem8dFpaRFWIosJvx9GZlk4Efc0ejXhWgvkgdUWDtNjr2hbCXFgjIG1KGsrjJKIKZNVMQu0lKjrsveHiBvSi3qbgzXMivsOM/J8PRNCYsgTS/X7hDbMcOkOrX0u85QZwYgGVZUYBkeUtmo5VVdcT4PbZXXkoXZ5kGwJDYkcc6f0CClFVAJTzMytsDYoJsQ8UWNDSWj+yOfzEweqe/61ggLD5GrzpMS8LlQTvPbT/RxzHliasdaVKQ0kiTQrnHXArFIskccRsYEoufOtXLV9rYsr+NeKRU8hMkwuvAugjWzmKcmvEJZ/3/arCK5gC3RuP+DRbkGRSugogL/+KoN3Vy24v9/Jf8S771V5HWy9IX/c0hk4/wvYzGuty/9vGqZbPBa6evX9DBJS29XNm4VekbVdlx+/7WmkbSbqQdvrS+rt6jpD0afkcJd22Y1/eypwt03Z/naxQO0/gDcz5luQsb13fxktQLKAqqc+a+jddRcAvZ2gwz4Jy5tI7fU1RVGsxZ1suXF0ivAqU3G7Tb3v7zwf/UAedG4o3Jq2awv758CuQF67ttfGg0t667M7zPN14CJKVpijpwo3bp2gjmTdaYVtjdZ4q7gsnZNXw1YV6e9vGdKSnUemvYJp66PWx+YNJXRkyGLvoru+dZmSrbl+/A0B0yCgnftkEKKnD0Nf0ZgAUf0+v6lQ3Z6FPaiKULgR3HfyvN3uofeh88dK8r/an8HUzT3jbxy5en46Q5ygXh2qipGW4Fkb76ZErGv3doOQDqT+pIWQib0Mf4pQ64CVSh4PII0hGE0iq4aeMs9EIMpME6G1xuqy2l27tMEmuNH5gijQimv/qFLWCtEV03McaWqouelyjpFkSkp+g0s7k+PoCH/sXGHxdOZpyK4b2F0vLmVGwsiyXNH16imk7v+2PZ/BNpS6EMx1oJpWYjDykEkSiWJECST1iTMNB4TAaoGKmz+7QrkypIhw6Kmu5tVxqfuPpgAqzOuVMY6k0InTKZGSpyhTFIbxAcV4F90bMuUTP1ahGfzx4cDH0SjlSo4jFx0Zykg1Q9ORtbqZc8gn55vGhQE3UG4EmoKmEwnlQWY+Hh4RNf56DTRgbco0ZgI9PahQUVDIQyQSUVWGnNEGLXpVo0qDkBhjI1BIaaSaSz2oNZeKAFqvMhUzTCEFtxqS1I2W6Zpf6QQBJIxI8PbEkLA0MmthsUZZGxoT59IQGVjWQkorq0TWArYUBoEgA+e5UFokzp+JHCnSCEFpBBY5YWWl9pTqUl2XbcwBrc+c4wiWnOssmdKUy3ylCGSbKa2SXJ+Bzc6J+QWa0qQjxzGBVuLfo4joPXhzHxt863vf2r5SSf/Z80jXavr2j7sIO3n632xv/7/24GOfL0IncN+t6r+1/z6d31V13R+Xu0AvBPlKyd2PK7xufQlCtAAAIABJREFU7NcoXUhhD8627+wnfP2OIz70YC4AraNVHXF5iyp9jTL9wqT5tqmvTvwz7/fjl036+5vHlJt6+VYJ+WY8bH24HXJDYqSTvkWEutkK1Q3N7Hwvbvvdb2W7z5s8wZttS5HuRXRv2n/fRg9+t2vwv28J+/IL4+lbw/krsVDxMbpxswy7IXvcENq3xzXsxi/b3n+Lqm3X8fp0eKrHtZmEGzfrtyzDAHgH1sVvUnRRxRAHDOHpcuH7d79j6sjjvFxJKXPILiNggNaZYoLUwpgjQw+mlvXKaXzgnV66BUnkOi9oPrDUCxcRBl+ucy0NQUn9pjRzwUjEp3papfubgFZCitRSSXkgxuRVYqIMQ6aujpSs6wpD3GUQACRE5rJSratl96q+UotXnRHRHlTVsjiCom4arGRCdGuagBJDYG1emTdX4f0gTDmTY8JMWMwLAVQLQ34kRoU2ux+feL+rzeQ4dm5aZchetrOWmZQOjGNCRBijy09clspVlUM+IaF6WikmDuNEkEAOjVIOfKkLdX3Pf3t+5uP0nkNQHlPlYTiwmrI0pcWMpkziQBwryYRDODAvV1QSn64XpugVdtoCGgqjwO8eDnxeGi0o8/zMcejivqJkgZAn1mioumGzaKPi6ckQxq7BNWB6pclE00iziqS4G7k3U7QVJExkMYgRpYE1tAkpDJTqIqvESK0F24I8BIsnFvWMUQsR40w1mOwC0KUssuujlWdy8lTAdXYJktoq11oZBx+rh+OR1hq6XokpcowPnFuhsFLbSkzvyLIQ7UI1RdfIl/mJNB4J3VGj0jhOR1BDyFzmK1xePAUeXO4kJf+hbXmAl6e/6WP+qwiuVMINadrfe/2de8HMt8VGjrJA2EnjG9rkN3tL69mdsNVWUbUfX/0cGx/EYWPZESjrk10vLrgFdCKU4EhBTRDV/Lg3+o8vCHcSsvUqwa0hQomeFtimfP/TV6v9a1sKZkM86jZ5GWySCnJ3njkpWYW8IWN3xOcm9oprs82Ssv1vHZ0It7ggccfNka20twchG9l6Y0r3tm2BQrl72+9LxGJHjMzuFOH974ZMbVphW+C5BQxjvSFCrw5899f0xrtL/Zq29mw3rwU/qd+OXkUTu+VRP3fU4Ck+M3cLMKNEY2x+zCJC7PyDEKC2Hih1xHPn/nX04W2fDPU1524LWEoK5HqH0vVe2k2mi0tt1KxEvYGwm2ju6yKBXnAojqRuBWVVnUtxL5UlCJvnXYh+I1svGtgRq35sjUZq9wFa309cMiWFSkIIlryCNZmjf1G+GRD+ZrZxgNKDl65YrrUwThOH8QPWFmQ6OqdlOgKVMQsxjFCemHugMeaBaVgRr6HnQ45Um31B1VZASLFy0YpgSAysaphkprELUrJ48aJkilaWJogMbociI6VcnahdVyR0WzALjmBJ5FIUkcC6TeAIa2vkNBBChGAMEgkRmlZqrTxfnxEc0Z9EiTF66i4PzpuKA8kEbQtY9EWQ+GomSudRxsi1NSzBvK4MKTlCXN0cOlIZh8h1MdZyhWHiUhoxZt5pRYtxGiciXlOZxiNI4CG973WZoBIZhsSijSFGR9JSIqQjxQq1LLR44KdWGEJi0IUK/FAT0mZOGb5/8BRvjZGJyCllPl0XZoscwhGtK5oGTGHMI80apZ+nFuWpusr88eGR74nYODCXmZwH1la70KgwxuOepVnKlTgOnhaslRQD1SoNl82oWgnZ7XuSOWk9qBtdR1PqujBOJ2xdoK2YRaqufk/Bq+/EA+NaK4TIl8sLtSopKEMI1PmT34903DX3qE8EcLX+UpEGMQSsc2by9J7aFg7DBFXRWkAi83VhSQ2NgckghUi7fqGUlaKFKMowviNqQWdfWGRwIVlTtM1YLQzaWFLqpDugKnVe4PAI8/mX+TX/A9uvIrj6ue0eGXmLbt1vMciu5v3mCK9WyfceZ29Xzzu36ufasOtJcff61h7f/xsVWvGrr+/BXm/izyJ1vaV9nx5Yvu2DLTWzoy3+OqtwX5y3G/faHWr2ZvP+/flZ757/ljoi5ke776Pb9svVhlt/31rztiLz/iJFhLSZIfc2bODdfr4tiOtI1Xa0ncf035FSl1fw4Zv36diB3PEEN2J7whWD36Bub/d/2ydl8828C8a/2a6+x0ZMt0FcvmGTS9jXCbYvBPrLvTn3z1DsQaV2yYz7FO9t3x4sBXcMeNu2nxsppaN1ZsmDz34/9N4X9De8TUlYNWDDI9b66otCMMi6EvPIZb6w6MC5iqfpwlNHkXDjZK2OfoEvZEQpTWBdQTLEA8cw87txYl1n/vjhH/j08gOShWZGaULKA2WZiQJFZvcQPJ6gKFertLa6tlaMSM4spYA10vCI6gsBR8dMlTg90MpMNTw40cYaIm194ZgesHLFiLTVJQAM47peXYQyJtayOmk7JqIqaUi0tbi5ugQwJQ6Dow4UjmbkcXSCdTReLJHqmSkGYps55Y/UduH9OCLjI7NW5tKY1wttmDiEmUbj3fjA6ZAQXakEL90X4SGPWASzwKmthASNxFqNQzLWKpwePvKnSwNLNGlcmxcMLM1FTdfS0GVE7cpxyiRRWJ8ZDweW2pDhwVfPRSFnBiuIHKDNlFIRCtNwIDBCXZh7IGNWKJYY48CqRsrRCxriiTAEfjcILGcuTVlkYWnCIT4QQmC1wkOCUl8Yjx+oRck5sywXWl2wlChlpnbCu9aCpQFRp4iUcibkB5oIpaysqo5Kh0BsFYpRxUi6kMcDFiOpVdoyU8uV0gqiiZiNRmUhO7/UFJHVp0sF06tLjJYZsUBSo2jjfF05DQfclNsI1QgSWNdnFylNkVgLS5sZ0oHSLozjwNyqpzbr7CnvdZMpydBWD7js75BzlcQrit6utu9kf16tdOVVAgKk9c/DloO5pXLMvFrK44CtosqPcq/QbcEQE14FdHD3nY5ABN9/n+BFdq+8qOx6JPFnph4RTxntvonRXMqqV7xtYqO6TaL9/NHEJSTqdpx9lrwR1HvbG05+hDsBz/1C76/Ot/0axQOSiCOHbk5tN52qbd/oHIPQveW2tobOYdqk70W/3QebJx1YJ253QUp9E5NsaAzS06T9+jqfgzfHj0Fc+FUFgpDMqw1bHxLfNJLuQyaqexRuSeG3hsl71WYMe7Up6v5ptHTr3w1h7G2rm8DihjyJ875KpwFs1YRpH+C2I6E7ksRN/LVm7UGtp1fu1eDNun7ZfcB5F1Rp6IrsuHYOPQiLsgWrvei677O9FsCCCyPu2mVqJJO96hB88smqXqm6VQ1KI3TjzM3rsXFvAv7b26yuLjKss1czW4MwUrRSrPh9DF4ZeC6u3UTrAm8EeHnuC5mtmkYozflshAf3SCsrF4386ezf+9frj7wblXfjyEAgh5XWzpAyQY1ApIhxXs6kfEJacCXtPLh+nESOY0/t6RN5OHhKMQrFDixlpalX9eVpwrQylytBApUraVNMjwnVyuV6xUScxN4KEgJGpqiiDcpakXBgqUYO4mKbKkz5AYCiM8eYQBKtFXJ5RkIiZrd1eSnPHE4Prt7OwhQjLUVkeKBqZZy+57KcKWvlZA1tK+MQeH984DI/M+SRwbzqOk8HmkFpRutWN+SJIsIhFL57GDAzl6MAkMSlLCwqvIgyjR9YAsytIAQOmsjpgVkilzIzii8xVZxTF8NAlEgcRlQLB3lAtWHRWKpXE9ZixFhRU5eUUOUQI8vLM3IcKFoYg/Lh9MhShOJLKapkDmmgtYUQldWM0maOKVDCSFlmxmFkWRdSPjGMGbXF0aDOSSpEnucXAm5to1qhVII1F5EFQjzQ5spwLLTgc0wUYy4zTYy6+vEG8ZSty3D4YqG1F9S8L7MTRV0UViJzHqjaUFvQtRDDSIiZQQIWA/O6oChTHihlReLA9XplNcHq7M/MXPxZWgvgnEDq5W/+nP8qgiuVu7Sb3a3C30wUNwL218tfuUOW7tNJXrHUOURf/aDLL7z6N97tqY/NpN16wHaraLNX+37FV9pQMHM0ZL82uX3j5877b23f4urcXrv9wT0KsU2Y2/yudAseF9H56hh3F/H6HHhaag8TdrTs2zw37QjQ7iH45tK+0j3bDYO//blq96DsaGB9U2n5ptn7obZwPNxVVNT87Wu9f/0zIJfvv0VbG7LY4cmK9KBGvxrf24Hvkc0tOH7ldfhmAdAPvcV7t/MBit3GGtvYkJvoKK7yvKcbe1rxNj5lfy5z9Oo/Jxrf2nATi1VC0J20n0XIfSGidmvTbzoliAf7U4hctBCkImmiVcNaw9p7iMJf5hHaJ6bxwIw6GiXBa/PzI7QXP5h2RVjhVgko/SRsujEK15WnJfA0CNQrj6cHvhtPHCPM7YnHJDRNFBv5slZiiK5oHhLnZSFI4DBkrFuNFC2sLSIt0liIMSFxIEYv48/xEbVnFzNOmUqktEKp7hkYxViKwDiSqoIuBJSKsgjkMKCWQGClQoMiiVIKSmUcM9XAgvJlLfzh9MDL9ZlVJyAz5MS/Pi20mKjrE++P7xiHI1YXhmHip8tPvBseCaZ8mSvD8EBqjcv1graZusI4vPdU+nrlmAMaR5Y608JEKSuHfMBCYwqFh4ePXOcvxJi5tMLpeGRsEdLAGnJfZGXGYeL58oW1zUhK1Ba5toVEJce+yDKjNfXFjEWuVskpocGRu5wiqVZaEAKJJhkLhSqNMD3Q2ozGIzEk1laISRE5EPCK0SSN4/HEMl88Q20DqkpZZ/RwpGnhD6f/QNFK1cZaC0E6F0yVWWce8oF5OZPyRFkXLAXGMFLLhVorquJFOuXipt/LjFjhNAw0BTOvbE3iKWZRQ2nutSgjVMVsdT5eWzDJPqx1RVWZ0oCGhikspbCKPwtDHlgbqEbGaKzWWNYzEGGe/VlJRyiro7+ruGAu7AVWf6vtVxFcgf/oh74K3xGS7cd448PY16TqbV+QV5VN4DyhczKOa3COpmzk5f6FO2JX6Ptu0gmxyf7xvdo4+ES3cWH2OdiMNd0yY7rxj7bJ9Q1itA4bgVrI7ZaW0f36OoLStjL8XjHYZ9Ot6jA1uGZHKE7Fr03khibsk+p2VOuAz9317Dlx8XJ6M1eE3xXb++fm3egcbrmf8Ps15TteEx5UFQFNRqrenyXc1OalB0INY9CwB9U7t65XKO5Whtu1bAHtm3SkaEB7WizcoT4aXTB2F88MOwzFjimJUFHXCDIwvZ3slWAsd8KxwXGdNd1VSvZTpDfCZGYbsuASEKlzn4p4ny0RpiJYVEef9hWGt7UFv9epht1CCb4O4raKycParz2r8/765zcifSQGQ1pz2wkz7xfj1ViXPtYieKAV+z3RLpXS032bnAbNOWreVjB8goy3uJUc1P3EfqNbvcKLFpDq2lJamI5HtK3UUBgJfDiufF5PzASGWGjmAqEtB6o2D7a0l+9Ch9IrWE8zmtzSHGaeUjRgKRAiz5++8DxEDseBhxixMPHXpy9UFb5/fGTsljFqjRRcBX3txuUioXO4hBAjzXqAbq7MrjSaXZAUCJp4uc5gldLVw8WUQxqx8kRZM4ox5ux+lnUhiHUD5Y24HYBAa1f3TQ4j6ws8Dw1rioTIf31uaM2M5UJOA7k7m6x1JecPfJ4XIgs5JXKb+X76yJQDQxT+9fmF57XQgrFoJcQTdYXD+xNP12eeLk5k//iQeHd4j7Yr0RQpzx1RzJwvl17NaNg6EwmEdGBZLwzTA2JGTSM/vHzmlA/kMHPMAy0N0IQhCD+dr3y6VswaZTlzHA6YzoSQGYaJUK+choTVmWHMFF8SurH9MBF0YQgZlcQqhWkaWIthNrDWmZwcJUrBpTlyHKkNIq6AH0eI7YWC0Mqzc+kuz8hwpNVGEVc7dw1HiDGTJLnqviQqSkgHF73Vgq2VL2uk1EaKmSFNEBPSfSszLhKrxn6va1lRPSOSKK35YrmtpGi0WkkhowGWuiLiArIiGatXR9hrZUwjGgJP1zNTPkKYYL7iDgWrp39a9WKS1stXTX+JEfM/tP0qgqt94hfp17mlxPoHb5CKnwNv3vqpbfvtoqR90thX0HfHMWew71o922etB25bNnbrsD2xdZcSu9ckv5G8N4TgTVttQwpu6NcNR7n9sbi7n7x6H7wflsFTRtHu+Tx30NWbbeOHvSoYuOunIlB7Bx02SscrpORGYP/62Pbq+1t1GvIaOdnBvW3H6P27l/bfpXVfIWdfnfDb1xb6s/L2/a8GxNbWXk0Z75Crt/etJel8kp56vLuIe67ebpf0Jqj9FvJkd9c4Nq963EjjezryDoEUcSSrBW5K72/O87D4f+X+yb7rw/t7Y2rkJLTaFy3h2x19P661B98xyh60bddxqzjsi5HtobGbxxz42xK+PT5/E5sE9wMsV7AEpszXyjhNiK0YD1zXF07xhKSRdTVCjCxl7VWG6krrw9Sr+gTqzVLHvY3U+Ukt+GTSq9wIk2tZRVemvl5nrmLkYhzyAZXIX5dAbCuhFYaciZ0/5JqC/nC5gHtFZUY17EkBpbGUQg4jUrr0g2Rqa5h53n8yuNoMcUBLw5LreZnBGIXDMPA0V4LMzqPRQEiBtW4p0oJKJdZEJiLWUA1kiQRLtGVGU/JKxrJSircuSXBtsZT4p8uFaRh4TCMavB8XlFM8cq0L/3Q1/mX+Jw+Ohokxn/jhpxdKe+H7g/D9w5HfSeF/mTIE5ctyZQ1u+SKMvMwzIsJFlLp8RmIkDUfg4BIXYeLT2lBdWebGMWweiTAKvH/8gAqsmlEb+Ly8cAzKaZwYD0dOITK36kgOwhgnWptpzXhIxpIfXBg1BMYUCWkkRzcvHmIgCR40X1f34DNQApkDWgqzCtdSSId3rjErrn21Ng/p1vkJiZkvL3+lNiPnI8+XL4Q4AYJWcamGmBmTl5676kgiJnHqSY0ohmqlaaIuZ1q5EPMBxVEta5UpJYzFLYjU9bNMIuhCDANBlHE8cNVCMUVbpZ3PhDHT2opIwAYX6iW7FAcydgh+03vTv0/kalO3tj6TrB0OyrtL7TbRBVoz2gCp3FbYbrDrCIWqodFRq4ajAjXzSsMqVCDJXmIOW5Wf81JC507Ztko3u/mkbUHXjjR59cqcjFwDEa/EU3FuS8UnSqk+8WzViINtRtX9GqJfoXN+b+TtFgWNRlg70uNgy95nYPt7xsYHc9f02tRRlSCMxdtZevBQum9iMGGNxnHtopAG47bg3a6/tzuakOU1QrgpkqfWNaRSR4lEfHWDT8LdMcWrxbZqs60+UrzCbgMzWhRHwDpr/T5kBb6S3pD7SFHuJBK2HjLfZ9dx+lYxAx0lFNkRM9vgmC0AE08531eu7vHYRlh/3dQdyWtiDO0WgO9m4xshvbd3QzxTj7hVXT4kqdLJT8QGm/5aFGERIzW/z2tfHVhysufU59ea/F5vpP4hKGAuhhjwH5edoGdcR2NYb4ilmj9TCe6LbB1x3jh0xi4qiLHZYu6d4T5zfj59Ww78m9qqo0rjBHqBdEJiQq0R5UCLyv98fE9KQtWZcx4ppvzwjCtJgwdLS/eFaLMHVXXxEnPtaQ7kRoKvfYXeXvp73VoAhelEUaGYkg/veAhKKQULR9ZW0HYlx0gIwU2N00gOiUCirs2RDMnUdukocKTpgohRLe2ggITksgoGVYun70OgWcBqYxoy4D6ED8NIswrRveaaNtIw7b+fFj5g1jpfd6YphFiwtjANmWmaaHVhISHiE8rcClYLc9BuYeOmzVWFYZxIBv/l0+ctqd17MNHWyrWdmbXyeHrH8zyzXF/4kzWuCo+PmXkpLMvCu+MjIWRkfKS1J+I4sSwrxEhKI0OKrKcHLAyUVlBxQc6n9eK6Uwhjdm/DUSJqhe8GY8knLAycly+sy8x1iJzSxHE8UEyoraDxwS1qUuXYf2cO6kj5IO+Zm5KDEcSIVjlOA1Ms5JxY1sqixhiM9fqZuHqwYUWd2+UjhYc4cNUZDK6XZ8wUU2GenxnHCVHnx9VE5+ep88hiYoqJZTlzrUaUxpAG1rJgFjmvF6IoaZgQGoc40IhYSCStFB0ppbK2C9Yg50CKJ1JuPYAaWfWArZdOPE1ogTUGV9fPAznctL1M3dYn5m0yEtrf+CfpVxFcbYHPdm2HTu7e5Ac2oCGqr5gbr6vyYufWVHPtHsV98/YVtXpgUfHPRW7l/1+1pf+959OkEDDMbVise/LdrfK3oEvEkR9Rn2SMrSrLbte52/LoHuzFcCcBsUEcd7pMdERvIxjv70NHJTrxuB9Lt/NH8XQMPWjo3399vV2nK9w+uyGJ/UUPILcOceTBAwwXwHx9zE3IdJdYuAtsv/re3bYFy9XcA+pWKfmmzZsGld0m/63vRL7WHn+Tab0d5y4A0zcDwm3JfgkB3DGpb3LF3vrxgRck6BZo9Hc3Jfefq5S8Fzj91jU0NSSyp4O34GkbexbZ+YyBO3Q3dF9J8WxSjGEvTFAxTuWOOye4idLPgE07Eszr52YDrjYnphZ9XIxv0uy/te04BUoDwsLD8UAjQAoc0oGUIimPaIyEPFHmK1ddkZDIeaDMz5AHkMkjXu1IVQ4eNC1XP0lt/lo6Ef7xQzdt9oo22gpx8Iegrh0eDxQ9uxdhMKhnR9jixBSFh9Ro6ci1LYhWxCKjuJhmbVeCRVYtxJhRhGDCwRoqjUYjxIkUjNaap3e0IQmWYkiMLLWQg+tMxQjVBoI1tK7ENKFW3XFAAnV5oVljRsgpkkMARiQURCLneWaaTgwCtRnNCsNwwCz5b0Q15iaoJlQLxVZiCKTxEXR1W58UnWytiphwjIGHduH7dxMXObC0xv86TFxaIU+ZT1a5PP9IWy4M9Zl5ufLu+Ds+jplnSXy5Xrmq8vzyzHj8wHp9pvVg+XR0aYTWKj+cV9IwEmMi0QgSySliOpOHI9omPi9P/PXpmet8JuXEx4d3aLtwCJFVV6bjI63OvHv4yJgHikZyMwqJa4gM9cqlGDlmUl14Wc6kkKhNGYYTj6GSVTmlymwj6+po1sv8BRgouhLT6EhmqyjFg8KQu4SRYVaZG7xczxTLPIwjUTIpVCAwr4UYR3ISpqzkfCLUSgw+py6tca0LX4ovBGtThERKhgWhmjIvlVIKtafAJSYX3U1GSgO5/+LWtjKEiMZEqR74DSlSywtIJoW026z9rbZfRXAVo6MnW2NudJUumheEVBzFWKOjSLUr2cdi1NzRHwJJYewTZd1U3rcUhIorR48/34liHd3ZJLN6h2tPyWB+Y2KDhyLMmz9e18lS8bL81Dy9tgbI1YOXNfqklqvQousbZTVKMkSFEo1h7QFhuyFHo/pgciBFdiHl3MALsVxviNgRT9kCHiHoa56aCWgSVwgWf+3t6akw8xSpmpGbo4giArpxjthRnCZ9Adz65I4jTpsGleBprQBUkd2AOPRAYotttkm49VgmItClDcBV7IMIa+fMbWlDFU+nra+U2+XmbQevTbV7EL6nH82PobIXOPrr3nbEUbGtWIG7PztZv8jtYGzBi+3efrtqvrl2jtwJd/p46wil9eBy+3ArykheOp/NrTi6yDmbJ2Uyt7Eo0TWkgviYa9F1ztZewJS6DppEf96sdiskNTTYHgTWZkx7UNtFAf3FnsoLetM5u++T2NHYpH4tC96mKtLvW+vo1r0Ax29vm8YTxwSrNsb0gEmhCaQUGceRcTp6tZVAiBH7vHItF0qbu/ff1Qd74KY6vf0N5uiVFeeUhEAvv2O3vInBB3CrvHoKp5MHWmnwiDkXH2yXJ14Oj7wsjWkw/uH4gWNeESISXKLh0hbW0khyotVKCIFZi1fQae36atIr4AeaNSQ2WlUIEZNAiBEJkSDJx3bM5LuFsxXDkpBxCxxrhRQDkUYIRoxGDBOCkpOjFKYuhJnD5GKZrRBzwkJgXl/2Cr9aVjQK0/GhP/PNq14lsi4zhMyUR/4KfJknlvVCHCb+cn6ikaD8yPvTR4bxO9YEZ7vyn/7hD/z5svJjFeKYYX0ixkQpwHwmp54aBi5nV+RPQWgWeDr/QC0rIY6Mp5kgxuH0nhwTZX0hrTOXZeU6nwkS+fOnq/+uDwNxvSD2I+8e3mM/fuY4HJjGI5ZGkMA1Z1QH1uo+hbmtDOGBSiVbY4iQZeTdEMhxYWpCqw3UsHgixMTDOLKW4m4AMSHVkFYJrVKJaFNUV0qtBAqkE4sFdJ0ZB+MY3cYmdHmEinJ+mXtFcqbURiViGkkSCBIIcWGICagUFV7qiu5oXybFhEgg6IKIMsaZY86gQrWI2UJVY8oZNWVIgas5qiBhQNvmEPy32X4VwRX01b524cz+u3sP0218jhgEqx2hkm+s7HmNpBggyXkyQX2lfksB3r63oyYKxHs7lB4ImAcYIm7yHAzmaMzRUayh3tKGW7av86X7pAkiRqzisgroHrjdI0VxQ7aii1a6LlXYNYK2FGUV74MN4QidbP5GpWLvnw1ZSyas7TUKoWGzgOmISw/iQqDzZe4CK/wGVb0hNrdqQ7shkP36pRPZNdzsVbb7+hV/Deso2f6Bb9HPHXrKbJOOSOYE/p+j72zWPT+HmInd+uUu/OSenvVzi5kd1Xkj73B/L227tn68IHLjWe3H9z3iqwrZLk0BHtTew0F3Wwyyy13c9Nj8+FmFoclug9MG/F6q0TaZh57uuzWqz8WvTnJ/bf0+Bb/nojcE0Z9fdrcCVUd6nQzvq8eEPyuhybcu5zezPTUQSbjwh/D+8M4nhhAIeXR7m9N7Coa+fEHCTKuNkBL6/Jf+w7XluoNXQVnxByeNQIQhuj6W7NUdQAJdPC8bDNT5XtCrEC8vvgBpDXK+VZTkY9fPSBAP/OtVGTRxzAfKPGMijHICWbgWRULyijGJaBeZVQaMDOKViMgAHIhcPQAiQLdVycltWySN5OGBWl4IZWGuhiTpqcKCRCHFgTEHUh4w9R4N0ajVqF0FPuWR0grrumKiRIs88eb3AAAgAElEQVRgF3IaiSHRdGE4HD3wX2cgsrZKiiNGo2rlMD2SpgGpSq0zwzBQqwtztrqglvlp8QBJQiClI//3S2XIE8dhdH2v4yPLfCZ3sdSmBcWr5kIeKGvh2laGNBBiBmsEycyXZ2ptWHhgmZ8xXRFrSGksVQjDSFX1tIk2hqochsyn5xemMXNdF+K5uLzTWnhpjfHhd1gaWa5XggprSoR1ZZxGJAUeU+KHtZHFOEQh50caM3Ea/SehrQxRSXSJiZhY5gsSAzFlrvMZSxmNEzEUDqa+skuCtIba1X0qy4L2tMEESEquzT+OLLXQUKYYuzf0A6sWTBOtzu6vOY5ECYwxUbrZs8YTOcxMKTOlyhij+1XmkarmkhrdfHgIR66lYjFg+fg3fc5/NcFVEBhxtfJNXftQhHM2Ds1YQ2BsUEyR5AFKxHkdoacZBHMIovVUyC3J1z8X6nCHOlQ/j6ntBOaafEUYNyX2nk4cDV6S8a55DZ1UR0iGLSjceE/RJxtT59PU4JyvYTUyPWUZ/biheqrEtCNAJjf1dHGuVTWvRLv513Xyt8gexHkBgPBuET4d7LXP2/bPXdA1NKHkWz+b+RdTV22X6AHW0vssqCNkoXlAVzoslZpfQwLW4EHAFiaJdNQseeoK8wCrJj9/7LysFdvnb+d33ZHtrStC33G0HO3rr3v6bU8LbpCTsUc5mw/ffaruq7FndxWJHX3bKxe3QLAHKbfqvNcpSRUh6Tf6fbuWEF6dfRtvYUPGduZ6V2zvchn3xuEghC64pQLNbFfFD7ohf67YXlNgjiD9+6MGlqBYkJ6G9lNuAarfNSPEwNybMoqh7RZEb0iiCP5MdF2x2JGoSK9o7WNqEMGC+8ypGmJGVGMJ8VVV6W9te//xd87VDMopHzgcjkzjSM4jtcwUEp9KQ/IBTRM6/wldz+i5p/UsOLRNJ64PAeLkg3ZPFaob8bZeGSXmQdUE0BgfTizLArpVTGmHmFdID45ePf/of9cu9xAT88sXiMIsA0/hEyBOrDegVcKQ0eWCSCTkAdMZoxKkYq0SJLo+W3LEqLVAyj5yxGbnyrZISgMSjPnymZeXH4kxMYr7zYkpKQRiHIlxcs6gZ46wrskkAnlIpDSxrsVRLKsYDQmJafw9tTwTY2YaBkQiEgNihXVdkCY8L1ceTx+ZTgdKW5CqWFemTzEjrRCkEabjqwWc9mqaqhUKnM+f/BmJgUMcaSgpR3JMDA/fUcvKZZ4JecSG91hzjTCJAzFNXC8Lh9NH1nXmp5/+heHwnjFHQuoL17XSTKnaiGOCdPCKP6t8+fzc1ToyRuXQLoyHd3z58z/y4TCQ1pXff/zA//vlC+fZ4PGRw3DiB1ZyOPBxCDyXC6UVAsKAkgafRMNwImkjFggWkIPz1L4UN5ymQoorogNC7SbKL6iMfGkLFhKi1flX4wEsoKXyOE4c8sQQM9f1hSyN1ZpLlUgk5oFDekdkdrPrPHK9fiEND5QutLpaYLaVQxp7JeLKaopK5DAlX5DHyFyUWa7E9I7S/g5FRGO4Ea33zTwFONzxXmqGgwTWHlDsq/W4VfN5WiVsPKd/I4fqvnm471F/b2iw9v9DD7zoKbBj3VAIT9Nxa5pP4Hf4x9a20L+jdMJcEmqvTnONoe7xdoeQlWTk5qmU2NEqbqfpx78hLibCedp4NP3cu7Bov5benortXLaN/7Xxumh36ModKlKzBwN7INARCO1pr2aCRum2AsIG1tADwC2V6W91pIVbBeGuzYRQo+1CqtIhrle6Z3fXYj3gkNC5R9s92BG1uwCsk+Nf3f+71xuaFt8Mw23bChS2g3zts7hd838vJnM/Vm6N2bOCvbnbfd7SdltX5Opt+CUgextj0NOjgd3kucfTb8R5PVjKm5At5mgsN/Rzb1uQnoL2e1u3Yo87FNBrBH2y9DH8c+Htb2v7wx/+SE6Bw+BmvsOUmfLEmAfWtvByvSDlylqeuFyegcAAyMOB61p8xVCtBzzBuVO1Aouv6DTDUsGuzrMaTx6U5UQajqgpy3z299IEh5MrV1ty5EtXhrDy4R9+z/X6hY+HgXOp/Pj8CeJ7rzYM1QOamLoYo0c4ujjb1FAsKCqGSCakbrcjEdFGwihlZTwcUWBMoyMgAikmtFXO12dOlvjDaaLNZ1rKPNlIayshGjkloldHOG9qnbsw70AMgWVdqaWhGMfDgZMEX5AQCOa/8rou1DxSdWWUwjCeSJLJ4YyqMF8+M6WIFCGmgFllEPftm8aEkgjBkZpxOFKWmbW57INpYK4XJDaO0yPrOnOh8b4YzxJYyoLlE++mgWyFmYyVz1QR8nDiMj+TUuN0/EhZZy7zF4ZhIgaXnCmtsc6rZx5QckxgjeW6oLVR64UYM6YNDYbZCuMjn68VBD5/fmHIj/z5n38gT+8gLJS1IOsPaBBULjzNkFJCypVpOCEhUK9PtNZcENb8b1El5xGbz7ynQY6+oKwLRefOiWqkODqyFw8QAikM7jHZCsfpAFZ4TCOrXhE1ihjExCQHsMYUo6czwxUJwU2i6+LjGnVungZym5nSO+bWOLfG09XQNBElklrjuxwIJMY0staINlj/Hu1vWocX13SbRWtwbk4zc4Gy6iTtRYxD9cDj2om6rijeJ6eOfkS9wTfVpXYYOyekdUPiEIXWjBgF7RykEOBUvAouiE/2W1HNzqkyT9u5P58RU3Cy3SZaeTdxRvUgUbvhduhE7SZGmSCV15N1iEKuNxHL2FM2GnG38tg5UuZo15YiG6pz1axnDMZ6Q80AqkWSuWhWAwZTV84NEDR4XBT1rt3Gmnt6x9y0Km76Ux3paAGSduNkg0xgyc4pK3ffOS7elqSQF2EZtr7onoXiwdGSITfZ04IlGYPKtiilJv+8diTEU1tC61WJWwps3apLexWb3amK+zl725JgzRjN06xbGi9ERwpDcNQS8J1MdqX2pR9kk8waqqNaW+pzC9K0uwPU4Km6twUJ0sfYFsgGcX2o0gPut+sDN5t27TsR+mRy117xVXswRaOQOverJk9Db0GkmI8DzRCbUoLt+mUEb8M1eum9qi9yKp6qfqjm9zHfQiUR5xrGduOZaV8YhF6IsnZkMXJzIvgtbn/8/R85dBHM61KotbCiBBpDDgyrMNeBL19+4PPTF5bLhRwSdVUPolonV1oPcAjO3RHpPKrilYNx8pjHVyQQErXhKcTBRUmH8YH1/MUDtjz4l2tjtcRfyjOQeS49oAsf8KWnwHhwhCwmX+UEAXFSNpIY0ujpLSuYKmHo1XfhSBRFo3J6eA9txSS5t50aMk28fPoBgDrPPIfKVQJnvSLLI2YX/Mf4wjwvBF05jEesrqQ8IDSqFVpIlNIYxwM5Rdp6YcgjpTTCMCAGRykc00qLkSaukq/zzHh6pEjDmvsbDunIwyHwPP+VIT8w5Mjnlx8YpxMxJs7nF6bjIwQli/IwjBzGxPsQ+ecf/pkXIrSFh2Hi43Tk//n8GeafeBxGHqYDWT/xv333nj/NAWFikcj/9f/9F8RWzs+Zhxx5mI5cJSAE5uXsfoXrzJAmhnTgcvnMLJVkhdIWjOhOAPlIvV66MGLhXFbEEiTnO4k2/sNjYIxKY+BaA52+C7YwpYmH0Pjw7j3n6xeqHjiXhYfTA+syI6ZYKRzSgamcXbbNhNWM8/pCSgeEhK4z0xDJwSdCpS+02+p6Xk1IOTKk7BWG1ThJ5PHwgWaNJhnVyiE0dByZNXKpC+/CgauANqOFTDGh2MowPPLczhQ5oK1xDaNnwAVYK6FdYfjAj4tXPmqrvCFE/Lu3X0VwZWzVfP13IIgr+6pTAFT898SrnQIt2m0l3Um5O9/nDrnZK7FEUFUvkw+etgCgk3I3KQfpXCZL7MvvLejZDXCl876sE8qT3Pmz9U1ee8hN6+uJyIO411IM29bENi9X/+pdimtHZe522XlPG4+oB1stbem111jBVi0muqWbbpP9Wy/HY/Wzzn2fpQeIm6J66NdhHdXR0K1yogtLDj09a0GowXZ3ou3ebUO5CkjqKGIH3Tw9KeyWODdQzAPq6PZBukEw3JXSdmRlu/QtAKLLbOzn7dSTsn/fdsFYxToB/9b3eod+vRW6bcnH6W5uvKE8Xarh5l15a+SmWr5pc0Ef67Ld1zfoFuwVn95c6/sFAhuSe7vfht2JofJqM+uBz6srvH1m5sGgSFe6r97nXgBg6OCVqP7sbby7zg+7H589mir3yKF9k0L2m9na+Mif1uJ8tOScFBXl8/OPzNdnXp4/o9cFq5WjXZkeD3z5/IMHL6EP2lU78iSMyTgME1PO/DQXiIk1vADdpHY4OF1ifHTe1uUF1hfPDlx/AOt+hNdN2C76ezGC1v6j0SANvB8SaivPq6NXDxmaCiFmSp0hjwwps5YrrZ6ROEL04Km2ypUzrVWGOLJcXpjr4gvbVhkjPMSR78KFj8eP/KjwUiLX2nwVYDPTeEAkkob3ZIPHCL9/d6LaiWudWeWBVc3lAOpKSgMpRtZFiUF5d8wUjMcwkm3mpT2yxAMaI7ZcoV2YivFBr7x//Mg/Pl9orXCUipjxPz026nXl44f3/OXLhRIqw3rly5e/wOE938nKeBw5hN/zf/7zfyXH5F2vjVJf+MfnT/zD+3dY/MCTVn6qC8fpI//Hn88gBS3wH4/G//4f33OtxlML/NN5pYXKf5bEv1xeaKvbCoUwoq1Qg0tJSPVqwxRwtCoN1Otzv383zoStL3i1aWAV49Pnxh/fJQ524bvje0pb+DA98i4HVFdmjWj9zPsxEG3mbI3r9ZOTy8cHtM0cZOE4jkSplKZcNLu21nDgWhvH0yNTGGj1Qh5PWK0EKlMGVUVkhDixzBcsKB9HccpDCLSmvJTGpQlVEikdYD4zhUQMjTEEbBrJtVCGkf/2rDyvLwwx8X0MMEZ+Wq6uzF4r+RD4w9FtkL7PDYbvua5158z9rbZfRXAVqlAGIfcAae3B0V6NpEaT2wKpRtl5RgH/kY/4b451xIVw0xCqERIBHXo6saNQbXB0O4fAHD1aTj3NFKpRO3ICHty14BpQ5+Spk5K4cW9wCYlVfIJp5ia7oYEOfVLvvKxp7WXpdzmUtqXtolIjDKX7A6pXXJXh1l+bD1+uUDZCtDrSk6sjOZj3STMYVCjivImxKiXdo4RADyKWJBxXoaSGBliTB4b79KvuY5g2n7voHEoTpSYYa9dBEhhVKD04WoYe/PRTtmBovKFAiKdBm3ggtJHEDaNlIfQgeOhE7G27+dN5P26SA7nnEYOYczw7sT1ZV1jfuFzqKdUtENLodi1ux3Tns6jGZXB5gq1dOxdLvKJuD2S3Y8mtbZEenPd4rwbnJ9HH630qMmgP0vuh1uxyF5ucwStCmQiGqxivd8F7EGFsLh9SghFDoIbgtJtuq/WU4aTCw2q8DL5wsW57owGWSYjVg6+IEbKg1shGF2l15foYN0kMowUfk/eo3qYDFnpwqeocyfYbFhFdGXlImc/nv/L09Mzzl3/ly4+f4DrD05+dwxRGWJ99B+mpv3aFcYQQ/YeoDtAKiwWWplCKB0ZFCYcP6MsP/qNpBw/MrHJ8+I6X5dIj/gpj8nSgKLRMr0bAI3gnIHdhKrDCl6UgIQEXspxoNiADHFCGcSAA45AoaeBBv3AKhYcp8f4wshQlhIlPX/7CYYhIFH48vzDFkcCF//S77/lhHdESuSwLOUDOQosTj9MJVJny/0/em/3IkpxZfr/PNl9iyeVuVSyym80ezEDQPDQgaP74eZUEQcBIL9Jgupu9kazivbxLLrH4ZpsezDwib01LM5L4QHU5cCsrIyPC3c09wo6d73znNGijcEqwXU+OZ3zTEWOE7oaeiAmBnIWm3UCcMNHTWjBW+Nlmx/N44h8fP3HvDG3vOIbA6emR2Qc2ciBJwtrErCwqLtx3G74/H7mTxA+PmceksGnCWMNrHZkx7LaviSnh3BsOwfN0fiZmuNu94he3txwPzyUU2QWesRyGZ0y/Iw0TWXe0neP5MDHOgf84Kz6r14zjzBATKUAKEzFNdEqzaVrY7PHDwDKcCFkhSrNXPXn8xEF3+LAg84LYBrKQoy/XNa2hqb6kRohnsh2jHtnvDe8ckCzPwXNG2De37E0khRFnil/VdkygHZNfGCTjlWEWS0gelQ1UXZrWmpihdS1xmhnsyGazxy9FX9b1LdrPhUnLkcaW1buIxmjBoxmngSQLXoQ5eTyK7I8oFK1u8BkUDRFBNy071/LLlLA50TnNKUWgpVUt76OGVmixaIm0XYcfBySPbLeON034zz+s/x+2PwlwpbUqQbK6gAFqKSymokdSVRsV4JLVW77U5SstTSTXWeJqFfByW80e1y/8TDUwrgDJVLG2CBhTxLjxn5sDXhAEF91SrGy9FE1VEXev+YMv/anXyae8gU5fM0aqsnVZlbFYS2hryeuip6olwBdzOKu/FFI79QClVfEFW1v7FUWw/4IBvR7dC80WhYXRuZzT5fhqiXJ9fnneCxqqPpxzRlCVevua5tGyTshyuS6rzUBSVw7lvyZH8f9uSwhRF8E1FDM9EZjq+Juw3j/r/sq/H2uRtF79szKRUpLu6udwtYEopJFcHPvXI4/UMuePQqb/S55fXz/x2vH40gW9/LdYKtg6puuoilRiw5SyHnll0MoTulhWs9MLZLd2tVopGkYjxXQ0Snlc1bJ3vYXKvJ2vJT7JBRi39YPnVS5i2pXFW8/hxzTaT2w7Hz7y+R//N56OJ8AyLyP4iFqO3L57w8Phib3zHLKGJYMypcwH5T4Kc2GVrINpgCXW55TsOERI8xlUWx5LU2GlgmFQASMe20KrXbl+COiWL4Mvr5EEacQowSiKb5USplCMH1vreNdteZ4iSRJDjAQpAdDvTMTkmaY3dGIQVTIF56zZdYpjgP3tK962QqM83+7fMgk8j1v+aUp8OX3kr968ZcSjs8Fk6JUhhZkk0N68RnJAxZneKka1ZQkeUdAoCwm+aRTjPLCEhSZ7bnqHtls+Px9Z/BMbZfhXuwbHzKwnvv/4ACL8yin+6t7w2N/yP/0f/8SrJfPNds8fDh9Y2HJqDDHp2pGmWHJkWhSm2eD9Ujy0QiBXJWS/e80iml8fRsYhEtJCVgaVn0Eajk8HGg1P4xGVEz5mtHWEZeL0h+/ZtC3PwzOYG1rXcXr8QHAGazvsw+/Z91vcTvNwnvl3P7vnP3164OevOt6fA67fsklH2nbLf/j8zCwLxHTRvt01GR8iOiuUnUls+Pg88+n4gTlH9s2epC3Oee6M57v9K3w8stEjm60jp0BwmtM8M+iM9prJKw7LSDJdAbXdDhNnjFYlKgmFC2diFsQ6HoaAVo6sDI1SGDJJOVKMZNvxcHhmjomN25BloNEGSZGZEtQcYkI3ljkuuOiYCQznZ7Z2h+iGRYSOyMellH6VNOjseb1t2EphfltTooXm7Nm03R/1c/4nAa7IRffkSeULPL4sEV3b6ZUqMQmrmHotIV30K0bXiSRX/Ul9/MWurqWZa9kQAV370mNlP6hdfBfbgHydeNVlgru+t0vCUKkUreRi3pTzCgQL/lAZpqYwTFAmIxEuO3KxALzZFMZqjTOZ7fX4i3wxv8iJu1YL13+x+k4FyRgtpZQnRR+VBNwLMLQac19ATX1c59KZt9oCrELmVee1Cs5NLgaml8ggWwFbHQNbn7cer/HlWNbflYDUTsWo8sWpX2e5lNQuflEvtqU+z63HYQTv00VI7U0pU9qqqYu1K33N3Yu6HOd6Xdd3X0EGFBARV5ZGFRdrpYRpZRKrB9e1bFcBSEUS3mQIQrViK63iimv80Qo8LizeV6dYSoZSwN3qfF7KyrVrsJbuWgqruLJ0sR7nsmoKFRfrBJECgKICOo2uKwiVcsl5k6KtMvUxo8tCZy2nywvLkXKu5fOkYslIjLUV0C3XhUw5lzK2L27bn+T26df/gbAsSE6cjieUmmEZSdrwdC6BzIfRUywVpP60MJ8hDJBbWBZggeTrwsaXzj5nKxATXm8VM4pIg/cLUQsyP/Hu5p4uw0RDSoIVT0hw24DWkYdppmsskjz3+ztiCvgsbCkavK2Bbe/YqDO/ePuO5/OZs1/YdTdYlcAYzuPEGAxP48TTNGONpjENwzxgrOM3g2FjLOekMCkxhpKzqNuf8denyJdJYXXD1ln2jcUZRZMSS5i43QqGnttmxOAR1aO05mk611LZjMez6zf0dsunw4FPxy+82fXkLMx5ZrNp+fA48/l8pBHPThqGFPgfHhrGD39AupZNtyVox/vJoMUTgqBVJMwjrXPkHDhmSzwc2fQ7luUJXF81wKDwWAFtDGbjYPI1jseT/ELf9fyi02z6HX/7/ndkrQjzkRwWTtqw1QvfOMP78yf+rNmy+3bDEITsH9g1ic0m4VVDrzz/8f0H3vUtUXVkN/Bh+MLkE/EcUCnRpMjbm7c8nJ+Zw8KcDSZM0FjO3rCMp8uCyhrBmgmnhOHpM59Ny1//4Qu9teys4s3+BlRiow3OlA7gXk9YVfRSSzoRdLF5OKQFPXnu247BD8QyOfB8/gErilNOvG17Gmf4wzCRXINH0OMTTkU27YYuH/Aq03YtPhui90QsB78QvGWI4dLgppRDxYhPiYRw67YgcEypMrKavz8tNBmcLTYO/rzQOsfWuH/+A/v/cvuTAFdKKdoAo9GYwEW4stQJbWWiMoXlUjlfdClal78XEXsp+TSpTJwWMKGUTNZSk0oUNiMJ7UpLZVg1WqraHeSca6ceF5sYEQhOcKEcz6V5kFK+yaq045MLA2Ok2EMEKYBLV3auicWQMUeNIhVQUoFZXlf/1dvKu/L+KqkSPF2F+lqnog2rp7BqvFK9P9amNVO9Alb32aRKmSqs4vvKqkQnOL8G/9ayGpnJ5QtQSEg1q6xIwJSxX8nUEtRchdFlGJHKHpXXr2gqXZ5fjrWENptcrAISYClWDplSTl1sKVOtQMtGuYAqCh4n5lxijerNIVIaJSQU1kVJKfsqKayKvABFkL9mEeuFDWREK2zIBA02qnp9CwDRyEXNYPLVEHUV0etUzHHD2pmay/jE2phwAey6aKCyFJF/klLus+n6efhaqaTq9asgrt5HWgroFFZvsAJQTSr3ctQlGWCNw7Fz2fd6MyhgoTCRIadiLQJFrxbLe68moet9kVXGGy55hyYXMH9JVqgDGnRGJONeVDZ/ituTT9isQBzSJlLQaNcjZOJy4ra/4XmeyWEq5UFlilBdl3IcuXbn6RbmCK4BEk1juG2Fm/62+OMpxQ/HCUWm7WoQMzuej0fM/oacPKItT1PCao12HWOa+e7ta7YkfBiZY0anhfv7bxnPR/aNYus0xnToXc/zOOCahkxm8AFjM2YBY4qLtxKLVUJWCm0d+3bDYT6ibcNDBhcWklH03Y6QIKXIrC13uw6NojGGjoSkmUllvt0rEjNaArnZMIwJI5592/BWL3x8OtK6wuodlsj3h5GnYeLLGPlOGx7Oz3yeZpp8JqpEiIk/bx3Eiaa94WHOzNkjSfPb40DWC2CZYySSyX7BKMvTNONs0XPpFIjDExoYxgda1xa/pjjzdrvjNHxhGGOxFYue3jbEPBG844ckxPMTf3b7htPxmYf5zL5VBO0YI5znB/ocGPOW3z98Yomat23Ldr/ld0/PHOaJxjT8m7dv+NtPJzJHlhjpMfQ6YjUsKiGq4/H4e9qmozeKtJx5vbvhL775Gf/w8T3DHPFZMErIYeR1u8EyM2TD0/HAYThz0B0f3YbfHN7zr286mt0dU/BYEhsLOxPYtDMqa4YwcPSez7NlEc9vTwd27Y7neKBvu+Lg37R0IfCYoJlGzinRKUtHZt/17LvM2QtxNmTdMIrlsAhBt3w8HokxspBwpkes4FMxW210g20MOSZORRiK1Q1Zl9gboxtO0wHjl2LTIA1R90x/5O+kPwlwNVS9iH4x8UEBJznXdvr8QlwsXxcWbCiO7Yvk6plXvJFmA8qU3DWt5OIU8OMy1cutMCQv9SuVyahA7CWTJlVftYrb4Wt2JeWq2VJ8laO3+jcBV9uIugUDqbpyrzPvCriaWrPMFB3Oobv6L62+Qeu8ZS7zcdnP2WV6Lxe2ToUq3K6l0vLg2tX2YkBErh5QaTUWvQKzlwVPFXLxnKl252vH4KrRWrvlVj3SpfNOX39fAcVs8sWxvPNVr1MRrsQCQGZbwJepXonrKQd9ZYTKgXItm+YqPn9pBgboVN3K5SrQzjmXKId/xsZhLR1GXX7avLKiL3crlzJcKfNmmnocjnJPhlTKjWRBK0UmkVMJb82p+LLknC/Mna+l2zWb79JoUSOaTH3d2nTxVXlTyVe2Hpcx+b+Qly+6jIXJZVGTfTWwrUysUVVQX5vWVkbKq+JxlWJhz6ggsq9dvn/srpz/v22iLGMOaHyJgZFQMvbChOiOkw+0zjHlSF4Ht3EwB4zdYySTYmAJGdoWYsRaTV9z0k7LWIwzpaO5afBzQOWIVcLrzpJyhzMap1rmMLN1O1JWfJkWJAuHcWCzu+VfveqJ4ng8j8ws/OLdO2zyiCTO44m7pmXbN3x+fiTT0pqRlCy9U+ycY9CRzhoyzwwpMg9PhBwwqiHFQKsVPidC0uhlpOk2GMlsHaAT2WyIfkSsYt/sMHlAUsTRkaMnqEAImcFnPj4/0LsOZ1qel4EP58TjGFEkjG4RBt6fzmgMnQkkn3BZ86prEQPZNkQRjPUYr5hTJEVFjAvLdL4Ye4op1gEWAVEIGWUy++0dwQt6PqCqSWhyHd8//AG0YessWVTJDGxapN8SwoxRFp0957DQOOFXmxu2JvC7g2eKgZ93HcM8kfzIf//6W7qN8Jv3HzmfZn6xtXzzZ9/xeFr48vyZv7y/Qy0T3mR61aEALVWvnIQ5dCQi4xg5Bk0G/v73/0RnFW3fcvIzSluCynw4nDiHyDk3KCJ3u21hIjIAACAASURBVB3TMjEmz5I8f/3Z8/4801vNXdvxhzCwsZbWCb0RxBZ91ptdw5QTo9ccwhkfhCVGtDF8PnymE0vbOHTb8k2j0XmkbTZEFt4fTsS04ZganpeAkIjLxCGUhbrHoHIiZA9JlegdUXWcFYuu7LtpyClBjojS5ORxTRmfHANaEjYvvLb/AsuCyZYJzNQYFYVmzY0TqcJskYu4OKkCTdZJOrnCIulqqJhVmWxdLKtpkczF6drUcpO6Ygq4lhfX7imhYIjVbVrpsn9ezMmrlkdUaY3X1TYgUYBCtKX0UubmYk9gqhOjqMICEb7OWctVD6XTWta81o2SLqEnQukG3MSyr5Ty5ViDKpOhN+V82lQUAH3MoPIFjCVdzTkTpNpCLwq6IMUaI3NxwE8vJl8TuBhn6jp5J12OOZvCfKzPvjBLFSzniqZU0pgEvqI4Ve0XYhW5rxYPIgUErQLpJJWB0TBzBT7pAuLKgYVK01xgg1aVZUzXUmou+9l4OFvIWRCjUCmRdBFmj/W8qJmFCCxNCbkuLGW+gDBJVBF8YXZKLGgxdFWyOpUXYJFzZhFw6z0nwqxqOTuVjtGzqIsJrcvF1yboGmZemVYRiEpK4kldhEQFkvXlvtDqhf4wg9TYJbMyXpKLP5oSbA0Hl3pPmKTLa3WCkBGjkZAKI7vq/eo4NtV3qwj9QAdwqlp4aLA5shiNkljulR+Lzn5C233X4kNgpi9l7LxlmQdM6jHzU/ENihHXtPik2OmAk4DelMiSzrVsjCKqTMgNKkSeQqJtWoyKWO149oacZ3TKbK3DKE3bdfTWYmzD0Y8sGZy1pKiwWrjfNmA6TiFxs7vl5Cwuw+v7njQ90ItiDgOb7g7PHxgjfBonPj7N3N70/Le3O25s5m/ff+Y3nx9YcuS7u7e8dRqjFVY55mXkEEbuuz1NYzmczyzKoIJCpQP7fsOcim1II080W8vDyTOGgU1jUEbxxYORlsc/vGd3c8eu3WCz5qMv7RdzfIVXJ3SzkJIwhJmm3zKNJWi4F8v2tq3svgPJ+DAyRQjJoaynVYrBe5xSuMZgXEfEY7DFpNRlnOtACY3aEwV8OrNrO5LSYC2iLUYUIgqjDXY+c9/e4GRBGYUNkTl7GmPYukiXIZN5HOCXN5a9WKwNZB+5399xe2P529/8DqMCv/1y4G8+K94d4Wd3O777+S9wMrEzt/z282dcf8/n5wOPfkGlRK8sD8uCiomUAzpmWtswWYBAShFjutL5qYQQI8ZtuREhRFMYH9egcIRYHODnEFHacJhn9o1BacOcDEsAP5ew5TGOBDRWw6u25ykk7nY3KFG0bk9IAWU1QRIf58Sm7fh+jNzsb3nb7vgcFN9puB1OvNo0PE49n09nQtI8n4/YpsOnhSQW5TMLiWUeivt7TiXUXixtt6uGo13p6g6+ROcoVdj6xfNhPAJ/+Uf7nP9JgKvNAojCy4/iNOrPNX/NC4iVixXACo6CvgYXw0pUlNnE1LDYGPMl3NbE2lb+4gveq7Kyt76yaKkAFKTogXwVpq8h0Ta9YDRW1mL9R5lw2lgBSkUcwlUUvIbNKH2NDbkEEefruV26rVQRJq95cCvmCqakVRR/JKGhMAjtXN5j0aWuue5n3S7MjBbaBR67TDeXY1V1wk4vjkevnhhwEccXoHIdw7Se2wth9bq9DFr2BnL4WuO0WmkodQ3sNlXLs46FVlKtKcoDrYfJXLVb+bozhKo1SvX+kNolylpeFrzK+MglH3EtM19YRiUXkmVlS1fdmcqlSWAxhd3poypGphX4X++Lyl6tFWhTrUMq+yMvrolUPdJ6X6kqLJ8p5tpRSoTMOhYpF8d7E2Aymduh+ovJ9TjXHRcj13wBvMpUYJwyTS11zipfNFEhZ6xOlalVaA0qRVy95qKEQCkZNqncXyplBlO7G7OgJVwaPpSKxeeMSBL5SQc3BzLd/bc0YjkevqClZNuBxyrL/bbjNC1McSnNHxT7hb2D7d1roh95nBeO50zXGt7uOr7t7zj4QAwjne146yyHcWD0gW5zzzQ+4dyWMwmZA6KbkrVnHb1VF/d0pTXbBKNfcBmeTw+VkUy06ZExgvffk5st/ngmKUPSLZ9PI//+4YwYgz898Yt33+L9zH/6eITk+TfvOnZ24c1NsVB4PB94d7Nl6oWcEjEbsmzojLB1hiGcGOeOm42jUTPTAijhOAlLipxFMestYVKMfmBnMr1VxBRQLNz2LUev8CEjTcsSMq9uDFkbhnlmCiVr0S8DrWvQRtOK5TkIKXiUKBqt0VLCjhsDQktWGk2ia3dkpbAolGkYp4HbpqO2D9G2jj4Jc/b4BJrMdmN41Vje7jd8ehrYtg4lmW3TcpxmnmdF8guvN5q3u46+bdmagdi+4n/+9Qcevkxo/S2pGfiLX/2Sv//4meOQ+LvxCz98adFhxsfMOUxM6e8vuYud1gzO4bRD6cgSEtJYPIl90xX2RmuUtix+QUvxYDvMgTlDVpqUhSkamhQxqgVtcQo6m2hIJWRaFYmIkYwxmt46XHAEd8NCBKPoomGYJ/a7G0QFtFg6tyvNCnXMxe3ZOseUZjaioAY0/37wHKaJnIQ8P+PIDMcHdvuewzRzXkssypAkYLSjlYzLifH8wJIy1rW0XY81lrZtGIfEtIwljij+C+wWTKrYFthakpMMiyRyLXXoWA40co2Pgas+JudijZDUtWyXchHH55qjp1SxeigTnrqwSevmKmuQ187EH3XiWQqT1c3CaPNXZaZgwIV8sUiosq3S5p+LdUFQGZO5BBDr6iieM5eyobeF7VFqjb0pAGZd6YuUkuml2VCKMNmrYlewOnDHmDnuYDeD15UF1OUDvr6UeiwxZRYrbIIgesUShTVTkgqLIQqyXDMI65y9amliZS/WcdOXCTxfBOlwfW+X1xcXlgRdYiW0FFuE1VXAUBjLVE09CyoqwMZGYXHlXlnnaUvR/rhqUXEBqTVzLOtiiSGUv7WpaKfaVP8WIWbBiDDVYPCwauFWti6VyJ5Y9W4qF3AXUjknr4suLlbgDAW4XwT5scBqWz/HSVadVin9eV0aAGwunwtXgVZU1PuzvC5WtC4VmPe+6AFVVbZdIp7UtVvwEhFFAU+IIFoIFU6aKpxHQ1OvWzV2LzF2sjq1l/u6XRczqhx7MJlNFSJ6m8hZY4KQVESSqdSqIcrXQP+ntt3evsXqyDCe2MhEC+xbTVKGpdHAjJLEjetQyjMEAykx0ZHnkV2juDe33O0yS9Qck/B8PuNz4rbpmFJinmeyGG5axcYmsu3xRtGlCEZw1qEy9H1fuq+6nskvRD/jQmYZnpnTwp/v7vj90w9MHhrbFtatVSwxM3aexvTc6gLOQwhMwRPabzl6y3lZuGs0r7qOKTb80+mJ9vhISo6/eGV5nh0Kj9KqNA8Fz8eg+evHkZumJeWZL37GZM12s2MIJSD6db8liiq5ruLxUZPF4XJg11pMo1A+0xNZjHCeDmzbjr21PPkSIL3Z3pD8RCMNjcrYmJlY6ETT9R1k8DHQGI2yPdZEMgb8XHRL3Z4lLBgCKY0Yq9jmuRgzI1gT2PZbptBCioSwsMSFfddwOh7Aj+TsudveE9OBNjzyzf6OxjVgGh7PM3//j//I745w9B/wZsd5embbbBBl2M+PqAwHiSxRUMNATAnvF5RyBeSkorUN2tauUM2cS6qDCoG77YaQQdvy5e00KGVonEGnzE1nmVLG+0QERu95WoSUDZmIEcW+7ehVUZ2aRtOrEm90XjxKG1zywFiAk93gWs0YepxO7J0ji8JSdGMLEdPe8Fp5xmVknCc+jWdUCGXxIQk1zXRaOKbEBCjX8+ngUdbSKfBhxoeFrISoR266LfvO8OEYCCmQloHzeKRxHZkF8QvDNGNMQ7J/XDj0JwGugi4loVXAvMaWNDX0M5ja5TXDs5EiiH2xXUKHc7p0binWYGdYYdIlHJfSOHDpFqQAgpV5eskcXf6eQJTC6zKBtxG6BYY6Ca/amku6nhR3dF31QmukSISLtYRU0AZVHC/X81GV7VrPYT2cJGvJsgY3V4pk1YEVxuJabrq4ZaeEoUzCJ3stmRmjqmdY2XLOxKqZKjX7AnCilCxBgYt553od1mOzWZh1Nd9U1UcqX8FSKa/9mNEqY6W1upaK1h/5ev1KearoB2y9VnFlZvI68ZfzGpvSERgrldlKMaJb1lLwRTN2BYsSYGoK+5cFugmeN5nmR/kySqSwOLmM1WKKJ9nFw0sLTYTZVZ+ses6pgvk1U/AifKcAsrXhINdy5qrl0pUtdBTN1qXDMq6M3+o3paousYaTVyibfLFpWC4LkvViXJcWpQv3asOxarzK9S0aK8kV8NemDRFBra0MSV+YNiWlBKiAiK7jrUoqgvn62v9Ut40tZhabjaK/u0f8Ea1uGcYzcVkYzgdalWhMQ8Jxh0BKaNsRc2SOgSCaxrT0Dt5IZLvd4rMwzSN3u1v8fAKzKaHAovAB7oxH4gmTNUtOLPPIafzA3eY1u/SBTlmUEx7TgO8tp2Hg88MHeon0TQfo4m8WF5ZpobEaI4GHYcDZOxSwaZqSuac0c9cQYmmZP44f2G3e8DQvKNPwvz56nBnpbMscTuzaPTlNuKaj2WoWKdEpiogzipAjt/2OvStO3jlBc3+HUxFwKLdlOp3QjTCfnxCdaDpDu8kYXjN4w/M8MfqBX+0Mzimc2RP8wONpIiWLVZaQEzolekn0Xc+cNDpONCrROCAL1lnG+UBWCzkLbR9osyabPd8/PrO1kWk5c5qe+e7VDqLnu7dbYoiYNjAen0kxcOSe96eZHAPnZcP3p4GgW87zyG0reHXLqxvhxkeOS0LChvn0SO56TmPAiabRPVoGfMy4pqVtNhg0IQwoUcXtHGGRLZMq3z/HkNg2PR+HjBHPbaPpnOUQDbOfUTEjGO72N0ynA0knOjK3fYs7H8ghECqbldLMyQs+RzbSkyTRakXWW/wSiFnIEWLbMSaNE4uPEyGmUlVSiRgWjHjEtIyPv8U6h5WGjYVbSbheY3zgttvw8aD4hzESXMt4GpA0kuKMzomsLCElREwBlllxevrCIUWWJLza3/B0fCQpxbh4RgI33Za7PtMROc/DH/Vz/icBrozKmFy67QSQKNhcusxEitdOTpm5zTQUBstmuYiPywSdMaLqxFABSV3pr2yWRCGaKg42FSxc6m9lkgprOSnW2JxqXGko75lMmURQwuAyQsbmqvN6oeoVCgjIfO0UTzWNvEzGdfdRFQ2TvPh9BRlr2czXSR0Fkoplg6l6sDVVN9YMkxsvnG2qZaBMMIKPmdEVvVDMq/9SYR4u10KKtQIUsbooUCRSrhYTFEZOayH7VMsW+cJy9KFMxilez3OdUM0/UwpKegV6pZNvqaJsgKGrGXqU8bWsZcEahZOLVmrV3oWq3XPhWipMArMuq3VZ2coV4FSjsJgBU5isbEoUzdRDVxDDhQWCApIWip7PmxJAvWYnppxxAYb2Ks6fTcYEquD+ei+Eios7X/VLdcwNmkBe7a1KthZ1YaAEU0FVahLdAtpUHV5KKL0CUiHEuuhoIjGWbLqgC0Pcz8UuQ1EaMbLIZeGxNnQodRX9B2oXY8Gx5XeAXCNN6gLIEkFgsAoXClOaaqwO9T4IFkRHXPjP74WfyvZqf0/SqrhrjwesdJxPjzz7gE0TzhpaMejGEqs1S5xmUvI03RatLSoviGS6piXngJFE0zi23Q6dE64vgcbnENnZBmO3RH8mpS2Lj3ThxH7fknLD8/TIGBVtEo5xQQIkZUli+Nm+wylhyplpzswxcZxP2HaLjxG/TPx8f49ViePoyWpPTjNGL/x8Z+mVkNod3z8qnsLCvtW83TbM0aDdFstMY+4Az7Z7RfALo/d03ZYp9ix5JoYEzrDkyGZ7wxyFZUmE4cioPE17W4Xkmml8xFmF1kUbRV7QJF7tWu7dwn9z/4Y5LKRUsg+VF961HZ9OI2OYaJ1joxW3+x0qz6QY6EzRI3VNyzLPPA5nTnNmGB9R2aAzLNFxMhucnGl1w3d3rxmWkfMCkgz/428eWeZMIvAl9BBngmlpwwd++eoVPxw+cspbApmUIjlFRDIhwlNa2GH4q5/t2VrLkuHxOKDMhsM0EfIenwIQaW1G4xhCx3k+gxK8OHScibkl647GZLJ2YB1LWvi8DNwbR+8skUTIihgy53GsPn2OL/MDb/WWTdPQbDSPo+a3h2c6Z3C6Y44BQkLbIkzZqMi226CVZkmRZtOBNIUBazqiKMbzE2evEXWHmIAz0DVveGMSy/RMwjFow989ePIyYtXMzlhkPnIaMrp2u2olbHdvSUvA+DOtdTTW0KSJITuCVpCETMBoVaxwWOhc4o3LbG3LMn8it+0f9XP+JwGuLqvxq2imdqJxWY1rfe2qUzWOY+10WgGSr/U4Eysok6twVqROcC+pkxf01PoeLpc6TaJMVKvwXV5qrPhaF7OyQ6XXq56LXONSro1/LxgiXSe/sO6/vEe4UFnXzMO8ankUl67FlSmzosg+VZO2crwplddoKxe7g3YpwGoBtM8l4aI6cssL0LMGVac1664K/pSC6MsxiRJCSBTzppfnUH7qmHFKqtM5SD3m1S5CV9pG80KTVE65iMLrMLWxTP6LLuC5CcLZ5ivbR7mmL5vPdNWL5QxBanekAushGCHGa5eienn9yegFoivmoskWEN7EAvgurXD1JUvRahe2hsoo1uPW9VqkdNUfhZrTuN4BfZCLLk24Nmeo/CJ4vDJFcGU4tclAaVIQoSwK8tUpPcaEMfrCjiIreFs7U1/c9i/uZRHB1mvuf2R/so1l0eLtqr+r7u/1ZCzUkGdd2+nLtdQSL8HlL7c2QfMTdmhPWqGMI6aEsS0bJ7S9ozt94TwbbIgs1rH4BZ9i8ZtzHad5wk8nUvLkXEp7z8OZnEtor1UaZ4ReezZOo8Rx2/bkcCLNTyiVyGJpdMK6m5ItmRNvdjdloQHcSNGghKBZlpHHEWAhJoOWGY3j1f6eME+gFa7t0KqAlU2jSemZV/cdOo3cbCyNbogMvPv5K8b5SHJvUMsjm/41Kc5k1RXdX7vn6fkRqxXbpqdtDfN0RltHqx0LwjQubLotO7HMh2cwG7qurZEvpkhB6AlJmEPAOYe1LTHB5DM+wBBOOBUxKqNRLBIRFdla4X7ToHTP4+mRf/gQEfE0OqJUhhQxLiB54sbt+eVNwN3eMIXIzeYOmJE08YfznuMw8/7pyLxMLCFwToZPk6a3IErYG9DNhsSEV1v+6Xmk27yhzwmtS3ykVQ2tNagU+CY6dLNlyI7nwzPOKjq74c1+g7nfIjaTw8x5giHOBJ/5xhpOgwWJPAchpg1RGW4bS063WNexJM/HCc45MWHxw1QWWjlgtcKEZ3a2lG5vbc/oM2NMLMHinPB206B1cVW/qQHcKc7EKcNmS4yhsKxZGI8nxEaUdZimYR7OaGv5RhTihPMSSItnmr7wt9OJqHuGxRN8quyrkLJne9PRuh078YjaslUTOmusXdDqwHavcNYRlGWehOn5E2/tlikmJiK6N0TVELMn+4kpj5xHOM2WY/4XqLn6cYjrywDhlbVIIqz2g5KK5kQhl8k55xcWAqvGRxWmYu1qYxUFxyvbtW5eFQuA4sVT6tUmX716LhorVdrpqZNrzMUBfN2XqsHCa1efqrYCLqqiR6rHqMmYkOvrSqi0l6IXguKLZFJhjxZXAIrOgtKKQEKnUoYLtYvtwg6lcrBzBeFZQ4iZaVOAiklCNoWNQpexnapovI9yKVcZXRoMyIVRUwjBlTw/CSXIV1LReuXaHBCkHNNa4lKASplZoE1yKQ9mfQWq6/CaCCiplk7liYnigL52DyZd2asa11M6I4VgMrbaRMSUL47yhdGhev6UcnJWRaflojCZdC3nRghtvdC2sIHFa7SynhRwtDJPa9cluoA4u5Trv+iiESxZmFWPVa+LZHVhW3395OlUzinV6znZjE2FOdKZi9msmFRBejXoBKKt4eFZiou6CDS1u5Xr+0s1BZWcqwYDWsnFzkEpVH3PiCAmlqRCJUwqsw2KpQrd1wVCrsyaVdcFRFlcCNEW7SS63je62KqmXLWCIugszOmnC66WCNlPZEKxzciBEBIfvXA8nbhtbumMoVEdIYyoHFAoXt+94zCecCFxoOE5lk5brRRTgpgmrHGsPbu9U5jjGZNHjEQ6t2XTCJ3WTESMyVhpkbgU/8CUSCmyaXuSX0jW8o3SjD6TcyLQMgUhxxNtoyAlnMtY2xJ8JKSEJTDFwGEWfr+U/LgYwLqRTXNDOnxmjpp7/0jXtrTGkn1giJF5HnHGYJg4PBksnjDMIJlv37zDbD2fzh8AuHUORSQtp2J94Ec2jWGxEXE7JBvO54+k0KNy8V5qxRLTzDg8Id0NIUNanhG9xzmN9wEbP/KLVzdYrRj8jhgjMUeyL/oAJT1DhpCFc3ZorfmbUyT7Mo/tNxsMPc1SshkbMdwby184TSuB+fzAdn9HTBmlLH4+oGzPlIRGtzTWcZ7PTLlFUuAQYfaWxiia9MSkerLu0TryuzGy6B43T/y8tZgGOu9KV7pqkM3CGDa8MxN7B6flgefZ0XY7YlqQYWab4eADD4vHqMB/9+e/5PH5GYXw6ZyR5NiYTIqCE89tWyJtkih0WmiMJQn09Z7KSYEsjPOETz3KOZYYWYLCpgWVNJ9Pj2gtjHHmmOG8/J40DmS3BWPQkwL5wk33Cn37ilYmdvlM5yd8OnO3ueM8vkd195g4kE3Pp8cz72fL5ynzqhmQMHH2gZ1r6dKAWwa+e/OWMQSejw+01rHd9ryfFFbOvLGRX3U3f9TP+Z8EuFpDc9ct1Q45XUIAkai+6jaD64r+8h71C351qE6pAIBcW9uvHWmyJkRc2Jnr6+vqPqaLrcPqubU6mou6xowoJQQlhJhL+REuk36Z1IplQpeKJmwwmW6pQvVcndsrQ3Hpjnw5FnL9f109qWJlNtasufU4fjxVrTgz1vHYzQXIzGRszRWMqTh6b6ouKNTxcFQRtKpi8Vryg8JsmSoqF1Xb9qGcgxZe5gUtAtYoTE6oUAAyVKCoFLEe5cpoka8lXHgZll31QKoAqxU4FP+xyh5dyrFyEZtbv8Ym5ev1lhIp1I1X9g8qA6lKmHJQkGJGjFz0SCvLtalOc0s1a805k3IuAngRdKoMYwVlq5u9ysLQQTsXzdbaiZn0CkyKd5eVjENIKmEiDE3ZX1+92lYH+DWQWYoHQ/Fxq9daci5mrlphwjXQfL3mUF3f633j6nWYXGmnXc1AX3qxra/t0tqRmC8aP62Kvk1LROV8KSurrC7A6tJ8kjJjvor9f4rbdH4qq/EcIQVOPnDKhm3T4uyOj9MJAzgtLEHT2RaVEstpZkgg0XHfGH612/I4PBNUR86JlB0hJ86+CJu/zGecQGccrdWo80I3Fw3WHBJt67jRJ253e1IKDMtYQEg2TEuq31maHBVNu8Mk4bZRhOjJOdG4lpQWMA2u36BFMS0H7pTl9SYhpmUOhucIptkSUyDYO371zTt2tuXLh3/gy7hwmBd8OPPOKEISjoeBv/zmjiUa2u4eHWbGZeRxghzOdP2O1N5zPj3SOcc4zYyz4mkStGrhdMLZ8t1m0xlRkZw1Ws/YxtE0r8hZEWJAbb8h+AOvb17RGwXGsITInBS7FmJUhYHPipQSyzLxqmnw44IohdGCUy2I4cv5QHIbGjOy2d1yHhdCDNjOMc+B7w8zp3DPq7nndH5GG0OKe5Quvk/Py8jOOaJs+O1x4jAbnAk0eSbGSMbypg0sYWbfdzinaMKAX2beB42PC851uLgQ5UzXtLTK8P5woHEbrNnA7BmnkZATu01HGxKHJXOcZg4p87/8+u94u9syhoXjAj4uJNFFa8bEL/Z7bmwii2fT9pyWQBaFopyrtqYw55IQAn4JTCgCiugzKUREGWKM9FqV+1w55q0ljBOaTLdv2Db3bGThTn/B9T1LaHj/+MQod/zu6YHZW54/fyYqTRLPxrSMfmL0nh+WzE45TjP4wxmlNVla7n9/YGMDT3Pm3dby7T5hreHsNdrcEuN/6ZP7/2z70wBXdQJfKOzR6vxMLvTQ2o0klR3KlRXKqTBXq3BZasCvr/oUnQtACMAmFnbK5cJ8ZV1WGl7navtfHluAPmu8SpALo7QeyroVFkyQmAvrULU/UkuYJuQiflfFRDTljLK5+JjoImYWhLn+jKqwM9ZzcTPXlIxCqWxNJl81T3mN6akMXc4XJ3mvYOOvuYyhejahSti1NapOsFyMJl+6iguU7rFcOk2SFDZpPVddxy5bhVRQmTRQw7SjuZaXXKF7Lpq0rGqpSyBVmJDz2vFYyl1STqgAkwqKLo7edTx0LCVHV9nIvMav5MIsqlR+n5tiU6Co16COX5NgsauWrWihsqawdFKF6BVAIoXFW01dx76IyddbVKtCGxpfymVeFY8wlcs5uepaj830lZX0gK2M5WprIbpovZpU/t/kjDLCNsVynTXkXER9RXdWbTnIiMr1J+hc2lVtBInFGy2tn5cMzlRdYu1g9WRCVtXwtgzoWsbWwFDmm3KuOZfuW6VYOy6VCFFKs8TaWGAksUIztah6jTKBVFmu/+qvhn+R27+9tQQEkmH04LE8Dg3vnz8TxbI1wlJNQo0TnNYkDOl0xGYwbsM5LgynCcmW3iSW4QS6JcYB8bB3LTl7UkooNJgtMc58iQeUbIkKnrzhhymxjZmIp80djRVuzBZjWyQEDAuuf1NYYFuEejZHXNNhK+OZJWBVKdNsul3RM4ngs7DrW3aU71W0YfGJw3FAp99hzJa21dzaiV0XQCxLVrziNWciWMe8zIzhhrbtWfpEoOHzPBDnwH3/C343jFjX4acHQvCc5oCipZ0Tjd3xygW6PNE7izUC2uGD4GMgGss0LwT7Z3wZQHLEL6uPOgAAIABJREFU2q4sOpcTXX9fvMJsg9UOP39hji3z1KFosFEIyrF4Ressi4k8jgGnd+QlcfSOIYJKHVp69OaGNsOD7jk1O1KY0FpxnhcYF1JsGLxn3xvicKLVBic9YTngnSXNmd8dEzHO/PDliX/7zZ7N5oZlNjydFPedYUwtP0zg54FvemHbJHprOHrhzvWE8AHPHZ+GZx6C5UZnHoYzHkvKhqM4poMn5MySI0ZqCSTOjAr+5nimEXjddDTtlkEm1BKw2mO0RnKmcQrvYVpGVFZ45YjxQNM0NDc3+PMjWkHrBwgepTsaidCd2GlP273Dy8zDLPzvg4PjE8M841Pm1hbj1SXN6LzgcsuzP9Ltv8NmyxKfaGxLEIUTg20cbZw5mQ1fpoEvc4tWml+fPb8+nME/lOQDNdG5f4Gaq3W7dG7VL9+U80W7tP4sWnK5rIaVXAXNyRSgsJYH18lZKQWxOLgrJbUDrpqMVjHyOumrOrmqqlWpMXRfuZaXlXplm6BMrglGJ7S+MDjBlLb+F2QbF5NFuMzOJpXnewFs6Zh8ee6X11ZxcVV/X/yUYlZVUF4E+V0V1a+B16teKlW2LecrSIOrbijlWuqjMIeJElotZLyAdoJeMkYr5toGGGrpp7oV1O6962brhVlM2U8Tyw4vYLhq5nIq52PW5rOqG1vZyNWGYh2+xUAXYOHKRMZYAGrSRfR9aWqgdmdWdjBWy4qVBSvkYL3v8mozsQZuF8uEzNXfTM+lzNtG4WGT2fpaGjMld9CRLzmUogsgSRpUKvdCQ0YbyLUBwVYervTnvIzlKWBzvXZSQQ86FRCZr/ma/9xWzr0MQMltXBcsdYGSa0yQLqa8KWd8LoyvSeniEQZUV3hBJVVuwBc6xhWEI5XdFS6Zk3P9W5Ryfn28epn9lPHV36R7LBETziR3w+HsCXpkt7+nmT/SCSTrSLHcgxHPMg9sNpbWFco0Z10WdWEiIBijcXIgiy1+ZTLSakPTbrFtT86KKJqQb4ohsjb4JEx+wuRAjiXA1qiMkwCmQzrLthc2bo+kwBJHlCoO2EopjOuw1rFMIz4nfExorcr7WEsTIfozwS/0ShWNmUCJk3nNbvcGOz/RdIJ2G7Iobndv8Fh0SqSYmKZn7m1D8Au7zS3n80i/3ZFzZowLu32PiGbXbjAp8HkeiX6ppSv4LJmu3WAlsW8dvZXiyC2CSZrsFzopulKfhTF4UgjsNneA4hw3ZKUI8xk/tYToaayA6jkHT5Mj+0YY/UCgpW8yfdswT2e2/ZawRHyc0TyRaQjJE84DP+u36Lajc4bT3HL0E94nUpwRt+XtHZAVAc9o9tx3luPgGeczOXVMRvOPzwvjw/e8avb8+X3Lw2Fhmt7zZdT8xc7SuB5MS1wmZp/4GM9s+xummLg1W3QCow0/u7tj9J5TLCXjzsDiF+ZgmJMipAmlEjFQTEi148u8wLzQdR1nH/gUyoKx1XBnHT5MEOfiSG8sxjhiGAj/J3lv0mPdluZ3/Z7V7eacExFvc5usTGdVuaqwZKoshC1A4AnCEyQGCNUACTFCYoL4BowQfBS+giWYeWIJyZItQIWM7LJdWZm3eZuION1uVvN4sNY+ETdheCWynFu6942I0+299t5nPev//JuPJw5ugBIZB1NzF33kOieM2fGULeYiLLbaSfS+oKVHh4FyfWaZzxgXOMcZo4KUhDNwPn2PA94fPPe2sF6PoJmDD8xjxyiOWTxzEVQ8JtxVM1hjmKYzOWYm/g3kXElT6nWbqQ51QrGu9vpNcYjNFV1KtvJWEFxDtDal00sYbnPHxkKpkSzRQ0AwpTlae1i65pKdM0YsWRtfJMjNqb2J724FXBOQQdlMSWtvyZQ6uWx8ItXK99YWDqxwK47UvEy2S2jEZ+pEtNgNnaocqNjUZqW1sEpD5TbzyaBaeT5tYsyNtIy2Aq21uTbTynpML0akVirKlZ0wt8nPNi7bFkA6FmEqBeMrmmYbmvWidGxImDQUrB1Dkc2yYatyWqFCLQiLVLksFrxNKC/+Y66RplVBnOBKVY9aaFl9ejtHpjmKS9GXlpZUc8vc2lcgr1qOpRLtqfEzNVdRa1uzqTmLNhJ/AJdMHeMCMUCfDXOoysBtf4upqtGEIdvNvb6eG18vaDxSXegVrK1u+bntcJFahAVVstobv3A771ovbPpcDb9WcY0r1ZAvWtQMzdLCvixASpMeVrS35Q7a2l4N0E6koCajvrZAyIpnQ4Tb+bTVF8zpbe/obaktzIYIh1xjj7JAKAVtz1fVmq2mgNUa3v1bup2uF2yZcNYhca7obLhjyp9QRka34kW5AnMGydVRu8hKSgUjihRDsCvD2NMZQzockNxzP95hxVb+G4n94aHaddie43zluBYezyfU1EL5/d2BLiewypIs1/XCp9wTlxnnC6fU492EtdVY0mqlBYTQMYollEIuHmM8a5m4PH4GcyIJ3HUd3lTifV4S3tW4GNFM3+9JYuj2bxm6Ht/1CI7rfGFdruT1iuv3eCzkhd5U/tbdfiDmyBJXuuENRSBmWNYZ8XvuhnuSKLYU0IXdcIe3EEKPEhlCTxDBU5EQt0amWHmYNifuJeJdoBRljRGfDMt8JeKJznFdPrAuBqNXtAhXqQKhVJTgLM65uliznohhSjNg+Hh9QiVircd2nsVE4rxyWTNFHI7MQ6/03QOLJtxdB6wUHW/RUkNZ6O7vmBYlZcfjZWEojmDhtCRwHXZ4x986GOblwpw9/8/3Hzn08Dv7gc6NlLwQOkOvgpeA9YHueqZIz7pGxt6wdz0LhafLxDV3LNGTi5Diid56YhESjqWALZk7b7jzgS70HLNyzYlBMvu7eywL6MQQBqztWLPy+XQkA8cIyxJxnSd0O4wfiVrIBcp6IbuOWR05L0DHRQMfUyEvC1oMwfcYCRgtLOvMaT5yZ2HakHYfsCgPxvIgkXk38DSdiQWcMSwpcZkWBK0dsx95xfcbUVwZu5GsGrE2SUNtFGMNq1SDS9/mEmPahNkQgtlXr6FtkybfioFKum5oULJN4Jbqa802mZrqq7t0whBp6MoWH1IRgo3Y3hcqj8s1pK25fkdTi6aNc+Ra4O3mW3RTIxY423oyN2DKWkOIhblThsXcEIHotPlitXEywtrQldAKOCNNTm9fRZLAC8DQ/vCiWORGQm50LwKmZtxtBZje3uIFnXCy9TPrYxvcJy+qu00t+Rqcu6nF2r7IrRioxVujDNW/bUhVeXmOGF54S61oVn3hAW3HVa+L7W/1+VmbqztVwVZcPScu30bppqBTkUoyTzSz120EXkGP7TwaI/j2qG37ZbQWeVvc0YayUmp0TbEtWWBzz9zOPcLVFUyL+/FFqWyXdiybwvJmtdFe+Or62cZme9tbZiQNJZTacq6/y/8LNpKNnX9D7l7Gvv79h8fvJONvthUvkG5qPkA21yJss5d4/frSiILR/nBcf5u2aToRJBG37yBjSfPCF+Me7TwhL7zdjYhEelP41afPpOIIosRcvxPnlPlifE8qK7tdoPMd3g+IUR4jRC1YHJ+18O154vH8gWkFl8+ghrf7kWI8//TjE0YafypealRIbyhupDu8w0ok+EDf7yDeEXMEUdacKLNFyOR8xZQVpwXrMlk9neuJYsiayWroQo8NoRLWjeKdR4yr9ADXseRCXE8QVwZvULGMgycGYaEji8dYV/k/OdH1iskzOScEhwSPasYapSuFNH0m+AG3fOIoHXfdgDV3XAVmgYTHObAeBkATVFZBxorBULBZSdcr+ICLiTUrvu/JOZJirrSSMrPElbf7A4fBwFJVcNEIPp2Re+FXz5nD8Ib9OBBj9Qfr/UAZTgz9gVgyKc6UYjlen/h6PxILlNJx2O35cDpT4sq7ux1TFtanD6g1vD94Pl7h4/XC43XiJ4c9P9uPmDUTwx5K5g/eH5jpeL488rP3BhsGgg0smiEVulDYCZgQSHNh7ODtKJQiPAXhtCQSjpwUK3fklDhly3E64bsD952vLU/rSKJ8IbBGz+N5Ia5n7h8e6P1PucwzH8/PZCzRjjw9fcJ4x6wwrBUtz+uZNRfo7gndAe9HjNqWWlHYOUdZJyQEnGaCrwHUBlhyxBrLNa4c10wIAzYVvlsuPOiAiPJ0/ZbQ7ehtxsyP9GLpBo9Jiet0rd2jH3H7jSiuNAnGvXxJW2PAZKKxBG2RG6VOgGoTURxeYXWFLgtDBhWtwZulBQPbpogz4NWySEMkhJuuPVS/d3AV0RpiRXm6JNBMOtUqIcJiNnJ03Xyp8vdrcyEPW9RNU2MBN68up9wiVZKtisDkWxSJUbqqW6dPFZGpE5jeig/TeGMqYFthOQelT7V4VNuUXFKr9pA3wnv1DtssALo2eU4NZQFudhBehaUhO7JxsnJFZKJTStsnkTomS6oOvabxo7LA1VEdugETK5rk8gvCxWaMaTJRLb0UFiwmJp66zC6a6mtlflCi1WMxemtLFdO4Hu141FaksXKcYEOpQkNvVCpTzwo3w9RN9GDYcvHApNf72fhqpbq0GwBb32e98eK2wrdec8W8qB+DmlZctqrSZXKLqkkOwlpqgV4MfRamUH2rJg9dTLeC05dWlzbBxEb9M6780Nrj9Q0lL95jtj24cfluyNPmz8UL7ytKRXdbEGK75gRJymAqh864hCsVLd5Q1G3RI1SlZDJ18bA0eb81BZWtkJe2/viRv8n+Cm19OqOmBgCP/V2dKHqLSKKopTu8YbAz1+j5No/M/cDl+sjb/Q6TV5y1vAt7nqYTn2PCPwt7v/LFww5sxzleeOjvSOuZz+cPDOp5eHdgXldO0YD0kBKrFt7cvUOsI6nD9BknkUM/8uVo2flAlxeW9ZmcnqAEigkc01SLAy0kIEWP2oGnNeH9oaKz0iN5wZjAmDL9EFAKS1pZqOphw4JD0XjCi6En0e8OtZ1l3nNNkRI63jiHpMhZC5GCt76i77YitrYUxqTM04q4EVvOmLs9oQtQoNdEKGeKLoxhxAZfhQGlLsC8rffpGiNIIAqs2aIU+nGkZ8QmpaQJ1R7JpfpKpYQm2NemIocQ+FVS/tXnC7GAdyMPQ2Dnn3h7uKtqv/zE+8OB3SA8XQvH019w8H3lJYXCu37kNCX2w8DHxyOxd0ieGPqBLgTkOpEOA3NyxLjQeXg37hBzwMqJ57nwaZqJxsB6xjuLtTPvx5FBApe00Ilw5w39fteI6J6Y4HNOPF7gu+Mnem/xIiwKFMFY6KTQu8KXAS5Dx6TCvvdEtSyxMK3KEFa+2L9BTGFZI7N6PhwnjjnRhYcqmogRG3YY57lrRZm4wMHViXnRTCc7NC5YmXCa0RJYrp8I5cyXwx133vNIDQYf+pGny5VzHDjOZw7ec07CGYeSeNPtqCu6A0tSkghvzEyKiZhHTvNUbSL/TYy/Sb4SiHM2lRDbcFCVJrdv5jxKnRQ7aovQZ8PiGzokjR9l9MXVurygG8YKfgstvq3Im4pJGyjTeDi3OUth1Rpq2qEtIPmF+3W2lXujpbq27xdhcXXp3yFMG0L1qpBRrWG+G5LVqyHZV8VOKwyy1EkYalSJ25RXdSjYJWHqwCwNkWt8qX2qrt6xFX2+WTO4DIvb5PGVmF5a62rbF2c3ZVwbpwC5bJJ7bpw4myusWqjvJW2MD9lUE1YqYuQkcR2Ubqk3jdGaNbe0IuvJWXZz5D/zT/yDq+e5C5XbZKqjWYkWkSo6ULihMV2r1up4gi+luvxrRXw2gUQuBWtMDVGWRgiXptJUbk70sI2T3q4Xa6vSb+PsbSrU+viLUe0tCLptm4fX5JqitK0ZfFP7RacMuRbE0Som/7DI2NC7m5O6Nk6cader1vugtNyfzUNs483Va/fFgV+o99ENMWzA1aYW3NBJAG9qy3FT++Viq22DA19SO/7KzTNaw6ZrkHrb97ZvEWlikxcUcfsM2aDQ32JW+1dBKK4qzTQ/EcVTisNbJXQHztPK0Qw8zonndELFMfRfcJ0u7KxjtD1/+eE7ckncaSa4C/duYNREdoFL2TEVwI/4d3+A0YrO7IaFL42QcZQE353PrMWwrDOdyQQjaEnE8wd+8axAIoUHYq73ipSFMST6sGOMERciHcJVEktc2PWB3hpGGzCaSUFwtsObQkoF6Tt2/Z7BeA5+BZ0YfSBrNUdbMlyyYYqGq2TmZLnkFW8KYoQ5GYTC6Aq9NxyMZfQeKYVZCp3doymRGcHA2B0qgqfKiY4ZwzmDztzSI0YLcZ7oNWEFjtfHmgYS7okSKbHaZawCwRqUSNEVr9XK5poD/9fHExYIVnnTd/z1w8zYB0qJeG+Yfc/H8yf+1XffMMVM+tZyTc+krHhTkw6CWdkNBxzK0I28I3EpHY8fz3RhYFpqxJoCw/5LynLBWuXeKoO11SojVnf8fTcQi2eOgRAMIiPHy4UPR8/D2HFeTrz3e0gzlIpOZQUJPddouRbDJQZ2neO8zJzmE9YK994wBMc9nm5wPF0jlymz5oRB2I+75o4csa7H2YGQZyY387MwcFxPDJK4yoIPSufqgiKUFY0XNMGcFlxOWDfSDwPOekxaKGnGyMTd+zc8nk/M0lFKx8kKnx6/J4cD36wLIRy45ti+Hw0/GwccZ4xRvh6V5+uJvYw8p54VMCXS9SNuPWMan/HH2n4jiqtBS+XyoKCKcXVa6KD2RVxB0xalAUg9h41ei2tTh9CsDCzkXMAaijblnNZMv4oK1WJKTeP4GMFvRpdFyP5lMvICYpRc6kretzZYEr2ZnnpT5enqgWb4WUrjAolhsa3d09CXLM2R3tbVk0M2DI0c6gRps1QO1ytCP1CRIqm8FhcrD8Zv5pCl+mMlW32htrDe0iCP2k4EkpKsqQVdgqWrBZ0vtdhbTPXuEq2O+EpVU5oNkYs1X9EUbuHCKlVh6KTyz+ageDXsckY31EQqAjOkwt8JR6YPhf/8b1/gdxf+vU9v+R/+z8LSR7q550/SB+40Me/umP8y8ad/awHx/MPPhf9teag7UmoLNbULo2stMG0wiW2ZZ4aWWUlVxW2Bxl2urdxV6r+b15lrCGKXX+wjtsJKpJHbW9FQGupjUyWj3zL92rVc1OJMoUgtRruoXEIhRIskbu74TiD1wrhWjyx/UzK2Yqv1eG/4biv81lCNTqVdj69Dz40ILtb28gaFbS75qXl5hPLiZm/LxqRqw2uhK5murRCkvLSrpSg5tvFtKwNJ9Ri3MVOf8KlmmQkvql4r4NJvb1vQDp6v9g+ktOBdYDTC+fzIxXoyiV+cP/E4LRQcD+M9PTNdKRhrMRamtLIbdoCjs3DoK9fnDBgz8DGuXJZM8B6RaiVQsAxkdhbS/ExJmRSvDH5g3xVIK1JW+tCz8w5rHas6ohpct+PT5ULUvrYQtdIVylrIzjH2d3w9BCRFLglwys7MeApiJtYCoXGRYvtOPC3gzMjnBd76uvja244QDKuBuGwWLMo1LrfXr2qI0XCKiU9kRDOHviPIrirDncFbiysLZ6nu4mNveGNgbyp9458f4ZTrd3kEnBuIUnlb0e5Ylolvzh+RNJPiGW8cNNNWbx2lFIoWrHNMS+L+7o6yZh4vZx4vK1mUeXlkna+EsKeYnrxeSLrHB48xhp3xeG/rwsU4LLALhiF4vBQyhnf3O6ydmVfhen5kiYmuH6Fc+Wq0jOOIsY41HRl3X/N4fGRaIztWvDEsucMYy3S98PCuYy7CNQofjnDRTGUkLcRiSXHlsiZ8t0eLxYjl43TFlHodGjU8acdxXvhmWpnjhc9ZGN3A8Xysi+0Pz+yDJ4tFcwFncMwsq/LT+0i/noldj0wRJ4bjfKHkxGjAGceyXCkCP78Ds5y5Z8b37ynhC/7sl7/kwzUQlhNuvOfx4xNu9MSihP49y/VCoONdZ3nvDcfzBdMbiIqRIwnLLgz0wDh43njFrImlGR+v3jO/zsP7EbbfiOKqogPSJPX8YNVe/9UfoAS2qctMQxs2l3RMRVqGXFVYJda2mDWC5eX9N8RKN8hF9dak2PywtCnKDNXzyJktf62VXUaw0iwRUkXJrrZUhEleFIW8el+kIg7ZvaBo2wdvxdOGfMTWurS5PaU976b0ay28bYhsfjHaBFi3sVJwrvYzBeXYwZCrA7jYOjabo/1m97CFZ28oSmnjq6/QGxFuyMe2/7Yp2La/mw0xaQe1CQ7+/f7E3/s7H6u0pPOgHYQLv7/AP5F7/uYY+S/+REEv0E3w7yaYetAr/+E+8Pf/vO6XTy/eZ9oUfjeCOS8O45tK1KWGWhVeEC7quCivUDzZ+GavEKRX1+rrzTS+kpPaQtVXN6huC4BX6FbxQo/ceE7bY6HU8NpiITlF4tbGa+Yj2/lvPzjq812RKppo94g2NO92ngyNI7aRsOoHLq658/NyDrd/N2WhcRlLvrVft7dYtAkaGjpKbsikSQgQojAHpctVviu2Fr2hbIHUUnPFfks3zQvreuZ6iVzzEwsdj2qZp8+cuTICzgQOnWMvM49pxy+XIz2ZrrcMxvHX7u6w6YT3Oz6eZtZ0ptvd8+nxe45rwHpluV4x1iNkdp3HS6Lb7Yh2j5QJ40FtT8wz+/FA5yzTsrCK8O3zhf7wNbsQWdYjne9wdqjeg0ZRuavmmgLZDKzXC8Z2eK9cfYdxb7BkhERve9Y8k01gVc+clctqcP0BMXBa64XVOYG5GfCWmvIQRcAPJGnUilL5Xmo7yJnOFYgrIpZLqoXPVGbedYF1XgjGEi8XShSssxgsK4W9CywGZM286yzGgbieYEH8wP0bYYqFlDLeOi6n77FiiLarCPh6QXJh1+9wTpCuYEKHlkLSwjSd2buf8DTPdM5hzQ7TvMFSyhhRvDH0nee6TljbsSTlKSfW4ng7DMh6JZiOKV/pd/cYMRQt9C7w/ekJ799zff6GS+q5fvqWohZTLKIQ1wsRQYxDsdjZkudHMIbPcWF/Njzs7rDWIjax23+Jyx2n0y9BRpyvTu1L6TFSPcFO05W0Zg6DR+gYHVyOzxQsS4nEZYJs+HI/MBpDsELf9/zlp+/4xXcXpEykpPzsMHDXGf5of8cxTTydVnZ9Rvs931wn/vGHyFM+cHhOZPuE5RPn5Egl4Yulmx8JzpI0ErSgs6JpxYdMWR3/7Jz4NC+kxwn0CmYP6Zm3/czeWj5dvyX7HXFdOOzeEFU4rYnp30Sfq20rrlRPpTaBlM3OXC1ilb7U3KOm4WO1uRYHqVS+S8uK26JGnFockKWtvLfP0doisjWDhpyV5GvUTM3SU3JRghhKEpzUttrsqMgI3KJ4bIvhURrHqoUiV0+IpoqSarUgGHIoSGPMi3lpGy2mqr1sriRnVyp6pFTrgZp6UltBk1X20VREibrCU1NbqInmodQmQtMKyGQrtrdLdSIeXG1nim3FE9Qxlapi89RVXW7jZpu6zgGL1R9Myh5I1iARnBTEFkagZGn+UYliLRiHJPh7/1aBfqjqAKuwKryD/+Y/PsO/iNUxM0V4MJAKdB2MpVaM95b/6M8+8Q/7d4hNrSVpqq2AaRab9fsfK4WI0FVviRshSlxCs2PLu+uoxWbfKp3iamtZ5ZWYQEqFmovFZX0xPm3FygqVmyBbsVVYi8O1fMxcFGMLubwUPb7ILfsyunqtZFOJ76m1Yrd2cWlqRaP1I7cYG4FKQN8WGFsvsG2pKfQWaUkFrhZqm01Gaf5eANmWpow1eE3N58uwUsfPmELK5sY9FG0tVEBsIieLNxkczeerAc/Yqha0hdUJtiinVykMv23bN+fCn3/8wNB1OCN0ZuZ3x4HkHzgl4bhcudJzScp5nQhaeDAG73p0nYnxyKPC0HWU+YhZr4T+QJ+f+UoKO59YM6xp4ZI9vXPYJZFk5dvpXONh4kLwDmOUr/YORPi8JJK755oLixPm9copwS6MHJzli97ibY/rDzzOE1EDKStrSvT9Duc8Kc+UtFA0Im6oSr4ys+8CLl3wJRBE6P2OXApzUcRbUoJOQHz9b6fCTKWEpFyNgI0VEoJKqOHk1qLGUoxnTTDniDWe0TnUwGCHGrKeRlYKBVszQOPCMS8MznEIlrhmnk9XbNeTjWeKlh00rqJjTitv9u9IGc7xisHQjW9RzQRN9ONIWRfedpFpvmCN4c57YjrxQEWmHErvhb43zEvinBzWBz7nDouSjeNaVko24Ayfo6ArvHGenR8wJXLoHNeU0LLy1b4jpiP3b99QSmKOgnMdIoYljmSFS0wci6385JTI3bsaqH0+gvEcUyTEC1kN5jKxG+4qj9aD08g5raAdx/lSbQtixKI8P39m7zPvhwO9X5mXSOdGhjdvuRsMn4/f8YvnlS/ev+erbse16/i9nfLdo7J7eOD71bKs8M++PRKL4WH/wGTvqy1C/o5sZ+47QB3WDQDsFWJJGAqGzJIX3pZMIjO4HhMTeREeg2HC0PU7vBakDMzLBfU7jjiO60LS2moMoWNelBQ/8tO7e+7Cj1sO/UYUV8XTFFINtWpf9htnaqtoankAcpsEWqVjq1Gj35b3rf1nN5k7ckNmaKt6BWh+SN4boirON26NSA3tXZXsG+m3VDNJWmtE2srbtUlWNl5Smyyr0vnFOXxz1t6OSVqA9Dbjd8WgWYkdECuHS0pVVfXpxSsoOeWQqgGkILcJbjNYtWycmrZfGzK2oYJt9t0QJlVukS42cStOTa5FrnmFlG18GWtrjEtyIEkIJreIGYPYXIMyaQRvA//JzvHtU+T/UMP/+MX34OeKyfvWXuuk7mifwM0VmlwS6NYDz3UG3wHrzH/6d4V/9L8HrF2BOtEbI7fYmxdUsv7d/NpEboyQTaakF5Tpdb7ipoir5+0V7LS9/6vnmlbFbud3eySZ6hRvIz9APHXb19fIWbs2navKQmnWFUap8Ua8FF0bosiGoOoLRw6pgeavrRhuKlFqIW/zi6Lx1w6rTj4KSrVGye132xqFJda8JPHVsMxKXlcxAAAgAElEQVSaLSqqwqviawFYXMEabZw5brwxlSoiKCjF81u7qTFo95ZjnFjyjPeWb2IGdWST8MWxd1dW9Rg7MqmS14k+J/YBBhsw6TMeRxFHMsK6XCnA1/cDdxQuc+SqC/uSWbPDFMPolJ0t+P0BI44kA8+XM49XMDJxLgF4xPqO4AJGMlIK0zkTvee5QB8seX1kF/ZYSQzB8zud5a4rrEZ5nD1GqsnspJDEo6Xwq0vk/fDAUhLXZebQrdhyAXtHEDiXhSl1t2vxpPW7yiMMBn62F86RqoSM0DnQDAcH+w68g6X4qvpLtYOxpBr95RHUW5LWNZuEjpwzS7ZoAiuW4A6YfOLOCgdZscuZvSt0zrHkzPP5mcP4lnubEFXmMrEkOGvk+ukJZz2dBTGZWDJDCBh6jDqwhiVNTOUOiSMpL4x6JpXI3eDJ5Z41JR5Gj6QrO5sJcmK877lcnzibkcslcZk+s+/uyGIIYaCTBadXppw5iEFkRazj/d07vns+44d73OlILgnfGc6nK1k73nQ9U6yRNJ0almHPfPrI6fQv6FxHJwYlM8cJ6NhpIq8JbwWTIw9ffMm3T584Xa4sJbNcF3ZvAqPJfP/8RFmFfT9yunzPP1VHzA4xGcI9J/UIE4t05GanoelMiQPX5RkXAj8/3NVYqBCYpjO7wwNZayt2zZDxGFHMOqPpynl5xoSROU0MAjtnsdaxGMGbHivvWNcTUQRxb6syPgGSWedn9mHHSTvOz9/+qPf5b0RxZcoWslzz9+TXJkPVXNVd2SK8tAe3VsdiTPPRKY1jQ1U7NQKMkeb5ZMzNzbzNP7eb2SGQq69RFw3JFAiCpcaRrEFJAdxa53nb3ltN5rVbtTYinUhhKLUN6E1GtHqnZF/z4qIDF+UmtTcFkhe6WHPnhgxQJ9Qi9b0Xp3TZVJTKKiEpi6+vFQETt4N/ObHp1kqtCNRqlF0Uor5qAwlIrpyqDYHbyOuGF7K/oiRa/p8IllIRrgahGAuqDtFC7jzMMz87rfzd3zvDHxb+9OrgMLd9zHUvb/BL2+u91p0WVysJ306SVrSPpUCEX/WZnyaDQ/Gm1IBrq6CCSrkRtd2rVGefKp+spE222DharaAuFmzrp2Zqerq2sS3GVJTRlaYsaQWkbRmSKgg1pLR+rsFmrYpAwGqunDTXiPVbka/b+1dTVFOqKetqa7GrbVc9FYUSWluctgZpiNWW51g23iHNIuLWYheSrYibauP5lZdYKABpwpCNM1XK1mJsKK0vOCn0qhi/FbJ1oZE3QYBU24tQBNlUgkmIapEiN9ZY/+MKc/5KbZ+eP2PtR77ePTC4jvOSOCdDJuJEebsLuFxVeUVXHro9iy7s+5GfDoWvRsP354ExHXHdyOfzmU4ygYguK5do+GZacDLydgcfLxc+p8jiBuauJz8904WOzgu7zjMtNfh43xlyNqR4YRS4GwfuQs+THPj2MnFeIp3CvRdkea7ZnzbzyQx8zsKCQla+2tWQYr1e2GnG+x0rHimJXhwPvWdKE33YQ74S1z3zmrBp40YWpIlIFjFcyHy4gDGhIivOslfoGjd3ibDO8IWt1hXOg7eZoespWvhYXF2rKVAgmsKbkrk3Z4wk0nIBDGs8cZ0c3hnCIBg7cE4LlzXxtFqe1mcGFvpgWFclpowYS46JYkxVLTuPLRHpLd53OLGQrwQBr5+x+QOfk+Of/uoXfHn/nnHnGa3njYfeG7Spba9T5HGuDulzfK4Lp/E9p3XGdTvO08znxyceVxBNqAnkElFjuXNPzGtiHHpSBNYnemAoiTUWul1PbwpCYc4r+/PCTwdLCoZ3Y8/peubxcuKdWhYtqDouOeHsjuLhV09H9s7hncPHxJxXPlxnphjYHe5Y/cp1jjzFgfL0zG4YeI6e+/GOT+dn9sZzLYkyvmVnJv7gJz+FdeV7FUIALStWMoPN3H/Rs6TP+OGBEDqulwUxmXmZMYeBNVpyOWC8xcsbvPEEmyq/2FYLhmQMRQem6cqyzhQ6rloQteg88qb3xHTFv/vyR73PfyOKq1x7TjWyw2xap0ZERm4IC23C30jDuZGTTXtsQ1c2V4fNTGzMtYmXk1Ty+cbNaeiEVBb0iwljUaS9VhVSLuAq2fi24G8FQTItp21TsLV9TU00v7jqW2RSQ0xeieZv/K22+0ZeHMcjtUWpGwEZ6OOLCtBI/SxrgKbYKu7FLX1TAG6Tq2lFjDXVekHNBoFU7y1Lndw3vk7rJtbHKY1vVfcvqr3xyoypHKKTK4Tmg3W0hr/+8Xv+u397gXGBrhacvH+Gww5OczWYqSZRLwxqlJvawLd+lchmWFVn7UHgTvnb/+TIL/0BS/VuUmm8p/JycqrLulYxhBGQ8oLiSIu30Rc1YC7gGhcqZWnnQm8CBmkn6oZMvroOSgbvqopOhNsxverQ3cKPoTnApxaoLFtgdS2s9dVbb0WuTyC2WniQXrniO26Zk8ALl49tv24lNMZu4ohaUG45mVuW5yZ6EBpC1jiHztZz7aUF+KbKA8Ns7CzF2KbmVG3SbSE35MxKwQqNeF+PzOuPTHD4K7RdOWBw/OJq8C6QlivO5UoiT57pdGXVIy4l/vjLLwhyJR12pKQcF+G0wlyEj0eDdzODJPbGcNHEep7JMjIaWNLEh+uBwXZ8aQI5rYTlhO13TGnleP3MoB06Bt4KSDSUvLDrR35+6Dgy8v165bpEnD2wKyfyqkSFz2vE2x3ZZYKe2PcjwcCC8M+fIqPpiezoXGJaLXOa+fkbR04nVhWmbMjzgjGRzq3cuQ5xhpQKBZrKtzRBjmdephodZqoHyDelQ3PCGs+alcLKvxCDuMBdEFLKOITD6Hjo4Gmu/lYoeDVMY+B7CfQKZniHtXC9UhXYUtkIOYEvGb/LPPCBlT1Zj3yeF6LNvDOWbthjZUZKIRjD95+fEddhUs1LvF6eucwFxoHrJFyy8jt3B3729vcoJeGWmbmzPJ4jzgeeroVMIanlOk8MNrMb6/fKdJ04Pn9iNxxJKljn6fOMSEX4S0nMl0fcfk9ZJ7TMOBf4uCauyRLEk3Xi7XXl86Wa1/ZmxtnEn//qEVHHx8svEdfhTeDgPYexWmC/7Tx9Zxn7d6zzM3Et2PnMYg1f9w88zo/c2zPT1ZFL4tAfeNvtmCXzs7sdH59OzPFEZyzZFKx09Kz4XLsvD+Oe0/ETT8niinIh88Uy8/T5ka+/+l2ul0eYAz0rLl3o/Duu52/w+y8ZvMUUwdmRJWciKxoLc4RYHlnWiZ2DL/fvyf0df/H9J7rwhmNM/PnnyPPyiNKk9D/iJq+9cv7/2v6n//JP9bVUnGJI8tKOmJ0yRmk+Ui+Pqy3VqXxDkWxFYDY0Krfnm1KRq2zlBzE22qT7qRSMbUVVmzxza21ZquJp8RUdc9rUVW3YXKkTdm45h9EKnnzze4jU4su+MltEqjLNJcjtuLWhAze12avT8lq5rlon2tiKv6DlB6qy+py6/65ojc8IUFLl76jVmsH46rnJmFpY2Drp+k1AoFuL8WW6r5Er1f9+QyHEFJ5NR5qF/5Zf8jf/HQ/7D7WAUip5y0gN9avGw+13U8v7rZ27eS2sG9rS/qftZ60rY84O1o7/+c++wDbF3639pbDSCuyyIZeFREVxtut983iqlYUQXaErWon+G4G/oTb15/ZezX7gZduqrHIzRDX/H7dU0Fropa2dLC8E+iRaiyeplgtCbSeutnqhlaI3oYLCTVjw6x9j8ksxl21rAdr69/zKosS8uueVVy1jV3AlkxvyakstqlJT0m7j2zrezboBJFeuXnh1ilwRsivVD6610lNrBXZZcQX+q//177+uPX9rtq//+3+giUwqGTEGbz05Rzqp7uddPwJgLZBXbJYaOSUO7wJLWiglc54uGC2Y0GPKWk0t04ITj+aF4AZC11PKQkxVrexMIa4rJVOLaWMYdu9Y1oiUQtc7KAUVQ7AOIyvXpESzZ3SWkZld13HwllngeVHmGMFQXdwJrNYxlsxX9w84sbwZPLve8t05EnXF+5F1uXBRw7vdgCvUlk7nMbZHJZBzIiJcs2HRSFYIUhPZlRrbEmz97i7WIzgMdQ3mLKyxtpE6V+HeXOp6TeSFfilUm5vgapi6KvS+ocKxRmpZgQcL78xEVy7M04T3I0UKz5ePVZU9z0wlQgjEeQGUiZFVM8t8QdXjQiBoQsRzXiam6wnrLKdUEONrTFYpuNBBSlhjwRiCaj1G45jnK2tcKjqsFkoihB5Jl2riWRRrHTnBMl85pURCyBTKmvnJ/Y60ZpZ0xNPzfhBcJ0zXldM8kd0dT8dninGkDMYafmdn2QfLrgvNuiHiQuC+D3w+PlJSYNz3jOPIx3MmmJorORe4rpEoO1SPWBNI84WlWDqTicUw68Dz5RPBWySt3I8jYxdQPTBL4i8+feTxnElDT7BS/S+xdVGXqiuyE4u1PV0zuz2lyJoiwQSMifQI35yfKblDNGFMB1R0fnCh0nvEsC7Hamb7v/zXP9p30m8EcmXkpT0iUieEvPlbAftc40e0SeiN1MfN1lrZWjft9S9tkRfOiUHoc22tbSRyaT8EW1cqYrZWmMG1OONVhNMOwtrMTbeJZkPP2j4VVydkF4UgcsskdJvZY5uctwkKaahEa4FKe7w09Eryi8v3DzIGhZaBWH9PpkIxDm7m38ZK5ZmZRoJOwmQrCX8/wzTILa1ZkGaAKjgSGNA2w2/G+VvhVyN7XlR4W+ttxfGn5ZE/erPw7m+cK/ve9UCCYFpVp3XQYq6fbVr1hnBj37/etg/Q9nhpRVaUil6tUx1fa4habsrGWw4hLwXpzb6jOgfQ6Ra23fZLBZPh6gqu2TV4rYXFZsZZx76e680nq14frdAoza2/cQbl1TmDzZH8Zdxf3q+dx9ZmM/mlkjZGWNH6fVJay7bU111/7c51iZtFR72euIVfWyNYm2s8kbo6rNu1YuqC5DZMUg1xq9ChclZuvWup7fkgNUx6Wy9s1+LcFhjqCpK43dDZvtTWUNWQP3KM11+pzduMMYYBT9KCyROexLvxwGiVQyjs+wExgS44UM+Sr9U8UyCmhVJgSTtyiizxhLLnEHrWspLWheta0dj3X77HTxfOeWFOK1HAdAPH60Qm8nm68n70/Mlbx0+/+B2+nwy/+PaXLBiEROgPfNV3zNczX90NfLVzzFMi68yfXy3fn850ds/Xg/Anv/sTLlPm8Trx8ekDp+PMjOPTMdGZTLY77oaB83Rkv3tLnGa++3SlD469TXR+R0onnIedD/zyaea8KIuxYHoWyUAmWIO1hcUmrKmFpehE7ww5KQklYTBiWJKh5IgYw+YerSXhxKDGVAqDWjRCaAkNscCq1YMKMnNc+MuSEHF4uUeoflKr+QobHM+6sq4zkqAzZwYM6XrCWeXrdz9j1TO6zORseZrP7LoD92PPlBS9PNO5EZGZvd9T1gvR9czrtaYwIKS48nmZcKZnvVyAGV0uPASDTD2pG+iC5ycPd5isXNeFR008jHtSmRFV1r5jjVd2Ung77LjOM1l6ylKICmim48jPHzw+HOhcwJmZx+PENEeOxyfOCtfrhWT2/OTNnt8f4Puy8PlZGaaVD4+feff2J8z5yl/78ue8Wz8wl5UlRkQC9m7HcU6sS2HXWyaxdIyYLvB4XjEh8GmxXIkoljC+4/3OokSsdXiBuy7gXEdaM5mZj7Mi0jGvJwrg/QD4yh91gRPKF++/JOcFM3R0LhCWKzZfgcoXfZyvTMHfgI0fa/uNKK62VXkNpFWQ6q+UWg5bbEG1XreJq7qtJyOstvrv3GhaDlzU+oWulXhNa3EkSlXXmZZbly1iS239NPJ75WkpXoSo1RsprPUzlepNZQRMm9Czuhr1YSsGZRovaXEQNKO5rpG2Y4yutXiktpaMyaQNcqMS27GRbGuYKPoKJqO2wFbLLXakSHWKX0vl3WhDkzIZIwbnMmqE+yQ8dzXL0BYQW49H2ApBvc37ppHjt5gcS5vvrWKboadQMK2QKSr8B39whW5u8kcDrVBDtFp712ViRbGgfoM1FVq9K9phulY8tYii1mP9YXvQCBwUL4XZ2OpirrC6qnQUrW0qSjXqLMk1xHPjR0GiAIpYRUxmTMLZGnysq/liNpSuFb9bq7HVY5jKEeza76aNFXBzcocXovt233rDLXB5Qxxt4XYnqpVbDSY0LiD1HEQHV6l+ZEPLatjc4rEvPmJmWygU8AhiEmR3K55KAXFbQ+9ls6JkMWRTeYGb+Wh2ipNciyiBtUj7uVbZ2dDUhLVNbAGc4nKLGyp137dOtBbzww/+LdsGpxQx1WiTQjEBi+dpWVmdJWpi0Qu5XJH+DpuPODPUTEaO7PqROVnwnqgzSXriemWZzngyl7W17rXmCF6mI2Y+8eZux14MMyf+6Mt7nHqiOfDtGvi/ny78o+M37KLlj7/c85yVNRuO04kyPXJ3eMPz+ZHvj4lpTrzZ3zFa4ff3A3/jy5GYJubzI//444TJK5063jrDm/2IauQvPn1kvH/gcq6TYInf8vb+DVwK63SlfzjQy8KK4uj5dF5xJKwm4qVQTEKcqyR7a7FuZd8HvM6Isahm1kVICmoNDZBgXU71ujcGTXWSzlRkziI4I8T1QuccEg2JBS+KN4Ecr9xZy4M3uPVMKSuDjTx9fkLkjvddIBjh53utFgxJWKZEyQu6K8zJMV0+sRRlns50oXKwHBlL4IswI8mypBWH55vPH4imI62fWBRyjogoD2HH7x4OpOWIvBvw5p6UZ/7yeGT0nqAGcY5/+e0vKNYyn67IcKBMM4FI33XcBWGRRFTLc7xS1GCtxVyf+MMvfkaOHvIzX73bI97w/eMj3r4hxMSHeeb+4T0ffvUtn/BImng+jvziWKkbY+/4aejZv/mSJ125rGCeHknFk+ZHQhj46V3Pd/PKd8czq/HomsnpM2IMX+/ekEPiMWd++uUfEq+/wnU9Ka4ULezDHcYURhsYx4EPzxc+rgufTgsqyjx/4I//2h8xdr4ajMYZTE/Uep7dutb5YD0hy8ppWvl0PWLEMHjLFA0xJda8/Kj3+W9EcbUp6GJDnaTNAkY25VSpIb35ZbVvG9dKSlNBISSztf9qC8KXOoFuRGCfXiFAVFK5MTXEdnvslRCsojqaESOsr9CLUpTk60TRxVxJxI3bsinWKpn4BeHwpZYuWVzNAywwhdpKSVTeEzRzRwuhnef0a9V010qg1KCzIKWiEBUtxW0+Rpa68mms9GSEoIrzlQ1dstxQv64RlzEtT04yu1V4blJI+wNkUW6hu9nU9lSwmX/5TeD3fm8BCbVwkq1H1Q5AaYVUe6NNTaDtb6VVcRaI+lJdbIiVoaYMZ4WnBL90TJ3FrK7GqwBjEmbbMu0yqKRqWup45V4OPhWME9ZXjKjb8bWqx1HY4oO2x6FaOm2E8JT05vIe0st539R68ALKua31id48nkp5SQjYPDV94yVtrufm1b4Z0XZdvbTYXoN+0h57DZpV/lwtyEvR2yLEv6puyuaJ1biHG+9vQ3atrbFC6+3abkWhVAGHbUWUscoGE4rUxZKqok44rNVSpJLoeVGh/hZulyiILHWx6ANSEs4IanuQWNchSRHrmaczc4xclo+IG9CcyfmRq7qaSGAMxgXuWflqHBh85v3ecJkmVvU8Xs+IdpjR8PE8g2S8sbhkMbkwhJ4vup57IxQTWBS+uaycc80E/Nmb93QeEiN/uTwyr5FcLI/HBcWSc+JfPkfGcYfnzLvRgRi8LnzzvHIpBSueL+/uGAaLjm9Jy8znyzOfHz8x48kJvn984to7LvOFrtvRGQEKmmv2W7GtaLEd5+WMBl+/m/tA0GpvY8QAhjkmrApGI2CIAiklfAiY2NZo1kOJ9LbDm0guC4sbMaka7Y7lxDg4jCrT6QO6TnTeos4y3v+0etUpnOPCnA2XeUbF8v1JWaJhSUqhkOKCiiGuBVc+8/UXb5Fo+P7TN0zzhW44kJPSuYFdd8/BZHyY0SxMK/+avDf3sS3Nsvt+33SGO8TwIt6UmZWVNXRXi90NNgUJAumIhmyBkkMCFGhJf4AgQH+EZMgUCMiSR0OWIMiQRxGkBKjV7GKJRXZXVuX48r0X453O9A1bxvedeyOraZaR6DzGi3g37nCme/Y6a6+1Ni+evUL8BDqwlZab+3tce07wiVW94LKtedxu+frultZo6hCpFyvqylGvLFpXBFFMIbBYLKmsJY2a3QG6IXKzHfiL/RumcaCb9qxrYQiJ0XvO2h2Ntozdnr/7R+f83rrl+XLNnc/xITEMrBcrqsUFztUM/Q5/2HHermmV4qArmvYKH0beP24YXUO9fEbqBvZ+RInBx8Sb+3sUgnWBt+/+nE+ur3l5fk6/ecuvbjaotuXr3cD9YUs0DZISPkZEUr5R1YZffPErLtsVQxwYxsgQPV4E0S0xegjhRNcrC5TxI6GGqSvdkb+mIaK+pIrDk2wlRd7oUphiyQxyKadhm5ALgphMBauUw0LHOjviIpDsE52VykSQkoQWxRKVY+6CBlWcWqVa+fnOPhZgcRS75ZZRpBRFm9/YlNiEEnyNVQETspbHRQW2AJnsbUfpmdBR1CQmczqwlShiVZiPBMqEfLcP2CRMNhc0b06Dc1XUuZDqSLDlwBqQmNmp4AK15AiAREZ3OpZWkg25oEZFFE2F0DuoiBSm/BjPAKcWVi2ZHdFR8+eq5pP+kCdoJ8kR+qb0siL5RE7lYCtOfdWnFEakvHamOCQrxfUcMJEvuLzQcOH4m//7HX96/oJWSoK5ESpNdqVZ8FFhk8r6IcWxxed1Zg7t04+p8zBsU0CIQAab5XjqcvyPQ6V1ft9jDtUcqFkww28H4h5HLh2ValnTYIoAX6UsOJ9RXDYyKsY5/dxnADfvtskJNoDYfH4uQhk9k+ToAow6Y1YRk3VpqowxifKtsT0zA6lKO9Nk9Igm566hBGU0Ls3bOI+AKkyg/TYNpdOMvlIBh/lmBJVZUZP4d7eCvyfL1SJri1SEgxbQKw5jBvO1HllVLas658LtfOLzTYdRbWYfVYWqNFN/yC1wIHQPjEnztpswStMNGxrTsq4MP70+Z20HRM5ItWXtUh5TIpGkz/jV4z1/8eVbklujlUdUxKAY0sSqPuP2m1taZ3B6R0AzhogQOauWWCcY0+Bcw3aYSBLZeovTNTsJnJ8t2flAGA68Pxj04wNWQxgHXFUx+ZGz1QUiIwdvCKljN0A8DEwyUNsWnwYq4zDTHVXV4qcJaxUyRnZhpBsNy6rG6sQYFX3wOF0zhJFIYF2vaCXgqhXbfgfk7kJKCaMNUXV5uzXg35Ome0zbEKoLdt0WURUBQzItK7fMvY9g2Y2eURJ97wlpQqLDyETwkdjv8FGoRXGhB5baULUDHz5/Se0auv4NL5oXDLuJVb1gM3bcDVtWokgxoauWKU4Y1zL6wKdv3xLTiB8DVGvC9gEk4dyKhKJulnyoBB/BVY7dfoOfNP040DrN9fma5AL7KfHFzXuq8MgfPH9JrT2Py2fs+44+LtjsIx9/dM3n9wcEw9lyxcMUGYcltr3m4nKBOox8ff8lHz5/TbVYYl3LWeXZbN/ycn1FX614HITN/pEYa27GEa0NZ1Zz2L7l44srDuKZ4oAkz1I7/LjnrG44dAMPUfGXb26J6ddgJTOOqSHPLFJAly3XacjFMQrRWrpo2XZZa5CihxhQziKMudbWSyR5kiS0ZNOAUQmkQ7tEJNLo320+zHcEXClA0Dq32iz5guxLxMKxwD1NBy+FItiTQ2q+GdYqaz8mJ9T+VNiMVhmEzU0em1AyB4fmNpyUi371pIjOOp7j+uoTOzCzCtFB67PVH0oRU3mIss83c0g6hT/W/oQzILcQAYIy33aYlfbLnOGlintt/vyjK9CcwJ7ok6h6tvsbU5g/TnlLPCmos7YrqcIczkwOCkp8QzoOSSyMBoq2Ej7SHdeMTDtLVYeMGlPpF2XHQX5dkjJ5WM8Wt/x4rWe74uk5IoXpKizX/B5aZbRnB/7e3zU8/NMDd6sWpVVmV+CYvF+X2IN5RM3MuDiniVHK3W7WJSUN7ag41PmJ5sl4lqcaq9Nxz7lUs0B+7nSZ4/O+fd789tCBeRbgfLRzuyxHN8xLEjkm9s9M6fz3RqfMfHmT28JmFuDnuZsSbGHeMhPiOJ1sWj81KWSJXE6sL63muZXpcpZVUn91+zXz9p3YrqdzKiFvD+XcepqI/z0mrQD4cu8xReWfAOuyzGBpFMMY+Hp85OVa84PFglfLir/1R684jJ4v9oFf3BzoJzmOYclf1AplNUoiGqF2FYKip+IX7x5Be6zd8nq14KJZErzHkojpht9bXfDqpys+303sRyEmgybxOCkuW8uPL1a8Or+mJeLMSGvy9bJq1ijT8vX9gc8PioShj4pRFJWrUSGyLa3/y6srlNbEqDj4gJgWbRTX65ogGnEWp1cchgfq1mK05zAuCWis1SgNhobKOM5aw6HbI8oyjCNNdExpwljNomp50a64e7jPsXiupTGGIRj6/QMvl4ZGR87rhq+3PTtpkDLhIMVAcC1BveAgmjSBljOsgkVdoW3LGDRKPAttmPyBqGvWywbhjOGwod/e8NoKy6sL/LjFR4+Sms32jpthwS/efk5S0GrFou7RKCZ1R6LOl79oSBiqsedK7zA0+G7EGUOtz7Griu3jDS8aaGvH7cNXLOozdGoRMbQKUj9gfU+KO5arS3aTZ/tuR0wD5+05Bnjb13z+dUC7ik8WkW5a4cWxMYr92wd+vBTc4gynBlplSNWaf/WX/x+jOkPbluvVGu336HbF+8PEZ+9vGJPHHRJBgQoBqx0VI1Yi4xT5ertjZTwbIs/Pl0zjHrd6RScddYDNcI9E4aJq2SAs6wqnwJiIiiNTGBFlOYQBnTQ6OZS11E2LMQYjnnEaCNFTK4NqK5QxiASSaBAnXycAACAASURBVFR8pPIH1o3lYrkihgBaI77DOcN6dUljf7eiq++EW/C//0d/X0DwOlOyUwEf0eR6MNvMqyfzAedsn3ntT02X039Skm+9xuiiI+IEin578WT9ri4up6i/zTzoNOcMqdPnkUGNC6c2nujMlORMK6EOea5VNDntfX6dL2GguXs9C1hO/MZcrAY0WkVcorgPs8aoCjlAVQqOmV1nAF0lLKKCVFxsCQ6NsPBPVhwQMssxt4zm3aKV5PT60saq4uxmy63X85S4CImfLHa0SfOzZqRdjdBIfpOGXLlNLIrsorEynABTUtAUi1wogErI6HAWvs/BZJyYnTzMEXiz5n+4ucpOT+Y/Z2bGijppsfwJnM+Dvmc35ExfHYGPzun1SSgzKROG3HKLx6iLPL9yzokySuGftMSedMhymOoTjJnPv9J+KwAzza1Csv5/dr0ez9PSonMhZ2DN5I+yuSXqxtN5B5llDLrEZ6CYyaX5GGuVjoOY5yOuji27AtQSjFrKa/P2RZHjST7HUZg8mASNxhBJKYePJpOw6dQSPV5qVJ4V+o/+t//1ewmzPvhv/oVAIsRERHDGkeLEJAISsRKpXIW1ikPfUTmHtQZFYgiOGHPIawieGAMp5RgHyG3klBJKWVLcg+QGcFWtSHHDwimG/ZYXly9Y1I6V1dyFwPtO5zZ/0iQ/gKtxWqMkG3ylsFpnqysiChMeqOsFE44Jh+j8+VYitYW2PoMwcLG0VEozRUG7msPYcXOIXK0a1mpgPzn+xqXixeU5n95seLZs+MH1Ff/v2we+3gxs+pDb4UQWrsZqYfQT+3HEonKLk4moHCkmRjQm9aAcRmsiiQUC1tDWC5ZOUZXzdnsYSK7GascQEiIR0VkUv92+J00TVbWksULwI0pp1k3NenmOj57H/YEzE2G45UevPyCFHLDadXvOrz/hi89/xcYP1K6lUoF+GrBKc+kabrY39Crxey+fE8RxuXCM0dH1Pbt+z4QimWcM/S2fXL1Gc+Bh16ONZTcERr9ntVhkcX+caI2mqRwKoTaRpskp7RrHPiamqJjCBFg248BmjDmTKwi11SzaFSpu6YPiy/cPHPyWV+tLVpWhbizWj+Aq/NRziDUDiso4pmkAbbECi1YxDRO1rSGNmMUK8RN+mlAKdt2e8/UrEp6UcgbeZrtHTIMSReOELoxEXbGIHus0xm/58PKcRVMzBWEYJu62B6yrCCnybFmhK3DujCGAD1N2UsY9OkzFeet4txt5P055akHvue88U0hgl6CFtfEsm4pv/sl//Tu7Jn1HmCuAovNQKk86sblgxSfz7CIZnLiivQq/xSC53ypgOJUtpQUKzcBKHf95sg7lZw4CPTny5mLliqYqmjwgdypdvCo+KRo8STsHEFXep8zrUycRs36y3toUZkPUUWis4rdX0Oise5HCBMzA6jj/cF7fJ+YARRYTj7YAQqWoY7b1z4zIvO2i5hZn3iYo2gSnjwkJatbBqewwdEbjq0TjKhg6GqUymPKFEpszrGwRoc/D9ph3QvnwOdF03o9JMs2XCtsyK6kT+fGg4aBBat6kJpsSTG6tieRi7otAX+b1LsdxTuCHEziYW12qIJk5A0onIUmiqjQhyFEWNq+is7pIyMosRV1aYuT2rcBxvuR8zlmZmc38wYbMfsnT50nRVBXcMx9HgKFWmChURWsoBZy50j6cv9A1uf05s8DzCT4zX+bYtlQzEZlNFvNtgwLRgkNIRTulS2yGKfTczI7KMQw15/GILrMpdQk3Dans77LfkaMD9fu47PoOrTWSsnhxkuEYD1BhcJXjAs/CWpqLNToF9iHykHIi+RBGGteyPltxv5/oQiTGQEyeWFr/FCa8XSwJKRHiSOsqPllafvTxj5jGwC/v3/Jnj1uUWBwrJrugloFEQoVANFkf2quQv7IR/G6DNpqL5QVVvaACmrblcXOHdmsOSTNKIPiehbVMSXNx1rLZB6aoUXrFh2cJXVX0XmMXir8cFL/86oD3mk87+Bdv36KJkDwXTYM1ijFMTFHY9fk6c6kUbbtgO24YfGA/DjRVi48TBM9VPfLxekGKE/dDYuom7HTg4Htuxy0hRFrx/PGPP+L+/p4xJVb1Bf3UUxvFm1Dz1c2vubq8xqO5qs/xJmHVgu3tb/JxO4y8lQljKt788lPO1hc8PtxQVxXju59TUWGVULk8o1MNHVE5Pj8ENoee8/MX7HtH17/ni3cOWyW2O08X4fX1NWP3jsoavnl4x/XFc7QZaGvNs9WCxeIlw7hDJ4/WNc6t8DIwjQM3weLvA5NtWS9aalF044ambui7B57VFc8rS600g1aEOGH1njQNPFus+PBHF8S4Yh9btHHEcURVht3+nt6uqOKIKMeZ3YE1WLWnrhvqlAiNYKynVgrnOgYm9nl8By8Wz9jFjqEPrNqGy9bi6yW1Sli7JDmFlyU+aN4/vOP9duTrfc+fvt0SMQyTR2ihOqM2CWVa0m2fhe/+DozNGkRtiOOOZnlObQck7LC2YRhGEoZkKkwTWGhH1+9gHDi4rMH7XS7fCXBlSmbPWAnNJDgl2CJOtwZSQThBzVqUEoA4Fwx1Sqgm5VgESj1JJrdWVGEjxOYMpHkkybzEklnpSrcqF1HJeT06B3Qi4F1mAYrUKmvgVDaxGdRRgzPZRCWZVapSYdiMymbgJ2apulQ9VwTckewAs0rhQmaf8qy/UwE++vFc1ngpkSJKz9ueyCNGarIN3kpmxWKVLfuzyH0u3loKuCutIoMmpQzonmSNQkkjVynr2cZMLXHbGf6o0UwmUA8KlGZKKovyG59DnqzOY26Syul8MX8SNpUcDDL4ssV6FvP+yu5AcuvQAaOGjQfR/N9fWf45LVVBJL6wP9nlqbAhIxMdSmq5gLEKJadssoKrjqGZsTww6ezKU0YjPp9fo0q0StEXNgiKrExmjyZZXJ9AHGVWpSnuuawHiOIywJM8R3MeRXM0apCy+xR9PEcyGJ+ddwk1x5ToRKvyumjyzcYxOyuX2Jyez/x4ZqycqG/dEKhy0uZtSYQMfzAanDq5VadimjCzE1Ky3X9eUZFEUDkCBKCJee3FzPurPDHq43O+j4vEMbu1ioBOG5fvG2JgIuLHnn0aqHowesn1wtKlkRBzUK4YxzYlHg+CxESKAVc36GiypjJGlNJ5xMs4YCuHc46oDG984tdff8F/9rPf4z++WDPqn/DVw2foUPOLg6LrcoSB1hZjLErF47mijUVrgxbN9rBjN+5YmoomJmjO6btHlKpZ1TVOQpmvWTEcetZJ2KNxVmhtyxR6GtuSGDFGUVFhlo7tYYdyNUvjMdKQQqB2mkpZrPS8OFth1isedntSGhFdkxS8rkeIW9CJRaNBAje9JRx6Xp8rmhZqA0nO+FdfHnicDJ1b8U8/3fKqDlyv1uyCoe8GehVYKOHVix/yTB949cm/x1dvvuEn11d8+vVXXDIxyciPLhp+ubMoIs/Ozph271jVS5aLhu5x4PmlxVDx/jBwvTK0z57Rjzt+dNYy9Ze45SUx7mnsko8XK0wQFp+subv9GttY0lDx/MUZoyhqNbExidoGmtUzdoeBqjK0dY2iYhhG6uaSaHrC/UiqO/rdhmuncK7mcr3CjzsWy3MMkcdh4mF/y3ndsKgWxKRYrc/xU4+zEaUTqdsyKuGbmz13NLREVgsPoviXn/0Z//Bv/21CcPzrdw/cbxXf7HqqNDLIQG1WeL9liBXiahauIcYtC2qU6YixZR/zjYFRgtYtjgMVmr3koc/R90zRo0yN1S1tm8esmRpUCmjrmZJnsVigQ3vsAniZMO6SMQauteYhDdRa+NnrS97evmWyjqvla7yf+GzcEZ3lqlrxtr/9nX7PvxPgKpSMqPOY8A500MVSLsU9mAtEXUqYmFx4TnP78j9GlyHE6kmLp/R/JAnOaqYiajZmnrI3sxtzxECutMHkWISgcuuuSnPuVHEBlrbfPBdu/rySPUolOSQ02Lz+yqijuPnpMoeIBndiA3Sx0SudxfgunVilTClwBAWzBX5Onc9gK4OKo2aqbFcizwWE0+tzF00KQMx/88WVllVJiiqWvLBZ83ZkgSxeR6LLmUhEAbEEZbgdDIuVsNwa3CrmPq+ZYaF+wlQVyi2SLZMq5Z2oBNZPaMBMPeZ1XTiYAnYUbKXzsRM5tr7mfaqMOjJVT8Xmpjx+nPcnmc0TyeAIoEoaaxS+MKdJyvmVFKuQ93dKOZjxaXjrVLZNSWY9TQGnKtmy3+Xk6DsG3j7RK6m5LZnPq0ZlofMsqJ8DXk2VAbIOJ6CUk+NPo5gA3PGcyyuSTN6d9gm6inHW2xWgVmJBTMzbEstxd4WNm1lbN5VUfDMHrc5sWN43Y3GKfmuoNPyVWY/ft+W8qUByBMvkR2LKbdUMaDMbau0lCfASeNePmJQI4njWTlw1mqSW3Owe6ZXGMpGiwtkanxJOaWIM1M0SkiLGkI8Pga1PKCr+yb/5mqpqkPAlpj5Hp0gi4mqXU/1NhbEVpBHE5XiSOKIkIhIw1pIiTClSTQOH3R21qzhbrxi6DbppCKPHqYl3k6IxhjEZolf08R1iHD7uaOsVC61QtUKGA7VKjJsHRp0I8cBFZUn9RIWiFmGx1LRp4Hk9cOgeiZVBrxu0qkmc8c1uT5RIPyi293fYdsG/vRnRaeTBj+joWFjPWnuQhoUz/MGrj1HpHapSfBHOiH5LCgf+6OqSvV9w9/4tKxd5HB/wYWBavUD8nrc+8QdnhmU1Mowbzl/8gCR7HrY76g9X3B62VLbhT15a1suWw/YevWpZrC64SyMfvDxj6DTJH0AnjGmZ/B3nS4txkVECd7c3LJdLoqt4dnbO425P6jfU7ZrtFLjZKqxKPFuf87C9w2pN22oeNoEpwW8eH1laaKrssgvGsGjP8OqR4C2pMjQCo7ZsBs8X7w8kW6PigDIV05Sw9Yrfr0ceu5phnNj1O5bLn/K//Pwtl82KTb9nEs+lbQhKWNk105hwrqZSFc62GAmI8eiUQ2wrZ3mhE2IE0Yl1E2iqFUZpUhhZuYYhWlIMNKtLGhkIfuJ8fQESqSvHGIWbrWf0B0xjUcYRQyAEzSFGjDaM04hWjjBFPrt9JATNFCIyfcMhJJLRuAiPhwdWv7OGYF6+E+DKqHAsti5BtNlOb3VxBaaUJ6UkjdcwJypWlKKKLTVaEFUyjgATTRbVugzSpphFyDomkpI8DVzPBVgX3JJFxnOqdDJQFcG9swZDHqo8u7mg/NSZWdMF7CQgVUCZJwhAIg9tlcKKFCbOW0ilnWRElbZcFlkrBalSJ0G0ZCYsFZG1d4IKGTCoqI7zCoPNFNwcKlknRfVE1HwMLwVIOjvEdAGhFEKpZHHNZ4kO0Buwsw4tCpI0u6ToTWarRLLeSerE261mheMjUq7oSRV6RJ2AUoLiKADvQEKmZaoZUeV9CFLYrsJ+ieZvXSr+r86QtC/YIQdbzvBRYm6vJQpANQobhclk593RmatOw51F1PG9gsw5Xvk4uJj3X7Ily0nkyD7MY3dEgwklUkMK+CIP2s5p6/n80Oi8HjozA3JEf3PLUnLXtIDsSSkqFcp8vrwdXuW2YzAKm2I+VlKOpZlNB7qMvBGSCHX5znjJ/FP+2BJZUgYxV5IF/0+dIqkEvioRnOiiZaOMGCr7xeSk+XmfUgBr0HKM7xBgwHyLNf6+LYdpotVCJZ7WWabQ0fkBW11xGN5RVed42VPHQFMv+P3zhks9sq4E26wZ/B5Sz4/aJZ23fLHf0IfEmDyVcpnh11Vu8xqFLuNRiHtEJUKKSIApFo1W2CAqzwBM2oA2WGVBElY7RAeSBJyqQCdS1IQ0Ym0OQR2Sp64azpoFVgmDUmw2G4IWztoLKjF4GfEhUbmKTT+g00jtDPvHW2K1JI4bXrYNRkeuVGBpPD/55ENkf0tnlyxtzX1/wPcP9KNna67YsWYSje+Fw3CgG3eMIbN50QcOfc86RJaqYZM0xmtcZRCz4HV9wyfXFfe7iX/99S27qDGy5UUzsWwXqCi87Yec6VXvWDRrdNrx0j3SHR74wfPXBGnY3L9B2ZZkLO9u71jYQFPnyRQXteNyveJ2CvzZL+748IdXrOrXrPSOH7++5qA1D2HJ7W7E2Uti2NMf9nz86hO2hw1tdcU3j/c83h5YtLBwHftuw7PVOVWV6A4bzpvE+uwZ0+QI44BuFnywalhMnh9enBNE0U0OaxRn5gGtLIN/x1oHfvjK8et3j3TSEvxEL5pF3LKwhvXaYunZa3i/6dhPhjjtQAmX1YJV4/HbLSt7z7//8RXEifX5NW37IWk6cDh0RDFMRB4PI8MwsXCXNEsHocNYWNc1e1nxm9t7NvsBW1f8+uY3PL/8GZ9uerwPdNPIYvvAyjWIbflAGbrdI6um4Zve51qp83fKGsGHLcbU/GSVeNsHqqrlIin2U8fCNYxpy+Rb1mtQfmDbR8YJOBzA736n3/PvBLiyR0tV/jEPCjYpMwCx1F5ScR7pkDUuUedWTsl+EtGZlZgZChVKuGJmupZWiDGgit1Pl1aRNqpkYoFX8912AT+FodIzJUVmMH6LEMjArDA+SC46ItCIRkK2omutciilIjdy1NwSkgLgQJXcCIm55TI71Y5ZYKYwU5y0aM4qQkw4rTFVIqqs/XEliTwXum+zBU/HKCUbmVPk51mNM0ic3WCQRderyNERqYvea68th6DZGM3SaUyMrJJlaTxrR3mzvK+IKYOpKGQGS7KQvQJkgtZkZgqetAs59SbH0kZ8qPifuxWN6RhjlQcEp9PTRObZgAWylDaxUop2Ktln8rTAn1gXKQzOrPmbKc5ZizUzMMY+6e+WcQICaFs6oOnE+uRcNgU6p5uTMuMWSwtOz6CqaOqO7jyXebhFhNYYGslMljeqtO1SbtOZ07a4Ro7TDbCxhOxqgghem3wzkTh+SChCvTkLLNmIcYoQTWb1VHYQChptFCbGcvxnUDlnY81jeYq+MJ3Y2qPrtjCz3x4h9P1a6mZJYxQXFq7rHmevQRKiHdtBY1PkqjUYCcQEbRs4d46mbpimA2f1kv0YeJAVD4dbNqOwlQHjI8q1aO3Qrsr6OQE1u6BUVW5SEzElKDlQKKEyljANUOzoQe2xrsZjckRLjFm47FqUBBBNDBNtvciv0Zbd1HFOxXqxwC5WNDIweY9VkFLFVR1Q/SPPpefsYokfN1y/dDTqEddqGnfA6AVar0AL2/0tbw/C+7FHuYhRNV3fc5CGw3SLQ1GbijEGfHRUpkXrLYJgXUPdVFQpAVv+w2c/4svdI2cqcPVsjex2/NtvDqyW5/ze62fc3X3BolXYGHh+vUZFjyjBVWs2j1uWK83kz1leNThr6fodje54/fFz3j3c8frDj7h9f8PFs4/w44EuWG73I293lrY55+yFp9IOPbzn5/cDU/0KEw445Umqphs7DlNk1b7m0+2BNFmW00RqWtxwj5WBISwZQsOnNzsu3Q3jmLg9+5hqc8vV4o5NWpL2kQd7i0pCPfV8cL5kVXeMyuLsms++ekdCCN0bPvrwx/zxz37GZ1+/AwwvrfA+CNsRglvg0sR9fyDYnF5+uXrB+mKNGR7w1TX3dsHDbsfNNxP7seXh8wc+Wg2cL1Y8P7+kGzZYVWH1GQfV8cZr0oOiH8t4LT0hpkMnjbTnfHE40K4/5l3XgbIY27B2BhM9Xd/hXM1n3kNSfHW45XJ1BRr6YQIlHIZH+jES0pb9o2ViwpgdZySWVZsBVrXk45Vh6DzeJ6Sbij5xyOL23+HynQBXJiWimYHGSTTsy/TkugRwTnY2iZe/a52T2u3JSUUCbRM2ZNAWRDA+jwBB55bMpFMOY+Q0jC3MoUexJEoXQbFWmbJXktuI1Sxst3n22twqC6WISMn+UQVgpQJEDJmwmWfU2aIiDia7cSC/fypOLKVLfEKp7VJAl0k6z1Asrb91gkjK9nsSEyVsQUeiVbTKEELCMeBlgakCXmmqzuCbATc0RDySBwaglcKT6OuKNmRJeCjAUpVWWrLCxUEzOFAqMCnha1VzVnlGr1moiBA4KJgmeOFURoUmkYd4pYxApnTqn00JztQplqF+ojYf5XicMjDT8CLwX8SRf7xtsVqyfmp+jmRdkpK8TaMzefxKYa0KAYXoRLAZLC4HU9pZJ4BmYybJgiuslVGIjoTirLTyBMiXINakQc2bSP49/z0zg0cQpVPBjQojCimty1Bm+lUqYrQiKo1NiUXZnpQpU3TKeNSKZADoMjsnsxXVlvE0KaFQjMyzFJ+AmhnF2cwiVik/psr3ID9FzXp00AkpuDi3rjOzKTNDHA0eUEbQ8QRwrSREC2keq0Tiyb3K93DRdBGUttw8jtSVR+LEQgeu3cDF2RXBd4xSsRmENBjejzv6KIxhIEpHEoukR4CcOj70WNsy+QmjAmrq8/nMhFYrlApoo6i0xY8D1miMsfTTHoAgec4hSSMScvjkMKGtxtlVzgY0I0OfWFVVMWpUEHtWjCwWKy7x1NWaZ1XHwg1cWEdda7SJ6LjHD1vkwuHcAtKOpn6GaZaIXrL3B758CNw/PNIpDUYTvMIZx+04IB5M7FACTR0xzYKu33AIkXXq+Xt/809YN57x4HjoHX0a2d2/RXzHynT041/wJ0s4f/4hQzjw/9wuWbtbXq1rYrznrA10Q6S9fMWmm1ivnrHdP7C/+RylV+ynAU2FcxYfD+y7gLGKKHvaynIYYQiRu/1EHzUvrn/I6/JVm7q3vLg8QyVL5wderlsq80Aa92wPgWp1hak19VKIwx3aLYl1ZNdb/P4tlW7QMeHiA37asCw3OFcXZzT11yyblwz9hoXd06WJfgzo1FM1NY/dPdtO0dQgumPRXPD5V1/iqxdUW/jL959CMtzv3/P8/ILoHU6NrJqKQ2+5OquIIfHZZosYx7SLOdhVJkyz4EoJMXperkKO97CQnPDm9h5jHUoHunRApR6nDAmNWyxY1SDVgulwD2LZjRBS4NrU6GpATQNYSxgjVieSFVADY1TcdAdCGOnGkdqtGMd7Prz8kLVreZRH7nYDe1+ug2qgxwFdbnHrrlC2ki/O05i7JbrN2Vm/w+U7Aa60VmVg7kkrAhy1H/O13c1D0ObUbxPRgIun/wM0ehbCZ7eLqdK3XjezGfOMQcisjAK0jrjyzOwazC1IVXRVMo8p0Zqn9eE4f45cszQn9gGe1LFj1SrrUhgy0XFeu/y6dGK2stFuzgmaNTazZkiOjIyIYCSiyJodjaLXEakiIxeshp7JVyQ38rgWXo7C7UXE9hUvuOdxrPm4PvAr94KzoSeqfBdrSnroSiVe68g3SaGNJmjNaPJdyH20VF5xoUesEpSBKpQTbHb/HSMgNHRSeqtzlHzZGUHAqW/vPFd6c5XOzsIUwIGtBiocTimiUoTCjgSVaUOVMnNVFcBs0KgnkQtjm8Xuy6DQdmZjCngWRSiO1MQpVNbN7Kk6Het8PhTGhrlNLEcXJ2T9F0/OCVM0cFXKOsP5rNeko3ZsFrobk4c7o7I3AMCoVMBg1gAOKe8/VT5A+wKS1NzuPG13Xu/SFtaZtVWAKdq1WM7N2fk6OyCjkmziTIYQ842BUEYIlVY65HFVOVcrDxVXprgNOW3X93npD1u01vRdQqnE3lc4Y4hpwifLzc09zsIUhO1k6PqeSRLGQojT0Www97VH32FdhRdfGGddGPWM8pMKKBVIwaN0Hqjkp6l8HQsrHTxaa5SyIIFU2MkwJZTa8mx5jrINrb9FQsPzusb7jn4YeDsOmO0NnyaHXWnS0LF2NamasDoyhYlatzh7QfI9dV2xbp6ztpbD8MgoHd4/EKQmimEMW5R12RyUeqwFFfMgZmLATwplAnVMNGgW60v+2b/6l3RTYtv3XLUVRjYsFgssBpFztPJMIrx7/w1SnfF6aZGwZjfssFpTLz/ANp62rqmtJdDx4fMXjGdnhLDn0PU4u8SHA0MfWbUaEY/oNalueP/2a6Q5Z9o+0FjDN1/8OcrvWZ09o8XzYpEIbPnqfuDLL9+xrp7To5kU3L//Ne8OCVGBNZ6FgY9fXtPvb3lzf09TOf7op39IayLTswqla6ZxzzTtae0zjEqs1yvOVi3u7BU2bHnz7gYxDe8f9jhbcfuw5VC3GDXSnj+jHm6w6ozn5xc8bG541p7zdheo3YJq8QF3d3uIHo/iwno+uHoFYWRZWR47zVIlOj/gFitiDJwt13z+5ktudlsad0DFilpZGHbZbBOESk8smgveH97zuI344ZEfP/+QVJ1zyUS/3fMXj1+wqK8xWkDXCJE4aFKcsqNdDC2gq4a6WRCnHqdrDps7BiIaw7PWYRaJyraM/oBWNTEGtFnR2IoYe4bJE7zn7OqCOOxZLrIm7Xe5fCfAVdS5OJnCGuhCQQSbjhb0SckxSHPuyTWSL/Cm2I7nYpcToAEV8sSUI2BLWZwe8t+NMkQEsVlLLZmqyrlEQgmjzGFbYnNooxhTdF4y9yoBcIWR0jOOMKrodISpEoz/7Vv1UtzKqjnJeViqZCdhYgF1eZGikbEEQgXO5z6bN7kwpnKRbEPW2CCKySY+2nv+waubDFzqin8+af7OWQBVge35x+Oa/4jIn/zkLjNGIfHVb+D5i4r/cduy6CGagf+8Svy887zRLcscF06KFtFCJZqfjhPtIuBUKCNXYGElg6KkobMZbZlCu+iirM4blVHIMB83TgOOFSe2ZZzfz8BCYN3zX62F/+nzF1gdSWhEkTVHCpJWGNFoMo0USzFKJouGq5LoKpZ/R/QFeJOoQ+aXpiORpktUVw7MqwIcmsKaktu+IvncyL7H0/smOZ2jvmQV7bXFPXlWAnSVm3B1zM7RSBauK61yaoUCJzGzapRMKYSxsKVOKAyXYFNpVRbhvZ5vVYomTBDmYHh9zFb49kVmxGTdYHFVyCyuj+Dn7DA46sqsAilZWUZlotOTgAAAIABJREFUoF+lnPOWP/rb+/r7tsQYiTFi8KAMMW1JrqKPgpkAiRgEkURMEWMtNiVSGlDKoDCk5FHKFZlEQlJ2DRpj8WFEaU1lHHiFUX2JjNPZdq8MUYGkiMKglEaVsTspTVCem38qgja87w/QH0r8RqBrEiF6lIpYXZNiQkSR+i0A+74Hb7HKoFVkNAPd1GNRDGHHfjDcaoVPYIwQvWVRaYyKNEoT/MhhOFC5Re482IopeczhLcv1OR9ff4SebglSofREWlZ8fNHyzUPCGI1NFU6tWDaKZbPkMOzwdsk0DtRSUdmREUtbn7G5f0PSDbUS/OhRovDe8/nmLXVVYbTibjdwtl7SD5aYhMdDj9bn7Dc7Hh9/w/lqjRu3DIc9n735itpYfvzhBxgTOETHn/75e15eXaOCkDjni76j6wZsnHj5/JKfPhfWjcPolhg1USZk0Pz41UuGpLl7uGG5PiOMPdE/YpuGYarYTndY1yLJ8fbXb+nrO9QEw3ig1u+JSeiGvriQD4jWBFEslZAWgXqcELskEljFHcpEZLjHxo714pypyGhCHIiiaWKgWZ4xBk8k4FKkdg6mnkV9xg/bwNnyHJNGXNXiY02YNmzGln66B6s4q1oiBr1Y8Zha1HbLolZ8cPWCH/CK3bDjcfdAqxKDKF5ftOwOEyIKazXLusXZCh22yMoRU6LvEyHldTW6YtE2rLUgtsGS5ze6eoVJOyQuIEa0c1hXk9IVxjUM41/DKIZKpeNMPcUJXKkZ5Misc/p2CvTMIBQjFq4895gkXrQyc73O/1XMk2aCylyDV3lsishszVfFPViG9845S3q2q6u/OkG71ItZQG4KMxCNovYz4jsJ148vU09/n9eSo5Nv/ntV7iRF5+wsMwuWY46Y8GUf+FJEbYosYuIfLDdQd3CtYDrwdzBkWm9P98UFxmj+5NX7jC5bB0Hz0R/uYGOotitUHXkRAh9ef86HdwYOa/5P1nxlLY1AM0JtPVoGDpOmrQw1KWttij7JK8FJyv24VNyAdsoAKwWOwVtisiarV3nC8cxm2ZkeKjtKl8evBR4P/Cd25J/1C2LtM0NTEl+VObV088tLS27W6D2ZJXVkdX7ro4LJB3fO/hIbMTozWzEJ0ahvMaDFRspU3KeaDLKj5GPURHUE8ajsgLVHRhaUy3qoQI7zmmcWene6cRABjyZwikWINu9CbwQv2dl33H9S2LTT6fVX6COlwJf9E3XWl3llcxyKOj0n742n3z85QaXSTjw+T2XNj7aCDlByLlGB7/WSYt4Bx4lXSQhDAGIGpWmClPKdeYLgJ0Ah2iEyopSD6BGVQEWUGNC5u0vId45KFD7llp8UB6sIKFtl52sKBexHFHmQNFp/+1wui4pPHlQKCEzjvrhDA2iD0hojCZlygRKJZOewQ6tIFYtpQimMsTloRBk02c0I0A0jSmVr/tK1vLpaYFSFCT3PFo7d2KGXl4QgbLZvaJuKl8uEqSz324HKJH5w/RIVOzZjxd0o+GHiofNoCZyfN2ASO99jCbTVCqs8Vd1QN0vub98yJaFualyaOEyWLnac1Zp29RF/9uufc1YvEUksF5eo1BHGA665ZN/vWfrIsrH88Ucf5diWZsV2iJwv17y4gvvHr7la1FTseaETcRloVx8xbN8R1YqbQTirN1RNTWUdz69fsNvt8kxDccgYWLcNBw7lXlnjmhdMIeKM4g+XH9Dvt4SVYxgNcdxzvnxJs7qmHw3Enk23Z91azi8+IvYdSbUo29CPA82LC7p+wFSWaTpDq4CxLVrXPGx3PMaIPXvG7cM967PnaLbsx4ELqxiVxlUjYUps9/cosfjHHalpWKSSGTg5tuMdEiZ8SLiqpTUbAjWWcxozMowjS0bWl5csjSAScHXN8/MX9IcODxwGT4qefhxprac2lsN0wBmDnzp6UbRp4B5hPwQ+enZFNwzYrmKze6RTFQnP6B+5rFYMURhNwzD9NRzcHGyOAZI5p0gXV77iON6mKq2wzEhI1s3EE9uVlyKk5VSQXDhlCTlyzpRW+TMmIyw9LCehdxpfFV2OTaWo5c9TSrLmQAFRYRREF0iJ46xHHTNjU1HqVrGx25jXKSCMJutkAOZkfK0VNgnBKoycGJMq5CIcdHaGCRmoAdQxF/d5UG/SUDG7w3JhtCa/9r9T1/y34wBdgssJekraqmHxow3/ZXwg51jYIjYn/1wFWiVY3/D3n2+gq6FVECcGr7hk4ou65vUBzvzIheRC7wsiVOTtslpIUef31DZf/M8K8+RS/p2U10upvEP9VKIZVG4DBqCdRbmcWK0hQN/w8zERXaYrlVJYA5MUkJ4E76Qk18/0EyXTKiI6u+3clOMVjAjJacwk6CQlID5HNeT4BI0owaacUzUbA1L5xZfmVzU7C5MqOrWASgZv5piDctLqREwZtTdTdo7GkB2mUYS+gqVXjFZYTPlGADUbHIRQgYq5XZmT0vP8wdmgOpO9urBRM4CqYna0eiPoQg+bYJh0npQgCiQqbFS5iHNituqQ2NeCmJzgbUQwMY+rihVHADcbQ9X8HRfBqphz6L7HiykMtVU1EY8yBgssLfzg7IwhwiEExqHDS4dSNSGMaK1wEqg0XK4Vz1cVtXa5yKuaEANWCU1lWJoa4xp2U+DN7sCvHu7Qek0KkRACWhcG3lXIcEBbVQIpV4TQU+kFnVJoxsK26hLpQGbEJLfpLBrvR0QpamvRyhBDvhYmPyIqkLTGm8zEGVtjYmKaOpJrOa8sQSJV3RLGAWKH0ZqXzYLd/o6qWXJeB0J4ZF2d82Z3IE0dAKtg+cs3b6mbBZVtuTAbXKVonYL9lv/gBx8whcR6ccl2s+GzXUc3TVTaY6sV77YjQxJsrKiHB+4PEzIMiN5zvVowVQvstEdVF+jDO/7G85eIGRjHgZfNe17+4CXt8veZAvybX/6Sz7/8nGf2Jf10z9XVj/js3Vcs1tds3t9w1TY8X8L55QV2DGhnWCxXWBxfsUITqZsa6wzjFNiFhq7fcLlecVEbkg6063O+fntPsi+orOAMSOh43mj8OHB+3uAroXWW+86Q1Ifc7T2NWaLVLXUjrM9/n/e3nxNiZFIQSCjfIVrzzUHymJ9tl68kQTOEibMF7A+el1fnrKuGQ4i43T391HFeGRa2QoAPX79iu90Q4sCX9zsW9YrLBSxlJBG5txbnFqwa4f37Ow5asx8UVkV23SOx0kgc6bodlY08hMDD2OPlkaumBu8ZpMcPHVfLa4xNTFEzxcDKLREmLtqWzq1oDMQ40FaKTReo3AWHYcvkFlxWK7bTiDDwtu+zXEQlrP3dwqHvBLhqk2CBvqAkrTjOigOO8/7mxSWVByk/uUOGoxLrNButMFtZr/ukx1aWpc+swtDkVtUiPmkhzj+K3kiJPv7fJLKmqRT67DDLDIGo2SmYwdGciB1tovGQisJZPfkcxYmRG6yw8OroSlPl7yprTY95TVbPOiWy0PyJnuxIjGhYeQ2+gtBnzdM806+RXIGjwKgy+DE6t+2chhgQhFemh3GCCwO3wtYqzkT4RgxXUUiVMCXFTaWJWEJS2JDB4IUJIPkicD9ZntmUGbQlmbVSCXyExuSEPx8ye5UKqIoxh4qGchB/eyid0bAW/tM/eOT/+IvAm2qRibEEpNwAE5NdbEorisoalbKuzZb8KmJmoEQDMTsvbUbVx2yzI0OTK82RZQxHXVL5eWRVCwNZ4gxcOnVAyxOOAFuprG8KrrC0JscpVCkPx+5tnu3Y2zzOaTZ+aK0wkqiUygYLIv8/eW8SY9uWp3f9Vreb05+Ie+M2797XZb5XWX1hYWSkAgkkbJAKgY2FZCFZjBBDTwqEQGLEnBEIECAbSmALZlWuUkkFwjJlmypsVyWVWdm8Lt9tI25EnG53q2Ww9jlxX3qag1S9Ld0X8eKcs5u19z7r29//+75/Clkyf8zygiOb+5XLmjDquKzImV4xZgbYvcVuleRYkzC2V0r+bvyFEDnWgdHNmwRFiqfX4M7AKEUW1YcR/Bb+x87j12xRuUP5+C+gx+yYg/d82fQ8mE+YJ8ukhLNizrzUFGpC8oH5/B5D34BvqWUixJ5Br2i2r1BVza7zFEPBteyROvF8a9n0DUJMCK4jCYFWmpR6ZqZmqj3r84J5NWGqYTmr2W0kwVT0UfBiK4jC0LoeK8A5m7VPjMYEJVFS5SNxo51GSpwPSCE5Zskdf8ahQZYTZipxMTP0Q8ujQhFVz6Ats6qgqia83m8YUsQftgxhihGGSncUoaGuS2pdIGSiEnNMWSOJ7IZETIpXl9cchobvuXP6fgBzRewb1mWiTB5ZahCROjScy0RZS6pyyjfWc2yYsO/gi8tnqCogw4B0r7mxgRJH9J7HF0t+cOn5/usdF+cDtS5om4GnF0uK0jCdPUHGW37tL/xzHHYvGfwSHyf42NEPNyzOHjIMjsOhYWJa3n3vnM2uoe88PlT8/M99i2fPn+PnS3pb8Oz1JyS1Yjb0mCQ4n0UudwcO21turKCup8gQudw3nM+XLFcV791bEn3gomtwvoOyprUd6/OB+8sHNPsND9+5YN9ZXm8ik0LzzVLyxaZlfX6Prm/RhSbYgX1r0Vqx3V8io6UoIofuJRO94s2+Z9dFguv59NUbjC4pRUCHxO3hNS69i44GURTsrp7RU5FUmfOzzI7oAkPI1QAXalLskUmwMoH1bMrZoAhCM1GJJCs2u4CuahbzOYehw/t8vQE0oqDrB4LfsBcF+0ODNBN0ISmCp6xXJDewDwPz6YRC1DxKZW4MLuRJNvKTWn4qwNWjEFiagR/pgoMrT7b5oyD27YkiGCBkEe9Rx55UwHhxcubFlFmjwmeQFo8T4zh2XZGoQwZCQXCacaIRmJAzqI7uv9NkyV0qd8wsPE7n3oH5fXkdgdx3T4uYGanxGBUSJRNBREy4A0oh26coZQaARcqaFjFmLBmRRfRxLJmaXGvCKZj0x0qMwI3sgx+F7iYkiij58+4GYgDtYJrLCdQjUlMjiFHkcp1NOeZAl7C3BKlIxuKjRrctFIbpXqKkZ2OmzEOPkwbvDG+04ZfUjpmGP9kbnuDphGIQGhEdL33BWZEyoDK5nHEa+CwAGUuAI/hTgpOLsFS5NJhSBn4u5X9GZqdHK1lbz4/KbALwIiBUoIi5ACjGMT0GoR7B+il0MyRUzPEFgrG8Mr4njAGZRzggZKRMwJgJFccAyCDl2JYp3l0PglNrGCkjFdmpqWVAhOwcTGMTQpViTiUWo6mShNB5H2cpA596zPtSSqCO5UaRW//IlLsJpCKfaiE9XkhCyr0sj62ijM+sby8FIgqqsbm2Evn+yNpDSWMSiphbCIl4agsFMJjRSakTs0EwmDi2glTIcQxjTHeIM3Gn7UKczAFf10UJhSgMKYbxoUYQY0ShcU7x6mbHo+WCuRy4V2vKSnB1e2CbIj+6fIVAYYIn7A7YJAmpxXuLOgg6P5DkgFKG5LYorTHJIkXBoj6jtZdURhPFgoXyLKrI2bRGCZgUgs466umM0ki6tudQSl62Hd0wIMdIm5giWqos0xA5s81ICcERhESKiBICETPA0lJQSceimuAxTI1gcIG+ucQnyW0oCanHpcRtjLA/YFSFUiVd2FOkgIue280tk+k5har54upTZvPHNC6R2gPTIj/YFYOlAO5PFiyKHamGx6sVKRr2A/zw8x9wfSv42XcC33zykBcvPme9/Ijt4Tmifo+bN9+jKgo+WtWs5wN6ckawA9pMWM0Tzf6AqQoe3V8SfERXEpk87z26z+a6RalEWS9wfeD25iXbwy4zTLxG6ykPz2fc7K6RusQFxx88u+VsrZDRUegEoee7P/hj3n/3ffpWsBKeD+59SBQNIrUU8wv8cMmDxzOkeghJoMoJ7c01dtB89vwVf/D9W6LW7JuGSu1Yz8+RtmO9XPDs5YHtoeO2D/zTTz9hVhY5q2zYo4slMdb8yF/RBc9sOqPttoQgcLZBVxX71lIZw+1u4GLe0fjEzf6Wh4sl69JwaPYELegxBAexv8oRB0OiFwnpO2TqmBaCUgEaqskDUuy4v6iwveFgA0IaIDBdLOiHniEMBDnlyaN3iEOHi56z+RrrPZv9NWV5RuEH9tahpUBXmr2cEG2P6A6U9YwiKhpnMdJhB4+UAZk8Kib2IWLDT/Z76aeicfMP/8O/mITyFCj+bzWhVeYol8oTWLr73Y4utOPfYbTKe07NlQny5OZK3GmwTmxClNlOn0YZ0Ph7SncxECc99VsP2UfGADJ7dmy6nMhtYnKqeS7HqTBqZ8YVRplLn54MkOzYTkeMlMIxbDJP2Ak59iUMYpyYxpktHDVZkVPboEA66dQSmSWYWIHTgV81Oz62Dcw8PHIgTJ5Bj4M1hDttjo9Qa9g7/vD1jD9W71ANjqdO8C+ZWygGGALPuoo4ZmD9f6rApYpfpOeD2Q1gsIOiWFno4TBUvBAahacaJO+ctzCLkAJjvgB3vvw0siYps2ySfJJLCdMRDEYy+HLp7uF/M8C24n9unoynfxwjJ/GkE8B52zH39pLLuOIr5/fH9O3c3Sd3/Ojbnw/ILB4/sqwxP61HMQq7j9ooBEYEpMjXwXBKi8+GjUDGnpo4xnzEU5/IBCd26FjqHlMU8GoM4JUZXCUZGJIgKjVquEZ2NI5AKyYIWVx+em1k0+TpPsmORCeOTOp4bOKrY2FGYOpkwsQjc5eZjXzMeVE/9uX1137jt76WFNb8r/8vCUaTgVQkIjFGHk5LnswqotAcmh1DgkIFFnpBlJF9d8W50aymE1qneXFzBWZKCBErEgc7Bta6lllRI3Ti3VnFoipYTSJv9o657qmLmkon6mpCVSWmCrwsud1HXuwd370NXG02RFPjhgZMAS4L6xE6Z7HFNF7bGUhJKZmUhhgjwfYUZUGhNCp4Hq0XVMLzphmwUTD4mONTjqpBWaCFxDBkPZaUlCZxXk8pBAzJIc2cw35Hch2ddaynC1KyWDdQKYeIUEhDYRzEmouzyNVtnmRnRjKdTnhx21EnRzUtWS+mlKnHugGnBuo0I6UbUrUkuhbpPEFMUZWmLEpubzoca4K7oukc89UZUpXEfsui8gTvqZfnbG8PLOdrvvPdP+RbH39AHHq8nHE4dBijsP2WRx8+ROkF9nCNSDM6f81stqa5/QJFwZdfBmRdcNjbbL6xgfNVwXRlmBcJ4Q37fsque4MJHSnNOHiHdxalJ+yHK0LbcvAVD+49wIaB56+3KOmYThcM7Q1meoZQc5ruDSYGUgp0Q0DpNSFcUqqaJBymvkdwgZQsL5sDu77n5+7do0YhC8uu6xlSiciiEEqZm4xXxhBjZvo8gYkumJRTPrv8kqY9IIo5Q0qsyilCS3Z9Yj2tqIqEd7nFnFRjplm7I7iAVpCoGPyAwnOxWKCEozY1MRVcNjtiKJiUgkOzpxCaN03H5WGLqedIoHUDh8aBmkG3QRYTohLQ34LQpH/4X/3EvpN+KpirF5WmclDLxC/Hlk9NxStpUCN7c/KEMzq8dJ4kjYy5DJLIYaHjJGrGScCpRBnEHevAsQ1KoiADGg2QThIpRBR4DXLMmzIpuwSPeVZpLDWKJHLvQGNx1LmPnXMMdWThDL2MeZ0p4xgzRocHPfbcY2ShxonqaPcvR8OC17lfnR773x21XEfHVTRATCPLkXVYg0r4ImF8ItaJ6QDlZYQHLs+8Wwm1zQBLpLc0VunYE2UUxBqexXPWWBoKbrTjD5ImxIK/EBuezN3pifupG6A4sO1qsuBGUwSfnX0FzMqGjxPgK4YoM+UhBfQS9iJfgSaMdJG8cxCewjtTRqRDyuzV0dGmRY5tgMzIycj7b7Zs6zmbMUFc6ICMGi/DWP4TBD2SZuN4JkaWSd+dhzFPFcgsUhwZLq8Tyr3V8Xq8drLQPI2MYrrr8kM6xRsokc+jSZm9EQJcTFl4Ti5lRzKoQmcmCnIMQnZffhWo6Hi3A1Jk4buTiRRUbs2TcqHYx3QKWAWwI0ozXhIEDAoEccRNI9122kgWONcpQry7De/a3IxsoIojizy6NeNYfR5vvOPda+Qx0CS3lvq6Ltooal3S2w4ZEz55Sl1w27ZsBg+2495U8/jsHstKcB5vmSwqhmGGG3pM5Wit52KyQAhJIbKIPSmDkSV+iMhyxmo6JUWHUQ4hpxQltH2Hma8ZguPz6x3bxvPpvsFSEWxAa4GQCh8lsesotCEGkCI/QJI8IcXcIDcCMZK0JsTIbr+jLCpKqTkTAx89WGcy3A/E4LlYVDQB2kMLSEgVfXTIYPn4yX3aviB0t5STKWkIzKWjCwWPZ4LONph0Qz07Q4iKEN8gwxRnDM2hw+sC9JTe3rKeCG43PQ8evMdyWnC9uWJSRhalQbgDbSdpuw3zCVzvEqXcYW2HLmdU0zn7rSU5iSgF+C3aGIQbuL/uqOoL6mJLijdUUpAKz2AtZTVBiC3nDyqCfc63fukpRhv0gwV4WKkF18+/5OLpz0Is+P3f+9v8/M98i5vNt3n87se4ZotiyfOXLxDFkn/47T/ho48e018PlKXi2y8q7m0LqsKQ/Iaq3PP//uBL7s0LJlXDWe34wRct77z/LbY3O3Ztw82w49s/uuK9+0uC9Qz1HGslrxtJsX+Jlm8YnEDVBWsjmU2WPNtc01mF1oKrbs+iECyU4OawZzKfMysKbvcHdiIiVMX1tsFUklJblNI0rseKkkrusO0Ni8U5StTYoWM3RObrR0yngZt2S2oGlguJD4qoLcMw8KMXe6wyGAZkChgZWEwvgMjBO6ZS0nbwurU8b2+IboYTNyg3YKNnUQhmZcHUaJRK6NhSRM9tE/DNHso5hA5Cbjod7S7rjZMGPfmJ3uc/FczV3/r1v5ymoyNFx5ySbnUGOrns9RalAPjT7wER1WkiPB7J8VUTBO6tKPI0Nm+WjLqQt+iJY4mxDLncmCekO2fiUT9yZL/C6Cz86+aS153gwXKAhWL3UvK/yzPkKCkWIYOmYwaQjGPkgs/hkUctTCC/rx736VROHA99rDblbKFcHcqW+7c+bxJEGTNTUSRmFn4lbFmGwMNJD/MEC5eFWUFlvZMgz36KvJHjJPtSw4OCv/+dkpdnC74VOn7R9jDrcvkwhCxKLzJAC23JDRPOU4/UAvqU01GLkAHSIMB6OCcL1YPIB1mS13fch6NDIag8CidAJXPOlRYZuIhxkEKCPmbWbS/Azvhfu3tZrJuyBimQgQQpl/n80U04BlgIIIQxZPNtqpKR7RrPgdOc2rj8+HJssxwlKM/Y4y/3zYI7MCTH9jpHKupYyj6e3yi+ylBpJCGmU8/D40We3ipXk/KwBQkp5PZKR9foMUVdjiDxeHgxijHK4W5bMRxZ1CNJFTFSEMJXm5wflzHXlmHsIqBIp+MUcAKIR3CVxobZx0ysv/o//ebXkrn68G/8btp1e2KMY3SLzxk7SuHsDqUqJqbG+h2DmFAgEWKglAUxdlTJs5oYHs2WlJXg0GyJes2nNx3BRWZFYFVJNr3g1pf07oCs1yi7gX6bNSp6QXIHhBKookYmTVWvkGGHlIqm3ePdgC7nVCL32VS0TKYLdoOgbbaIckaKkcqorAPTGvyWyfQxtnvFevIQl/INY/yBi3vn9O2Ge/MlRnpKASJlx1IKAoXGVCVYy9YNGFkxKzTevuH11vPOgyWD98yEYlZLnl8fOD9fg7PEELl3XrHZNHx5W3N/2vKnP/hTvvnkXbZM+fDdJ7z84R/xrY8+YOh7tCnYd68QzjDEgkePHxB9hfdvCNYSAiwenOG6DYf9Jcv1N4jsiW4gGosQubpilMAdBrY3PS1Tvvhsxzfeq7i/nCOLgs8/eca7H35E3+1ZnC1oNl8Q0wzX3TJf3afrO6b1fb7/xTWvrt+gU8+Dx+9gfUFzuGWJ442YoVPLF1vHo6nnXJdcXMy5bTwvNvvM3OiC9XyCEi1PlzXNZseud3y5GTCzNc32U4a+hGJF21mkcCxmc169fkko58wrxX6/oaympOCYzdYc9ntWqwUmWaZ6zpvmisYZNl1LM0QKKUh+wFRzhOxpmx3CVPzKex9jRIsWgc2uo5gkdq3g1U2DNVNutleUquIw3BDjHC8T9+qKtt9jk+f+4pzLfY8bDswKw+A6nq7OMErTDhuEqNg6x6yoiVZwtpjzstmx6S3BhnF+85S14WIyRUdH5zZc9obYt1n2kkQ2WEWHUJpKK7pmQ/rD//4n9p30UwGu/s6v/5UUYiKOMQP6rfJBSjAVniaaU/NiKwUqCiYu0Zo7VkmOZQwTJFYmypDY1oLSjiszEdkahhyJnvUhR6HlcdI5lRDHEsnJbpUnJzUyAu2i5f2d519bX2eBtg15FikC11cX/GZzQVi13H9juCwkhcxOGz+Shcf2JHEUzMuoePtciOMbRkX8UR9kxdjaRHHKPHp7fXdgMKJToEAwVQN/0R4yMJoClchlN+9zD78UMzITjDqZNArJEwwJ+3pBIWZQ3ox7N6r4iwCVA2XgWhD9HCn7jBTkyEyVOovnbQsXGoTLn/VxbE5noLMwH1msI2MVU0YkOqdFEVSOoxcC0CP7ZcfqnMpuyGhgG/ndwznbVFMocsuXcUyO7QxTEgSZu0gS0ljiyEDej2A8pQwaU5RfOS9xvC7eLh8f08rhDlDoEZDIlA9rEDG3wGF0HwaTz6nyOAnSagoRiTqObYvS6HDNuWdiZOm8ZkSDY0lY5rwsn2R2xtos4h+SoJARc6xSjte1JztJT+h93J+vLEIwCZDjKhPh2K5mHMej6UNGmZPZjxo2KRExu8OEgGKQSBGy+YTc3zKFhBgB5b/zN7+eZcHVv/+30qKocAgGNxAFCGEQKZKCI6WIVFUOAw05NTqKHA4qoj91kopJAh6pamKKyDhGPBxz5grD4/U52C3RWe5PHcvJfRaLGdNZRQiOpumRdcnQDby53nC9ecUUI2jvAAAgAElEQVTGF5AUjdBoUROdBVoskwzIbYuUBVIXeGeRKpFiRJuawED0UJuaUiRismhTYBQsyhrcgXlVknzL07P7vNztWNQGPbug3V3hh57eO+qi5MNHj/nk5TOerhdIoGk7YvQIpblYSIJPtD1cX/2Ab/7ML7K52aLrgkl6zXUn+Oa73+Ty9Q9YLx+SkmXoOmbrCyKHUU2oMGVF9B2ubSknFyA0elLz5Sc/YrE6Z3u5YTEDXSi0TDgMr1/dYrTkzdU1P/tzPwfxDXpiSMkjxIQUPNevA0Msub2+ZLlaMThYLVtWM8OzVxIVJO9+cIbvO1RpePb8hubQ8PDiPpdvbnh8X1HO19jWIYNnCD3PLht6b2gC9Klif9hRAQ/Oz+gOe8q6ZCItz/eOgy/QwRHFFu2hnizQpuCwv0XiaV1CaYOKHlMWXNx/Bz90tEND00f64OiDICSopeRsZljUkkpX7HfX9CnQth0+KIwqscnwZrtnSJZBrWgPVygk1WSODQOFKinjgcJMkann4dk5PXM+efEJSiSM0rikEapECMurXQ+hZ6okLhXElPBJEJMFUUBySGQmVmJEmRLhPVJmfatPkSRrCJaUJCJ5tEwUpiYES3JDbosncsZNsC3V/B773/0v/myVBUPMwEGkt9uDHDVE4JNkqjwN+tSEVgvoaiiSQIxgbCJzc9pBS4JIdBjmPjAj4EMieMV+7ijGMCphBCnlkkh53Je3NVZwSmQXIZc1IAveP9pE/oWL15nBkWV2vKksBj9f3iD7Gb+0P/CdYoU0EjmW90qRG/XejfzRhehPx37cthQipyvn/In85K8iUuZ2K1LfyV9OpRp1x0IElbFHFQTRGKTvMuAZjh+Sd6W1QWRGqRC5BuniiDhH/ZR7fVcrkzFTaoXMegwfYamQu3Z0HErYJzhL4HoYSgg1tD1UMn/eqLyNMDA2cBzLlOJuBLKZalwitCKL2A3Qh0zVxJG5iuQIh7mh3ktaIYliLFfFO80ekAWyMmdRCY6GxAwIjiVlRO7teIwfOIb3phRPDClpZDTjneD9WK1zZgS5CYQXp0iHRBbV12TEb4XMJRcdkYT8e8hOxhHm5yEf74ciflX755MkhoSQId9DBWN5USFI+DAaMcadlqN4Wsu3ynLHy+HkLkwcxHhPpLvtH4/7yKIJ0vjyHZOsZSSM9VahE1JG0vjkMsh87/y46fPrtlTVhMa2Y9SHyA8iIlFISeValvMVUkW2XZNDkoWiLA375oAwBZ3tmSaFlxqPQsesM4hCZd1myhqgQ3T88LXLhg3gs72nKrYM7pI4Ph5Ye6A0JTYIku9QqkKYKSJ2eOFJ3WvW8xmCilIlSl1iagPe41NgNpmwXq+4fPUp77/zlMY6JqXmxfUbtC4x0wdsL7+k1AUTEVgsZmy9p+8GNkNDrRJGGqRLzHWJnizR4sBSN3zx+R/wMx/8AoN3vPr0H3Hv/CPO7j/g1eWXTOtvcNNvmNc1+90Zly8/4cnjD7l8/T3mTz9g27ym7RrWq/cppjWu3TI7n/PmuuGzz1/QFvdZqETXv+LxWc2D1Zpd+4KrV5/y3oe/wv37K4wpWP/Ce3RvniH1nE++94zlTHJxTyCU4OnHPwvaM2ynPPvuH3PxziNsu6VezZnNIma44f7TOaKC+t6a20/3/JN//Jrz1YI3B8vnmwMPzgtEt2dazzirA2VR8PDenD/6wYG6+JTJ+oJSbJiWa5JJSHvNajrhwdQy/fCCz54deL3dsE8TxPbNSUoyqRzJeqblAy43r9hfv2F97ynBLKh0m3WUYoI0luT39P1A6DaURvHw3hQnwHvPptnz5rrl804yryts2zArJdOZIaqSfTfQe4cwHi8F1tcUIiGqRb7CZETYiBSRgyjw+y2Iile718T0GfP5A26ahhg9jkAMB4yeEp2AlBiEIahEmVyeMiWUVUHlDfuRhZ2WBW1KGAO9FQhh8CkRvGUiewozxbsMrKqiph0G6umcfbMFJRnckBl+u/uJ3uc/FczV//br/3aSUuDiaNvVOZcoioATUASDIiJUyCLKoBERagKL5LjUGqs13nguLDQSHgwBLS09Ex52Ax/GLXjD/7mq+Fw9YOEPDEaTVKCOgWBLXB0z2EiQdP5ZWskwsdTOEEKitJIdBlVH/pr9Iaw8zHQGS2pEM4cA3dEK4aCZ8jvbBbaQdLokFh5hJUEnUhKUncKbNIqUx1LKkUk7TlzjhOZkonZ35cTjxJaEIIR4EiM7CRVQqAE1lPxS6jjrEi+WiQ928Mkq8MGbgWoRcphhTWacypiZLKUyu+Ek+NHZd5zVdYSJy5bFmHI5wI5sVhCwUbAcZ1HhwOg8riHkGfvY1dqRQVZIGWAd53sV8nr9KGIf0+pPpUs1xjUEkQFZEpnG04a/98UFu0JnUE4u5YkxJ0yGHCugRkPDEctl0XY6NX4WQiCOLtCxdHhkv6QKpKSpo2cQmfFKMSGjHPsyplPJT8icK6VC5PjYcCwP63jUyeSDVikzUOqowTviV1IOfQy5Fc9xOeLK43JyPqa7aygxlrffkvEfk+jj8Q3jZ49GgmODZchSOID+yOYdgV4SmJj/fnwEMscsWH9Mg7/TthkpGEgUYxzK8WHoL/+Pv/21hFkf/we/kbRSFEWNCx5BS6UEZVmihGaqFI0f8MOOLsHBJkIy+CTwziGKmipFPIlD36JUbjgb4wFQSFFklx6BWak5X6yIKfJys8E6nwH+2NoqioCIkaosKbSikBIR+hPDnvo9tWxYnz3FuY5SF8TokAiUCHihqaZnCBIvr77EmBUiWdokiT7wznIFYSC6BuUblvMV1eKc9nDN/emSEBvssEGZBdEPJOCj99/jj7/3HZ4+fI/ddkO1WDMvPTe3HaWRlJMV28s/QhdLlJpRFZHZNDN5RWFoDq9wThOD4/WbRHAvWZ19i7KsaNqeBw+fslwGfL8lpiuGJrHZzSkKwWazQ0jPclZQ1ZLZ8oz2sOfq5RVPP3iP6FuEXlHOVghRM/RfIqWjvdpwvd1y/8EZRTkhxSnPX+wpyzySy/Ujbq9fYsUa42+5eOeMvlO8uNlSS4cp1nTdjl1zw2L1iEIIRIo0/sDnrx1CaFrbsZhWzIzhYD1d61jWjnmpKNSU84tzrBvY7npSlNwc9uz7lpgi+0PDZLJi8IEYI/PFCtf39P2ecw0UE1q3pSjPaINFh8hN2zDRNc2wp6zWdC6wOWxou5zeHqMjRIeu5wh7wFmPqCqs7RGipCormq6lc+PDmAjIGNHllEJLRDJs2wwIpZCksYG8FgmZFNrkZuGLoqDWkuA9Q0yQJCkFgu+RqkBrhdAl1vbcto6pkUSlkUJjhz39KDkSo7bRSEmhp7S2w0fP4C0uRoSQ+L//X//ZYq6O+hSpjxND1uAoJFEmvIqAZxISKSbe7S3r5KnKljexYhIDnRvY25pp2fHnmh2rJCBqvi8FXxYTgo98U2/5V4YB4hua2Zy/J+dUveJNKXCFQLqKKrYIKdgnlR2EKjHpJ6AcUkA3ASUH6gEQNTgH25gF0YuRQRFkkBJCtjKKhn/9yRYcuHbF79+s+BcXlr9dFugOhjowOfZqG8tBR7f6KRJonBRNlKOzMC9HxkqLDJLUiLpmVqBTokbhpOfLoOhU4roXNDJQ3Gq2GoouoNQAKw2TNgOioGDvyQptMpgKY/nH6Pz31owsVMqvFSq/7hJMY2acbICqgMFnlqoaQZgkA6RT+feutHRSmR8Dw466MC1PLAo+jbovMaaukvfplaaocusYkRJajL0Ex48JlZGUGsGC0OkERpTQOUssZfZT6ZGhiiMoOrI1IrfscEh8SiSZRfhSJvzIvKYRzqQImkQpE/EoquIIgu5AFXAX83HULI2MkCUhZAY2QeQYhaPr7+0lE39vXRfjf9IoOj+9L/2zn5Mi56gdsfNxLceMtiPrd9xmUvkcGcbrdSznJ/LpEONDwlGz7mNuMRVDbiYdJF/rpTYKYyRGBxZVRfIeJQLJDVRFyb7dk3TB9aCJCdzQM53USCWYTGZ0oUMkgUiBMzOhj4JKDCh9Dx96jNKAxXmBwqKGPdWk4p2ZAlVjbUepDDdDpLcRVRqInmgtQmvKsuTxfEalS3pX0fYTopQMIeJFwLcDpTFYkXJfu2JHYeDj83MAhJyihaHp91zMB6Sq2R0szlZMi564f46yghv7pzy+9y7L+j4hRoTQbNuB5vYWIyVVIdhL2Lx5hi1Lbm+3GGPw4TnnqzX15AIpHOvzJUJNiP2GFAO3N5ZismQ1r7i5fcbTxx9S1Uu6oeP+aopQN3z+eU/T97i4YlZGXj7/IUYpfuGX/xyvX/yQz1+3zFZP2D07IFPivae/wr6JVIVGpUi3e41SBVIr4iAoF0vOK3C2pm08Wmw5W1Z8/vkVj54+ISlHaxP79jO8swhTo4vEVGscgtvNhtmk4un7v8xnn32bojrjR1evCUOkkJHHj95hUixo+wFn95Sh5/HDNdYZvLNctnuuLFgf6IeO5XzCbdMxKaAqK+7PawYXUMWS3f7A/rDHdtccusAXUVLKNjvXxSu01rlhfLAgHZ2T3AwbnMsRH5PpnN72TMuCmVGkVHDtM2saB4GlpIoerOVsOkGlHm1m7AaLICGTpdCKhGVtZigpMUSc0EzKKa3r6Ls9Qk3ohgERAg7BoW/RIncDaPqWpCvOdaCUBVJ5FlXB3EiMqdl1LUZGvJ4RYiQET2U0zdCSpGaIHVpGKlOwqGt2Q3+aS39Sy08Fc/V3/+NfS8ApqNMWX329DEenlKCMgVWyvG8lUTi6VPCubxBK8Fpnl9h9O7q3ooAysvGGb9c1T63liejRyeb6ihJ39jsyUPgHxYKzAB/6LudOLQd+a/OUYZpIWlH6nl87WL4bCyZpz3u1h3YP7yqYjOyOS9mWFUcBdgy5hHZ0vjmdS3OdolUV/8CvuKwqgnGZEJJQenGaoJzk1Dz36JaTHnIS+ldZBU1mZ46i6aMOWo+NoU+aIBy1L/hVtYXJLSzlWB8jUyYxwq4EMeTAT52y4OcQctnQysx2HQOSSHnjOuUyYV9kENQLYJJZJ3PIr8fj+0T+/BHij/ucwfU42/uQ2/VICbj892MOQeIujsEDagq64R9dPeZaKJLMjFEI2aWZxr5/FkFts7sOxlMmxmBRkaMPokgEIShGsNsdwxATJ4YrpnTHII4xHHKMNShSQnswKo6AaARPY7kxyVF6liQqJITIzJUZYziijHcMVLo7j2KUziuyi1WprAmLo/kiHfWHY7SE15FeRmqfS+pJ5jL4xOXLcxA5S+24ndMysm95n8draER+VuRtHr0ip3JhyuuIRwZ2PEXa55UHk52PhFyu/Pf+h7/7tWSu/uW/8RvJmIKZhOmkRJgZr2+vueojPjkUBaiAigmRPAsz4eAOlEpSVxXWdZwt1ry6fMVkuiZsnlEs1izuvYfd3zBb3WO3vWXfNrjQs65qggxcNonrcYJKzuX7KjhkMc82fiGZmZpCCXZNQyQx0ZppOcm95ZTA2R6ipy40NkRETGidrfhBeKL3GClZF4G6qKirGVfXX3JWKR6+8w283fPii08oJ1NWZw+4vLmhEBJVVrgQaQ5bhFY8vveA/e6GXTPw3pP3+eLZn3B//RjrHL7dcDad8M67Z3SNwouaqkhsNrdcbjY8vniEkoG23dI7jZI9xqxQeELwxGRZLu+jlCTYW66vdyxW95kaiVUCHwRn9ZLO7pFGIKKhsxapBbdXV8Q4Y71W3D+bIIzHDQ1CZz3h0AW0ntIPnt3migcP3uP51Q3eKerKYpTl4vw9tkOBjtfECN1hw7ZtWMwfUlfzPBbdp6yWTyim57z44jtAjpq46fKN2TZbWusptKBPU6pCkrzj/voR7XDgqmnZbA5gNIe2IUbLWfkIH69xoWffC+qyYt+0eAEpGWxwVNpQVSXtfs+QEjF51nVBXcwIydO3B3ohwEtcGCiKCq1rCjnwwaMHxH6LG3o+3zREAVFVLBRU5ZymtwgR8bFlVi8plMZ7y27wLCZTtvsNpamwbsDHSMRw23REKXChozIVs3JC22wxRjMtlwg6dMwPeHvf410AqbABEBrKKb7riClSkOhsBykHj2ql6aJDKQ0hf0c3/9d/+RP7TvqpAFe/85/8m1/ZifjWl33OyzlOMoIqRJ56x2slKIWgT4pl9PnJ2iWkEpAUSkZs4fhgFzDKc1lp9knxcO9YHRXu5i0BSxxLUSZArzMYglOqN6KASnG7r1hXhwyeijFUaBLHXng+P7Yj8ut9zMAhjaWrapyxOknOZSCzL5UAZ/gHdsVlWZ4yNCG7v46OsvEvebei/HFjW/77iCmTiHlXxo9qEbLOfPz/3giqCOex58/X2zwzSg9VyNZUPZbl3ogsfK/Ix9IBQuVjPIrPyxwfgB4pNyFgIAOeqPJsG3KT2uwGZHQMjqXDxJgKDyfVkh91WSJkrZdgnPBHxiqNM7kSmUJxCmQPqQA/5bcuH1OYhoTAJZnLyWMprZMig9i3xi/IO8ZMpiz8PjowhchjmvV+YyFsLMMdS1+n0Nl4B1IqBIaAUwmRsmEhjMDEj+u1MmFCjl4QCGTK4M5k3/vp8rTjTZFGK196K4gzvlUfPLkOESBgYnOobZB3oCylfNlGnU/xqV/zuONC3OWt5XXmlR4NJcRxXT/2OT9ennL829EUosaeoGEswcqYt/Hv/rdfT7fgX/rPfjtF11JriTaa772+onWJd1fn1MIRpSdGSUyBaQHWgtAS4sB6NqMqpzRR8abtSNEjYodIC6LwVKai2d8QsaQQMQSQhqg0K1Oxsyk7pKTLzK6UCD3F+UTf7ZFa40Kgruf0fUvvBrwP6LLC2wOFmCBCR1HWRBtoww7lFevVOYPvCM6idMXucMtqUjC4RFkYZAqczacMPrC5fc3H3/glNpsruvaaQk85Wy94dfmcSTVnPS3pvGVW1fTNhvvnc968/IQPPvpFqkJwu2nYdo7LTdaYBu85W5yjlGbfDHjf0XUNyihSv2M1gbKagqrQKvD03Xcp4h5nLZP1Oc+/eMHL189wcYnkCxbTMx49/CbPn3/Jw4cfok3PvSffJPgDyUOwe4SQNPuO5f13kJOIbxuamwOTxYzkHUJW9MMVfWeZL5YoU3L7+iXr82+w2zZMKoWzHV2UPH/xIx5ePGVRaarlhNA5/vE//VMeXqxpXf4m6toeU1bUOjGbzdG65tVty5cvX9C7SDd45HhDRlkx2B6jDGfrFXjB4A6IKGn7HqU1bW8p047p4hwRBrZdAqVZzSeI2LOcTAl6wYvL10gcWiiUVqjYY6NhPlF0zcDGRrZBUYSWqpjRuB2P5nOc7Uhqxu3uivX6HguViYaqKAlDz653DF5w3eyQBCotiT7nnhVFRTWZIFVF2w9IlXh9syemyNlyju8PbAZPFIbkB4RQFFpQasPgLUIUNLbDR/BuwEYJQt8FPEuTJb0hi9xjjKdn9Pj7P7mcq58KcPWb/+m/lSgtppc4KZn5iCsDwWpU0lgT0SFRx4BTmjpaXCwy6STAKskiOMpesJskZJDoIKiMY9VZqiSZeketemxX0BeJICtU6QhOIqVi7gNT5zKYWrgMgEyfGxSq8XG8GEtabYSqhOhyqQuXJ3gzls+UzuBqCGOdJGZQIUcdUySzWzOVqQMXQRXQL/mdqiTKIuvLUiKkLM4HUClb2CWcZlFXDuj9nJJc7qu8ZDdxp0gHHSTNdGC21zQTRTlqLpJWTKLFCc1fGnYwucn7OpEZ+PiUwdD1aG1VMUc5CD+CK+BG5Mj8uQA1jCBK5TEsdY5eCBF6k8HSETEaMgg7evnv4u0zihGjIIoIU5EZrhDvhEZH0FjIO7ZrcLlMmYAWUIZuW/F7wwW17Pmm6/l/5ktEzI1h9Rg6G496rNERF48GAxUJcTzv8JbGbQT8bzezBVIUlEiCCNgRQ0opUKMr7+gFyAwTyEHgxstEKUExRjxE9c+u93SHpkTiq68f13l0tSqZG0oj5Ri9MG4vHvVSd4DtrdUCOdlejzESFpV1jiIHm/oQUVIRQiKO501EiUnhK1pBABUjOjLmYCUE+oSJAeQI2v7qf/f11Fz9q7/+N1Nykag0Xap4PE2slmdMZIXV0HY9XpSk5GnbPS9vb2kQaEp0zAxTFHBvZnLJuush3KDKVXZsSoUqF2wOLS40xGCwMbCYrRhslwn7focWkrKYsppUFCZbevZDg/WB1XzObr+jkJLmsEMoza7tqbVAuJb54pzWWqYKBj+wXpwjUqBxnhKHNAWDHXK4MjkXy/vItFIopfHtAWk8QhgisKoLqqpEAsvzMyqp+eyL73N7+RlBLpnfe5+m7bMz0flRvjCwmk6yG7Ga45zl+RefcP/hE6oiMSsSTdeiigk+BITvePTkCde3N8yWj1Aif7dH55HRIssDNkzQKDabDh81777/BCEE1y++B6JCpMSD93+e4fCKdneFCy3T9QOSP3C4dRTliigC3nfcf/ANEgO2b1FCIlTJ0LfY7hZnPeeP3ucH3/4nnF9cEEKJMGs2t1+gZE3TtcyrCZGWQKDrPN/+7repF+/ihCIlSdvtKLXi/npNsjtsqmibLV5oSIJCF2hTQjxQKoWSHbXOzE+n1kynC3bbS5TqKaLg6ZMHuLjks5fP8veoH2hsAlNgpOGsjqymExKapAPOSvqhwfY9NsJ8Zii1YTHRbK6vKOpzhDa8ajzd7sCma6jLAhcVqyrRO0tlJgghGKIk9Fuqas4wNAit6QdLTJKdSww+ZscsOTYhpYBPgsF1CJHjdkQCkSJKq7E7QJ6nfBiwPp0CahubHbcxRqpC5e4IKSFNwe3/8WeMufrt//zfSKWFGYkByYCiJBGlp9MaM2KTWfD0UtFKKFNiGvxYWlEYL2lNzqhqjMiGNK+oRWAiB2IQrNJA1UgGk1AI4lDzZgKHWqIdrIbEQ7ujlIEvJnPe6Txr2pExsWPblrF0pmXWVImQ28loOUa+xzvGJnEnnipGHZIYy2sy5fwEq+/KW9R8J9Q8q2fEOPY0DLklStA5FT7KhIriZJ+XyTCNPb8a3oCc8rtFhRcKOYICpwNeCbRTrHuLNZpEQpmA7Wsehp7KDHzsIsz3GRjVo6OvFTnsU6QcpzAf2bk4Hm8Xcwpl52GqxuR3n8ueMOq0xPhzPNmRMfU0jWP49kw/MnxeZTBWkIHeAKOAaARpwNEBl8hjWcjc3TuO27UyA780At+DB73k9w5rYsxhn0dNUEoZVKW3GCD/lgYpH8oITmJmYfSpPVLGe61JKAflePhHjHQslR2XlDd4AlaTEfQcQdgpW3Pc+NHMmfOx0p027fS2zKbFt1inGBIgx6iHO13Z8XUSp4iGu5MCUSeUz2DMCpl7aCbwKZfzTuMz1ihVzKXCMGavmXQUw8dTVTEf71fjLI7Ohb/y33w9y4L//H/0dxKMngzbIJNBS0nnQGlNihmMpGgptGZSTYm2pSgqoh/wtslxF0bivScFRRcGtCpR0WFiZp5SitTGMJWS1WKKiIaDgBQ9Q9uxbxuCS5hSUZqSYfB00ZNiT/Q9SEkIkRgVWjhMGlhMzmn6Fm2ykLg5XKHUDKETQ9twtnqA0pFKDsgouTevKIzHeYGIHdPphN2h5eH9x+wPGw57j0WyXJxxu23p2pZFDdVsggwds8mMzz77UyQBK0ruLVYoHYmpINgWKQVFPWWxEDi34NWLT9DK04fIsl4gijUxvEbKFev1A549+z7r9YJJXTFdzpGmxlvDqzeXnN+/oAgWZ7dYa1meP8b3O5SZ03YSUs9sMSfJlr5tqaYGREBX93D9K2IHXvWU0wuq2Rm3z/6Evm3o2opHD9/Hu0gxNRzevERIw+EwsNne0DY9pa45tAOPn76HcFuU9lT1nJAGbnYV0be0TYMNPSRB8h6fRAa921uU0kwWKxaTBVebDYMPCKEQ0ZJCQ2CCVzUMB5RJTCfnNF2PtRZTaKbGo6VESEPTb7h3/wnJWrrDllsrafuBGC1S1RRlpGsPxKRYTpe0zRaHwDlBGyyCyL3JGYPtERoqrZlUC64215iy4tAdOLjIspowEREXI33ILnjvBwpTcjaraYfAzW6LDYqkxjlFKf5/8t7kV7Y9z+76/LrdRnea27/38r3MrFdZRbqKkjGmERgkg+QJeMSYAQOEEQOEJ/wFMDAgY4GFhIA5E2TJYCyDBbiwsV2WcFWmKzNfe++77Wmi292vZfDbcc7NLDNBKZHKjMm595yIHRE7duzf2mut71r92GG0QgtB8IHJjZhqk8NqZ9+pUhpiJKVc9eHsiKkaUoy4EPDpFHHk0LrB+54kDPZ//4u/XOCKP/fPJ1aW41RiB83rRYHUkdZJnreCZsgINJUT0RtwJReMbNSBcayIMbAtK67rbNa6n1JyNAimBFYIvBB4KVFe4FWOKDDCsjhUvNlYhIAHN4FnveftWlMuPB8cOw5OQRJU0VPFmEGBCBlITDpLYosIDUDMACBC9nHNYCCQmZuQAMPc/pwZokJlAGESKPi73UOuFhWX1nGmHJ+bipAS0QTag2KSkmkB9QC3teHfuHmX/UwqEWrN/zp9Cycn6k6BLviX7JewAaziB+MD3lQJx4Lf9M/5QAy58ibO0qiI2YRTG+hyhU2+BVjrLIFGcS/jTXNxXRDwJsHD95iVO31XcJeNJcVsaBfZKB/lzFZ5qGfGzKu87ZQym3aSH6XIBvcTUtMzspEe6vkxUuQuwjFk83wtsuFdADbyd64fcZAto4h3crN/D4Ccaobc+6bsNDcDpKw+ppSn94Cc0ZIiMSW0Ergk8CpRTBKnEpPITFAxg+WUJNqLuxwzP0/InGpy0tyX5Gd2yPiZyp533zRPF3qRcpehEndTg9Lfh9UGde9TBCh+tjcr3U9GxpgZplMCqIwiA/KQQ0En9TMP/ZlzxilY9z424/7jl/7em3UKSF3zrXcAACAASURBVJVzFMqf/RVlrv7N/+xvJYDRe9QcEzP4iZvBge8J0QAJrQXT1KGEBJGrZWolMVoyxoCSAiUNUQniNIIs2HdXaNkghcRZh4qWmAqkqanEgJCKpQ5IKgbvWC8WiLHj9XggoKmVAtkg/TsaXWLKgpAcvbVUoqFSgarZMI49znuatkUmy2XTMg1XXFxe0FQl0VlGepTrWV18ws32hm/e3DJFjZAtTVGxO1wTXcTFieQHjCp5sG5RemR3GFg0lygJq2WJiE8Y3I9ZNQsEA1EoZHIUpSFJSdG07F/vWWxWVO0FX3zx93j28GNCnNjues7OK4rFhsluWZw9xe170NCHwDR46qrk/OkThNIcv/6M2zdXrC/PqTZPiGNi6re8ff0Ni9WGxaJEqgrXHaHe4aMjpQp30yFVA6bl3bbHe0+wkZ0FNwx859ufsmornn/+93n8aM367CF20nTbV5RFw+LyId32wOdffQ2qxIrEB2eG22Pi+TfPSXrBoqyzR1IpbMiqQopHhJCUZokWsOt3mGpD3+0YnYWoSELTGMFoxzwpqjXJDaiyYpwsi8Wam91bahlpVEBK+Ojhx/zg66+xlPgk8c7j3JGYNLVJBCSNqQAYXSDEgapQWJ9I1GjToqRjsGMOtk4uBwgLyYQmuIAPHUVhkFEQUqKul+gYch2YSGxWZ7zY3jD0HVJpCIJjnDKpUC/wvsNFT/ISFwJj9AiZgVNwAVMoIOKdBVFzCjK2PuYcuTyrmNsqnMf9H3/plwxc/ft/MmFdvsw3krkvBmLBEGp+vypBZHPuMgQeT5FLHHhBKAVfioJOCCY1L0xzhkuUEuMiTfKMUpAkTGQnsYowUNKmA1OpWUyB70+eoox8ZS2eksfB807WNAoe2h7wTB0YLXHeYwpBSAENiELOYGFeBQWzVyjcV7Xk3pzsXXIBkuKuVy+SDfGI+e+GN6HBBsnnpmIw0MTEpUu8KSImFvwz/QHdRHBDrrW5NlAEjqLlJ3XJNQ2P9j3fb/ewnAM3jxccfWKx9Dl3qnUzWkgQTQYkxfz6p9kfNQCmhI3LnixdzHJfmIXqObNqa6Dx9yXLzOyd9DOFMx+3pzR2GfM+SDMrxiyrxtnk5E778eSxYo5oSHPMA1myND6zU5H83I78epCZUTMziA0S9on/q3/AThikhyizt+qkLp5whD6FbM7Mk9PZQ6RdBhf9yZNHzs2CE2sj5tTzbHYPZFapOH3N1ExYzrviFD4axGmf5VuY2Z07+VHlGYPT76PME4Pvf31PwEoI7hLjT4wb72VTwR8hwLIR/fTL+eJDpvyw8LN35mcfO28z/lG58RSmmt9j/gxPm/uz/+X//CsJrv71//hvJKU0eg7ZDC6P2p81BSF0GF2T/ERMkeggBE/vBrQukFhkqrjZH5FaMgwOrWSWwfsjpS7ZDz2RQC0Ng/f0Ll9dJEoQllYpjnakLgxSGpRMmFmyKrRCCU8wBUspcRjOSs3ThxvKInJ7/YoYZfYwRU93fMPZ+VO6wy1lVdKWDbqsODurcU4SpWN32DN2iun4Dav2MSKMDNPA6uIR/faWs3XD8swQfUbnui7R5YbD9WeARZuSaGtIHbrRKP0QhOB4uKWUEUHJ9XVPjAPrdknZLLndvaOolmyeGHY3e4KVLDdLinINtuTN1VseXDxgigPtsiIMgcBEGD0paozxhCgwVUl0E9dvvqFeNIz2hvPLj4nRohsYpze0FxcU6wvcDRxfv8aPW5qzB+xfJ4bDG1TbslmcYa2iagqef/UZx8FhpOTJxTnVsmFyAz/40Tu8XAGayb0jhZGqqKi1pu8OrFuBDwXOrGcGJmGnPUpqlDakoIlh4vL8nO32dU7NkVC3K5x3TOOUc8pMpK6WfP3yNUVVcXnxAdvtOxSK9XrN69srJuuQItH1ByqVKM2ave3m4e18XHhnMQrq0lCZAmMSzlmcExynEWc9RdnOYc4B6/OlXnRHQhAIUyGkxNqRFJlLviVjSCQCWmsIiRA8KWUvcEoCpTTOW6Qs6IceoTWFUTnbMcXMQqHxkZkFhkIXTNYTE1giBI+UZQ4bTQkfI8E7pt/9r35u56RfiCiGr2n4iAOQmE0f+Q/aUveKjyy0xnHsNBchYvyQF1QlUAm+UyW4rvjReoH1mqg8UibaMUscnohDsRg9LgicNlgDv+av+HA65lVVpywrecm3SpEBSyWoD4Gd01kCNJpy6cA7ygJQETkAdZwTzwV3YU0qzayKnEGKys8TI6zIbJVV0M1sTunhQK6UERKc5VGwkCQ36ZI/1W/nGPbIY9dybrdQ95lWSAk6k5mvMrLoRhamJcWR78djBhqpAAKEGxYRmGaTuDXQdjmKoXNZEkwp19cIlYHRRQFp9pulk/8J5qbiLA+KBJsw0xNzhHwIp7E68ujiDJA4eadEzrA6lQJrMqPVC+ZuDLDM+sl8sETytk4Moor5vlFkxi2ke9P8kMC67G9rDAgHleKf7uCvNIbmaHMsg454X1HHPcfCYGxChpK+AKQjoaitwAsYy4RxipJASolRSiapZoO7RIlIDJEUJSIk4glV2awGlzOLpoW4V4XTCY/m158xWgZDSYKQAicSRog7sHXK6PL8NJaVdzJkPg6L94sU51tO2BJ3Bv2YcsRJLGY2DpHLm1LOvTpZ7NJJo0w5FmIeawAh7khIJxNmRo5iBpukPLlYcQ9i/0gq/K/QbdOWue5GRAQKVRU58dwPFIWgtwMBRXI550cmhakX6JgwZSLYxJOnF0TfofQZRsB2f0svJCJOVDKhzYZuGokqB+E2pqYsC97uLOfLJWZSWD+iBJRFQS0cm7OWsqrQsqTQljJGRBpYtaCEZTwcOFud09QN09jjh8CH3/oe28PXfPDdT3j+5g1vxoLuBuKLVwg3cbaOfO83/jm621fcpjU+eG53Bz58subiIsHmGShL8CO7PZRVQ1k85h/87v9JUW9olyte3Nxy7G9YNWvqIvHr31kx7b/G+xVdfMODRy3Ls5ZhWDAkWG0ec1FVSJ0Io2DZaGy4RaCQdWAav6IxA1PfEUSFeOBIU2B/eMH6coXSCnfraB59QPSW6eYdDz7+ELAs9Ybm2ZoYJ6a+o7HnNI8/5PjlNwQJ1lqK6hyZLlic3yLVE7a7W3zxmBhfsr3es7n4GL19zvEY+fzVke7FDRCQUqNVwtlrWlOgzAJTeOw4cnH5gCJGxqmjlhMuTOhC8OTbn9LvX7LYXCJDlmt3e4uJNYlIVTaolDDLhqFP2OhwVnC93VLXDYKJ63c/oqgXpOmInzzfefqQq5u3lFoRzyoenreI4NkPBePkuD0mrBsJITJR4+PEwWmK4Ybl4oyiqCBGRFGBiqSQK+dM1IzJkRLUdUsQgkqZWQ2YoxwEpLHDR5Wrv2RCywJvB7QpmcaRgEdJQUqe89WG49Rlc7qQeSDILEEaVBiRQiHSRAqOtjAEFA25uSMlsGFCyjlvrqx/rt/zXwjm6os//6fTettzLiwsEtSnUS6d5bIygC/JvILnLnZbp1nGihA1sWp46yEmwfkxobXNE09BcBCaq7IgqOzPul1BPZT8Mfs6ay4VWeY7MSZa5Mt2q0hWIcTsjRIuL+ou3scAaEn0iT7kvKOUEkbkpNwkQYlM38aQPTDUMQdvhgBJwzHmWANEBnghze89m8B7vaBRt9n3pMgBpQOwmcHKKQ7hBN4Oa/6gWFN6y3cZwE8ZmC1ETlgNzCafGbR8ODNMYpqDOU1+7nGehmxN9lUJAet537gE9uSRmgGlZ564nMHOia2LcBeUJMiv9cT0FdxPAgo5M3kpS6aODK6CzFKrFHNO1uwDK8UsL4acm3WKb0gp74dxHkIQs0fM2szO3QY+dxu+LloWLvEwCJZx5O81LYvg2RURETTPQs/BFUSlGETEzYSqlTk+Af5oZlOuRsxgQsRZuptri7y+l/cceTA1S2X3U4j3WVLxrmw5vcc2pfkJzRypYOdD5s5zNedshdlBL/8xX+/ACfPeP294z2+mTiXR80ehZiPYXXuBiATy4MjpduoUzPsoIz+R7qVQQd7OKccN4M/85b/2K8lc/Qf/3d9OADFOCBFRQkEKKBF49+ol1o4sVht8iAQcZdOgWDL4wIvXt0yD5Wp3Q1QVpRIs9MghLXHWEYKn0AqZAst2TaHz4qL0ipvrF5wvK6SQSASr9QNqGXlwWbDvPEqD0QVuGul94HbfkYJHqRKXavrpFhGzDUMpiYuR4/FIN4w0y3OeLgRJX1CzQxqFG/Y5hFfXCAGVESQMEUXX7TIDETSlGohJYp0DITGV4eOHZ5SF4OmTx2yvn9P3B8r2AdOYmyqiSXgP1gq0VkR/ZKEcQsOTZx8QnWN79ZbNBx/MtUAKYoGf3qBqTRIJoRK+HFmsHhJcxxh76rpByQWhk4TRgV/z7tUb7P6Wumrx1mYAW1cIeUA1JbKymHbBdHNA+HP67ZGiqum7CQFsHj9l2F3x1VcvCCny7KPf4IvP/gCtS6Zhy2L5kPVyRXe8AUY2Zw+QMaCVoO/2uGQIwy3aSKSokdIRo0MXK5K8Yb3+hKnfYS0M4wAklm1LWVZMfiA6coTFbOouy4LkdmhT5IsqClKKIAumwWLDkSQKQoBxnCi0wBQtk/Mcx5EkG3TqEGbB8XjEhdzXKqUkhYjQ2VPcFDXJdUhdMLqRacpNAqYoIZCnA5XERsX+cIvSBcSEOH0flCYQ8MHjXCAJTSGz7GGUJMqCfrjBFBVSKpztiVHgpSEEcN7jU8SFPBmbm2CKPDUoDc4NKGHyUhVyTMe7v/HzkwV/IZirT8ZdBjdmBhsHDaKgt5KicmirMsBiAmVIR4eNFaWxIEqIgqvScHnjuWwFAZf7hMOM0YRi35b4UiInQScl6+3EMzNC8jnocx+yVyeEzKY0KvuJ0Ag/QVXhj/mAB58Bw6gZZSBOOSdpVA4VFEIIBidRwiOjoiDhhUAIyUK6DEjc7NKXs8fpVAgcsmyJnafuoqahz1JdJIMXHTKwUgI6MgsmmdkkAfXIP2EFiCG/p2mW5UYzs1hkw/ccqsku5M7B1mQw0/u8avcJHhaQBpDlXJJH3lYpoFdznheZYkkxJ7C/bzqH/PvEHRBFyJx/Vc1USz3rT3EeBkjp3pQeZ6BFumcYyYAZP4PNk9Hbz/ezM2g0Pn+G5QLe5GqcuaqYByEi3IRXkq9Wkn9hO/Gv7BzUO7gtcK7iy6Xi++Ga43HN7z5TfPJOkKQjINkpxa7UFP4elKR5N/hZqXSaXBY+3+UuqYNZwlO5gid3B94zVZBBVAKCiEiZfQM6pjvtzyYJM8Z04l5qU8QcSDrfL51KqmfwE09Ka5qBWMpg6KdOBLMHTcyTiqfeyhOCClGigKgSpQ8MJiFcYBkdr8IZj6xjaMI81JF+BngJgrrP8PpVvD1YGcboEDYBASUrhCjxzvLoo2doodCmYppyVtObG8e7tz/ExQYxm9WfXTwkDh3We4RPXFQ6A7HFhkI2nC0dZ+cPKY1kcg6BYngM+6Pjet8Ro+H2cEsnIVVPuLp+TZ2gmyxPNxsSEwtpkNWKq5sjVR1ozYLj8RZUhYhQyUS73lBe5iEZ5yaUf4kXEu8NhdakMFHrQFW0jMOB3bhDSMO6PUOpHEIa4yUhdiweXxCcJdoDx8MWFiXBXbE+37BY1ihTknxmuqVaME0TWjikKgj+jL7rkYVht434YBm8wL+6ZlWWmMKQ5DXRCw5XI1It2HYW7xsWZotg4uzsE378zee48RXdNnDx8CHn61cEO/Low4/orl+yfvYtDq/foEuDrBakekexXCGVwTTn+ONItdSkCZbrGqEN755/zbJqefzoE1KauHn7DavFBSmNpDHw7Y8/4ouf/BhTONbLC/qD5dh1lJUi2ki7iBzHjqcPNpgmEUdN0TwFBP1RMh6vaS43VBE26oxp2xNCAhkoK02QNjNFfqQfB3QSUK4Y7cii2jCOIyl6+u4bqmYD5lmOKBiPpJAjD6YAwe1pVINuFIexRaCxIeG8xjNhVEFVynlbFjs5vLWYKhFDIoQsJzs3t1PIRPAe5yaKosR7KIuW4I+gJC54QpQ451CmwcfIJARCRAbnCaGHpLGjJ+Eg6WxkF4KQLElIiBEpK6SuENHm6Xs/IEOPUWYOdI7gLVH9fM9JvxDMFX/uT6QYE14p8pStQMhEpRUYOy/UM2slBNdCU3lovWKoBDWBaVSUosvyz0RmLVKW5SwCpzWilCQP107Tq0ilBe04sQ4ux1qdFvGK/FPJDLSSgB5c1JggoIpMI7wTAT0YlJQUwjPFbJGOMfttNJECQUHAAlpKSqco6ghVzKttkiDmehjJHT0w+YBb1rytKz7ejch2AOJ94uMY58TzWX40Kf+7EXkFP8q8v4oZiMT3TOJhZpdOhvEqZEZKkWMMhjQHgBZQB7hIMOYFHjXLdyHl/Zxmn5aQs2w4//tkVlJyrqiZP8LT9KDUGf2KkKMczIwQTr2HTpxc5RkEnoCgDLMsqfJPLeboiHgvQab5ZzODMydnBizkTLGhhah4TuJoBJfW8CBd5yiHEGEwsyFeZxa1DsR+RSciSw8IxxHDD4olY5WRQ0w5hPSknMaoMugRcQZX9yCLeTfcRaxFMDNNdcreOqVUBHHyWMV87XGX+SYzxpyjHaI4SXozqDqxXXchs//v33Pxfq8OmTW7/899zu6pUH2GtzmuLEW+6ywfr49QT9BX/PXDBlB3hKZ8j+XTEUTM++pf/cu/mp6rf+sv/A8peo+3O6BgvTknBMdxiAyHLReXFwTbIYTkcHPDelPjpg5lHHXziLrdsNsfudlvKRQ8evQhZTiCEgRRUpY1+zGxvd0iosUGjzENC6OJ7hqlS0T0aG1Qeknf37BZrxHRUhUVk4DXNz3CR9zY008BUsCnmGVn3TAc9xhTUpWaYXT46EHWCJEnYExRU5rENE1IIstqSbOoMDLSdx1oTdOesd2+xscGqQust4QwYeLEv/in/wxf/PgfojUoWVBWDRdPKtxxjxQttn9NRFO2K5RpEEpDkvjphmAtUl1gJ88XL17gXM00ZVZPpEBbClIKROd48OgDvnnxh/hpolg+xNkAMfuhpEok1VBynNmqlqYpGfsdq2ZBsdDo5UCqerAeNzQY0SCSJkXo9xPeW9rVgrJdIZTJPp9xxHd7gh/YHzuK0rB++JDD1Quurm5x1vH42ffpux1KN/jomMY9pSnoR0tVtJDCXYKNUgKSxUdJjILgJ6p6g51usMMVj59+lxgjx91rqnqNKRRvXnX040izWGDqhr7vqVTB4XhgcJGbURCiy/l0CWxIrOuaabhivTjDTUdMURMpcDExpYSdRgq9IgSPjDuMKfHB46OjKFrMHAA62JHgE9pUDP0RYyqGsceULURw4YjRRbZJSIMxCrzHns4/KV8sSJlrcGLKE4fMv5MIQpzXciDEABg615GEQZOXC61r/FyUHucsvzd//T/95WKucBArzVUQ3EyCRz6yWuucP6IFBEHfCxppYKEJFmo3QBGpbWaAyoa8sg0eVNZ8U0gIk9fLOniSzbLfRiswebrQecWivYVDNss5ZSm6lGU7smE7BIUVEi0cpxylUsAHVcGYPJUWgOTNSKbL+7wg1SFnvBSVpC3IQIABZDF7iBrAZgAQZJbaSgHOUeqCIQjWzudsIyszsPAps1FRQOdnrUjMjBAzAxazhBhkrqnxcy5MZJbRZre2l/n+C2CYMjvlI6Dztp3PE4KdhzjB3Ec2p1rOEqKd0cTMVMiThEdmsdwM7hI5qkGQQaBIUMwx4R3Zh6bmcjobIBT3IaOe2cc1v89IfryZwVc3L/UnDeyUj3CcAdcp3twr6CWkA7DkQ+sgGPbHCJsi++xElfO5ksqvW2TJVKYjS1cxBUmvFasY+Xa/Z7sE2Wm6WjCUBe3R8/JCUYZchCNFIrpIkBL0fWRCSvdqqBQQZJbzTt/skwSXZmlNh7wL/fxe9IxXhczho4UId4AnkSMShBC5kkcIvM6ThH6+w4lNSom7ycVT55+/Y7nyMVWGvK0TQLOl4o/HNzy/2TBWgkWcfXNWgI78Ttzzd9U5WuTjTM/vJcyHajLvgbdfwdt3Pjpj1a5JImUZwzuatmHoHPa4RWnN9TEvPNI8yEGIsqHf7xCHLVLuOXQ7mvYSNwV+/PkLhJAUKuCiQknFMBxp6gXOjsQ4ktI1enOB9YL1UuFSIsWC/njkODlcmhiHgaoMCF1y9e4NTXMONGjjIDmStzRli0WyeHieM4VE5NF5O3syC3y8wIRb9nFgvbhg3Tym6244a1tSGKiXF7x627OqG2K4hsqzrEeCP6J1TcRSlDUv//B/xG63qNWGdvWU6bjl3WeGqb/B+xxBcH72iJev33F784IUNS7sOb+4RMYVzULQdwNV7HD71yyLNUnlCIf+OFBVFTJ5vv7ihyxag00ljxeGzUWbAzBNS3I9t7trzi6eoqXBjhOFEtTVJVJOSBXwPlCmJTZsEfaACweGXqB1QqmaQixw3ZHu+hoxjpSLJxzGd0QlWbQPMNUFw7AnXffY3mDKC0wJb27eElOgFIZuvMKYAkJgsz5nGANFKUluxI+W3nUIEQhhCQqGYyRyRVWvWW8aXIoEG7n44NeItsM5ePrJOc5pqmrJ18+/YPKJZvOMFN+hk0TGLWUyGFMQ3cSLt6+5coEpGa7f7UgyIcIRhCbFRMCRokOoLP0hFHrySCnwDoz16OORznbIJCirBaO1qKIl+AlTLkk4QvJoXQMBbQrAkITGkUjC5+eaA7Tt0GEKhRSKWmliUkidz7shRKaYA6FV8pTtktoLoMLFkRg8zh+pjSZGgU0nheTnd/uFYK66f/tPphubGJCYwWDKiBC5k62UAQQsixnsnErQ5hFK5r9nj9RsYokxMyCqIoSYx991XnpeaoU4epqyhLPAegy5YmU2Hn+2V5S7msZ01IXIkzRaItoIuLyIGJW9VzGBqzKgqw3X14I9EL3CJ8FaOKpSYWSkFgFZZw8Vfs6KKkSmKuJ7PXuR2eA+B5A6Q6gSaozZE3bnIiaDjzufEfmnI4Mvmf8WvSYKzWQdOkB50o+KMBuEIixmSc/LGezH2fCeMhOYZo01ygxgRJo9ZzlLZE4SzIigmE3wcmaaTt40IzOUlyLvr4rMPsV5DK5K81Tl7Kly876A/J4NmSmLKb/vQt0/x+n9I/LfTo87yZBhHs/TcvaJFZAUNzK3p9so+CD2eV+cXmsXwOewPmEEpEgXEteuYqqBQlMIyTh6fOGJytDpAukDTkgWMTCkSGUNRyJjKZHvTRieJuhSSj8FiviZ7+PPfj1/OjMrocR9/lQ6bXf2nJ0m92IEqeaYhvnvdzAu3WPSU3KGmJ/Ezts7BZ+eFNh/srJcHnZgS/7QtdTlxEdLD/UIk+DF9ZqfNOans7XIHrSU7qXPf/kv/S+/kszVv/sX/2rSZUUqCvbbW0pZMQwDRQFp6hFS0pY5q2fsLVJrTF0ipITkOOyuqcsHDMJz2B7YtA1Df6QuK/bHjhB89qsET9uuUEqTUsTagaPNPiBJiQ0RLSRts6TbvsOYBhe3bNpz9uORVVUzBkMKtxizwKdIZSqsdxQqIpVEK0lTlZRFyeiuOXv0EHu8YfOtc1bnLWFyJHqkbHFdT5rg5s1bFu1DlFqw3d4g1DmH/TWyXJBExbQ/YH3AjT2RibLQjMOI1jqzId6CyFlPq7M1tS4oi4jv31A2ivbiA1Ic0W2L7b+mbD9kvHlFfzzQLM9wk2SaEkJMEKCslpgiGyKlAd20mMUKu7vBj5mJO/YvWZzXxKQZdzvCtOPi0+/w+E99zGRfM/Uji/Z3eP23/iZKn7O7eotelZxdfpvD869xw4TbvsOUG4S7ILieaEPugiw2mPYxoxtJV+9IqSOQJ9GLxRlTuma1eEwce5ztadYf4KaBfufQsuDLVy8pqiJ/P2VCVkturi0xTRwni08LkLDvO6JQOHvM6x/Q9SNt3aCEpJCefjwSyzOC7an1Eucn3CmoUxqUVOg0Ioh5wi4kjNKURTUzShBjpCwLRIyoouTY7SmKOnv9oiUSkLJiHHsAtFQZVClNjAFhWnyMjONIUTUMY4+bBgpTYt1EIKKkyce5nk3oMpGkQfpczSSlRMRspE+6ZLCWhEMIybHrKaQgkrDeolRFCp4xOr78q//5Lxdz1TaBohKYEPlm6di+kRgDTmpUo5A2MU0WVeSpJOSYfVKnCTY5o04xS2RS5xH0yYOWSBHvJCupIsIUNFNAHgRUnpdJIq9zJP7ZWjHGPedmBhVlAmHz6mdmz4tIGRRUfvYTRSBSG831RE7VlrmjKqtXglpKXh0aGulYF5rJSUrlQbm8vfkKQKSZ5Tn1D1YD6lgQlEYhIMyp53NcQZIJEQVhrizxZUJJgbMth0qjKo/dZZZJC8EheKohsqiAVciSoNMzUzN/IEWaJ/nIQAjupx/bE7hNOfNKAKOf/Wopg6PTfcUMqJLI+9/FDExPhnkVZllT5WgKO7NSTZyPzPk412SEoHTebjHf/xRjkWaGa2QGD5wQRWahxMzizUxkBnAjS7dCJI+Kfh67mwPJgoVQc6MEPkQmB22UiBTohUFUgroSFHZk0jWfWjDa0Q+RZoAvikTpYSByXSqmFholmIjI9xgguMeGUb4HsOAuq+30mxMgOYXDppnByippvJ+wnTeaZP4pI2gtEDFLOnquv/HpBO64z6E65fTNFLmW+Sowy5sCOU8yun7ka7mkkZK28DxdpjwskAoYBT+sNRuXA4DhHuvK2cP1i3BB9//nrRssm6LAuJ4PHz+iG6755LufAJp2ccZgB5JuiCnmUXudL2JKU+GnG876M/rOM9zsqGqBjZ69E1z3HXVR5qqk4GibBbddjxCSzeoCU0jWeoGdBhZNpNEly/UDprHn2lyCEox9oncRrQtsspQSlpsPqJuKmEaGsSP16zShRAAAIABJREFUjvWi4JPv/HqWmw4HLh58wO3bxNXLdwzDDW5oOSz3LBY1F9/5bQ7Pv8J5wf62Z+oX9INDcORmdyAlSbQHKrXFjh26SjTNUzofMcUZUyxQ9Y5SOEp5y8WDJ9RNgdSawW5ZP32AWba4wxLdrNDlGVIb7HFLYIPWS+rLFbG9wQWPmm5oykQsSpqzBl21+PFIEG9QYk3zrXPqT56wrD6h5yULPiYwMPINYRgRRUn32ZZ3f//3+dF//RU7XfNgfU4vb0nFbxLTQNxp4rvA65/cIJFsr0ZSWlKXiuevfp+0fceDs3OW3/ptYgG315+BCBSN5jDsWOmGwhRM/desnzxGlxq1eYLvJ/YvXyJSZLQDTbvi00+X6GqJm3qkzFOHzx40KFUQvUUVC4Yxn8eFVGhT4KYJN+5QusVO1zmiYu756vsj+51AFxopCur2Y6K7wTmHFDUxDUxDj9IVIUiQElnUOVdrnPAuUNUGKSXBH2llRElLVTY4JxGizPLdZkMSKg96mQySpPCMU45eiMsCoQwhNvg4oKiY3ABIYoyMzmUQSsnxeJsHQELAIonR3+X/aRlQs78rpsgxRoyPVEWNEJ6UIk5IXPz5npd+IZgr/vyfSNujZBcUb0eBQKF9YiEFK+NYGAtSU6v8+WeWJ19yxwRHL/EhU+zZCy5JNnfyVRIWylOVIhtZJp0X4kLQx0BDgKmAaPOivY6wVxkwaJ2BhptlDxPfm1Cc2aFJ3rFPhyi57QwdEVVKtJasU6AWnv0xMQnNygfWm8TeKqRPrGaJxEeBF7ncJEqJDJJSBFQRiMg8jeESyUTSlHBlTu+OsiI6T6EESSWWSiCk59VOoFGsteAaj3eKEoGwUKrAqo55QjG6DDxOMQla5PcX4E64OrFlKULy0Oo58mAGUUFxBwtO4EbCXfBRIrNOkrwfOzIYSPPonBfAzISJAKsZLBXcxzTAvXP75MX62VG48YRQ5sf4eD/ydpIH9ezDimp+QXFmxfL1/EEJjl4ylSXRwb6FYhd4rBxmEXghl8QAT25HysqjY6RUs/9LBRgUOyf5UWnoCw2lzDU4UtwBqdMt3KUJ/7QfK8b7ipoTCNJihmQzzRVkvt9J3ovz/U+XXeLUIThv9/2pxji/5dNg5fuvSyXw81bu8N3swVp3kUc+sTYd+2A4BMOinDgXkUoGaAO3u5I/qCositYl7HuvKUTJOgaWfaRrBb/1X/xvv5LM1b/2H/63qW3OUUbRjweMysdhWTb0wxEZYV1VbMcDplgwjR1FUVCXLTf7a5SuUAr8NKCVwAZFoTL4bZqapixwtkNWK0JKeDshU8equczTU3YguoCpa2S0uY4mCpJIrJcVTalZbZaUi4ruaBn610jV8Ob1c0J/RKqSJw8u86Jkc55RjBFpzpHkXkQb9oz7AcGRs8snVMtnVPUZ3f6aPjbcvP6/Oey29OmM65e/R1E/JhYb1iZSrddUJIIbebgJnF2UWSKqAmff+S1C2MK0ABmJk8X119hpIjgHB0t/c0NRV4zHLc3ZMxId1nakoqCqWmTS2BSIuyNJjpSblvNf/z5ReaY3O4rFEi4qtE10BVysH2Lqc1QrkE2JH/aQIovm17G8Yv/i99i+2dKuBCp+iEgVQtQM+3eIMLB/s2d9+YTODkgqzp98jzBtiS7gDj39yzek4BFakdB8/tVnyG6PNgXnZx+yefo9vvzyH+HGnuPhNUHVlKZCxoS1A0oLdtt3LDcP2O12tM0FKexpF5foomYcJ2LoaNpVroKZRqYpUpqJQmnaynPx4BPKRc10PHJ99Yqu67N4ECMxNFQliGRyfhSBulnjrCVGxfXNK7Q0CCExJjGNPULXhOAoTYmzkaAMg3MIKU9hRTg7IFRJjAKkxtmREASBwOQtxjQE2yN0yWgjIg7ZZ4XEp4hOBrAkkZj8fU2YJOXnmc8uZblEikB3PFBWDbvDLWd1ZmIn26F1nVk/Af/gv/+PfrmYK1LgoDVdDw81CGmpCsFFIVEq+6SSTggZIVR8GT3VlJikpHfybqTc+1zTUcXAuU6c1RkoxJAYg0D7ktF4blcVH+57GhVAFdDaTB0oD2PieVdyqSR1ChAkIcDVKKjbipWfsmFlBEi5+65KudvupmSKkfaywDZ5Gk8fLY2N6BpeOomLAhc8Xhk6GdHWobXE6sAiSMYCbnxJWEtU8BRCsS8ViYhBcHmcWLSBwnoQNTdJshCR7aIhqcjiYBE68GQZswndFzypB77wK7yLRBEZvWRzSNRDoCgki5WYc8MUHCZwRWbGpACfzYKmMfNUoLkfPZMAMzgKZIM+ZBbsFPJ5YrlCTrlnCqANo0sIoYkx4aMiiEQREqWRqMlnFun0WDk/Lr3HVCGybnV6IRKYJJFIQmS/kc/soZD5uCj17NuyMn9mYt4+mdnrg+aFgqfU6OPAbqNoU5GjBULFcTvRypGml2gd2R0FB1+iItQmUMrMRr5TCuk1WktqI/OAg0x5FPg9OdAoQQrZU3b/1sJsUOVuBgByEnwixycI8kdwUkIBylPMwbxrTunzJ6br5KfipKLeqckJ5efnUyB9Qs8meikDcxwTAGNKjDIx+pq901kqjApjArQeCJyZgqc9XJeRQAaUp7Pep8qzcjtkgt3b9f+nU8Uvw+17zx4y+QllBGJzzvEwoo2hdBPTsiLFiFIjjytDwUTz4AKpBCkpPrp4hjEFWgeO4w4VYQqKoR+JPpBSz3h4R7u+YFH3lEZjioamvSQORxwCVENwFd5FHJL98cjD8zP81BOnG7puxEwav1+D9mg7AQcerxvkpkEKgzIJoQVVaJGy4s3Xn1MtbhmHSPATj58+RTzdEDkiVUV0L4nhNXUrqBCcNQ3RaaKbkL/1T2FDy+54y4NHH7O8OOPmm89o2weEGPDCEVKk3x/YeI2WT3BpT5ocfjiCNoSbIy++/oJicoxuIL058OGnv41uFc9/+JzDboehoqprUrvE2oGmgSfPvsewH/n9/+lv8uTZA8qzJbvDwFpcMhwK6vIZb//hP+L45nOkbdm++pqkGyZR8vTp7zCVAeUjR58ovcTFz5G1Yug7TFHTLJb0V6/xH0XCqChKz+3N7yFMIuE57q9YrM5JU8F4PHK13/Hhh99G2oGuP9BulggV2WzOub7eUTQ1dd2yWD/mwZNPGJ3F6BUvv/wBITgWZx2pjzz+6J9lf/MjLi4MUi6Q5injfg/0FO1jtreRzZnh7/y9n/D8ZuLcClp1w6vdgeATVbEhyhw/5JIg9XmcXQiJ9RHdD0ghCUyMwVCalugsBCjKh0Q35NgD05AYKas1yXaEKBimnpQkUi1wRKRINFVBSp4YJ6LvqFVDZ7c8On9CQLJ2R7RYQDSEJBiOr3HxyEiDVoaIx3mLwBCQ+BBzEj1wONwiQgZkwuXnftdPs9olkNFiRI74+HnefjGYq3/vj2cKSs8LadQZicY8pj6R5UAfEk4opIhUIpJIGG3o/GmcPaGUoEvZhHucsy3KGDBNweGgqVtARVoXqc2QpSUPN1awMIoCydUU2XaSdZkPrJgSR6Eoa0XtRx5qh0TSyUCLICG4jYo3B02UcLlK6KVgOQWKCEPvuEkGi0FMieUiMEnF6ykvroWQGCmog2NpIqLUpFqifWBUkqQEVXAoKVjYkG1ZNuXX5TU7BWFRIitJuXWcTxajR25DxRdCct5Jbl2gQNx11ykEaw0yBS5rjzEpe8BC5NVe0XuJ1mDJsqyIATV35sWoGOIpJkCgQsAFxar0rEuJI2QwQ2Kw4o4+8UkRvGAM4ERCe0OSATf34WkDTZJEGanwXBiPqjRDzFcrNgjGEIlaIi0MKZGQCCImeR4XYFPEiYIayTZ4jJb0VlEApgwoEipKRtIdY+O9gLKg87Baei58gjOdvXE6wJi43ikmVeOtJxqRZc1CUmgwBp4joZCURLyE6BTBRcQQkFpiK0GqJEJEtM/J7Ysx8GuFIGH520VD4yST8sh0n0H1ftjmP+6rKmd6yc9euhNTdVIKf8qjNbNUkG1xSJhkwiDm8uc0m+rnCcWZuBRhBliTp0ESXfYMFhoKFTEKVilwuR4gOCgMHCC6xBUlD5d9lvEPhr1VvBOZrfv4v/ndX0nm6t/5T/5Kct6RlGQYB6ZJUjcLpM+yjhAjpdGkGDD1A1KKTEOPlAWm1AghSWqdGajhFpRCSoeSedzeW8vt7VuWy4pCCcoid+D1k0Ukx+OHay4uLymrM8I4IXRgGjuSG5CmQEqQZk1iot/dsNhcYKcebSTQUKxqmotnCNVSrh5DCihj8GK2W6qc5tIu7tYv8OCmXPQQ5+uasY+oaJGmwnVXCFmRoickgUJjpwFjyvl7GknJ53hbIdB1xTT2xBQJQ4+ROo/jh4LkHKIKTMcrlC6hSLjpyOLsA+ywx9Q1KXhCsEgt2T7/AbYfse9eokKN7b4kaMnmw49Rlw2Ls28hzMj25iVpew2hR7efcnj7Q86+u8YUT6kefZvtH/4YmQK6KWgefYv1dz9l9+JLdr/3FV9/8UPWXpCCxzz9DZ781h9j3N0gvOKbz3+CQFEUC4RaUFWaumiYugnTQBh33N6+IgbPww8+ZRpectzuWD94RhgtumpIfgIKYjjSPv6U4foLZKkYdzfE4Ni9PfL0099ESIMqNrx68ZzrreN2N6JLxTSOdLZDieyz8kkRU/Yrp+jm84IGofAxkrCkGO9YKGWyx6owJTHluhkJJCnnJoJEDJE4e8mCC1gXCESEUNRlST/2yCBpVU9TnjMkixGJQ3dktPlitG1b9vtbusEy+YmmbAEYfGDyfrbX5EvsKDO4CnHEyILgc2enEArvLCkFpC5JKebhDCl4+9f+ws/tnPQLAa4+/3/Ie5dfebYsv+uz1t47IjLPOb/Xvbfe3XZ32+4GY2AAkifYyAP/HYwZYAlLlpgigYRAYgiMAMsIZBAzZDFCAoyFhNwgkK222tXt6q6q+/o9ziMzI2LvvRaDtfP87m16WIOmK2tw63ceeSIzI2J/93d9H//mX/U7U9apoQovMbqkoPeAzZ3SYPadc+0kF45JeFJIPbNdF6MU7EBROFehyMRJDM+F1R2TzuuXCbWMUsld6S7sHc6b8+q20LUjXsN8liakOTkrj1L5qirTJfHYlJePO+ejUHzjWBNaYE6Z49QpbFifw15NQlU417BJZ+3cLp12A8vjxHYTgGU9RqaVdsiyR/7Gaed2V3qG6RwM15aUkxfmZLg5izTsONEwDLg7weUAP73MTOfKJ68ytQpfXIzZhMf7DU2R9L02pSfhcDC+/3rmEzG+MufFZBQxPqww90JNxtcno+3G7ZLZemU3eJljh/BwEaokuhiLdJImliVjZvSUuZmM2ozHlmnVw/SYYZmN5SDMNwGu0qy09425G10yvk+8v9843sIPXipfV+VOWoDpGhf+/dY4+8Siwp46pYEtINUjv6koS+7cTpBNyRhZeoA+iA62BiQjW+eBA9utMdfCqQm3vpHVWb3wOjn3m1MVTtn51TSTpsYrVn7yYeb0RrmchFyUM8Zyb+yzUhX0KLAopTvF4YKSxShPlT+3NHRJ/E5LvKhKE+fDqHL6Zpn0N69UlRgrPmuxPMCi9Ig5sMK3Ajvh20ArDUPkJsasSu8BrnREknzUxcffUBfcgwWUVbmsnVuMQ4Ilde5647BEPFyRHQ4Nvp7wOSNPBj868fS7hd9fXjxncv3Fv/2//FKCq3/9b/7nPueZeVnYtwsHMSqVm+UG0cSpN7LtyLzw/t09ty/e8OLlHXMGaQ8sxxfc3z/ilz/gxd0POdy+4XJ5Yr1Ufu/LL3jz8jOqd7JsKBs3r37Ii7sDv/Zrf45SJqY5U8K2C1Tm40u65OHUyrS2oylHCQKdXDIxSBY0pYg9gJAwCtTWQ+qa0rMfpQ921Dpxbg1jhCK02kBaFAsD01xozcKN3TfoK2Irdb3H952cJryPfKucsLrjxbDzI6iwb48kccTv8GzksqD5gPiCTonf/Qf/Az/6zX+VvW2s6wP1/ivS9Ibbu0/AjXI4kJdXbO0B2R9p7YRdTqTlhrVfeP3936Du75hvP+P+3Vd89mu/zsNPf0bKM+3+nno+U6ZbRF6yfPKG/fEr+tZom9N7Zz5opNv/+m/x+W//b+xT4bjc8HD/jpQmypK4fS2U+ciH3/+cP/zq55gsvHzxQy678/LVZ5wePpDSxp/5ld/i7fuf8L0f/Qr1dM/br96TZeHd0z1bPfODH/15jqJc7I7f+b3f4eKFdTshcqTXDfMaFTbidOuUpLTeED3Qe6M3f443kNoQjd691gzD6L1RlpuorHFjmu/Y1jM5h76r9UYyQVQ5Tgv79o7XL16ieUL3lcPhjveP76n7yrxk1ssGtSOqXFrl8XLmxeFASQuqytN+og3yBOC43LBuF6pF6GdOy6gCapzWM4f5hpIntrqS8sz59BamF7gZrV1AcgTbCohE9FNK+fnm+NX/+B/96QJXn/+tv+KOU3G+XxtV4KgGpY6wSufteaGvsBQlS2dJHU3QUyIlwPcQQk8N0gb1EKOlXSDtwEv6Csn3sP2/S3BUftaUvlTmdSF7TIwaf8y8dIw3rhb557yxq8Oqf3RFffNhQ8vUCT3QdSdXmiK10hB6FvqkQyMjz897lS5dFzlVYc+xYKbqsDu2w9ydVDoTxlITR2kYGZUWlIMqTxqz6GMHuuJFWc1QUT5vyosOB9mYRFg1kT3kZDkJ1jo3s0Lp7I/CvWVad1Jv5KS0KXPe4JCFYzHubjpf9il6lIf+X4jy7MmFuXdS81jccVqaIg7AwXvC9o5Y56XASYU37vysJy7NmdW4E+Fh60wHqKa0JvxAhS9L57YJ+5RoDWo1fu1lo1qi0CMWAsMkU3e4V6WbIFNmxvn5PnNXn2iT8GvuOMZXUnjqjtcYLV4kuvJWCTD7Jgt2gR/eZv6+GseH0E+9yo1fXXZuc+If28yqiZQ67spO1MoAyMX5TCq/MhkiGz9+mLkshacSz1OHRLC4syHoNu4DYuSkuGTEnCaNjODSUReq52CqtD87ArPBcQg9z2NhExEO1ji6k+gUh6M6xTtny1SBi0bhuVnls1358dPEl7fOK0t8QiNn48Zh650bEV4tHVs7rStPpujsvLTKh8vE2zQxE2n3/9x/8fd/KcHVv/Ef/Pd+c/MSITQz79/9Pvf7kSyO2AbzAZkKt9OBZZnQ1igq3BwX3n79U8yNe8l4+oz19BXff/Uasx1vCxXj7f0XvH75GTf1Pbc3C69e3VK0853vfYfy4iWiRppf09tbNB9I02d4a9gY67sb3ndSnkgyY1REEmYZ905Wpe6N6lBtYu+VvYZDcasXcp7p+06aJswTKWecEFLHuCiY6N47N3qJe8Jy4HG9MCWlPj3Qtkduj59w+vJnfH3/Ba9efxfBMTfmKUJM/9Ef/C4HyXz6nR9yXi9MRbi5O3I43gZwmCIWwLYT5/dfkO6O3Lz4FIqyt7esf/Azets4fPqbvPnzf4Gn+8+5efMp9f0j5/fvqKcvkXlGRZlunL39IfAiKsc6LK+/w/ruay6XDzRpvLz9Hm37mjf/8m+CTbz/nd9hf/sENbF+cObjr7Cfn7B5Z5tvyX5k3yFPC5dm9NbYbUP6hSkfg5lZPuV8eYfIkX2/YKpYfUDTS/q+gyqtJ6zfIzqzbyuabzmtj9xk+NF335DKC7CN771+wcP9j3n3/syP//BDMFPlNev6SO+JUgzRiW5GSplf/e6v8vT4nvvtETM4LEdqbxQSiz0AkNKBbT2hxyPr6X5skoWbfMvNkrg/nTlfKu8ez+RyR8kzT/uHj+BsuqWNTsnGFjIGCTNN7w0lKqKuOtDj4ZbHpw/MeaKUiaLGtp249IlpegX9CacGYDKo9UwZWTDmwbwpRio3YaSQ0IFhoR/87b/77//pAlc//Xf+NTdzPut7CGNtDrBUNJyB+xShmz6CLGuCvIUgJS2wznA6w49qiKX9CfItyABXdfTu+QL5Mf47fYD1hq/3O4oo21QDXKVr0HnMX6VfVS9Ra9NG+Gbmo9sKrrppR/vQmTynWo+ve2hvqjiaBG2ZduhYDSFqRP5/Y27zDXB1fX53WIbOyBTS7mxz4+7ivFqN6kqZVx5Z+NAh54lanZyNl1V5y4Vjb6Q08eSFGypzMr62hOwTvXSSwZ0Zc0mcJXJKbhDonVka3BQeT8I8KW9PNb5vjVaUSytkduqc+XRSPpw6xYRT73yaCl8cQoBtNZEt8kolKTfJmA/herucnYMZR3Pe6kQpsF2UAytPZGbpaFOqJnolRis3jTe7kWa4NeXLxZj3zLta+GR/4mUWloNE/IQZn58O9KYsuqFZ2ZqyLJl+2fBWqBNkNl7pzrvtyFYU30BUeDyGnqsX2Lvxw815dWMs1MjRyiOio7TQp63wSOInuvDanbdAL0IduQfiCU6df+XFA7jyxbnwh+lAHUYHcbjtwpI6TzucSsLMKWmwtb7xg2XmJ23EeblRDD5NY7fZJ7atc5mv5kwFkehGJNitSuLgxgsqt74hx8KnnLm0A+cufG1CtcSrUpl74YHEtnW+FOVHSzATS2+cUA4YLsbsoSU7JSXjSFfUNx585o5OS/Dn//Y/+KUEV3/zP/t7IZczo9YnRArLfKBvH/hwUWrbWWvne2++Q20nat257BdyfsnxZoH1CSkTdzcHettRKbR64vb2NSZnPnv1CsudlJaoBpkUs5U8vQgbe8ponmO86AY0RAuSMt42JC1474hGp6gBrTW2arQeSsDeo6lgqzvbupPSjNKGqHkCDfCEdawDopht3M6FvH3B28//GerK61c/oK5PAEyHW3o70yVx+/IVdXtERdm3xuHmDiTSv9v5EZUXUCp7/QpxUC1oUeY3b/C+U24+e45Wsc1Jh5nt3Xvq+QOX/Z4Xn/4A0YrOC8cf3VDuPuHDT3+f209ntq/OrF88sq0/RcSQl6+YJ6E97fT9zLY39LwjEmPYNCu1VI6/9pe5ubzi9JOvAaGeH0FnzHZ6bxg6ejw1+uk7VI/uUnGhWTCA7onWNqw3JM+0Fj2m3RqtGTqiNczBXGIMZoYDfXSPRmKN4SmjbrhnxI2+faDsEfr6ySff5fT0Y2Sf+Yv/0j9P1zseP3zF3t7z+s2PeHGbefjwc2g3HG6/x1df/T6uzof7ypdvH3hcGw/bjrMxa+Ll3RtODx942p3WVzYM2ztKDgasAzR0MJ+9dfJ0AFvJKXLXUmokV0QFc+PmcERTJrnTWkwRWm80mei9hqk9Cd6cNKXo7MwTUyrsZlhvzMuRuq2xYms0FmwuIzi001xIEtElv/Pf/Lt/usDV6d/+y64qTN5JxT/GKqQUBcKTgBltzUS+mAzxtMYF1BxmHUIT4ncvHV4RYZSPQyyUR07WWITONnEomW3v+CTo6qRJWVtimxqTO18dMocKs8HRjQ8iTF3Yp4/H74PRMnFyzViKVPaOIyO5u4lTutAyHFosLi0lsgVdPuGcRbEUkjzQj5Z1+ZhBVK75nyPvS5LTuyMj2ju1DlnZXDm0zrKGQHpLCtZIFuO0qhr2eoHTLpw0M2HcOmgOh6MYeE7c9R1uoD1UznLkRW7BpAmR2zXXqLN5kaK7sEYH9c0oNE6thRMzwYeTcJuDxTlr6Opu1el7J+nE73elufI9vXBbAHMe20RLyrs9NHU05ZCMi0EadQhLhrMrejKespJdWLuHoy7DbQZa526Ggzuf92k46j6e/22HWoRFYTbDMbQIZmAts7pzUsXVmbtEk0AxPtXGJwl+sjsfRPkL3SmzkczZ3fjCZh4tdHUMkfdDij0CWzhd/+xkfF93/umqvFtmNFv0/XXlh23j02Pkbv38ovz8ZuZ47qgIK50fpc7rtNP2zE/TQq6N17pyl5xZdv7P9gKxxIaxj/gKbRHZ0S3yr6QZ6wwHcV5XB+ksGmBsa2AH4XFNzBifJqd551GMf/G28rOHREL4uk1RatCcxTsPKXPpTnY4FDi3cGmqRCTFX/o7/+svJbj6j//nH3tJyulSedhOpLQgAi8OtyES7o19e6CUTKuxm2oe1SGiwTZ3D/1ILjPuKcIezXA3VI2coOSZUibadg/SQ7QrEbMAsbh4e8I8FmzM6O0SgC3f4MTNRvHIFNIDva4jLiZF9lF3mgtbi8Jy80Tvhj1HfeyYCbXuzMuRlIWUC605boZ4x3r8HRv5XIKRsnA43JFSRO703uh1AxptXXGBRSeyGCJK7WfcZ1p/JOlC906io5KCDUngtjHdfca+vUV6e87508XQLOii5MMBpHK+/ydIXfDpQOZArw08o2Rs79T9hPiM7zsw463G5t9C5+Ot4SS8NUQTvSacOtLEQWSmj/RypFDbjptAzsOFWem9IhabHwgAWduOkqjWwmFnHXUifT7Fc9YaMQlu4czrvdE9WDyRCVyeR3xuCbELKWXKNJFS5vUyUbczP/jOHXMxcrqhpcz7h0d+8vtf8/78wG/+6Hscbz/h9378u/zBh7dMeeb7d0K1W+YX32V7/JypKM06EwvnJnw431MKzJZBKl0P3M43VP/Auhr4TJ8qN2XhQ7tweli5nV/w9dM9331xpNbM2/VC740XU8ZUWZsxHV5y3k5MfUfyxGVbmZIwScIEOoab0Wu4CLNXrMVrNjd2E8wqicz//d/9h3+63IJ9Ay3CQy/c9k5h5COd47+2C6obrShb7cPFv3C0ESB62KEVzi1x7M5JhaMq8uhhkZcGPYNkmsKlTtzpzvHSIAmaDa8wu3HuiZqVu9qYgMdL4nYXijQWcw5HofqCWbgnHEgq3OzObVNK2fngHdsnVEaElDupRrkz2rldjfs50mNb7eFsq51yTKENw0l7COxaBuvOizXEzecUzxfBjoJXI6vwQpVzcjZJTFvg0QPCeheL6flRySlR0BCWj4N3c3aUWYzWlM+PxuuzUEpYWS+rhx6jVz7H7BMhAAAgAElEQVRZMi84Qw2qnBxMEJYituJ0gTzjlhEXfrqHZ+1Xj5m2G946VpRH61xSZtqV3o0uUFtiuWn8qCe+tsrv7kfuNuPN3GgOLhkTZ987yWLOaD2UscfsJHOSdF7Myr45W3JyKfTubDiLGPsBjqZ0wBDq2ElClAo/zvC6CS+yUrXxoQltG7EEk1FV0Co0FbYc792dCj/vE19XwYrwqhv79MgXTxPntPDdm5VPm/PZ4nx+MnZNnLOja6Q49yosKhxsp7ryIU1UN2QDVWW/KHo0Th8KP0udm9I4bolVY5d6WOChJk5yQ6mGyIU9KT+pd5R9Z/MD2TIrOzIrL8b5dlalttj9FhMyQro4OcFxU/7wLvHTd4ZqQbJzK41X3kjZSOeJ/+el8luXjXfvZh4n51VVtFWeUlBkX6qTq9HJQ3RsnGXi2Bt9GE1+WR/TD38d784dwi3Q6oZ7pdWdvTtuBT0eIStLyiyHOzQZ63piWV4E2ySQS5TBX86PdIvxSd0quRtt39nTE9O0jCg+4+xtLNONYOKN6gEGpjyRcqbWWNxzDaB2mDWYruRISqQ5SnmFCW81qlG7M5nTKjxt2+hfTdS6491RTWhSal2pew8h9GAvEmDDpWW90dsWFqGts52eYmRDAKg5Z7J2bg5CSjNmT6AB8iYA3Vn8iBv03cCipFenA9ZXvMN+/kOsCVYueDPKy0T5zmtShvr4wJf/6MeUQ6I6LIeJWacxFnWcJ1xmyE5JBasrpgqtIzLjvuO1BnsiBXzHpKGipHmJxhBzkiquRlcjWzDJAWAdPMCDppBVUBZmAoh2jCJKx0kmqDtra1ET03dUjOQ2gNWGI9TeEVGeAxCG6ziYScVoGAe6w+VimF/48v4Bdfi9d1uYsPsXMdaVWHfMC//svrL0jX96Mn7rz/wL7Hvj88evWffG1L7iL/zgh3x5f6LVB378xe/yZ3/4G/y53/gzvHv7wM/f/5zvvXrFJ+UFXzx+xYc1U9oT3/30DuY3XHpjqcrtq8IyZV7efEJtG8sk3L14STan943t6YF9uWFaEj84FFqfcFH2ObFMmW3dkFzIFmOkvZ0B51ITOn9KbTvWO7NWXBdMrlOqX8zjTwRz9ZN/6684wEmEnmExZxfnMA5t8ljDnYR2OE0BMA7Xug7NmDs5BaNyMMEcWsnjZIjMKHeJfCDXkZfWnnODrmM+8494UxVmiRTaJoIwxnOSyL5TSZhG/JWOIjUrDbJwbMrmFkHsXbgMwXG3EuO93Gn6MUIqi7BfnVyhVv6WxssGdXUV9iU+fv+b61Qan+fJoOAxngHO3ckuqBi5Qm/2/FxO0LCrRMbRdRJ6aEbRcI8l73QVzp55kY3WYzewZGGx6Gb0yXmoib1nDqa0qbGTwJSGc1TjSGJ1WMW4G+LrD+LkDtMIxtx7mACUzqSdi2VqgrnFDjdpsHoXT2SCtOzmnEfOGSMLtIqiSag+wjazkXCsKioSmaSD3QKlqXMD7NVoOf6Gmgz9mSMp3IolK3l3bmflu7LyROF9c4p1esrkFvmvmoSszlEbe02cSjQKrR69g2aOS7B7h+ECrsXJSUaDvSLSeRFUK20THpf43jwKvs2CzUvaOe+Gl0zrxtTjey+K8NRGhIPAEkEVNIPNwh04jTyzh56fmbxIwQhmrxRhwpjI3FujiDCpcpc20g73kqnNWEbuWL+q7Bkbget9vX87U+uv/d1fTkH7f/JPVo+FbeJ0+pypHLFecRfmeaHMCyUrx5uPWbjXJNZrNm9vUTzQmlO3NTYpg+U6lIkyL3g7UaYycrEknodQS2iJ50gZ8Hje1sLtZ6NqqXdodUc8RO7b5SGAQw9AZKO0vLeNUhK1J7DQXO17jMJ8XF+1b+zbGRWNHCQk2BJdQCInK7Lc7DkXTXVGzLjWkphvzEUoCikVUtmxsoeY2nY0RzjwNP0G9XKPbSHb79tGa/d4cublOOQlmV6fUL3F+gWkY90Qia1XpIgrnuL5Pa+IKGlagtGThuSKloL3TN/DpePVsC3jVbBasZbCIGKFVjfwGGGZVUQnzHuwdSNLLmnGvYIW3Dzcjr3gvtP6GesTtfao6CGDOLs5tTcgYa3R+hB7l0yiMy/H2Ki1ncP0mvv376lurJdHLL+h7WtseDp0b+GkbA2bjmxrx+ye1sKthxwiRiRNnNcTd8sNrTb2/cxx+ZRqO9mMy/Ylbz75AVZ3Xtx8SsmJ+3c/Z16OPFwuvDoceaiPACx2ZjnesrbMtm3cHO6o7cJhEdZtwt14/+E9qLJMC/Oy8O7hnrzc8d3DHV2U0/aAWSc5PK6NKTuXJpgWigcj2k6PpJR53J7o/cTd8SVf38fX7m5esu+V3/5v/70/XWPBf/w3/qqLQMexooh1njGOCMedWBBSpZhEIayEIQlg79FuHTt3eBwCtuKNnCOAs3gnd8EKYzGNaICkEjqCcePRZCNvMihvvyZfSxvp2hpAy8JOPwXviEwdc6e7cqzCZY4xoZkzI+yeUAG1FpodSewJZhujGXE8y0fZ1XBs9eER6ygOTMTPWG8klUi4NZ4XRdVYVG9qo5KpJb4uNLIJs4VoviAxTlRhHzcvV2JcuA7MmZ1C5zEvLOLsOLMI2Dbev5Fin2Cis1RhzxNb2lh2YS2JZDtrVo4dvOeICEvhQLu4BLA1oTRoJd4LUo9Ray0IjeIRk5WHqFzG5zvRaDWchSrCHjWA1KZ8Uiq7JXp31klIFo1CxeMGqgJ7zrRRpFcp0eZTjafsiIUgfOmRbG7AngJotBYsVu7Gk4Se4NgUang2Dyia4zWYwG4J78qahJQac1PaMCZksRijSA69hIamy7qNUoDIa9GpxoIqwrQ7XRVzWAkn1rF1TD+CozbC7E0MNaFKnCMyziO0kxosG/j4m34956676+tzIdTcOW7Knh0RD0NCV2wRso3U96tesMvH5FCHaVxv23UhF8Cdv/53fjnB1X/9Fr9m4/YO8zw2jwNIiUCrAXyuYDSP25DBcziiDCWE+DC9+MeYg20kcre20qrTW8U7tCvoMcV9j1HgGMu5ZSQ5SWdkjJGi+3AjpUxSHaOURskz+EouU7hUhXD14Vjvozg3jBTWBfNG7yveC9t6Yh/jHTOjtR4hpK64twBwjELibsOtCOCoTqg38I57DcY6ZTR1JHfKoqgkYIpzbY/3CQlQGTqzilXDmwP9OfBYckK0gQjOGqzZyHzz3sEN6x33scm7NhBIRdhJ8w0yxXthtkNPWF+x3qFn7JLwVWnVA4SZoKJjPPqckQO+IzKFDlcii86tghdUQdKEW0NTwmm4jQDSobeKrlfFbac2Y1tP3N695t2HjYeHt4imZyCv0x0//9kXfPbmJcepc/9woUnm9uYl++We4/HA+8eVabrl9PQ5P/jRX+Jnf/DbvHzzfVqvzClRa6P1ArZjyWi+QJmjKa0796cHkgB+R++N08MHDrc3SI36m51OSoX9/JZpuuGLp53DzUSvjS/fPoRDMc+cL09Inuh9ZVle8HS5YOxxv5mPtLqTPZrTkkxMSSDNXLYTqsqcCipKEw1pjFVyjhod2QOc/vTv/eJCRP9EgKt/9Df+mpuM3jFA0iirbUpXSPqN3TTRj9eTMA1wVce1t+zBLF1kWMd1XPQDkKThhrm6+iof38dkRhpMkPsI8AaeO0EY4z0VzAQbLEtLwzk4ntNS+/bv9ZG1wT6E7vHvYvG7ZSjfpZZwz10rSK5usnGjqcnJTTAJBiLvcZxb9hAyp44g6LVnLg2GqxOxmjpynTAWc0r9+LqK7UwunCbh3IEmiDglXZnBccMX/QbzMBAPUFJ/BnWxyMZ7bMTksEpiNgEPl1gZXvwNJTfwxZjbACIY1oPdm6ThOBuZdE1dF0GIseDc43OdJXR6nhsuyqMVZsC1YaLsNV2bHeK86mNhGwDCHXIKINxUqeNEEwEbwbRX0BtMUejdnsGp+kctXLruvJWqEdDpV+Gpf1wsr+HyXtqzh2FPju/f/vx1gKducXGMez3NJZi16xON1yQ9xsV1fD21ANyXMHvGqekfwbiIfHS+jodZjA5UP0Y5fPM+8XzsI1SrtPGa/kiuVkOufPCz29ZqijBgh7/+X/1yugX/y3/WPaVEyVDrStIZlRaMiIUo3HqDNIOHrd2sojKB1IESoMxTdLHhQMZtuIqTB6DhOoIr9NaYSnkOdRWN4lsfuUA5hb5r3xqtOfvlHft2oTXhsp5o+8ZUDtS6kUURTYhmcpbnRb21nVKUXA7kPJipa26WBChJHMCM+/svghlyQTU2QZpmkih7vUSQ6tCfCQmRzpSnMXKM19nbOvKqGlaHpui6Sx6F57GorOOdN9wKoh3VKSqk3BAJhCo6ga7IteZMdWy2M7WfSdd7vDVSXrC2gYdj11tFNT07LQFkUiStSAadMn0F35x+KvjlgPWKbR08RahvXZ9rrFqN673DAJ8BhLUcSdpICeZlRtMcI08Pl2C8ZB/vmQJKryu9N6b5Jdvlnk64pONnldNpx1xxQuu17Y+4zJgLSSdaG0yWTLgqSYXT09doucWbQFIONzdcHj/g25mbuzeQMnOC6gok3AuX1rG+cnz5Az589WP285nTtrN24ebmJefLI623yMqTxDIdhgj+jKBctj6qYhtbbbT1LTqFLm8q84jV2eit0nAOyy02Krf2fcVzZKNF2qHG+V43nGdimMf/6T/906W5+j/+L8P04+I5j8XcRx5S8muy9djRdMGy0EuPUcdY2HqKChndoXThMsXMz6cBmLYoq7zm7HxrVelCFsWlfgtcSY6L1Po1YFEiZFGNqUHTYLFcrrqBoWHQaPDex0mcpQaoGz0krQgNsPGHJm10gX2J3eiL7Xryf2Sk5Bu5RtvY4c49AlYlxyJ9Hchcrf7Foi2mpwF4VFgwpv4N7VZxskbcgJREasMo8Pxk41jSGI0K4ELOoTtTz1ypimtli2hsoLJBSsboKqclIfU47WqOXCp9SLQkLNXxlDANJqR7HrviEIfHJCwYvqh+ic/jopnUjSzCVBVV55Qb3gokoWq/ru3xngxgYqM+Kc4JCVpcGj4rPhgtJHRZZbCDMR7u5MEWOAE+n8bmgF4iDzfViFEY6efBPMbxdjPSOC/aKT0Djy5OriNRPRu409A/FuAk05iWXEH+dQMxDrtfF5fx2AfbKuN53EMP5+bPr0UGcO/D9yEe7/P189dxjrfriFquAC7MDVXj/chXECffPvYmYXbI/rGq4pfx8ZN/+L8jqngvnM8/pZQbDkPL5H1lmg9s+85eOy4pNFkiWBfQELD3fiKXGXDafiHlBZEYoez7E4Scm2+mTpdUkKG1Qjq5HEhJsNbwVAJk5YToxHau1LZRpkxtT4gRmwqglEOACDME53h75HR64LjcRSH05Ym2nSKSwSrTMjHlzOF4i9lXJEncHBaAwaLsiGbElfXpA/1yHiGUkZUXIv+NswPWSTmPkbmHYTzlAB8wWCue6T23io2zLY3xIYAkB2txv5exvIpjso7N/A4o3SfQDZmM+dWBzhPuhUTFduir4a0iqVBP53CgebjWUlVcjgMMn5AkpJyZPznS9ofo6dszEHVt65NTWEYfoCNS6PWC2UJvGbyx90bOLzDbuayhBVNVapsRDePB5VTZWqW3TrVgpLs57idSOkQuWSeOVaC196TphrY5kKh9wiWYnFbPbHYBwOQJ0YTuRsoz6+N7UjrSMfrbrwIMS+IPHj7HeuOyVeZyYLOKWaOkW443Nzz99B/iDbw+kCRjvnE5vSML5HmBdKBt9+y2YrqgmlExyrxQT2/JZeLuOEP5LiaJbV8je3xoAOeykDTFJGx8+qkkWl2Zp8zWGrV3ijpLmXGJe6/Zx2vlF/H4EwGuPn9rbOJMz3fluIjrPNiXPcpuiysfV0knTdeR11jk6pWettgzS/zcxxt57GCaxiJUPDQ6qoJpjJ8g/u02WAL/yHRN9EAMI0E8I5TnvKBGy6A9AEcfyGTR2FGpXRfBNXKuViGJQ89sONVlVBRGZMP7ccRlaKak1NB2DdqgfJS10ODZUTIN4NAHk1UGu6ESrFsyJWlHB2OWCM2SAFoM2yJ9W0ToAzAlYvGca7wXbblWIdjQ0TRcGaLaj3Uz7j069TTAyZjmRSilCjn5yPxKoB3PUax8qIAIdWiyeo5PuDEATZNgobrTPHFwpzbhUpWTCWec1sP63buzbjkAYTKsJTYxNoYOaQCLnThmDcRB1fj+fB0bjlDO1a+n37eZqI94vY3gTRm1NyH4l+cBbzzEBzs0AEsfbJKKUCUcfNeE9mTBBirfYL7G53cl9J6f948Rin/r+88///FGot/4nZYYuWQfC6bTH3nOPkCVNCeJYCKUDk2v4aOxqYGGWrQBxF+0SPiX/+8x/jI99nNED3QXxAttXXlYjayGqFG3hmqmt52+X9i70E3pJFo/AUpJE+dTaFZi1BXakRBTa+Re4QFe+sd7z/VcMO/AORbHfkZToZTb6OCzFWFCpbKdDdVIhd+9IqKsXJjyRMmwzDPtdI+fn7jsne1RI4BSHLMHcslsF7B9C6dWKnHfIAwbKqFlDMdghDtPeWIqGZGGlGBe5nmh942UAwSqNiTnGOfJGZ0CaPQRFSHScYOSZqS3MWKriFVUM7Ai3kE2XDqujTQl0psz01JibFgb1m+pW6WIs98bbg1vHZ8WzDbomafL15R8R06JnEowiHWM8kix0bQJaoSsnrd3JJ3xfokLn4z3ipDpRHxAjG03XJxUCloyKc/M1un7B2AhpYm6d3YTWgMXZ2+CpQNuR7o8YmS2Hn2PZkbfz/HcFlL22ioqN/TdaH0boF/obpjHuNYsBYOo4TLEDF9PNAPNewBgLQH+vVP3FdWZaUp48GS8vH2FaiGlzu1hoeTCPP8A18LlfM95vXDuG6fzmZtj4rxnPCnHkji3xr5F3Q4yU08bta+IOysRE7KsMeVpo/+rWkPHmni2MBn0fefm7jWny1uKL9HjC89jaNFvLKq/gMefCHD1lC74LFxizSaNNto0FN7nFIux2hhLidC7IVe1NzHqSFMwHnML/cr1vWpjlpKu2G0Gb7C0cE5dCaVs8TwqwVK5R2uHSuh9kgdrAo5apiTFSoCnXMOJmPexJI0dVB3smFt6DtIUBJNOT2HzdxG8O00gWYzYQvci+DjovmdyF3x8z2fjWGNRE4Q+GAwnwkazRFmwdkFzZx6A1bMzuZMGoBIVSBaam+0aTRAifpc4PaYBBKbx+eynITiXFiJ5U3oWyhCUpxK740UC4GoOl8tELL4zkZxePV7rdAm9xFoSKsITYzxcGtYd2UechQbr02vECuASfXebsrtwLzt4YnlMfBCjJWWvRpUeY7Ia7ioxp3YhW7znp+Ik17GTk2ewLsD7ARi4hncSeWfYYIoGiIIB7FuwU/tVqiceN6vcvjXWU5NnMHWFGu4+xkBDW1OhzY43oYuTehgynGDd4n+hvZqf023t+Xjgo/Tp+iUNIu15RBjXx0egJf4xseRKeugf3dANgG8iSIubiPbQK17ft2A4HUL//BGI9m+M3H9JH69vxz1BFCGj0qm1onlhqztg3H/4gvNqdBPWPfoymws5FXI+0PZHbGh/shZyFtoeydrmHbeOEiNFNK5j9/q84MxJmaYF8RWdhSSGyErlETfFxMa4qYxzoY2TKRgerwH66KFhSvkmwIQoU56i5SLFX2v7Sk4yxsZbqP4kxShtykivFFFUWmiXcJLvw9GWKCkjsuOyU9dKb5WcJEZUSWm14VTcK66ZlCrTfEfSGWunYGrtmmtkyDRylriQilIOByQXuq9wKqxPHZ0qudxQjpnpztG5gHby8gZnpRxf4XTEjrzcfsT27pH9w0OI1bcM2pEUVSt9XxE3vBZaW8nc0tanKHj2TO/hwHMPEF1rCPvNbYDOLfSWw6nTO+GWaxUzQ7SAK+teqf2Ce8blBqjs9UwzCX+UWaSxj849E2i1obLFfbV3zOuzVjRihIx+Nbm0hknGWiNpdAwywkBbj8oH9yFJGMem6mxb5auHt0M7N7HuAZTMf0IqRzBD0kLbH1GdeH95YG8tznmLaiORRGsrW+uQFJWJtq2kkmlV2HSju9K8U6QgeWGvO+yXcG+gYInHhydoyiYSzo3e4kb3CwZW8CcEXKGGVPBx024MwDC+7R4i23YVSQY4/Tiu8qEzacE8XBgRC+P3LVqF6ddXO/p7z8+i+QBWVQJsmDltLAClxyKyFw99EOGyy7nhTajXg0w7xYRLTgHIfGis9jT0KNfX9o1FtfOs3VILPFaTDxA1DNN9FFNjAS7cKQMI7SrP2uGmAQymHsnqpQlZnAnDDZ7Uycmv0S40j6oT6UImaoN8CLGtJrpBzpVuzmU4Ia9uxtRbzLKTsRPFGNphJ4wDbReKw547okLuHm+5hFttU+itkcTAhPsipCT0FqGkQo+b5sVIKXbCvTu9h8h180bblZqVrQu7O9X6YLKc9yMDS1aJsLjU6C1RJYL4dgnH0SkFQPIeI0dXnsd114iG7k5p8gwIrqO5qwOutmEpH+zVPoDKs37QAeloD+DqH1VIINAHKOqDTXUTmod7sgF9H+d7ivaAZPEMPn5Pxi3XZJyII+/MJFjG7qPomT7+PUDiN+4l+g2gNXIX8fZ8eX1kmjyOxeW6CWGk8BsmPGcbqXvUSxhoD7dgDPWj21L/GHbtl+kx3b6IUXeZ6H2jdWeSkCwUYuG7e/MSdyPnGWixAEkGnlDNuN8BV2lRRXJG0lVnI4PBajF6GuLnvm+YKfhGnhe27UzWmbQI+fgyLPvp1+n1hOYwzSCVpJ+AEO63utK2GBNdLk/0fefp1NjXJybvTNMNuVRsiAfVJnw6o3NiXhZ8vjDfHmgSrNq+PiGnRO9PcT9SQQ+JPC/UywP7vjIxQU740nl5+xn1XPFLwi8LtnXEYjzqHgG+1hqkQrNHVI9s64mUIg+vtQ2rgveO5kw7G+3DOQAkYfEXUTQndtnpfiLlFMwXQPDi4PfgUZTehz5KesJ7gtG752aILOAdrw1NC60KrZ2pu1PtQhuZYNfw0ABPjVQOAVTqNkwm8ZnuLQZdrXWs7jTvmAu1O0iYcJptYC1+zsYYmMhGwyW0Xm6YKd12tv19ZK3pQu+d1hokpbVGKguttXA6qtI8cs7MO90y1jcgUd2CabPrJnW4513Y90bKBTtfgAt734aWuOBs8V59szTZbAAiQn9lNcLCIW6oZmPneabu8fWWMuwnUKXiYI9QO5QJLo9EPUsBiRon6uNwjAwtg1nYZX+Bjz8R4Kr164UYN+c6hLJjxD8AxEdBkY2AzavA8Aq0TATs4y4ZGzojuYq5r4/xwT+zPQGg4v9fq0nGaDLb8/b/qvVJCNViQe3ax8gyDcF0pbqjnlAkTgKgjTFMlauOaAjAry8haQiaxzH0q3lkzMVrOHpjDGQfF75o1fkGcxVkATkpO51dQTQS5ZtFdtbmEVoqHhyNeAuXWm8wQAzA7p3uHkF+8FzBIxhJhOQxBrTxJmaNouOuIKZYDtZnaFBpOHsOgOfS2YksLddIgpc0BOMeowxzR83o0iGBZKV2Z1WwrKw9sxk0dboKzRKbGz0b1oRtEqoHsKuTR8ChhcttF49R4fX97yA47Y9cEUIwLfat82f8vEKdhwJ9vGdyZYHG+Xkdw3QPR570b9xEAB2zaA8ZH8mgZ6ePc1g9NIbXc78PkH7FUleRvvr1/BqarXGOjEiwGMe5YznE5N88ihF19K3X5+5MA8C1wSRjI9KixHOlLnGvG1SfDE2ZpXERWjhZr9fZFaT9gsvn/3/3+PFPPweBpLekBN43RBzYvvFTsZiHDs4GSxoLNECvj0zTEXfBrTNNhd5XoqYmAkZVCqIPYPKN6hlQnZhXxUzZ+kY+3bB++YeoKiKRKJ7okXouR1L/MYiTppm+rzAYbXyFbePV8RW+HMmacI+RovsxgJ50NL9m28/03tGnI/tpwrhEcGN5FcwXxzj23uGD0lMKgXy6I+ktdd3IF2O/j9oSb0bXe/zQ0KWj8y21fk3CmeY3wIluZ9zfk1olpUSfd47zJ8zHA73u9Kcdqxu2CbSJdjmHyFcM71NMFDizXR7JJfK/VG5xb/gemz6rHZU5Nmh7RczoHiPPbjsJ4bJ3Tm3FemLfTuAx4r1mvW3biujCvp1xhqzEzrFBs0a7rhWeqd3Z9pVaG90JQbYUtCy0LUZ+l/1MG0n8JnmM9oxmFpssCuYxKuyEZlbVuNSfI6JkmalXeYudYhwH7HYNtA3BvFuJrDFPsVh7BaYBfkbBeN/GDqxGvBDAegmgc/oS2gbrGegwHeL7W+X5DnWO0TfmsK8BrlIGmaGtMN/EDcU6UAZlrrDMgya32KV++r3YqSaB4200ujBK5t9/AbRv3wB/AY8/EeDqunW+vrapybe/n0L8dA3ORAyVGGdcFyD3WKBQhvidAVSGJitG8UAIavsASdfHlf1ijMCuHX9deS7PdREKV3AjcU5BAJJsNIckTgasx0XWBlDMLVxZkuJDbNdR0hXocQUdIST3kcWVxoV1ZRe6xGJ/XbQPzhgDxfHrEPBtAzhkCUC1i3DjjplSpFO/Mes5uNJqMHLeBMmdPkpWw+HYyVnjNeLhxKseJ6p0vIerxnQAs+7so8YgDz2ZpqDmtTs9x6isJMWlRa5T92fQWS3AlhfY2vXrIN3Zu2KAZKFuDTfBU2LfnTX34QIJrtJTwavHLrl3dLAyW/p/yXuTX9u2NLvrN6u1dnWqW7z6RUY4IoM0FhI4nVjgBpIRLRq0QaJNA+giWUbOTBu6/AtYokkHCZrIAiEEDZBNYUc6MjMyihfvvftucapdrGIWNMa39rmRbiFFIxRvS1f33nP2XnsVc6055hjjG5+YoUBQp3T3HiA/gyT9KU4hp7w3Jp15oTwOPxkXtTjJF2aomSxWdB3dIil6A+tGfT1V7WdoxGkAACAASURBVBQxVWFZBT9RSxPtXDV6zjsSdMedAxht/5K6cC1AZpGB2nK9LRtu6RwAJtM1zvdhKst4Xxg428faGJ3YPY+N1eXEOZiWStblmLyWFGfFEvd0L32Lyau720F+I3/AtxPeN0qZoELqVGE3jo/mjZlwXhO9c4XW7uhjz1wqtSo3KoSe1vYm/UZqmfFefSwXP4leydSPo8ksx7PPxDW1tIGK9/3TgrK+JgS1BmmuWM+2dGaBog94/w0uJco0nidiRQX01GJ5Tha6HGKwtHcxKPjRYj9sgd0moheArCXj/YjjhA+RPJ/w7t4M7APer4CCcztKflCRRplI3elcZRvTli51iqKoiYcyUeotzl3IzO9WhKaJuLUdU75T7ESodP0K2BHiNTU/UsYjuBnqRItSI+L6mtPDLTFtcH6ktQseHkfGuTAVz2l/yzE7puyYyi1zVmTJNA7m95koNdPHNV234XjaE/2aXPI5WiNPlYzjUEZmKtFHSonMpeK7a6bpBHmiMtKcwltX/TM9E5cKvPkkoDEcBVKaM+CxrKRPMi1XJ5ZnHDTv5hm6Sxj3UDNcPNdnH76BbgWrK02I62SfWQv8zAdaabC9gr36EJInSL0+l9YyDucC8wyxh1OFYa+Hw3Cy1esJPfQGBFeU0g9bROd/rb/ZQvoA+hXkN3B/a6BvBc8+NEDX60H85U9htxWLFTZw9RGM4xO4+zW9fjPA1bKiXbTdJkP1ZAZ340Zw1rza4SlN1WKtNWrQwz3kiG8NLHKh2oNj8U/FxTNkAOxXnu8Li2FM0PRU5oT3kq1qNVDu7QPe0Zo8Ot4oxRyCyX5KTIzV02pjjgrdq1but4R9LnEQMXty0D67BrEphqAYwFkqXrx5rJbXjIDmopSWpSZtMeFnjdExKhyz92qPUd7bRs7yXC19qc7SlVVIllSYKXiTx0pTA+3CTFzM8LHKfF/BJ4cP/uzfib7hvLxAxVf5zxrUlmVCLMZERvXX2kbtyzx7OpyZY+E4KthvzDrmUozFdMqqSVaFNp0vXTEzvTIChipGaNMqk1MTWBeMrayRWoGQBbT9QtXb5BPKe+Z1O0PO4czY/pdXPWfPUsgC+OfTbZ6tZWy2Zfgt/zfw45fYEPtYWcYy5983OIO0Zu+PWTsZbXw7fvVeWIJjXS1nNjcuFYFl2ZkntOkN1GobApXNK9PNVS92zvaqsziItvgMDWguPrNWmkBpfH/0ffte//U//Ed6kJd7Pvv+D7kIR7YXF4TQEzsnKSyoe0HoEiGsyNOJ1grVeQKPNNeoNeC9J8SBUgam8Z7ge0rrKKURY1Brm2WslYnUSU6cpxM++PPvaEvT5qjOAdXM8njm+cRqtYU8qnVOU67gPD8q/8klcisCG8as1TorBNQV1CxXk370zTxTkRAS1WdOhz8jxo/o+ytVx3kVHrk2A1nHP4/E1FMzSn6vlfUm8eMf/YLf/as/YDre0pgpzUF7UFNiCsE8PgCH/R3ebwh+S60/wRepDeN8IM+OkkdevXutakQC43CQjHp8d/at0Xqgwn4Cv4KLHRwnON5Bb6uoFpQWnAformG6g6tP4LSHzTMxNv0KfCfAMhwkfaWN9SZdJBoH1x9J7trfC4Rsd7ppw0rhZ8GLESoV6iiQUgeOOUI+2r1cnx5I3Uo+o26jMdj1AlsV/fvhFmIHFy90nP0G7r8SYFpvBMLmDCmIqRpHSW/DBKdB+1QOMD5o3+/uBaTWOx0/gNvAq6/0nR9/V/sWdlAe4OUPoE4CdastPL7V50KEfgerDfRrMVo+qTPI+kPYfwXb53Y+FnnP6xzOA3zxz6DeaRsObT96+OrPYP8Nqg799cKh34icq7/7Bz/8lZ0oNqNErzYZy1g7A5llgnhvwlp+3xpnD5FzUePQN2uVond7H6hFDZRN0TgDCZyqxmge5yulSFZM1rLDG9CLVqZePOaS0GtGk69vMme72phbI6WgVbsFLjavqkDC0+Skl8CZWyTNJQfLJl/n25llcw5CqWcDtXfyOgXzYsUCsUFIhRICHY0kdEroHNOsAMudF2rNtZ3ZstYa2RKYl/NdzSzvEXvRVtq3vjS6JuDV4QgGRGKFQsGvHLFk1j6Y90FsWWsWhOrbORgxF0mYMXpCrQtLz1CAAqUGcs241HHMlTzBoTpKU/J7ro4Jz1w8pVZmPGNu5/gIEAlVTMJqtDNi0a3wnt4KOF+UwbMghCeLOO/fO64IZdaFZaM+sWH2FW6hrN/72eIWd8sgP3vwDOgmY1CLml071PPQVTFXpbNYiqWRq1d22Hm/guK2S9a4aM0ZaKpnX1ilKpi1KK8rh4VNFeu5gCsdvVvg+18at7CcSG+LmfdlyeUlqVf32B//6J99KzGW+1t/3Djea1W+fwCOetD35itpBfbvsBYCMrcFp0loe6X3ZK/y29ygTzCMutnzpImqX8Fwq9/7Ju9Kq5rYPICVVtNBt1W1T4jQPUfRARtJAcdbOH4DNx9q1XJ6hEvbh/lBE3O/gcMDXF7DqoPDvSa+7SUMFeZqZr0KXYSHd7DdPAEDl3Rs5psU8zFBGQUG+q38MCHqmAD2b8SCsIPra034rgrkrFfQbYk+kk97neduDeMdrC6BACERUqCcjgIIIWp/OnmkaA3mibTe0EIi3702EJAIsSNG+Zzq41tdn4uXAheg7SyellzF0MyTfjZPekiEXkyPF/gkBvt/0nmoSf+eTgJJeRRLRIbLT3Rj3b8zsGQgzjUYD9pP73V+xr22s7mA2zfApLGQi97bXQqgxA7GI2yvoZx0rXxvx5AFUNaXAnm3r+D4FoGvnf6++gRCgPuvta3tcs0c7D7WPuUR8HD5HPa38HgL+72AGmiMTCe4uNHYzKPOzcVLON3rIVZnHcuUoe011qcRDnfqX9uA417nOXby7oTNe+M/wuUzjZPZZMKQBByB9s0/+rU9k34zmCt7zb86v2iV3J5mJucUL7AYZ/8FtsAe4xOemDzZsocEYp5AQnOVgopEF3Dlk7ERTYwGrWnyjwI7tar6rto2ploInSO1YGZ4Y8d8s9BygbEWHCE5fIEWJM+BSum9f1+2DJqsQlHbAzNbu7ZUpJmc1J5YKYBsYMVVsQqlWGVMEt830/Cl0s/6/QkzmQ+izZN3jKUQTdppDeasXJSlDc4KMXfeJMqYGnXOxOLllXJm0HfyetWqapKQrGVChk2KTy1YnMyeISglXj4QOy8NVnazeecYayUaE9d8JcZKm+VjwDtiEDOosaLnUvPqwxWSDOmxKRp8Mr+RtwMT4+RoTmDB+8Vg3oAiORYd95N0bUC+eF2jRXUJKlVWNV6jZf8e+H+Spl0Q3e+ldJ9BMxY+u2zfLX6tk4H5KMC9dAzwsarycVaBg6+KTxxEbT69FvbIQB9BBQ7NssRag654Uwc0BqKxGNkHeat4YmxrawJsjnOI7BljOt2rC4vamlUn2u+XIgHnFin7W/oaXsF80OS13UD3Ak5vYJr0wO/W8oxsP4HHV7DZAlGsxOLL8g7GrAny7mAyywX0nSbdWmF4hOsXmjjevRXT4t/B9gUcZzEOeQ88grNO9OleP58nGE+aqLoOHl6JRXAN+mtNVIvMc3rQ4HjVCZDNJ7EgFJOWlgEA+Mn05gQ3H2lijEGyzYtPBK52V5q020aT+OkILz6G4wktZYuOdXslhWic4PG1QEIFvvkKLj5SlXguQKd9cFfwOML6Ao5HCiPMDQ6PsLoQyJofNaHffQHDgTkmAw5O8pKPlDxT1lHH7zfw1Y/g9kHb6hDYDCuxN/Og811Hga+ug/1RAGW1gnQJddBxlUk3TloJCA4Pumap19iIX2jb7760c2nj6TDIO+Q9zKOASH9loK7C/g7apGvcTsAE8RlnE2+tOqdnFrPo+sWtAd1HgTmcGM7rFwI4Jei9xcPdzzVutisBoDZCciYh/uSJnQs9vLGHZtzCzSWsTEocJhiaGKvVhV2neyDIJxWStvPsQ52/y98Vg7W70VjwQcf/6Q1Mkm+ZBu0XRaB2YSDXOwG+w6PO85hh9+LXepv/RoCrdg4H5cxgGGLQSbfJyKHJTKm+7ulzJlfUIuDggVK0+lj8V8EvnxVzIfZnASzySGnCM0aJWd/XNIYrmhSLPSiid8y2ImvenYMzcRUXHJlJ8khTH7tpyasyw3ytCxAxwGfVe9UOPTcBLJqxLMuc75qBLw/licVqzj7vnc1olRbkCwjFMXQZnxtb66MxDHoWpeDofCVlGBDgid6Zp8qrak3tAVVFGRt5VDXfEuSZPIyuqo+fk6xWqmIz56yWMUVwk1IaRwqr4JhwMqrPFbJXw2LneMyVXJsYyFIpbfENwX4w87uLTFOVYT8kaq2EAm4V8M3BDAcDOD1osZoDuVXmRdYrnuYb1ckPp4wcPUScc5I5pBNTjOnxeSljFiO0ACjvgp4/FmzqzcVfjLpxzht4r+fr6hzEWYyTZc8Sq747m3G9cyYrh0byjhIqsZifKy5MEpKzvSM6/W5dljZJFgwaUGZOhck3wqzr7JyuQWwC08VBqAo29e/5dYL5/EKAWr2NdQHaYLL+gpdaqwYStXqImAE+eGJ25NgI0//Ph8Rv1etCk8dwgItnMNkK/PJSD7rxCOUDAY/eVv3H15q4L54JUN0/CIRtP4XdDHGliWbO8PEPBYp21+CMDVo/mH6/gxQFVmrVJJgnSVSnR6uqcAIl65UeyrmqR89oUtRqJdBUPZwOAkLrK7i2YwGYj/pM2MKbX4p16I3KfPaxJtlaYXwHnYGp0xtNioevYHMpOW69E6DYfwn9hSb3xwdjfDYQIVzcUNYbePUXYiouL+DxSwGSLuocuBVsr/DrAO6e1c0F5XBkdfOMnDfk1hjvB7gbJDs9+wTyQd99vIfTkfTx7zPf3cOb/xfeNTEwJUD/zPxHPXz2e3D7tSS+3TVa1Ve4f6VrVyu0N/CDfxUO78QsDSeY92Igu5XAVggCVjXrupYM/gIOB+gvgQjDPbz8DPxR5yoq/Z4X39EY+t3flzl8MYL3lzq2+3t49xfyX4UohvCTfwmOB4GT2Sr0jsYSfvrXdaxvf27ycaeFQTtoPJxO8LzTOL14Ib+GX+k75xlWNxqzklH0/rTWMa12Z5YQRth9jqpjgUPWg9FHY9TMazUnWH0ApyIA3qLYzVoEYkvU+AoNtmvtl6v6WalA1bHWCp9+H/ZvBdB+zYU2vxGy4H/+N3/3X9iJVhsuNnKLdLaaz4s8ZUvjcw5PWADK02R39nvYynmRRKyg8Fytt/yeWrQdA1fONwtxrCbhaCLprPUEQenebQkuNWZgNlnx/ZW5904NPIHmirESktjar7AonNvnOOLTvnH2WZ8ziEYaXZWpHtTipxjjpZNUiMFTfCYgD9M2BLbRyzxulYPOQewUBtpqI5d6rsIc58ac1LjYe/XnG0rlYhXkz6qV2Byd8C+teZJrJDO3bZzysHBqDhz9TAiO5L0mcztPCUg4Ql+pVddqnAu9V+PtsVZabZxGmApMtdJSJ09KDOQWGEZH8J77XDlMBRcD2SdqaxxmC/Cco/yalhHVFU9uyi5z+el61dpQQAXWC9L87K3hm9ipYq2AFvY0ewHklAHnmINA75PPfZH77P12zs5tc5a2R8sNvhjPnUnXTknqykJ7Yrhm3wjRKwneOYKFntb37wM4a9c+K7stzk/3ig04apDhPNj9tSQWL6zXezZEk74FHv3C2J0ZKsuPs6W1t18Upx6Sc9Rx/+GPf/TtlAX/9n8pE6HvtKqeBmMITKpZrfWnjWJmHl7LdPt4J7DQioDXxTMBo9c/E2uyjMfttVbxy7ZL1kTqHexeQj2JJUi9SUhmbPYBxkcgPrEYhzcwvpWEc9rr4fv9f1OenZK18kz272GE3dokPZPxTrcCbc8/06T/+I28McNBUk7sxebMd9r3biPGolvp+/Io9idESUnjKJaBKP9NLlolriTrMA0CCDXD9Sf6WSiacMtgfp4R1h+YDLaR1CUjqaSs8Qi7S0mP1YlJuftGE/fjG22jMzCx2Vl2UIab3zUGyYzRw4OuQSlweA3bl/DyezC8Fhu3VIzULC8WM7SjANs4wJsvJa1qZavvmw2MVauuy0XH5QPcXAsYD8YOrq7g89+DV78U+CizGMgPf6BztL7gnPOU1pJ2oxNj6ZL2bf+NgPv6SqCvNdg9t3GFwBrL+Kn6WddrHM+TrmOIut4h6N/RjOWh0yq/LIxtJ1Dne/1/qfYDvd8FXZu7L01Gv4IPP9f16eQJ5HAH6173R0oCnKcjrLdiE/e3AlOhQXeBSVomCW9p//sf/9qeSb8R4Orv/P73mneOEs9hQoRgstoCQhoEm3CWyjJi0MrbAhldfJoMJGOoD1Je+rV1gZwbXWw2KcSznHSubW8qnartyTMTg2ecMjE6ExIlN5lhheCdGvDWRopJAaeo55x3NgFZG5X3AR9Aq079uQwM4g34lWKfqVYqa2DQUs0XRm6RWjrvzZhux2qxBh6TZ6IntcaqBXIteJdJcWkGLU/asVZWfcAnmWkb+p6hKbB0bEpWjtHDWOk6gaxV8HQRsUy1cLUW0IvR0RX5p47jid571h5iCnSdsiUWb1eXPNveM4yZXCoxeFUwGRM0TYX7Yeb6sud+UkbLOnlOs/qFHRtMzTFlmJxnKpUpO0bfmE5ikopJfPk9z5CKFJ5k2HNLIPd0XWptVpH6BF6nM/gxD5VV3SxjdYmjb1XXf6GIl0gHh8BsaNbf0FselQHv89/vFeQ1//T7VgWqytSe+lC+91jonCNnyaXL70EMfnGKWVA2XFW/NgNT+m5vkp59bvFnLbIey21q3r+ySI62r7bPWBJ3LFqwLLhRYM3xR3/6LQVX//J/1Pjyz+Dl5xCTJI20htMXEK812d6/FliZjvDwVjKM7zShKnrcgNgFsNWEP77VxOXd0/tuX8HmCq5eaoJdd2ITksO9/JRWBoGVpQrEuyc/znCUtLPaarIfTgIB04Mm2/0tvPwYCJo8p0lVY8sDdzpCskmdqgnv6kbMU/Sw2ejf+wnufgrbZ0CE6dZM092TNBQ77Vd77wZabQRcLJWbx7f63uEob9U4CAikBJefwcudJLnTKNYoF5nOLz8WcHAmdfY9XL6UvNkjH9CLz+SDcz28fiWpL4jdx6+gHK3J7aTv7VZALzP65oVu/NPX2q+LnfxPixx481xS4Mm8dw0dZ6lwujOJq0nW26yg3Ats3Hwoz14JYiNjgDdf67xcfChwuL6W52plkQX7W0nSLcGL35FcWDK8eyWwkk+ITXDGYlqKtksCNs5rHHr0Xt8ZyO4F/NJKpvs6GxsVJEs3NK5D1FhfvmOezvlUpLUk8FI0lqp1L49Rx+BN5m3qwUm3FTDD/Fxl1jloxhbOs+TgVvT51GncnI4C8XnQsT28g5uPoc20/+2/+O0CV3/3D/5Kk+XnV6Ob83v/bU2sIhir5Z0awzpHCO38xF/YLPWDU1ZSCQry7FAD36XK/X0WcDG8p4VZCEpSX3zMJVf92xYQocA8V7Gfy0RdGqHJmxWCY3D13CuuLAZH27dgk/BSCeuwSdxku1oywYvSlpHfnxeTtUKIAndnti4IdHprPROLJMxVJ7AwA+ugtjvNVXz/VIq/bmoDQydw028i+8PMLgRqbRxi46J6+Z+iJ1WxJzO2wA2w8kBupMS5H2OqsLIig9RXQnNso2OYCutVYJcC41joVo05F/iVcAxZBkqpxD4xzwXXxITdHgvT4HC9mKzD5BXtl8GHxGGuFOfw2TN7eYfGqeJjYBgzPunYa2tn8LMA1gVPlFxp7qkR9gIICvLRnX2BlmuVLNqgmgzoq7Z3BkZB39P808903U16XiIVABaAhqpKl7lEKfj6jq4IwC4JEcGM7WEJ8iw6vtgpgJVqYbYuq+fhFAWO3jvfLjSLGfG/so+YOR9s7C/AfolVWN629DP0unEjS1WhJWKbjLhqAu9//Gd/8u0EV9//DxqHB00srgg1pxU8vpO0hhPw8lH+mcM7TZLNvDEx6MHRrTVBhZUq0pzpuh1wOGkivfoMqAJLLctwHNCq3RuL4Jv8WRfXYi0e3goIrcwIHDsBtFaNiTH2KCLmYTZZsYvw/JkYuN1z+PLH8MEnSEbKAoKPd+BmeHzUZN1tJSWNhyddOR/13XEtgJFHffdkJne/kpyWesA8QbHXvnuvyTjYZ7/7+/rs8GirTC8T9fCgz9x9oe1+9AOdy2SFAF2AD38o43MO8hTFoIfvxY2Yr1DkGxoHgZfhKECw3umcPH4jUOw6MU05m7/F2Kqwklfs8rmAxXC0Y2p2vCfO1Yd1kEz57DsCaLMB2Bj0vn6nm75NMFVJqId7GG+1/1HPAN3gFa4/1rUYDyoICJcQs7ab1qLIXTFT/EnjcHMtFutwq2NZGfPVGztVDLyBfHN51Lnpzd/UJklz897GndeioBogPx31HcGZlOmNybJih9ZgwQjTnR6KLuna9hcaT8A5cLRkmyyTLXbtcTOcBPjGwZiYonPcZtpP/5tf2zPpN8JzFSyY0LWs89mUNBtMqmk26S25OrkYDLPGu9FFxrEQXBG7QMLhmM2TkqomrdJkUq5BPii1yHFPq+wGEGhUWjEvi3mmsMk1ArFFWpTslr38JZ5AlzwhL0nYFsLYe2MHDDw5bXcdPA9TJgZdAlcLc2pPpvEgSSwkf84wkmFakmWbTX40uTLUZr0Sg3JRXCUkx9GY1ecpMI1Fi6zqcO2pTUoJXu1LHKxWkWEq+FQ5FoHGNjWGxJk9O9XGKgRCBJoqEGuF1Gel984CdF3nlSruq1XPebL3pHWgBccxz8TeM04y9ydnjGJ0bDaJN98cSSkwHCZS9ERXOWUlLcetx2XH7CoXuwTHQroO3O8rXXBMpRE3jtNQaT4rFytPdJ1jnFSliAcX7bnDX0LcyUsGxJgiL22sVY1TiFYJ5/8S46R/+6LWPpN3howQ6Gr17PUK3oHleC3zSooF157YT96rzguzyYbOPGHOPHXBE7KFrppg6Lwgf5klly5tmHyLpKKehwsL+1R9qs4AJIhZ6Kq2pVm5+ccaFMshU6AstKWyErGztVbzDVpwKvW8fUpjIj+Zcb+NrzzAYJIYWRPwOEiWsobBPP5CIOLujRigVjRZPPvAqsdmAQDnJEU9HiXpNa9zG9Yyt7/6x2JNtjt5gK4uJb989VMxH2XQRHnaC3xdPpcU9fG1mKDtJ2KRWoVf/vnTRLfq9b5nH2h8bzfalxSsIm5i+7f+XQ53r+Hrn8ubBWJmcoGXn+AvXrDuIwFH9o7nV89wIfL61RecpgMpbvjsw4/YRE/uVrz66kuG6cSm6/jow+/yzZtfUl3m3a1JnI+38i3NkxiveVJF43ZrEpDTOVwm8OEBPvm+WKbhVkb49qC2HJdX0Ab5zsqogoDrT3EvPqIdH9luJg4//Uqru80VvPm5tr290vU57nWNTgeWqCEO78R0xQCrFzB8Bcm0/oeTrs9wr/FR32Nc3v1UQLo6AYNpECi6eqH37G7g1Rf6d3cFVxd66K/WcPslfPMF7D7Tg+7Fp3D3S3j9SwHY8aRxM5uvb3sp9sHbin2wJPPVCr75WozPPMG2h1c/NVZgluw2nYyhqrYYKPDmK53DZX+qt4gIL9b2cKfxW5quwxdHfZdD+7YxALfszyITllHXPEWN5ynr33U2FiZAZw/WNug6MenzsYOHpdrUAVUM69l88+t5/UYwV3/0+5+2EsBbObpbzMALSl18REuwZxWAWaSayXlSVFxiq83YA2emcE+sRamyLekameF2tqThGDyNQi2NLsgzkqrKUKtfKucEsnwuxBDPnpfsrfpqtJC8EAD3ZEy3CSwsuTXBy9dkNoDe0uZzc+QgX01r7SzHxy7Q6tNM5Fo19sBkGqPWohmbXRVTkV2j6wLbusg/mZQ8pRYBlaUdT2n0weNyU9sABKLGPBGmgPfQ95FpLgy1kqI/e4mSGeunUOiSp9ZCVxxdqoxTIaJ98FQ6Kp2XT2ntPdtNopTMlAu+VfoUyF4grffWZNTD8ZSpzROjZxU9zkXeHSfGCm12uAjHKTB7pbGfisNFz1Qa89iIq6SmpN4zmGxas6oMh2LeIe9gFhjKNRCCp5XJWvKoWnRpB1OTU2XfHHTD+yzgMHud37JIv9US7S1pO6tAQlJtoKK4imryYSCZz1Dth5zlKbQs793ip/EmwYn99BQzbS1AOdl3JK+eZWpr1IiukUuzwMane6/xBOzOkrJ3hEnZbDoWq6pdFjuhmUTarNDIGEfrgxlsYRK8wmExL5f3Dp/bucL1H/zJP/92MlfbP2j4TqvpadKE1q01CU/3MvwOg655t9NkU0ZjK5xYjClrcuqvOGf7OG9y2858Jhur+rI8E9dBM7kmRL0vdkDQ5HMy4/TqRpN8WgEVrnq6iw+Z/o//RX4XaWX63Ivf07ZXF0L+sQM3gVtrsowdbNaa+E57+WzyDGXP1ff+Gvd3b+DhHkJh/ewzTu++gGqem35rxveTpXj3kqLevlLF2P5Wx956YyIeVTnYr6CtNIlHC0T95mc6+auV2Kjrl/KA+SoJMB/AFWLb8/GL7xAi7C5echzu+fzmmo+3G+7vbrn46Bnv3vycWAOnYeTN/pYffnTNyq34xe0D2fUQN8TDG27HE1erjqkMbHdbDsfC1fYFX9/9kuhmrnZXFFZ8/c1XHOqGh5JorrDrt9wdbsnTQPORzndMgzF70wGxdQXwxtatngoSWhUI21QBaT/rOu5P0G8I3cyLzQXBr2GaqNFx3B8Zpjsunn1AzeDKyPriQ4bDA857jnT4dmIaBbLVj7bHlREfe6bpRGZmndaaI7zH+UQIkd55cp3Zn/Z0eFrwTMMRPFxtr3gc1DXA1UqunlYzLvbqU1nMPB+LjPm9gbNisRsVATjnJf3O9pCeZi1UxpN5woIWEClIRl1diDmEMNcXgQAAIABJREFUp8VAa+Ai7Z/8w98uWfAP//XvtiW52QdHNr/R2SAbwDdVKC1sVIcjv9dKJEZ5jmCp3Go0N0MNFgCqjdXaCD6cTevLq3nLoXKS9UqpuNrORmQXI642uhSos3qpKVzUJlMLqmxBDEbNWtnPTgzFPGexyL2n+UTOWv1Ps7WIaQJsS8xCidp+HNXnsFk1WJvlBQLJkiF5am60XPCd/C8tF1JS2wPfQzxU0raRordwQUceTYZqyh0KFXznaD7TcmAVPXPJuBpoEcZScZNurOAdcRPZ1iIgimM/ZVxo7GIkT4XtOpKSx+VZKeoN1smx3no65zmNmek0c3MTZdI3JmacCxfbSKmN+5OuXyxVDNYJ/NpTqqMGDw8TpQQOvad6x36Gx7Hiek+Z1VrnVGAojhygWMgos/lIYzj78/BFf1tz8OAdeVblZzMPhA9O+Wip4My7GfEqAMIiOKru7xI1xrosgFLMH+pKO7cRUg6axt9k9NUS+eKcwl0Djto74rFRkoTTp0bR7rytwGJkF1PafKVrjiUkzlPP/SNxiP1cGLHgzvcODoLlyU3N+mguHQ2a7rnYaxwtbVmcVwPxCaterfKitKUNi8nV54bNs8b5H//zP/12gqvn/7bAVUCTZXNiIpIZ2Z99IImt62D/CLsLqwScBMZckc9m3iuSYLNWVdt6o1iEliTflAl+9v/InN1vzAvlVKG13pl5efneT8x/8gvYv5bc5bw8MD/6PwVSLp7D2y9Ndotw/V3JhLUJOJUJ1i/h/qcCRtNJ7zvey081HIAFgJlP5uIavv5TePFDsU6bnYzj4wDf/VdUTbi60ba3OzNgO+1DVnNonOODqxfk8cipzlx0W2o+MZeZWgstT1zcvGDjGrvLK/bHyuv9WzahI8XKu8Oeqzhymh2pjby8+Q5fP+4Zx4l+vQI27O+/xm1W5MPAJkYOacX17gWtFuJ0C6Vx/fyG41h4d3vg5Bvt7rWAZujgH//P4LaQH3WN11uBwNO9GJTNWh6h3UeqHL19JSZxmqw4wWlbLcHFJfHZFSlGItD5SJ8S+2kkeLh985W8e/dfi0n70//LqvfQub18JpBx9YHG4HYjhswF2PXsYmLTeebZEUJknE4UH3Fez6NSG3MRCZLniZh6yjSSa9Y2AOcibRqe5L9m8S2tSsqjaMHQrEBjydWqVexTc5YUUIFJnym2KJgsbR0vT9V8EjNZRknd+1sbRyYrxqCFxXiS3L1evwfyrRhgzhBXtJ/8t79d4OqP/sb3TYGTbJdTJRBY2USB99QaCKFRosOFSh0rLkRVrZlDvNViQoqM2iFIBslttkwlk0GW01fLOV9pqlaanjX5lSi/TVjysVrFZYWGrqo1oPWOfG6cJ+Yik8UcRHlcKgtoasQKPjrGImYml3q2ATi0mp9zNRO/GK2V94xexmbvZNju+6CJ30PsA9Mp02Uv1tRBK5V1n/C+caqZ6xYYmc4TcQxeEy/GUk2F63XHXAsfdZ5fTpVV8jzuB/rYaZKMAgOLPyllR+gKzeITZirRNcqp4KJjtYrkuRDIXG5WPB4mXm4S++OoyTh6nm8TIYoNA4HFzSZSigzszBOX2w23x4EYPacBBsQiNe8Zp5kUO+7Hgk+R4gKH3HjMWcUsDrL39DWRA+RZcmA5JyB7ptla++QZF71a+SxxHlWp8edig2XYtBnXdL5bNurZPY0t753R0IBXyxF59jwJPT9ytLACK2iYcITgcfXJGJ9Lhc4TRlVbjl4tmyRTmoWkKUIiugVc6eGWVg43V6YBbd8Vi4vTvZGMXhdIsn035jIZUzWYtNlsjC/9CUNzxpq2c9RD1xzFpO8FsKp5sNPqtVSZqLEWSLXx93/8Z99OcPXX/pOmMmMEMmbruzZPkkjmLLP3dJDk8/bnmjxeWPVbyfJFvfxUDFeexey8/ExAZkHoqdPDZcrmbXE2TpMxPlUgZQE80yQ2rBnLdXgn78zpYDKMvef6hbEE6ckb1PcCS77ZhBoVy9Al+hefM757pfedTpKMXDNwNyiuYbUV8+TkvaQVgb7gn6Scw2tWF8+VuD5JomZ4JybieDLvTy/J1a3NO/QAfmPBprMq6PYHGVBPVjk4HCQj1vxUBbk/Sh6os9i+hvmb2lMl5faKs1RVq4zYq428RXNRRlc2Cdyd4LAHnOTcaRToLbM8TH4loBOtJ15cqxVM9WLavvMD+PJnklT7GyDKr7YkrQ93cHqt+ITdpYD0xY1A7/WHGkunvT5/vIWLTxXNEIIqArdX2o/DXsA8GxBOW5gtFFaMAtSjwPM8P0meLkG2TKp4/TRO2x65c6+AI7gdytoyKv78tyXf4yFuzFgPUMy7kVThGFZWJWl+qjJyZlHjWmzlUml29h6859VyYt200DgCO/0/qi1Pe/j1hYj+RoCrv/c3vrtY1c/n2r1X4eSKNTReJhTzb8xWOVJqI/Wemk3a8Qp4dFPBeVNSmyYvTQ5W1VUmnAu46mlxYQqMeaiNGDxh5QgkTnkiOc9YKpRm7FYjRjEdxTVChpDiGcSMk8DHHBu9mXpn16ythaOdCo0o703QDO5iNFCm/aUqGHSpZvPm1ZnsWINfMr8881RZBcRQWYMb71URVgJsI6yzZ8jKvukDrDrH41xNzmqsVpFx1IO/VcflKolds/MSnTf/UmWeC1cx0kKh5sp67TkcZ1Z9wM0VH2Czijw+Tnx8tWY1ZdzccC89j29HLrY9h3Fg13kOtfBys2bOBecapXhiUCbUqzcnnt+sGAYxSu9OheojQ55Z9T3NR3KGxzrTcuDYClP2dC6wL43QRY6jQHOMHhdl3I9dOlfBKUXfq0jBCzzW2qi1mJwrkJwNPDkkpU6hiu2pVmFnLM3YlJq+vJpJ2VptVTVwdtA6gWiWtkjn62wM6PLsmi2889zz0hOro5aMC+Z/cjLat9aYq8VsNOtW4FW0UcO/+OxYQlx1b3koVjWY7OGUn7LYW23KywqOYl7TZgyw/Naa9HywmJBThWT9FScB8Wbb+Qd//pNvJ7ha/+3Gbgurl/a8G63qr2iCLrPmhukoac33xnBEMVirjT43HOQf6i80WbRZQGtnhufUP7UdmQ8QL2X+Xl+KuZgnGehzFvCJ1l/sZMGheVDIZXCSYWanSa6cUMBU5NxqpJhxuF9pv1IUqxA6WJk3jKgKtrixsM4MwSIUqpWY5kHRDgof1HbLbDlQFjjpe7WQSVaV1m04t3ip5qUKCcpeoOvqE/mUyGKIjmamnwcN4PEAn/xAUQRzBia4+hSePwd/C6sd2z7hw0TfelYuchdWtGlgtb5i0wbuxwPb3cek+sgwV2JuzDnTWuXd/pZaDTBmJ3axItA37cUk3X2j8zNlRQXMR3j+iRUHXJj/a6sKUu8FUusedp/A/pWAEegcbC2Fvu9hq3ZH9Dtd5z5Z6xj/lFAeo634Tham6iUnH18/VQvWpm3sbyU9O28AJVqvQsta80kg1UdVvG6u5ecK1Vino0DjzecCdtMM7V5AcZ50HYsq9pkHgeGKxr/z8m/5xDly4uJKIal9g3RjHisrgMAS5sd3iK53FigagKqxm6wdkDw4tD//7367wNXf+ZvfbQ6I1pJmASd16WkW3VO5fG344mQ98PbQP1VYe4JJic3QdLT4g4JW7LVl+V4MxK48tOxVjWer97mVc3J4bY0uOntPZR08g1ci9vv9UHNWYOfaeTJmEJ7ruR9hS/787AjB4b0nJfBzY/JB49oYq9ArqT0Xd85RKqWx9EsEhUr2vSrtYvKS2X2BAtuUxLxR8M6xDvJFtZMT0z81Va5NlXVypADT2JjnittIHlr1gWkuTM2x7oMWp3NhBew2iTHP5NKYh0KmsV5FwtQY5pmUPF3ndY4oDGPhcp0IGcq6crNOvHszcLNOiraIhTA5jrnQqHjn6DroUs8wjtw/GnVskul6Fdl1kaGCC579vrG5TBxOMnSfTgXf9Qyj4y7OhBbYEjiWxuiiASmgiQVyDsZJrNKChRZ2zjmxLyl6RpOyKlGFL1QSjmJMU7bKwN6KH3Ju9DSmpW+mBU91aWGBKnOuuJV58JbAMm/XG34llsF7wFsOWHCU5vG5Qazn97f2JBt6y2eTv8mdm5u7vOS3WYWkfxr7APPc6J6IPe2H8lIFrGqDgOXyicn0AavadVCaSYxOVaT4X/Hy5VzPob9//59+S5mr7b/V6FfweK9Jbndlk7+H/RcKpXQb5fUMZojubbW+BDumjarGQtSkuHiueku37lcmI54EsmqFd1+IAepW8hqRJLW9/UqAatXDi+/qYs9HsWFdMqAyi/25/kBy1Wpj1YWPJtlUuLjQ5PXwTmxDVyUlllEy42mvyblWAa/tToBjFcF5Xlx9TEmB01gY5rfgE6lbUacT19cvSS6zdh0Ph3vm2lj1F4SS8fMDj4d33Fx/wInAJqyIIRNjR50eCXnPu1PlcuvZbj7jWB/5+utf0jbfYXr4hpYHuqTImLsZsUjvvgAMWD68EtDrX8LhKDCStnD/hcDdaCBwfAPbDwQ6upVaAQXzDK3M1N0cdDvcs+e0aRTLdX+Ed39uFaAOfueHOBrby+dMOTNNB0JK7PxW46dVSqvEGAjA46jWMOuu4+LyhuPjI0Mu5DJymS7p+jXz9EC/vuJ0OOBd4JBH+pToYyTGyHBSE/BCo5RMn9ZMNGJwPIwncqvgA3k+yrMyF8Lukk3Xczjtqc6JWcrGRtWqMbD4J1oRkJlPGrt3rw2ENYGleVKafOyswONBLOk4aMG22Skodfehzn+xBcHtKz3Ynv+OwFyM2g8iTG9h9UzbDFHs78NbAdreZO/TWyCrWtJ72vRPfrvA1R/9/u+02jkYZPiWfUnm25QCzjXmXKnBW+SBjt/ZbBi8JqjoBFBKdrTOE51kiEwh+EQrDheLgCrgfYUW7OdacS9Ne3NTDEDXxKYtgM0XGeBnr+diOZXz/wWm5FPpmyfXSsVRnBrpMlZKsHiHUgne42JHTlCz/C9Ln8KqOkDKJDl0LMZSrQrz4ElRx9YctCkTXIXocLEj1cbKG1A3ye1+GPHesapYpZyxJ62AM39Qswm988xzYb2KPOTM2ibZmHXsx1boUmDOmXEqXK3MjG35VEPN+GkirTp8buw6z/VlfzbrhwTzaWCosE2R7AphqOQkGcllJ+lw52mnymEszDXQe3l9bofKocqXl5vn7ljoOzWHDd5znCB0Ad88p1I1fvCkGBnHTIlBOVq5mEzYFP46SP4cInTGRDUcVOTjqo1Yvfxqa5P/SjYvldjSXBuxFRYEv4CrYMb2Gg24mSTt547cZkpr1mKokV3EFxnQYxRz2dm4XOIMai8Qtp61/yUJKHYGymswn9TUGFNjNQcG36hO3xuR2TwaUxbmRu1VdOELZwAE2INV3it1AzA52xa0PttCoJqUGiptrNTozjEUtUKYKi16XFU3gT/+0V98O8HVd/69xsVzsTshaiI+HgRO5juBqO2lJqesvKfQtCiraU2tjfb4BnBmcPZiCMpsEhViDraXqjrsrzSRtGgyHLpwx4Mmo+1OAOn+nRKtS7YQz0f1nqtVYKvzahEyD4AxVYc7i0iwDKNoTFTste/zXkBsOqkyrvWSG3Fwf9DE9+IGHh60b6kJiD2e9H8QUPz6/4Z4IRN6tkm3VYg7JbqvNubJcVAsLf3hneSt7ZWq8w5/AW4WY9QuYI3atYQAt3fw4kP4+kfw8od8+vkVV892bNKa2d3wcj3zcIDjCF9+8+ecfM/p3a0m7Nu3yua6uMbdfMjKT3zv88+5CBM3Fx/z7u7nFAe7lKi1Yzy949mLjxjmgXG/x8cVPjhi7HHec3uqfP3LnxE2W2IeuSuJMj/w8uIDri46WnOQM9k3Wqts05ZcHpgnT+p2eF+ZTg+4mAidZpToIrnCMJ3INRPoxHp7T2meYTgQU8fD8Z7oEiF1xLRhGI7EGDnWmU3cQK2MeWSpr06xk+/Xbdgf7im1sj88sus3TP6Cvj3Q9894s39D10WmwySiwEe4/QauvwOWT+lTR822IFiys5yZ0V0zCd0iGlxvY87+9ub38hEoxqYhMFtmVc9mY8aoNl6qflbyOcut/ex/+O0CV3/413+nle698MTJKARrWup9MLVQP09RoZqq/tJquEsBF+VTWTnP5BreFXJuJCydPQQq8jrF6Fl02TJLAhTgFqibLaogNGMiTGrEfCixCHzVpKDSUJ4YBpWhyz/lnSN0njLLYDzbd7jkcDUylUycG2GrwMxq26k5U3LDxw5cIVTRB3OQJLXkELkW8GUitUjpIFfPKhSxX1nZUgofNSDqJEMWX3G54FshbTp2u45xLMy5chEUznr3MHLVR7xvDGMhR/N77YsCQnvHcchcJG2zOcluK9cY/Uz0MqtHdH6notY4+8PMi8uOkUwsjksnM/S6k9w4Zui6QAyqLLzPhdIitUxqGTQHyauzIzt49baw3SQOobJKHWWu1BQYB0+LjlIrh6lZVSgMi8k7S/KrtVGbx+0CzI2+OcbylNCfmmNOYm68a/ixQdQ1xUtOzOdk/4ivmaUnZoVz9Wtr0C2Bn3NR2xsvyeWswGWoJg07p2AQh9Qe5WZlM9wHM6gb4MkTLTiU7O+gqYqxmlwNKjGtXgUU7qR7oDWr3ivywLQKLVsTc694kqXuozYb3EELiDpbd4GgbS7ZXzhHzFaI0SD5pziL2iTzew9/+E+/pbLgv/GfNoUi2oq9oJO8H9Rr7f6dvECrC2MJRoGAk+U5ecv2iVugGXvkwa2eTNINwDKIJvPfDEdgJSrydG/m41m/v3ymKIPlldZYVYOkNmzS8528R8ODJjbnLI8qA15+mDJaO5ZoDah7kx1X8u9QlBQ/7RVjsO6VNv/VX8hA/+xDvX8xxLciVuNgPQyXn/VrhZLGJCD19dfWWmcUo/TwVuDt6gYOs8me9ckvNB7gs9+FL35sYaJ/BV7/WOzRfIDv/lWeXe24eXbFy5sdHz+74rvPLjkdb/Gtcn97x9Q/Z9o/EELiMD8SfCQfjnSXH7JLntcPD5Q8M863bLsLcs6s1xvujyce7t/yycuPaD5R60yMHbHrORyOHIeZ3e6GL+8feb5KlALvDo+kEHC1sdleUeaBeTpBaDif8DUzTVlxXSnQiDgfmKaBlDpK87SSFZBdTwTnOc4Cdblkaq1stle8u/0G5yP9asM0joQU1KkECEidOU317BF2VJJ3uBSZDicKjuw75uboy6BA/XmCLtBvL8nDSNnfSrZ0DWqSrOiTrcKaxrw31hR0PwB+tdK+zLNA0+bGfGN3T8UOyaIdFunT2XjJGXDa7lJ2PVf9vGTwifaz//7X9kz6jci5qg7apBUvCJgoed2btNHoq0Ijq01ahRnMi+UzEBzToZBSYKyZOhdYJ1naxgpbqxTD0VVPGRtuFUxqVURBqY1NSuRjJiTP7BtdbdToSJbYGZMqEb1XK5mDz6pM9I4QPdWb52RSw9vivdhP75iqLvLQGqsciMmx9oEaRMXOcyXZSiObCb7VmeIEdnxwkAM+NUIpDMXhwkmRIDXjZ0efHYOv1BiMqfLUlQBGDI1xnHDR0ydHXAdakz/q9O4EzmkRt88m3zQeLLZi1UfmQZ60ficPzcP9RB8d/TqQS2XjAnOupE3gWR94824C7yiu4kyyczUTGlRLeHfe8a7OXMXE4W6mf5nY+UbIjZASd48TcwbvCyGrr+dQZtKq4+4wc5gLH3265jBCq55aCsU3jvczJzpi8czJ4zul3udaiVWavg8Bmhiclho+y291nAvbpJiKFiI5eWKVt0jFDpKaN1c90zjpPm2Nzgv8NCcAAeAsU6M5jVH1wFTWmmwojlA92bxUNaoqM0yZGgVKSnKE6iC+J0dPszycxZMD+F7sY8tif4tfjPmV7CseyYQuV3yI+HVgtmgGnPxR1eIoFhZKV7vR4pMxVOlZkUaBkM/SYmvg0TXKTv0aqZCKmDfn9R0pc27N8619/fxry2JaGwB50KTfKnyNJprqIf+53pdHGdKpKCG0wovvWX+2GTICGv4I76yiql/L27MuQA9ffqW0735nmVpRwK0eNbnsHyR/Pfvc1pwWf5DNeP3655oMN1fAhewsx1eSYnYXAlXxEuKj9u973xND1v1Q3+u9wGDXw+u/AAqcLuCDz1XlePsaPvwIiJKPjreWi5Th0drCjAf47Idqa7N/ze7Fh/h0ycPhXsnxl880Gc9RE+zqlZi0d1+D3yoN/tnnAnzjET76IdzuIT1X257th/DsE/7jf//f4dp9yVdf/YT17obHuzd8/PH3VBT0eMsmFkrp+PSTj8ht5iFe0qWZq2efcf9wz/Ex8/zZFfvjUbE5oWc47Iiba8rxjuw7bi4vOfSBEm5wfUVtsztacMRuZLvd0Fzk01KZasf/+j/9j7z8+He4fHnJ2ODr2x9zvb3ienfDfjywHwfyfKTrtqSug5po9cjdOODmiReXFzweD9Q2MVGZh8rLmw+obuIxzzgXCauOL+/vSWHHaThwqgMPx3s2YUtKmeijFv+xJ4bG4bAn+o7KzDwH9g+3ENd0KeLrAG5gt9qxStccThP57mtG5+k3O0re0nUdU1VBhneNGDNTnuQ/9VY1uMjhQS2Z6jQaXZ40luasoo5gae0NGEwiX4oMyknMVXOWut9Qo+yisVqK5MulUOHX9PqNYK7+3h98/is7kc0b5ZsxSVOjJiAnnHPErU0klgjtnErqp1H9AUNYWorob58cbVRCd62NYNJKzpkYTc6xgpAWm1go73DJE5tnpBFs+R4LnIbMpuuIztEWs25UGGbNMpKv18nSriVpet9wBFppTDwZmPOZ1VCekidTSmOVEsNY2KzgWNTKpOVKKpXmCil5aohMVRWOMchjFU3CckjGWpVG68S+pdjoF0AaJUONo6rlFpP+WCr7WYxgmo2u9Qh4eeVW9X3gdMqc5syq87ixcXXZM462Xzlz0cHjSQzaOnlFOBTYdPBwmLhaJbZrOBzF8hxK5epiRSyVV7d7nm97hrnpPE7ygb2aJnbXW/LDzNw8wQduDxO7PvA4NKqP5Njou8A4NJpLdMlzOxXGsSgdHWhFTbdXfZJ3bC4Ui7eIIcp/Noo1LVGgIFpeGQ2iE2AM3qnKNEMNFrlh96evqq6sXmGg84TG02TtnCxnRUWHon1yrmq/NTc1uvaOkUI3oqiWCEyVED2pBHJoUKvaLM3S6ZxFLSy3dUP7uTCYUChVbKuzAhEsw41Sz/EMS7TEkgOWc3uKNGkFZdVb+yknQOaKw9VGDkUdM2pU1ITLTxliBaqfaQ3+wZ/84lsJs9zmX9NNGM1w7lGAZJklx7Us1iiY2XYxjY9mcB9PZty10+e9ebZQ1VsexVjFBATYJpNVOoGOcTBWoFiFXTqnoKfYqNWx291wnE/keZLP7nAAeuLzK3bdiru7d5JSphOx35Kdl/drf4Rf/hg++lRG+9RDm/BxTVjf4E6PTPlIbJnVxUfsD/d0wDQNhNQpqPb0QHr+gvn2QYuIroNvfgE3HygQ9PFO+2wyFTjYj5YFlnX+1i8kP81HePWnAmzRGgj38j1RrFLQT6rKPDxItsx38k6BVgL1BOkZbFf4qx3/1X/2H/L47jWOyP7hS0LacZorm5a5u7/lp6/veX5zzQc33+EXP/kz+u2Ou9ORx+M9r8YTp/2Jn3z5cyLBitscxMzVbkO/vuLtYeQqNT7/7PusVomffPULUtwQvefL+yP5J/9cDMTDKwGDj78r5i1PJqEFuPpQmWndRsd8anbMxye5tRaB9uU9aQW5akyWSeemuv+PvHfp1WzL0rOeeV2X7/v2LS7n5Dmn8mQWZaoQNqZkWSABLSS60ALRcJsGQu66YbssLCzxK+AXINEACcEvAEQDO11VWa6qzKzMPJeI2Lfvstaat0FjzB3HdrskSnl2JyJ27L2+21prjjnG+z4vyAPYPbQntLhf4Lf+BmDh7Q8hX4jDgBNLjZ62nSkoe+rKWJ7bxpAzPk6qEbaWnDJbRc8747C20cTRWulro6iYv4kWVs6qEB+nz9dZ7bi+fL3Q2F8KqpbVAFE27Vr5sRtCnrsmbgbr4PlbHakzQLkgv/rffrPGgv/g3/5UCJbSlbRWXqjlDTErvgbqaPAn1V1V80KUdvhgqZ1ynZM6k6wI1jak6RioopgQZwUzeHLR8QTShfMCUbRzFdB/u2g78bqopqXqY6WuqYoIwWhXJOf2HXtqad0tBaVWnHV4Z1hbBysiGqHSCrNzbLmPAgeLLVmhnH5kzKrlSV5p9Kl0oX2zmNawQZ9ffLHei4rSxdLHPX2EWholQDEWW1QrFaNDqsJEg7esJwWgjrO+J/mF0dWE613g9JwYor7fAL7n4JVU+WIe+bPjBWNgqML+ymB6zFBswrZV7KwFy91NwGVhWQrHc+bNbcQY2AfL6bxxOzt+/ZxooyGvjde7yNc58doGQMBZxnHk4UOiBT5GvTxSKUldkM57JegbwwlhHAKn50aRzCYGby2pCaSmXEWvDCwn0NYGEWQTWtcwtaxCV1Mtrgqr1V2ONWocIEg3EjtKZ1i1YLAtd90S/ZzWAvMluunFfuc6UHOpCmf1uVDmgLnorip0HIkLTnXGVgX2NaHQUlEYad10DOg7qgHA4BUZYhSwGq0gRceIL+PBUpTn5r2WS8bqTLugAc6DsWRqF+nzrxRTAK3q6LtaCM11gKlgjKP2osDKd0HlL3euJvAPv6+aq3/37yh9tTbY3+kCUFVbxTBox8l51Qo9f61FiusQTu87+LMyTztSWil509Hcy4jRjIpjMEB5xo0TNTuwGR9HDJn9MGCMZ/Seh/OF4CeoC0s68vr6jTrfq+Xrd/ddP9V6gVVUq/X4TjPxxkmLQOfh/r3SsV9/CaevlRbecqeQb0zXb1gennTs9utfaNG0P0AZeyDxI9x8qi648QDH2gX9TgvGhSmgAAAgAElEQVRBN2kGXipwdaNjP6wWAsOVLsphr+4weXEzrvDlX9NQ47rA41FHljnpgjseCK/ekp+/0gJu/6myvn70b6k78rnjKOJOF2I7wPao+YQGHY+2pCT7h6/hy9/R7MHxoGL1sWcrno9awBxeda1QUETC6aFrfpzG1oSoxc00K+V9nLRg3F3B13+mI03QwuJ81HNoHBXBYawed+05kC9fL3o417lQL+7AnFChcdNu6e0XXU9ne8xR6qM6dCzddJ1g6lysj+y1HujcRI9z9wY+/xK2J8b5ljFaSk5s65GcbedOSTdheGXmWPnO8blcVNSeO+Or9s3Gy5jPRh3pddIgperxpOm5sqyKrxhudOPSurZqiB1x0knt2BenjhaaISB/+j//hhVXf+sL8c72bpR8BFVmqSquc5VyqlQL4h1idFfvWunYAhVKh05Bb6gIPASrvKQmRGfUsWUdrWXNJ+3uMeAjkFEk462B5rtmJXdYaMR7w7qsNIF5il2bogVKM4VcKt4NKjyujVoL8mKxj56UOpXbKpJhbPZjELDgCa5xKrA3jYvRboE3jZYbznpqaxrDUvU4BoMzHdXQNWG1L/A1F3CWaTC62BtHXbJKMzDsd9KDnpU7lLtubT851lPhsA88niplqQx7R25ZGUlNC7p1K4yD/vlmGrQQpDLOAllf/4RhtwsYq8XvwzlxHQP7neFyKUxRr8nBKf7hLljenwtxMNzuB5ZS2A+BZuH5mChbpTrH/jByToWv32VurgfePa8YE0ilcS6CGQJZGpcqlAxTHNhqohTt6iQRvDW03LRrE5Qc7qthswYngFFtnu0dpEzFFlGoqpYemCbKoqtdbCnoZ28F16unj9dXD7T8VxAjL40Ho1pBZ42O8DaDw4EtmHVTrZJ52XDoeykvAifRYqf1kPImmkHZTMXhoVVqbkhUTIPJHbyYUUV6FazRkO6XJARj/Ue2lUeDrttH16ICZ0vXEwqCVKF4oxgHgFqw4rtGso/JzXdFH+go8Q/++S++l8XVj/7LfywVy+X0HmMja1pJaSXGkcv5xN3NG+b5wLaeNbarClJX1m3BjCPbemaKKsiNw8S6ntnWjZYXXfSOF11014suNq9ewybquOqLTBw96XgkHvakyz3zGInTLY9/9s+hFtynP2KInstl42q/I28rze9I24qUM37YYdyIOEuoCzVVhjHw+s3nnJcL43DgF7/+FzoqfHynxUkrUBbi2y9Jl/ewrfzgzZ66Loz7a8ZxJkbHFAb+rz/6MzietKL3QQvMuqnw/nLWYy3n7voCnjsrajfA7Q5w8PQeEIVkLgkYVHr46pa69k7X070WMH7ojLGFL377b/DLr38G68ru7jPKdmYaHDEX3h2/JcQDabkQY+THn3zKvDvw4f4dn7y6449//qc8X078O7/7+/z8V3/KPB3IlxOff/a7LMsjUitfHe8ZxDHvrtnKwlI2prDjeDmqxE2aJn6IXj92Gmnryri7Zl2OGktUBK5u4fkRpANA10fVt0WjO+W8akG6nDqS46hFVwh99NY1lMb2nZPXgqpmIComI531mPQO4t2nev+oqxZVQ4/Dsb3jejprd/X4oDqof/N3etHn9Z4TI+QzzjuktR6VFTTarrTvsAgvQvQ49MLQf+e2XE98RE284DpeMA5h0iJvPWuBli70AFkt4Br6mqXz1HxXRomBlpGf/S+/WcXVf/fv/7a0JlzITNbisRjrWEuGzdCCvh+W1rszhhgs1YLBkqru5oMIda2Y6LuI2+ma1wXMAUMzhZLbRz7VumlH6MVVNw5wOiecj5TcsE7HeKZWxsGBOHKuxE5Qr9WodGFTirijdyOKpThBmuHSGvvgOl6isFXDqzHywVbaqqM0twrJCtW6bubRzyXUTMZiMKRcMU0fp1jNTIy14neeNSV1rxlHzo2WVA8TOpNow2DFYEumlMb1EClVuJSKmz1D1GzEYA3BQCuND0sndEftiA1dtD1ZHYGuVcc90rMBi7EIhbuofJGr2TEFB0Wdhyk35gil/97xlBHRyKFSGptoofLDNwOnbJBeRNon4Wfbwt1uwvvA2TcGDO3SOBwGjgnOl8zdzcTT2jgvhf0+ckqKw8hZN4XJabTNYIT7c8VpD1GF5+27AtN0HEJrQsMzNAOxIkUL82w8vhUcgTA0mkDuIeEfiejRIqlhXwwF/iWn76X7w3cB5KWpwLI4gn/hBvXybOhdrqY/P1TD5vq5DSAZ0xy1GXxtGK9NkZeAaPpYz5uGX4Q0TDQKlYy3jlrtR4bVS6C475+HFcFUNV9AX+SL0Ab9DVt07G7EUZ3Bu+5wfEFaKPkWV3VEaqsoWNTpqPQf/OGvvpfF1Zv/9O/JZctYN3L65Z/AODEebijSqCkzTBOlbHi/o1LYDzuen77FBEPNHheFaAJCIdWKwVIuvRBxgWG3p9asn9fzO+3q7HawbVpE1KxC3qHDE8/34Pa6IO7v4MNfdMCkB5c7+LMTxc/Hjyyj4c0bnA+M+1vuv/0Vu92e3XTDenli2xYOV6/IrbGeHthyhvmV4ifWs2q1WOHdvT6v3VuQk3ZejNcxHVmLqe1eF8zaF9hWdLOyPutiSoX93Dt/ExyuVPCfk3ZXPvtSX4P1xNdvybUg3/5ChdTzCLsDu3HifLnHSePmcMuH97/i9s2PadKoeWHev2VbniAfubm649VuRxtecfr2J6Rq+cEXv8XjpXE5P1F7R/2rX/85t7eveL0LZBO4vr7h8cO35LSwVcjrE/N0hw+eJJZty6R05nD1irVcOC2VaFaup4nUZuBMrYUp7FmXjTVv3L76hIdtYX33tXaW0or95EsoRf18l94FrY9gLHb+AS2t2NgLmyIcpgPztMdg2crGKiOXh7N24vafQ3vQDuN6r12iMGkxJ1VZUyIwv4WvfqLjw3mvGze8jgRMUoxH6GaMgo62jdHCufROlbNaAKUM/rprB3dadLWmo09n+qg863kTg94f/aiP+fheMQ6gqJLW9Ge970Uj2mnN/e9rf3+aUuJl+39+s4qr//Zv/0i0A1VxWQWxtRlkCkSbkFW7ULVHdUh3nYmFVh2jVX3SFpRkXTsTy3WNDZ267kMEq+HOvhdT21aJ0X0koztHD3TWEQdlA2+wNpBzY4hBxylVafJjdJoPVxQO2qoWDA5HcTDiSdIYB410cahxJZrG4CzVaPteilCdWu1DexH1g5NKfYnzEXDB63PdEsZY6l4p8btzxTsdQ4koLHIcHOtzYjcHGg0v30XnTNYoTNRbbNZR5m4ynJ8zxRmmYKlo8Zm3TJw9t0Mk58ruEHl42tjtHOPguJyzOteKdvqQ1jsdjp131KZuvofzxhevRoTG+ZLZkmYS1iXjvOVuP/DLr0588cmE+EB92IjBcb8U4uw5p8R+HDlfGq8OA8elMM2eh0f9LFZbacYTnJLXt6Kf4allhikS48C6VS6XDe+1u+Wd5haWqpqn4LWArEUIUV2AGu1SMM1rCHQYkLwxhZFTJ6O7lNSdblWkn1vDvEhnrHbHSlWHJoBELX58x2A0o/gGS9NRm9Pzs4oGWVMgBstKL16qdqjEaJrB5qRT1186sUpZrx2GagchrCDBUWoBZzDyos3TkbhBz48qncxe9dxowX7UW7kiiBdSadiurjeNftOr2NIw/T004jrqomMbWsP4qh1QgT/4o19/L4sr+x//XRGxqqF6Gc9Msy5Wj/cKfvQzCkNEd+r7t13kPahge33QrsHDN7qYxQkdkfVdOqBCvaQk9Fa06PjwXgObvYXLqmOpYehFk9cibBi0CIK+MKOOrFx0YZ16dyBftENyXnVRNAEev1E8gbUdMXHSx6Yqx8Ab7ahN1z3gedWQ9EsX7OcE6Vm/z6Sdi9vXcHcHbNqtsQEvDWPU4eaMME87duPIFCaIkSjC8fJETYXT+ky0jeg8n/zgM2iCpMQqDcmN21d3TNOOtp55eP8N4xQxZMbplhhHvBWi9wze4Y1hy09cLguXp2cOd6rNWi6N/S6wXI6Y4LHGMgwD5+OJZCe2YnBktlKJzlNrwZmMYNjKSJJKa4aCYV0a53LkwzffoiDUDMFx8+ktAPvdjnG65pPhinV5IpdMGPakogyudLng51tSSpg4k9LClhaOzx84fftVH0EPMO24+eKHfD5fIbVh5BlXG2u2OO+ptZKBXBdu7j4n1ZVmDXYNZCxLSRgsz3nj/PWvMVNASmUYZkxLuDCz5gthvMJIo7ASwszoR9blWYkf04ypZ3Ja8RaaEXx1nPOiLu28IOIYxmtKWbHWU6QhdQM3MLhArhmxTmHhHdtgjMVaR9lWfa1Gnc8ivfuF6edf+250ay3y//5Pv1nF1X//t7+UU1M9UK2N6p3CJJNh8ytNAmIMUdCFIDVctEiuWK9ONU1waIhYvHfqUE4VU4UwDX3kpyiBlLUYqGJxBYIx+EEXrtEqViDVjHGFdTHYwRLRgmyT1kXhujjntXJ1iKwlU9eq2IWmOhwlgnvtTHThuxeDZAV5audCmCfPZTPMtfFIRoxjiAHbReXVg+vYgBQ886BjsV0Rsk1ct8aTjy+QWVJu7LxewKUUdtZRs2NxDV83xsETLOyq4WgaceoMpl5gZIs6/0Q4m5W4ecbJk88J8QZnHctaODS40BgGhzMG661yk1plapbTtiHAm9uBS23IKbHfRa4nx/Mp8XxcmWdPHAwxOEp30j3lyjx59tEyjSOtCcdT4vRYYTSsq7DfBVxr3J8Ln7yZeToWnkphP05clsrDkhh7MWg3g4mWc9Xic4iO3JoWtAaacbhgqKURxdJSI0yOUhoteHzVkGKysFhhsoJpKtBPTkgGfLbIoBl/tar7RqrpfILOtCraZX0Zub1AQr+jo/OxOP4I9mwWkxq2aEer7j0mCaZzs6RmsIbQlAFXDNAdrkaE6HTj4L2lNiUTV4QYA7ZW/FZI0aqza2097sZBK0jt2JAXjZXVjUfoer7WI55sVoaa2NCno66PsjLWB7zVcbVOIdT5mBD+4Cc//14WV+Y/+K+FvPAxSNmEjzd3YlRd0XytC4XQtUVOA4al86XK2v+/qR7m9ADxRvU/j99oxMzSFG3wsksfr3RX//xedSfDQTs8d18AfbxiGph9J6APyolaFtW0TDvAavdhea8dprT0er52i/v88ipVR9USH7MAR+2+cDjAzY0+3yHqY0wTk3dcDYGWPbUuzNOBKo2WT4gU3r56i+TKOI1Muxuenp7Y2hMlJy5Hw+s3n1G3J/Ceq3mk5IXJ32D8I7UWrLmmlkZKR64P18wBWkrM+5nT8z2v7n6A371iOd2zH5+wZuKrr37J209/yPXVHU/P7/DGMt38AOSeki3ezXzzi58xzm/52V/8lJvDK/7vn/xT1iZEl2hppbkrbg6vWOoDh8M1V7sr8EFNU9uZIoa2fMvVzSdE53ts1DVr2ThnlcFsyyPj4Yd8+/UveHj8U6KD3/78B1gHMUzE6YbdZGn1gslaDOa0kOWg0WFtozQ1G215JbqJVAqCZdvO5CKcL0847xninm09E4LHGc0kxQ20UnVdtDpGDGFgS7mn4gRqVbkNUjE2dOTDC6C50ZwWnbRCFXB+RCi0iv6MjhAAQ8krxjqaEcQ4dV2jPxes57ScqWIxrRDGmWU9k/umwrgBI8o3LCXh3KDUyNQ3K8b2G239LsRcGlSQn/6mca7+wx9LO1VlKJXOl3IWaxoZi/UWcVBOgvOqDzFAs4bcHJNTh15rGUfAhT7m2fQ4uTN/rNXizXvd1SeqZuxVoRlIVvCpadETI9ZXzOqwwbDlTBw9mzQOx4q7tthgSRXV5jTH0LRjMQyOzcFlLYSuvfOrBidnDHkymPXlM1Qdy9kZnG8ciiBecQy1Cr4Kp1LJQYXq7VII1rE5w86BTZZsK4dd1M5IbrQqrKUxDgqxjGKYgMe2chsG7fp5Ia5CmbWYAJid6nbKWpmwrE5jaGKAd+eV11cTH542glU0g7lkZFLNWT1XitXC5bpjLsxeX+E0ONanRCuVwz6ymx1Pp43PrAcD49C7RVYhlO0sPDyt3IdM2SzTCMdTYpgj2QjX80htwrY1fAhkJzgfeDxlRBxTDLw/r+zHgXHw3D9fWEvDOb2hLWvBDQ5apjS98UsNbGvFWnVXSqmMoyetigWypiHBYEXDlZ8Gi2uwP4OZLGyVVRrWqnGgBpDmaM0RfUNSZfHuY8AyfKe5eiH3a9yOXo8vYnhrwSQBp/eDZsBXdSsCxAKIjuCcM4hVHWEzetxJdCSHqXpzrUJ1MGBYS0EmkM0qkNc7vDfqaKRo4Ha3hKiej27eoHdS1VEpMqiMoReGL1wsa6sWmUZRIM6qvsE1Q7bCP/rJ91NzZa7+I6EVHb1dHsF0ojRei9xaYPdKNS01dUeh1fHI3Enf21l1TD4C505Mn7WYcUELmoLqa4wS0DWkdlLx86EjGeKIGa91R7+c8TtDnGZeXb3i3Tc/ZxyvmK+v9fGqQVi5PD1yuN5zKRY/Thx2V1BWtrqxcwM+jjxejkRpjD5QjXA43LCen1m3hXH/lnT6hpYV3jzGAesMFktKiTHuMKaR0sZ+2FE5E+M1Q1yASowDhorPR7Y6Mow71vMzMQYOccdWEmvZdKG3QjAbw7jDDdc4EttacTSud9d4t7GZkbxe+OXPv+HhKbFVw/n+p3z521+wn0c++ezHHC8f2F2/4u71a97OhWQtP/vTP2ZwB9682WHdFa5+wMY70nbPOFyzJasB8/YCtTANE+t5w7iZ1mbeffuBp+WZ88MD47BnmipxfMu2nllkoiwXUtlg2BOdJacH9l5Yt4X1nEhVsHamtsbu+hPO518T/Ixk2NxIrYlYGtlmvB8Rs1ISnC9HTZ2xDusCSKWJo2xnnAs0V4lxVAc5FeMHLuuiG6kQ2S4n1iqM44ypRfE21uOcp9GoJWGN5ueC3udqzcqrthbBa0PEqMY5FZ0wlVLwzlO2C6X/vpQNsQGRSsmJIUxUp7yuKo5WKyYqyEKa6aL2pmY1N9Hyql1dka5Bi11mkeAle1AakKEI8oe/YcXVP/n3vpTaDLULZtmU1SNOiAXWkHF1JqUVHy3JGEaj2ABzTpiaMVXYbgO7VUi5c6dWQ5uBaHvAs+Vm9mrTD4ZhMKRsOW+F0VhOgzAZC0mZHsU6Wr6w30WO56Q8omhoUjmYwGUtjKWxBYPxBXvOYByTh7OLtJ0l9BicWhytZFx0JHH4krQ7UQvO2Y9Q0rq30PxH67uU3h0oQo2qazF94c2lYYNjxJKaFpAvDsC8df1M7KNPr3o1V1XE7UbDWODoBbuq8845w+U5MUyWkmDv4BKFXfAsTrjprWLJKsw+rRsxOoI17AMIltMp8cPPZ7ZUSakRGzynwsU0dtZgDQzRE6PFSuP9logi1K0x3o1YawjHwrIWXr2eeH7c8KPjfM58+mrGGHh3qYxiOZ8rT6byWRz49VPianKcsSy5kKvBDA4pDh/hkoRSHcOsBTwV9lm7lScqIego2lRotWh7vQmhdzybBesDpm6a6LAZzLhhyqSdpqjaK5oW7rpbU6G76ZVOrQ1bmjpcB4WINrEfO1iAIhUMSGe+yYtD01lqyvjg1UFdG3HwaqoQUVioMwzFskTdJDSjaQeIMDjHliohSh8V9gKuCeI1akqLIr1WjDHEmpC1IPsIzX7sir2ER+Ma2Q5Eo6NOVwSM0+dkGzhFSgzlBTPRDRFetVl//4+/n5qrH/4Xf1dyrsxhxzhN1JrZ1sQ5JwQFH5uWid4yjAFnYJjesl2+wQ8T8+4agOPz14zTFeM48e6br7m6+ZTWGmMoOO+hbby6u8F5z3q5sKZCPf+am9e/RZFK2jLGTDw8PbDfOz779AveP574+usPfP7ZjynS+OLNHWu2pOWZ5XLm8fwOb/a8+/pn3D98xV///f+EX/78DxmnyOnUCMbwO7/31/jZT/+IEIX748rV4ZoQR+Zhx7IceXXzCuRCHCLjeEep90zjjuubV9w/fmDwewZvmIaR87qx23cn4+UZuy1c37xhIXF5+kplExlssziE/fUNNZ1YZcQKDOPIED1lOxN9ZRg8UjVk3oeBYYhsSTt70/wKabl3XxewiVYzUHHDrAWrBamF0oOFXb1myx/w/oARgWooZUPEY2pluZxoMpK2TDEDIe5JOeG8p9VGSxUfIiU/Kp3c7UkpMezfcLmc8WHg/eMz0zSz1dSZTGquQuB4PiFYxjiRtgvLcmJdL5yXC3EYmWMAa1XWUrXTKSaSOsagtkYqmVIqwUeEQjSe8i9tAtN2BuNxxpJywphAs5ZaCt468IHLongHg3arnNONlEaNGlyIiFSktY+THZpGZ5VSqVJx5jvspkoHGi0njeR5wY6IxqpJa4BjKxs+DOpMbi+NKUurRTd/prsepbsD9eBKZW7dXSjtY/dK/uh//c0qrv7g978Q6zziNCfNmT6OCFAGg38Jus4G4w1E97HjlF0lJ9UbjbbQiLzgrmsTXHsRNTduXOP9BmMYONdKoDIMA2IN26LMq7YqF0PGSPWOciwc9pGIsFqL5EaRQrDqRGxrYoieUWBdC+ZmJBh4XvSCla6dcV2IV7uIz3btDUbUudhdhdZYsKUvYOhYy5p/qWOh0TDSEd4mCCZrllzNjWH06kg0L+60RimCbeC8xebKuAuIF4YM5whha4QKx6rdl6sh4qxjQji5xt479s7zcNpY1kS1liE62jkzzx6x8ObgOX61cXszcDSF51Pizd2oYnFRo0Lp2sfDTsnuFeF6CFBUOF2tLviXnFnWwr0XrobAj3czj88b51J5PiXmu5kZR42W/JD51cOF4Xpiq4VxGDieErkarvdOsR4eLhWkas6dMZCXit0FTBFkUY6WNY6lbtRSyEa7CFZUE7WbVTRunIrUS3IQN4RALXoNNRGad7TSCFXUAOEavqh7XOKLS0VJ+qXoGM52RIOetNIJ/5qzKf2mIqKmCtca0pzqM5tgosKq7FoI3lFWIUchmEIzoTv8LbVkPS9EQCy2qdvPWNUGtyaM1lGLJTXV66WirXPp70MRwTh1CxpjsAihFgpe6fU+U52lrb3DnBVAqk5BsB3SJx2K+/f/6fdzLPhb//l/I9YOXO/2XF3fMt8EQph4uv8VY3CM4w3WwKvXbyn2kdPjmVR7SkU2xGFCpPH48J5SGrspggSsM4QgCLrQjfGGFjx1uWe5LGB2GHPiMH6CG2Gab3l++oowHLAmQxZijFyWe7y55XJ6ZJivSZdHct3UQLRVitQOPc7kemE/vcaaSpxmUrlnf/0lbRN8qBgZ8FZUKjDMVGOI1rCsR7yfsE7I2xPBR5o0nGQwM9N4TasbtSWW5REXPYjBlkQthXF6TVkWHo8FI0deXx3QkA0dZaW20gzM0wF6B8QHSwjdJd0KtVTm3Y7gJ0pOhCi42HocjQcMQkJaAgIuzrhh0IX7ZSy/zRwf7klb5ed/8lMuxyNpyzi/Z9gP7PYTr18Frm4+JZ3PPD48YJzntAnn00pjRFrjnBbydkGKYKul+UqpouM5o9KB8aAFYyqWbTsSwwEX9yCJ6AecoztO32PCvrtNR5b1RJVIroVSNqwf8c5RSqJWSLUxzYOO9sqKiI7eKlU3mqYRwsx5vZCbYxwGWsl6Dq4nxdQYq+PCWrDWahdLGsE0rI9sacWaqPFwLlBrIbWucW6aCGG/q+cQUZJ8qrUb/HSaY/614qqIQpFFGs4GlQ19RNEAJlLSqeMm6JosVNNYti5m79BRQP7Zb1hw8z/+m58KRlEJ1qq2KeHwGbIkMuAGh6ulj74EMQY8NBdwzhEks23C4CzNK3X7Bbi64YnFYnwCa3Et0Iy2Va+852wbvuP8i/VYNEqnNu3+lNoYB8u6FfbDwHFZOUwjIVjuWyVWeBMNl1op1XEIwmUpKiSOkWzVdeZKYsHggVUaMwMnWdl7x7JWZFatSqSRJeCsIVoNPxYMthR8FyyXEAgFNqvjRrbGEBznoIJn2xfTYtUFKJfMOHg+fR14eFgIUQGPNVWSqIJ+acIOS60Z3wxHo0HJzRSWpSBnBXRKD3Z+HTzPtWKrsLsOlFW7Z+fHM+HguRkD+wkm6/iQk8YYbUKVSs6VLIqbeDN5DvtICupSWy+JV7cj21L49qnwdhdZRUXxy1pwY+D6emBZLccls7qKSY1PxomnrbBsim7Yz1GfT8qsW6Um9Bgpaat6UJMCqSrWokEZAg1LM8qrGqrHOcNq9FxwSdEHgYpDOL8UyT0SxothEwNbxQaLdzp9MUNjapbiHLJlmuluP1fV0GKt2oRdUwBnVZheE4dvBWsqKUTVL1mH7aYN3x2RS95oWJxTl6GsncPT+VreOEoRkII4g0NBvc4ZanZYoxqF1lkx3kZKz01sTjd7rVjtEhfRTWDHnWy9AHSpZ0Ka8jFGqrZG6nSrWPWaesmo/of/4t33srj6r/7H/0NKSVgTSJcnHp++ZZz37A8H0raxLCeGYWZdLlzOD1g/sZxPlJIwbuKTTz5lfziwbc94HzmdThij3QJrdGSS0or3DmMq6bxwfXXH8XLP4XBgt79V+3sTcrlwOV+oW6WklafnB+bdFXevP9GIlXRkmA4glfNp48P7X3B99QlPzx+4e/0JH979CsxMHA3z/IpUF0IYqLWStlVjxYCcEw6wNuKDx/TOiBGDc576MsarKmuotSA5aYFVM9IyJVesNKwbECrBOebJ4UNkN89460nbI60ltq1LHXbXIGoGGfrjOiM453Xj0jKtJM3Y246E6Q5MF+DbhtgNMZYQR0QaIhnnZ01ikKajNG5pTaUptEBOGwaPdPOUzZ51rSy1KLBYhFwT1kbSmiitItKwNqrGDKfYHQxbyhTpppg+Zqu1EPzEltfeHTeklqE5QhxZtzOlJBBH7Xo7Yy3besF6nYpggo4Krd4bt/VMcB4foubh1opBheHVamauiHkxLlO7XpYMo60AACAASURBVMsMe06XC8uScQhpe8YCb9+8ppSNljTLt9CoTT+7JW/UpvxIaa2DvwO+P96al48MQ+dVL5XSinWelnsW4DB1GKrRP/dXPW9Sv6VaqvARkaNBwPm778FH1AWl6MgdkD//33+ziqt/9Ddfi7UG0wpNCk4CSQlDuKiAL2kgtQuvO+/JilCcYzRCNpYWHCOGmhq5NKoLmnVnVXhsaqNZ3elL9RArfmmwd6RVdwd2E5x1tA48rEZPzqvBEIJlSYVTcQxdYFwGHSWV6KgedqVyI5Z3rjBE5UCJjJjnzHiYWC+F0Wkss5cCw45gDXmtPElj9A6kUJrRQkZ0BLR5x+y0Y7WliglKBt+2RUegQUGP1KBdKtMJ4F3o7A2kVHH7yM5Z1pTZRa+AUhoRS0mVx0vCUHpHRAsLW3UB300Dtep4xxjD69Hw7XmjNMNdNBwrXB8GwprhVPgwaID0oVpiMTwOK5O1gGU3BWJUQOfT88bTMfHp9UgMjst55XIptLUx7SPTFGC0XHnPfhd4yoXLksk5cMmFb94vXE+Re7Fc1sL1teH9/YK92ytbTILu8L1hGFwHcDqSaDE3DI7mHCYJqWRyDwYPDZqvOk7tQdqDU7PFZWtEWylGDQy24xs8hmQcpjsPsxTNhwQygsuZWD0p9Dgj+Ni1erkUjYFqKjSPdxZbM0YgiG4uRBzN1J4/qC12J55qLVK1YGq50KYARWnp1RjdkKDMKZMLYtQd6GwgemHdCtZWsgm0YvG2EIMjbXoumeYoFHyHjkrW46UOMkW0MHxJSHgR5X8MWG+K2qi9S/t9La7+s3/yP8huf4d1ng/vfoWznrwdac2AbDg3sZaFYZzJ6ZHd/lOG0SFi2M0B6yZ1n3vL5fxESivDGCi5cHX1irTqsbbzE9c3b/Ah4rH8xc//jGBVY7i72uG8x/pIrhVvhJIb6+WBp8f3TPOeeRjIuVFK1X/vZkQMJS845ylVR0TGjWhg18CyPmO6A9ZY04smS3A6unHOUmvB26jQeGfJ6wlrLH/yz/5Pfud3/xbGFIXPiNNw9rwgkjHNMI36vRAmvDcE56l1peTENM5IK1gL63JmnA6q+fPgjCiY3VtKXfXaaxYjFeMDxhlKPoIxOiGSC9btMM51hxkIGeMEyQbTR1g1byCKiJAqiESkbpQilFqp1VKap5HZskEIWPGcNg1NLumkYnsM23pmHm84np8J1iINLjlj4p6ckzIYg9P3xQW2VrHWczw+Y/1II9FKwYoie0S8ZtT2911aw48DIo4tZdK2kXJCRLtCSVRrVeuKc6PqvjsIuDUFJxexlHzGhQmAUjLNBJb1QvADQ3RQE4Ih2qDHD3r/8zaQywZGSDkrb1I8RZRTWHMfG2I+3gtL10OXUrRbVWsXn5cOzl06/+rFUGGhbdB8F2bX7la16oZV9T0fUw9eumVW3wP5xV8eof2vRLbgbtxTJJOzV93RqB/Gi7G8IVp4GSE6S5OCKY08jgpgJII0vAjUhkkrxgZMNWSndnRWQaLHGcMQhGIEmyvZarCtLY39aFmcYGqiFd2FVxG8Edpq+HZvsRJgqdRJSdeDAGPEixpq1mz44AxxE0qFuUwcqy5aUlZMdNigXI0ikWzgRMPOlmg09NidVSh/ahUfPMUYzCS0U8Ebxxw0VHi1FTOPRIRpnil5ZTsr7ypOM+tW8aaQi+hjN8cMGBH23jIGq3E1SdhKpjnwg3Dtd9xcDZxSIgTL09aYvWq18iY409jvIqcKV9PE4/NCNp45CnnLVCnM154feMvj08qyXUgHT1wdu1eeS8qkoZBWKOfGadUW8MO2YcTwkBv7m4FjbdSlcWtUu/WTDw8cf7ZyO86MYeDr5yNXzzDcOMYrz7AId68iBni7G3hcDUyB+1RopZGSkJbMMAfaQ6aNlpRV/2M6SDO0QqTRvKegYitjLBW9WE+5ELxBoqN4jxWNNGpUxAqlWcxWAEdFuS0mJ5IfcNYSBsU+UCwSVaQcTKM423e+fWEyqG6pGkrzlGjYmvmY/efyiA2V2hEPzQpTMpydpdKIRByGErVzZUolWB3H5dzYvbDgRAuvtRodgzeLT5YRwyZCq5VBvOJBbMFXKAYyOrJ0zvTonUIUwxahyQtCtWF77BB4WoTowEh5IUZ8L7/iFLFeSGnhcHPX8TIT835PWs8Yq87inBMxfsnp9IHj8wcMjvXSCGHP5fxILcJ+f0vOG9sJWlt4/vDMbt5TSuHm5jU5Ceene6yxXF/fKo5GwJSEE2Xfffj2a7bzI9e3dzgxvLn+giqJwUbefPYKkQ2YyeWIiGcYb6ml0DZDKZVWVl3zpKhLrIqOzVrR4gaoWCqWYBxhGEjLwnJ6wBjHshxxxvKjf+P3yWkF42h5YzdPeB97mH1gHKbuRm6s6UxsTtl6kpC6kbMwDJMWIUOkStVxYBvIZWOTjeh97zplLAPeGsQ8qns2+N7Ryhgzd9ceiKTOlatIrUCglaTdtSbYXgm00qCHIzvjlXEnBidCJTAMI6k0hMyuFu3EhEN3MjpSHFgup36Nq304OvAtM1ghVUdb+/uDZgZaPzFLpaYLuzhSWmWrwloS1lustaRUqCYAhnQ6Y3tXuuak4z/jaa0xBY9zjkLQkWHRrmJOZx3xBdEOmwTyppqzJo0mFVsbTRJbruz3e2prvL/c8/bwlmwcKS3ktul72l2EIg5phpwdxniMbZo7Kt/dHnxwgAKKfYi0vGCtRahYNyJtxljLmlXwrngTqy4kJWbTHW7fwUbNTqnwIt8VWbb8pV/nfyWKK52TKm/KZE1QKLWRPtLTIVrBoBqQYR7YUqWSVRRcCzJrN3dzHpgwrTHZyubAVaE5oS2OODrGDJdJRzShwIzjmY3jKWO8IzmYQ9BkcR+wpkHa2DcgOC6jUDbtZBkRLudCmC3WwLTp7n3ZwW4BMxiidYiBaRfJS2HbKmOPtbFSWUrDbioifqpVZ9fNEqoFGqY0pDqKMf1GA1bDApmNEBHK0wmCY4yOwz5yThqMvDOGp2NifH2LbIW9wPlcqFHdb7U09sUQgycaSzGWdbA4J4zNsDxlbm9HpihcHgs2N7Yef5OOC8E4PnkVVSv1kBRzMGnr+cPjwm72tOA57AY2I5gkfLLbkQ0YV0it8eZ2oK6Vm6vA0ho7u/D1t0/87t0V19cjp45P+OuHO7a98I1v1GxxRcizsHeex4eN98eFus7MY+RiGx9aI+XKAc8HKm9vdqxbYTcHnG08Zv27jY51VdZTbYYiorZeQV1JzlJT1YveWppoblYFNSk4S+q7P/EWh6PFxnwULlEvsX1UHEHJml9YesepGqObK0Hp6yRssBSC3tirgk6ttXijhZmpQsiJZEfkYogxUNKZfG7EqwOtZJrveIWtUaVhQ6Jlp4aI1sjNdlSIoXb+kO/ohRYcaxbsEMlFWMcNL8Cqz9MaqEWwCM45PD0vzA26yWia8dma4IdA6nbsZgU2wRD5q9Ax///r6+n+nsO1YTl1WzsXpmHHh/O3fPnj3+Phw1ccnxdau8Bt4Pr2Nde3rwmjR1rV8Uz9AetZF/inx4Rxgq2RJvDVX/yc4AJSijKHZEOIXNKF0/nIze0nzOPAmi8syzvm3YHPP/+S6DOF76KzrKvUumFdVN2RVvx4vyNvDzgfsHbSMHjJOoUWQy2JtJ4IIaot30RyXTEIx5aoxTPNGkDtw8QsN/gQmaYDzjZqWfHsVF8rwmgncl5I6aKZcP33jNOSzfsBxBG9x7mGSCS1RE4XjASW8swU9P3wfsKagHOjutqk4uJMLRvGWFrJ3bHpsIPQZAELpk8CjAukumKJ+DDRctJzWZwWLVk1PT4MlLJSWyOXzLIlvNcpyJJyT3Y54/ye0+WR4Ee2prmAIaj+7JKbjt3Tpp9j2RjnW5Z1BTa8nTFGR7DOe9ayIVTEe6z3gKWWyrS7ohqgQjNBC8iyEYaDFrb0wlgcNMd2OeL8CKYhUhlGjdwpVTtL2lty1JYwTT+vKgOlniBXtt7ZjGJ4Oj3r40qldp2aNQ0XI54JIeNtU3G896rfTEUB7bVAbeRSQBzbunw30msZ6qKC9LxpTI7zihixoVuX+wUnqIPQBfA9lsi43vHS+7bCTsNf6nX+V6K4Wq1hxRNTIwZPWoUcLbZWdtmQR0FwFKsjGXd8wpbK6A6sg2EvkJ8qJXp2s8bMtOAQ65gGT31KRGtgtqzSrbkbHCTivKdkuJ32LKuQYsYZSMVQk2EpZ+pkCNUhuUFKDJPmBUZfMWVgZ4UgiWoMeRBssJjNUmzF1krbCuNk2R5WZQZh9DGskGroVXQjeMcwWZ43cEFYbWFohlYa00sbL2ZaFca6QYInNyKDAxGcMWzieFoag+XjyTUEx8EZzBx5fjpjpoFG4ZI96QjruOKlccgTH/aWT1dh8JXjOcHkuDycOVbhQzOMHtZaeT4aIpZnBPtcqWmjGRi9MD1qURMmx8OSmfD8yb3GEEyjx59UwxWNYgLqt0XHHM+Nh/cL1791zfWra9qV4U/uF6yB7blyMvB7ux2nzeC9MEdLE7gTz59PgquROnpWK7gMO5MZmufNPjIXx6/PFZcM9fGBLIIte/xOMLkhgydvBS+R0MccwRgymu0XRzBVYNA2uzxthAjJgPWRuVj2eeUhOmoThqWxmMYUlZi/FksKsLtUqhiMd4xN2G2GGh2PTlDZhKcKTGXDO6tFmBFkqzjJ2KzgUzMZRhaKcZgGc3WUYMnpotmRUqlJ8KJJAskGxApbs1gnVGmIFapT/pWiHxziGgNCboIrq446F+2U12YJzhBLgyZc/P9H3rv0yLZu6VnP+G5zzojIzLXWvtQ5p6osF7KEsbiX5QY/gh4dOpYtGrQBCatsYyxANOAv0QAJaCEhYakwxnbZ5VN19m2tlZkRMef8LmO4MeZa+1S/GqWzo7N35s5L7MiImON7x/s+r7GPRmwZSf71pg7zUxuuSoxBVBxiOw6EA+1HA/9P8Pbw9AX7dsO4MeUTuTxS1yvXlx/4f//RjX27sW/PxLjAt78C4LRc2NszxuTJrhg5PV58tdQqYT7x8PQlMSW+/Oorrh9fef74HV1B2LCxMs/w5suF8yVSonDOZx7sid4qr7dvmUshxkytK71WyjSTUmHEgarSdqfGvz5/i+F1KaYQQ0eyMUZy3IYpocwAlJhI6cSulZQjdV+Z8kQwCMOH87ykw2tzZUqR9X53/49mD/gMcVp99wqu1o4uRNz6J2YEcXVibzu9Hf4eCah2V/lvN+a5YARi8dd3AkQ7fW/kecG6OvYHJUzqYE0WbFRnv+GHnRTc0N1HPzo0d8QSVr1/VcIEGgh5IiCU0JE0s227Dx1lpmnHZCBiPqB25bI88Xz9FWk+kxFEKluNhHxha52+VRgFJDL6ib1/oExuHTDt9HiCCNvtPeBqFLo79Hg01jF4nN0fphponwas/U6YTqgqvYPmB0wye72Cu4XpvaLmq+OBokNoffXnh2WqDurmYRlut4Pun0Fvh++peUXO6M5dS+m4PnU+l5DHY0DCiHlyBRcfHJHIFObjMQ/04Wty7Y4T+dSMQXnjyIXgw7v3BgrY8bEIyOVQsn5drRoHY+vP7/YXwnP193//d4wSmIbfl61vTNWoRQi7R0eXOX1elzRxUvYlRvZ9cLfhO+VtEHKEpsQ5suWBhkw6sATTvbKpYrkQApxmpY1ISJHERFVQqT7AWaXeuv8sMUZMaFUu8yEV904wYx3NOUM5oCF7TFUh5MA5Cftxgh/r4Bwiuxhj656SiEI4kndjFK+pqXfaCMRtYFHQAiZCPuLx4TDrL9K9O3EkJBj31ViWTFBfC0YxT9ZdzvShPFwKYyj3VdHq9QDnaSKEwHmZiEl5WTf+0nnh+yYkOloSJUc+3DujK1mM9V559zBRDc4lsG0DEnx5SfxwrZ7ECcI0JVr1pKXqYBIPG/SufPN+ZVkScjyZX3YnsZ9DJAi8OxekGuuoPH1x5pvv715bswnfpMHDNLM3ZURYt05eMo+W0GB896Gh4uDN+U3gWgP6sTIyrF348mmifajEc2Sh0INxrYNc/ML/ogfBX2YKg3UInyBOshtxcQ6bjUAKRm0DcmHIQA7PXjh6C8UMm/00pEePbr4PWoBQItTByEYagRECRSHJkRyc/LGq6sM4QQk41EpECBpIMVD96olK82FsT6Qk6HBPVsleb6MhO9/MPEqeovPcxqGEAmQdqA568k7Bob6e0uM9KSV/fY6YEY2UNhh0RkugFc0Clh0zE3w4HU0hwSASmnnN2OHJ+nv/37c/Sc/Vv/ef/tc2zIGypWRyFLbtmcgg5oXTeSGVROudZb5ADozdVY/z4wNY4qtf/Jw/+sP/i9vrK8vlRIkLMS68+/rnDDYYxsPlEe2dacnkEtmB9frhQCDM3LdXtnVnOV1QVaaSkOBl0I9LZrS7e7G6sN2v9L2hRPI801t16rZ68KbVxm27czpf2Lb9GBoCJRfqfeXp7ROkC7VevRNWnKs0LQ9o9bXb0IH1Th87NnamVBAb5DwjDEqckNBQNfq+IjJ4WBasd2KZiARChNv1lZhOR1/jmVxO9P1KikqZiqvBpuxrhVH55Z/8U37r53+VZRKur9/T6itTNnJ+JAUhTTOpPPpAWA4v6+hISKiuSIxo36j3TkpvSPHMul2hVZoq9/uNdWusu1cVff/yylrvjDEj1g/7gMNDTSIqMPaOqXuX8jRzv1+JMSHRB437fUXSxN6Gq0x4GneMzugvbjvRQJDAMp+5Xj+gw+v95PAmSchs9UqWSB2NPpQSM00HXg42/HeqU9FTlCMR2AA9ggjG+LXfrUfFTMmFFOJRE+itDME4Un0ACbNxBGhc4+lDj48N/YxegCDxs5E+fUqpAaZKP9LXtF8blIxDlWqeCmzDv1vrYW5PP7YYmOOTnJDcsT/+X3+zDO3/07//C1Mzwmujv53oeImt3QM1GKVeGdPCmIR53WhEj3xLIpgyD4+tBoGSI7c2kBQINtA2nCQtbjy3U2YMyCHQjnLyoYM0jBwKtTilW8RP/Gv3i1YMmfbaOUuCp+Sl0kl8b7wp7cMzOft9P0fjZd2pT5l0V+Yls+FehzkcNTtBqQTKXllbZ0zuM+vLYN6F1hNL39hiJp4m73TCI/CtDgpek0Oyz8BJSYE9DESEZQ+0E+wzhE0wGywW6PfONCW20jmfCve1cVHHSswPhWk1rjYY8YAvm1HmTxF6Yd87wwbzHFlMqXvn4WFhDOPrc2bdBvu9wuQU3GVJrC8r23VFv5r4na8vlPfViesPkXxKzN1fdLkqU8g8vM1cbw0JwvcfVvauzDHw1Wmhd+UXS+ZJM39Yd2IUKsJ3z5U3l5mX3qgJ/s3pxMfN+JNf3fiYBtqUEWfevJ15ba6g6b7SDv5SV4eKzu99VfZxPnhh2mEYYcq0HoixMTdQ8xPxiJnbEri8NKaHzLoJQzY/pQXQPpA6SHuFHNDDQzJJhyyHV8U7+0qCHBd2i+yhEaNw6YMdpdlEyZGwdeopcDZhrXCZlf1e2bsP28bk3Kwh7NKYs3sNVH3gMxnETd1flyPTOVF79pRygGWD++QexFYC2RTd9VDDjIxx7R2VwPnmCcm6RFowGA4bbRIIQTjtGRuN29QRjaTNE6zDAqTAP/jHP83h6q/8J/+lxelEjoHldDpSaM63qvtGkE5IkTI98PH9R8rpRMmZ3io///lf5uuf/Ta36y9JJXJ++C0+vP/AaO8ZLfLu4S3vfvG7fPvLP+JP/vif8+HbX2Ikfnj/Ped3X2G3Z3JJ6L5DhIzjX3qvTNn9imN0Uuq8ffs1T+9mLm++ZJ7PlOVMnh/o6qiHVhvDMr3vxDhzW5+pu6+UJHm/6F6rr8P7znlywG0RIcbilTLTGe0rQ431+oo2rw+7v3xHKQtTiUQzchQImWU5oSq8PH+gzIUY3OMkGNvthVwCOZ+QWAghoH2jDUHrSkqBlBLzlJmms+Mh5gntN6Byubzlvr5wOs/k8xNmO6OthFwIYWZ//UCcjJAn+u2G9obI7FYNG9gQhDOj76RpxsbE8+sLyOB6r3z4/jtqrezVWDdD7YoyowQezo/ub+qDNM0EmZjnQm13urrh2yH4hX19pZt44KErqXjK7/X64qr6GGjvGIU8n7DRD+vDfhQYJ/bW2VtDdKAx+FbMKkE9uNOOIJWagjnyIIZEG42UTu7ZGo02dtroqIbPNHaRgOgghUgbDe2DEDNymOq77pgGDz6Ye68AujX0IKt7URxuxXAoH9Z3X+OFowh6dPi1YetH5clcsZLonJmxHcnAX7v9mZVh+Pyx/dM/P0P7X4jh6r/7dx7N+9USaCCV5Pv7nOkJRlXmGAm2EYfX26wt+ETcO0E9DWgiLFOi7TBwk3Ia0KOnohb1Prb33XtyZ4R1b4SkEALaDcuB2QKjQD+668yg9UCNxjIZ08bnaTtxDCLbIC2Tc6FUWZNgJaDvm7dadKWtjXqeiSEQx6BKxAiclkxdb7i2MfGwJO7saBXGquSlEI5C308deDEEpBsxuxohhpcDTx53zylgH3c6Hc2JJTlwMpp7ePpycLAE8maUHLkG5a26Gvi6N7I2co6kpVDboHT3fLXiT9T0mBmt83aP5BzZRElBeDgnNnO1blkSv9pW3p4DrRrjrvTD9/TwNlO7cv2w88Xbgrx0xpKZslDr4IuH6UAoKOkSCSWx7+PgyPh9mqdEILAN+OaHld9+t9CqsZryzb+8Otj6MoEZ5SD1txjZb43l3ZltG0cU2FcIjMx2pONCN2z2oSLsSkiuSPatMya/eCTtXvESE5uol44CQf3vn2JgvzdCmehdSbWSJkduaBbmkBg6kByIQ5EWaBKwdUdE0BTQKTANT4puefB4gz0qFhJ7iKRfI7tbGqQcCLt7ZZoa9IqeMnEYucM2FLGMBe/CzcO7IJMK+wxpFBLiXVyj0fNBVsfNu5tByJFya5Qc6H2DJAwpmMIswroPwtlDI1ETvQXCXI/7GRjDfrLD1d/4239gMSz0YxVidnhXGJgttFa5b3cCcNtv3K87O9Dbzmje2WnthbD8HN0/kuMgUDAJFIn01Kh1pcQHJFRXnWM6ejcjEmdsf6FMhaksTNOjhxUw3pwfENnpbfUkaXxkWz8QkzPajEgKOBQ0P1Kt8fH1PTYU0pneOtfn77k8fYmNV+pzI5fC6e3FTdpavGnHvBdQVNn2lfvNuwxzjOQkrPdXLsuFmIT9/sL54cSogXgyUpqQ+AarN4pEYkysTVxZaR/IMTGdhMBgXp68qWBaiKmz3xtjwL7v5PLAecncrh+AQpDOw+kBoxFwNEKIGZGB9evBWzJO5yduz99yuZz44quFNB34Os2oZmLyZofeKi/XRu2db775I1p4ADnR253hcg+WC9t6hZGxkOn7C2pKSNPBrHp2A7g5N6q3nb2PY3BsDAJqgykXhiopRNa6kpLXEDUbRIQxGjFm2nBoaI4RCREJmXVsWO/MOUNXqioDV4mCRJq6MlVHp5s/lwxlTsXTfKq0XpGUsOHXniCRYMLed3LKB25iECQwWnVFLLhAoofJfdMjPd1XPgE/g+TD5P5JaXLmmCcAj5uprwE/+aokIjGR8qemk06UQD9CBKO3H4ercawDPyVCf9MI7f/wrz2axkDSwH7plFuhFoibQXakfkCwLMShTK+GtI6cdjbesE+eChinxmML9CMirskHI+nNVQjpLFWY1sqLDDAhzemI5Qfq5j6gbYLH5p2FFgxB2fALSoyJoND2ytunzA/PHLQy4HEmSqe8wh5d8VoIdGvcCVxUWOtGzoEobvruqHclnoVlj6xtwEPEdoWwkzTSq0unMQojJSQIi7hykvKZ5zG4oJ5E2eF+FiIr767KazFaM1IN6CkSUnKUgnVf6Q1lLMHXbsEVh0twM/X9/sLYdnYr5HNGUyS87uxZCU0pDycSEz/Lyl4HL0cly1NxDtb5lL2G6MBWZIv040Lyw2j8WzZRHhP/5Fp5R+TdV86d2vaORuFXz52vvlxQGudN2IKwbo1zybxcKypCiRl9LOxbp62DWgc9G6qwEbCUmVJAnjfeFOHboNT0QBxKGp2hQj5NJPX6g3sNXELnHiNpU+Ky0Hcj5c5E52N0CT7XShjKICGlYkS0HWDOfWBR0RSYU6RrpGWHaYbd+S5xSUhVP0xk4LaSJLOHTpbOnmYnvXcjTUbvPriM1IndB2RB6OYdgqltSHSTusUAPUIwP5UGYVbYVJExvIxZIpICNg5+F4pOzj6yKKgZ4VOpNEZU+0xuH5MfZOKulCFYDjQRkspRi+PMuJgPJpuEz+vHIILcOgTl7/+z55/kcPX7f+u/spge2NfVLxwoKWbonWW50FH29RUxxbpbUbyINrCtrzQTTssjdb+CJIZWJ+j3nRCzV70MReJgjCuX5UIKkel0IqXC7X4nloUyZdbbR+Zl4s3jE4k7Zc4MNc4PX372SFnvjAEfn1+ATk4zqspaO6bOv+p1p42I5ISoMeUT51NmOp0ZfWU5nYGBtp15ujCf3vH68gPr9YWXF7cpaDemnGjNU2W9V+ZlgaMEWMaNkh8wVdq4cs6ZfDoRgqNhShIgMpWZmCpTmRHJbNsH3jy+I6ej5olAjDtjbJTlrZPRraNaAaerhyRImI7mIEN7p/eGmQeOEsWV4E8LKw2ggjBjQ31Avt651Y3ry3qANG/00VGdeLk983h+4uHyljI59uD19spaAwp8/Pieum/My4WuA0hMOR6DjA+mY2ykfCLGiSlFVJT99up+psNO7fwtoasRjmCV1s5eVyDRJXiqMQSqVhKZYUodzYMCwL7d3Z+VT1Q7iuXzRDTvsTUbuDsKBCUFAyKSZ6xtiOlBQPDVp1sNFDWh9kGtG1GCA7VDYKhgEo8V585wR64rVTLBuPtwZLhXy45hSaJ/3CqM/UeDegyHtwGviwrCjxdt+1EBM8H+QOYHtAAAIABJREFU2W8Y5+p//g9+YX7BH/SU6dLZ984lwZDEEGWsg24ThY7FyBKVHAIfX94zwkxK4kWPzWhZ3GOVoquDwp+pGBmzkFejR6+cUTVSTOxbd8qvQJh8MJBupDGoCcYmhMmLl+fiHXObuRJTLGJjRySx5UTWoyx3KKod2Q1yxEJGzRBrB4HbB5kUhb0LTIEchFMwbmv3MskUP3twHmwQRLh2j/6WMAhqSJ5oppRlIe5KSBu315UThdsAy07X1pCcKJ+NFAOXS+a77+7Mc+Lehxfu1sYYxoiZcxIYg6lE8lIYo5JsYt0ahvKzrxd062x757o1fuvLEx9eNuYpclkyrSut+UDx1eNCHZV1NM458SSF0I2XfeWXdefLudC68i4lehLygA/PO8sckbcZ24UUA//4jz4whnI5z9yvA/nFiVbdd/f1aUK6Q1xfXhtREuVcqHWnlcy5TIzoKtHzd1dySRjCNWUSQk4wtZ1ncbWopQ4amCSx6SDWw3MhkZyBZXILwtid8F+Pbszqa1wrvs6Iffszz8FmriJpDISojB6IJPaDTZU4cAUaUOmAr+C0CzkYGuNnrpaZ18hJDITdkHxI6rGj+0BKZIyMTYF0vLmN9Entcqitaqfs/m0WD7ho5yg4h7Q30uK+rWNmoiZnyol1COlHrpV46jDhz6eQwpFy9eF97xlJ46erXP3Nv2cp+VCwby+83F4YdWU5PzDNC8RIDPHoYzNavdK7edUIUPsgpoD1io6MqgMt5dNpPwZCMELOzMlZd2M0zpdH2v0HUips9w0zpamvfpbyjhD9khyjs4e6dk7TgunmrKw80dsO0ikpMs0PIBDzxddRknj+8D2SIg9TxKxhROa8uC/09pGukdqap53FMUSj3VETHi5PbNuNoV46L1G4HArxPCcfEMx76mprpJDc24Wzi+zw60S3KB408EBKmVAKQQohDpoWB7KOlWAwXx6p9T3nGFn390SbEVmxEHh6+j3W++YhKVNu9yu5LCzBlbHTkpjzSowL719eqH2i9URXQU3Zq3PCAEwShrFvzxD8/6u2j0BgKgsikd5d3SFlB69GN6ynnJjLTN/vYO6NtGOAqigylJgmXy2OnaGBrodCbYqOV1QSQmbf7txaQ02ctN+bM+/MfVB1VPdc1RUjYjFT+/CkflloYyMGt7F86krt5oNRlkgMYMfwYvLj30ZrI6TpECwi1ZRax/F3DGzrh2Pdp7A1lwMFH36Mz9siL1z9tXUgeHoQAHHRSxU0H4qXHH6r9me/79NK8dfAovbP/5ffLM7V1jcId+eE7BdqiUwbPCdjWZR7D0gULCj7vqNEagjsZaI8ncl1oVllrsZ+DvQ0M1nnrBv3XGhdyDGgUdn2QXwxYon0fdAkkUoi2A6zdyD1YaQQOBXotbPGQFiF+BDIe8eisTZj3SNJKpcKL08Js4lziCyt0sqJLkrOAilS7q6q7KWTYqS1SMwR6ebYCS2kc2JsO61VXpIwsjCr0Wtj9ERYEh/DzFSVUHZaTEg9gWwIgTknOpVtCbQ2kx8WPmKkJZG2ypycHq8fG98zKES+/5OVZH6BPj1OjOHdjF+nxH1uLFF4SYHXIVxGow+D0hjSWYLxq1+9cDkV5jnyu0tGN+PLxzPvP27c7itqjXfvCrercX3Z+Z058j4m3kyRP7XBv3y58uXiF5MP1405T/yrMLh/2PniMiHnyLcfd+baKadH/tX7G199dWbHeNXBw7sLdlfumxFa4/6q/OXzmV9FZ5roYyb2QWuBXp/5KJEYJ/cEvZ2d+Kwd9pVeB5IL+0PiUhM32Q6GkzG2xnyOSFg4Z+O67pTg4LswjBbcE1HjIM2RPc6IGhaa+wdbIUmgza7uyCaMxYg607UzUmVSf3w9JTyR88C2hpBowZhG4PXNYLRIGQIoQ9tRsIr3noWM0AlJjtWDYHVgsyFdoPUDOhr9FB8GEAhAJyDBiM3Qm8vk5ylRU6SX6MnEAPfjNMzoxBJQPaNm5FDpw7y/UtpnOOHQ3d/wWkIVJr0zPmHaf4K37X7ldC5c9zuqjVIKYXpE9e4Yjb6z7s0VDXFgZAkRG/3wZznF3OG0Xszsm5OO6d0TZanQ+0A4o2Nljsa33/4pQxMxetgAIillogRXHUJA5DiE2UwfK9+/3NFuxNgw2bzQVwPWV+f1hYDEzYehdmNZ3tD2F0Z7g5mS6Ly2K9NcyDmBdXKZSFJ4/8Of8jt/6a+iY+N6e2HfGjoCrVcent55Ck8aqsLzxw+cLm9BdldqJDCXyb2PQMk+rGi/MuW3NPXqGA2B1/VKaY3l1Ni2V0bPnM6PRCn0/ZnttrOcAqm846t3v8ftw/eEeCcbRK1YvfPlkjmfn3itg94LU3IAc5LvOT09AIVuE7ctYOsReNJBDwrRfUPbdsNCYJrC54NIDG/o3XEpNjpKYNvvtFVYThc+fviG5fwF43olpYiNQS4ZRCm2Ms1nxn0lp4yNOyHN1G0n5gwGS3TExG5KGztrH+Rp5izmUPIcCZrZRqNLpO6NCTAJ9PIIh2iQUySXhe3+ymV5g4gxRsODw+qDmAUsCNobIUT2/WC2Ba+9USBIgdYQlDA6I2WGHWT1kImxQISYJ7o5AHqMo55GDT9G8uNa8DjsEqcfxahw9AXq5iqeClAgnH6Ej35KC8bohPb05z8K/YVQrv7b//C3bZ4DcVNagybNE2/VBcF9SizJ6LeBTpETDq9rtmPnC7lBKcL1ZSfkwKnDLQIPmUszrquzRyZJBAE9+Qncn7AZkp8UhgT07jUMLQRG7WRTSgguTaaAfFIkcmAw/BQY4XJTOpGWBnEMcorstQOCxcz5iPJeW/UVcTro65NxXjLTrdHE2Eci0TEJjCSgRmtKuO/kU2ZUo8wQayCdZkbq6GpwimxdGeXEtF4J6l60pMo+BrspoRr1XeHUPP0WQkDNDt+M8ZRcWehT5NtW+SoI+Sgjva3G/BCZp8S2d/o+6NGJyVTvP9y/e6V+MVN0kFNgKJxPwnoPnOaJ1yPivapxmeDL00JcIrfr7lyktVNipq6Dvjbe/eLEKQb6ffD2aeKXrx2ZA8sGr62TTsKfvN8wTc7ZylAksKTIGIpV5cvfOvHtDyvPm7JYZJpc4ROBJRkqwr25p64UD0PwvhIehdYDc8yMI5UiU2Aa/ThRZmyEz319IRp9V0gToxtp7swxMLr/3Vfb3YepwqjDfYXB0GoQfPFt3QjZ06UPlrDeGKdMk8BcK69B0bgQpXGRRAhw7d44gATvZtwG5mFpL5IF7wPsAkOx7IpVJGGiIONoqxcMhaCIJqSDScVyIOxee5HGgK5IdiUq4gZli+6zaMHTgSaBOLlJH8BqwtJRnTP8AGop83f/yQ8/SeXqr//Nv2sSE1hk6MZoG2rKFM+M+p5UzpASFoS+756qGoaQWLe7r4pqw6yy7zspLa4Kop7IkoaoG68/r1tMud42pin7ardVVKCUCwDTNHFeLpxnOJ0y2u9uLDZhrcptuxPyme++f0/OpyN9Zs4nypMjQmXG2JzNOFydCtHXZqe0cD5deL49k+cz/X7/XO4bEG5tZSpCyjMMdV8Vg3NJSMy03olJmFJBtXI+Px4epJUcEr1tlOSvx7nMqA3asc6sRxdiCouXCgcfXLV1RAZv37xlvX9k3Tdymqi1c3v9QCkzMU2UaSEei/gggVo3vv7yCy4T0F+YzgFR+Pi80cbkns105nZ7QdIJRfjhhx+Y5wckwIeP35HL4soahkkiyUQd/QCvBix4vVbtgzKd6W0nJw/ZYPgqE3NMwaFoijoqotpgH2BEJ9AT6dbozVXKkL2bstZKO0zrOpQ+GiKuSNUxmLOXim+9OtZCCt4CeFQXDaV/Un9UmT4NuKqIbcSUGeYKHoCQac19W0GCb1piIobmRdIyHS0FAwn+iNdDWXWsBEhIcACXtfcfFatWPRk4GvSKp4mOVeAnL/tRXE1IxycP1tUwh2Qa2J/+779Za8H/4a+9M7PjwZoKLRYHi2Vlet6JFhnphbhe2CcjTAuIYOmOaORhH7xIRKdBWBW6eDIhewdUid6DtCvMU+QuINvAknu5qMpYEo8aXdGwxq+YOFVgdOraGLMQpsgSBOuwFq9QeUAYeyXpIz2upNipTYkhMk+ZK53wMtCUeZ0CUgXpjTiEnCOUT6tDN6OHjxAuO/VZiSlwPgVaSJxi5DUqucHWHRBZsnB7XonHi27vPpQmgRSD07A1cpoSbR9uhG+v3CWz0emWCHMkbe6HmbJXRwxNjAitRwLGlLxTUPrgdTO+TMbrtXKavZpGRGlNudngemvkx0QL0D/u5N137mFOaJqQ0Dk9zPxw3cnDO53CSMxzIjwk7GPn7ZQ5xcg3r8/89jTxXU5cs3G7eYJOu7Cs8HqGhygsk2MsXu4+pJQlot9uYAErkXsdhFNmm5XLrXN5mHlZDd3uTqhXA3NFx47aoXVMjir4VDx8RH1DdNTCelQ6PDHY9k4LhTg6QZTQO7kPugqkQp0gVCH3nf00HyrszNy9mb4PZUQn/scRGUVJTRAGW78Qyk4eTkTuHULzRI0IjBhJKdBjJkZjuSpmsEU3yge9Y8Ub6VMK6OqHgzBPDuuz5n4r9XXiCAnFKAPvFwuCJn/znpqHKSwoOUf2470jdz+Jb5M/Vl3E+z9DIAxFOkwlsAaQrqj4MPzf/OE3P8nh6vf/1h+YkOjtjjYlFj9JV21ezKvGvt4ZBIIlRJTeV8Sg4TU3kZ058rlKaFikjuE+ueHdeZlGiIkYAoRA3zficmKMirZOiYJY9xWiFFIutFYRCVwuZ0qZqfuVjne+WncwowTj7Rc/Z/Q7fbt7me5BTn+5VkKMFOC67iSgnDI5Q91e6SO7naHMtFqZpsRQZYzOMs3o6MzzE9avxBwp2WvIQp5o9Qokf80eA1jt3u4QWienQgqNSPB0mR1VXdFZYCk4QDOX2U3TMZJDIARfHy7n3+Z6/cYfgyHevyqQ88S6rkDHbGGehSKDIM88lMj56cJoG70az7fM8+1GG4FtbdR+d5xSemK9f2RZLgenCbZ6Y2sVCwGIiCo5T7Q22Gol5kEg0qV4Ypnda4EkEnLm/vKReXmgtZWUi1uJDOYyMUyPDsJIb5WB0LojCfqAMSIv1w9I9qRhKReGuflb1DAJ7PsdAEtOqx82uN2eKbGQpgntHR3+fqKmWAgIjheKOEJmH0qtN/9c8PvVe8VJrcZBYj7WcuPHtZ1EH5Q+cWI+zSkp++fHp3gfhxn9mKAkHANX9J897EdVqvdjEP01wCjH6tGcNWm/+j9+s4ar//Hf/sIAhhjNIlkG0hqjz/Szn+ynOBPKRvqotORD0N4gENEoEA2J0b03wS+Q0gY5C9dmB7EWWleokRyMczF6EG5jMBHRcyCuRjWQrEybMYfEunZ6dnPvm0ewblgwQhTWIb5aqg6HDNI4nzJbTGx3gV5dyBxKmQsjJB4m4bkNpikRqxuFp5hZW+d1DMLaierR+o4xnRZqbWDGnIXrvTNyYhJFcGjqcsr0QyrVPmhExz6UzoTQWmeeIqKFPpSnBK0FXu6d5dTZtk5MExKErQ96hGmrnLLQQzwKnAMfb0rO3QtZk5dCD/Wh6iKBlALnkPj248qs8NXXhZbcu/PmaeH5euPtXLhuDT3u+9MlsO2DMxCmQDLh7ZL5MCK9Dq7rjbenwvfPjbdPE7MkahtUCTwYvAQfHJDA827c74On7sGkrcKW4Pm18tCV6znyWDL3JtwPv5KZEfES4t78vs7q0XiSkFOgH+bNiGHDCHipcT4G19gG+/nECSOOwSaBLslLjKthJWINym0nTl5nY3CoTv4eMoYRasfyQHVBokLcUV2Ih4cPHOXRHXlFSO7XI7mKhGbvf1QDBsF2mkTsuAifTx6kGGokU7DOFoqv9cQHqbx1NAk77hGZjqPfUK/ekeiDXT88WiG52pb1U5+lwKpeJqgGSbESmaoDWYWKqPF3frn9JIer/+g/+zumA5RMU0CVvq8QPEllMXNb7zQ1D6lYRyxgQ9GxIZKIIbJkofZOGu6lHHGi90o1J2kH8wtrNZDkqaws2QGzY6ekyJwLMcln74yqm6CTda/K0U6MTjCPCaeH950YMzEu3PebKw0oQzslTH6dGleiDL7+8vfctzfcQ7P1inbF573i/d9Hqsu0HaufwFzch5wTbmIOAdHGvjWCdBCjlBkDclkQq0SJxGDkmOiWgO6sJfEi6ByPQWCIE+XTyUuqAiynhfX2Qgjp8+MwRnXi/OgkhGleePsQ2PeA6Y23794yx5W4LGhvvHw01hp4uW+sW+V2a56AT3Bb77w5PSFi7qvSjolRTjOzCpt2V9xGZ5o7xgPKfqyCLzxvK6/XF+Yyc78fQYhjKO3D13AxRXJIlJS4rk5JnyZ/bNu+sTV/zxoq9NFIeWKQPpvbt92/R0PEguMZwJP3jlAQatuJIZJSQUypvf/4vDmUzt4qKsFJ8GM/qm5ceQr5U7Vd8nqiAfT7MVSFH4ekkA5P1K8Zzz/dev+1rztAfBxQ0nAMacMcwRDn42d4atqZRTP06/Fjo5vfOfohv/s/f7OGq3/419+ZCMjqZaSj3kkNppK5itOl8wDLDlKUVRl5EDeg+AWiTSdGnjntzoC6WiSJMupApJOC0SwRgpCLF0Mny0c5pV8s+1BScpflRkL2jk2OMEgnxSShuzKJssTIfavsEsmTF+k+7oMxOSUdCdS1M1XlNCeWONjqwFLB6Nx7xKaFoo0QI9tNaY8BbUcXyvFnOUWjaqBoY1fFCEw7yKLEU2ROs3uF9s2THGl2U3qJxFunTsbDlIlitK686CBJJN+N6TGztUEOwldPidvdS4ofngp79VPEN3tlnowvb8o3Dew88/T+lTAlXm4rMiXsDvkhcfrYuPzswlR8QLleG09nIcwTA+N7qTy/7N6XFQPr88a7NxMf3t+IO3yVC7/zswuP88S/eLnzbR+MoZzq8IRlN663xvithd+ZZpaboefA/9M2+nPjq2nhexkHoLCzhEjJEauda2u8+2Jh3wfXLmiDtFfClLBgzCl7UWr3dJyEQcKokmE474ng60RBnKS+dfdrySCUBJoo2wYjMj6hMywzijCb4x42A21KmSOqUMJgC8WNuN3Q0b1A/IDTVsCSEKbMvMPU4TkpEaF1ZTnWkiOJox72zsiRYJEQhRCV2tQvxM3LZWMbtGn6lHZGT+KfX3EkRBBCbzSJRxrE037ButcBBf/7+jd78CEEQYIbkdNIUAJxfDL/JwZK2r3kWrIP+v/FP/r+Jzlc/Y2//d9bV1/TvN6unFIihMDaXhEVKr7GarURVIhpkMJMkMDehdYrSsSsHyvpmd527gcGREQYo5JDIYTMdqhRprvzkoAkRpSGpEySQM7FDfK9czq/ZVufMTomxUvLtTMUSpnJtkN2D6HzGo1pudD3G9iRLiUyzwuDgfZKjEKKyRUnMep+YyozaoFhyjSdiEfaWASmKCynGcxYeyMcfXQSCvHzYgoKXjZ82xs2dkJMtGZOpbdOKWfO50durx8xJiKrrxP7wELg4fLAFBO9VaaslDSjeiXGgoVADpk+OrXeiDHx+Pg16/qRcy7MU+E0+YChnLnfN16ud4ZEbvcrw4S9Gzk6C0wk0epGyOfPhcqdcKy9micAj/BBjBMhJuiDfbxwOr+jHOpg3W6uXh3G9z7EVaO+Hz2FAQvQauV8mh1+KhN139jEX6Pr7iGFra48v7wwL29Q22htJ6cJMWWZFlcve2Pf7pDceN72FUVIuXjIQAL78KH8U3WSmaDaSCkRc6Z3Fx+0rp9LkpFAwJ8XJtDaMTTpOJQqg94+wSiP7/lkXI8/sq5ahTz/mCBMR2LQ3HcLEz58ufrPsXoEHO4X5GBd/QYOV//g3/3SGF6QDJBVaVnoCtPRaxVNWC0SWkNzZMRCjBCSEl8HQz9RsAdSElOAvY6DlyVE088ekHyYSPcIdeuEHIhkZxJV84JOA+sDYiDlSJZE65CK76ofzol126mrR+G1ZEpSghbUhG4OuOwh+j+PJ0fpET1OS9qGJ4NShiLE1056cP9Ta86IyhK4V2MdgbBEVBu9dSYWmAOyD4J6cmbDPTUhOKJgrZWsxtiVKJ/SZoMEpJJZh1JRnqaJORkfPlaPTyO8XCt5Ur54O/N6U7ZakZNH96sFHi4n4u5m/OUsjAgP5iWqH1fHMHxBIlmnlsCK8vK8cT65rFtyoN2Mx5L4blv5K19cmAhcb431ZJxz4uXamHLkj28rD5fCssPjQ+GHq/cAqgkXjUwl8v//8EqdIxdLbNfB9VbpU+Dtm5l1G6QSUYzafIWZkwcQqiotwQOd3pVWB+RA0uKrwj4YCCEGJA4iwUn644gUi5CGsRWv4CjS6VU+y9tZlSaQ9BiCjgH+03tHKZFWGzTQJO5nUqOTidHJ/I5AMDqGnYILQlU+K+iOaPGBPDZ/PoOT3j+r7NV5Xt28m5D8Y2omkBiH70Cid2TSGlLKQaP3E+9ZHSuyx+ioBfP0Y1Hnn/XYCUkplmkJevPodbYBuEdP1JDhv/sP/sXtJzlc/e5//J+bmtLH4OX11YnUo/shKs7U0TyNFjLEgFqij+bqSy4+KJmQzK8TvetxYf5k9gVncQew4+IBBwzvCBK0Tk5GEFd3TssCo/nPpxxwx4ppPIYzp27XfXNopSrLlI4qlYikdDy3ofdO7cr5fKb3To7ZL6rmG4TEoHVnMzF25tmrbNQqKRVq3XiYF1KJjFa518AyORF8b8qUXRGJMRFjZoyGxEyOgZwnsEaKBRikXI5EXEVo2BicTmfq3himzCUf11XFFHJe2PeN83Lmdn/1VRZG2+7kPBED9H5nmWZigCQ7c1pofXBdX8nLW/Zq3O9Xau/sHRSnlwvBU6DiARJXnXwjJQRUldoc91D7IOWJdV9Z5jO36wvp8Epp8E2EJyoTIWQ3kpsPX72trq5H78fNITHZcdhLPlzV5r2WL68vhLKw7evnA71IJgan3YuEz6u/zzfx4QlwbtRomHZXvVR9BZgm6n4jpIWhK0Jy9lV09BFAjBkxY9vvrPsd2hEfTfnALhy/DHG1qjf/d8GVKVNXrT75zkI63neP/9bb5/v7+SQJ/nrox9eYHKZ3Xy3aL/+336y0YFpXbAmE6BeU7S5MIdJmaNdBCQUJMAmMNDlWoDdsTViORLsxZEIXXw3GFNkNmmSCCJMOOo3U/cLVThMjga53ZyCJhwauzc3rVR1GJtko2lh4punEJIX7a8dC4OWm9CiE6L6XaF5FE7Pvn3Mv2DD3rLRASImmRo9GqpU6EilGditHme2NEBR5H91EnCc+rp18r26ElobsoHKihIiEO3EVOt7lFvrOOQf6qoQQWeor8e2C7ko+GbVl6lCm5YzmlaeqxAESMrfW4Nl497jwEhzaWc6ZpznCzXgKifjk9RRNFbkPbtuNd0+J+61ynma+aPD9qIdKZzyq8n6sXpQskb0NVjG0rrzdE2tthGiclkSRGevCizb+7/fPnP7YeJbB5bzwszTxcDozbhuSAt/86cp1Nj7cK+HjwIrwb3x14Vc9UV4qear0LxamlHgEPn5Y0S8mvkqRYZ3HAXNOfFsbYpvXG8XIlmd0CF/HzLMNJLh/zauSEs4HHYxYCa+dA3dFjhM5VTqJeOt0DH0IyHYYx0W9a3IMgkFJvl62vlLjRO+Zro3xCIhhw6GuIXVCjMyy8ZpPlDiIZoxXby+QekdEqJ8unMfaT9ScexUKwzpZAy37AYMgyBBCCoyqWPzX5L3Lj2Xblt71G2M+1lp7R0TmOef6lvxAsoxQyUYCCYsGDUQTAX0a7iHxB9DhIcstS3bDHf4whEBgBEKWDYZyueqePPmI2HutNV+Dxpg747pEj5Io3Rudk2dnxn7OveY3v/E9BOlGXw0pFWKHIoToWW/5bOiihOFVRm9LJBP9gLgodhS0wWEedEoU1mOniXc+EhqSOkUzEIm7Z7U1qe9s/+/hz9c3r9QKeuGSV/bzE1FXtqDElN1Z1u4MyS707Qd9cjXnUT0wcdw5+wM0MSs9ynQLAAQHUjrm6KTCiBDaBFxGm4zGQDlfXf+X0gAOzPwz7H1nCfJdAK2qWD25l4Pan5AoCNXXj+Rp4Mi09saXz595umzs5/l905XmzsIck9fcjMZePpFDRmTh5erj6lErdVSuTxdaqy7KLi5utjroErGuaAi+riUzTDkOH3fu7UA0kqnIDBpNmklXZ6liWlmTueDfGkM9lqfUryzpynkeBAkzOb+gcb5nvZHXKyqdNSf+yq9/QvUVRuTnT50yhKSDRS/U4WnsHeXYjToa+/GNLldkFGJaYJyud5p+XcbOsrywLIE6Bh9i5KgHL9erG1NG5mgF045ZJKWV43zzSZjAcdyRtLl2r3WOUlFRgg5CSHAOzl4Zo0wZSSQ0CHoBafQxHKCIUvsgBmiTTXNGYLyP3gYOUroD+VaKo/3QQQ3OAkmgHPN2BYVDmNqo2Tm4XJBwwewGcfHYhO//Zq5fGRDEwRB40fLcpz0AsM3vQJuoJkB20E4v879tznvDO7jqJ5z4d2WK+f+8fv5CgKvr9cUjKUqj1cFLjAQTUqucAWr0LrJch6dpv1YfL+SCmGBxYxHDju5dfOfUXZU7IlB6RoMxuNGXQNzBZqrx6ALdG79zzkg3oghX6dzOk7E883P4gQ9qvB0FWzKjB7SfxCbkLXGMwSjDA+e6d+iNYMQQKMeOSmINkVYqIUXOtKCt02pHl8bLGjmbV4KYBnIO7Ecjp8Fl2VAV3uo3KJ2xCeslUiXTBfQ4UZXZHWjs5eQShJ9fNv5AIkf3gLscG3lTYq+8vnZuefDh44V7NcbrwaFGPY2nNXKKd/bVs7ClyFMSvr1VPqZIPwY/PSW2Nc4LqhBO40+vgw9b5PWtcO/w5dvJWITz68mnX8xp3qeV17NwboPrU8IY/EYan7662+J4AAAgAElEQVQd/MvXyqaRny6Z/AeJv/Q6ePm48M9ud/7gKaKy8fn2xilgnzsfYyBcEz/+pQvfOvxbfyD0uFFe79SmvAVhSGC9RORT54+l8quXC2cWPlM4LHIZQoxKOQfhvBGAX0wwdYekDJsVN4E14dqYNoXkZ8BUqK3RxE9Z3fzCEz4XRBo5KmVJbA1KcxB/yPAYg/WjjwbajsxEhBiU0KCNQTpPMh4aeA139iJ+WlPo1QuZERiappB1OOhZFeqAeoMMfQS0QlOlz77K3t7Z6rQGajXCmuiWMQv00xOgazS6CpoUiwuhe2m4jOFl9HljSULuJz1CLwtFIpnm62KsjDZI4tEBI92hdIwV0d9fdPW631jSwjm+wHF3sJMycELz0Qtjw+rhoqMxE6gfJ3UFJDlwghmAyBy3RBinj0xkhvYZvin15puIAUSsHfS80tv4Lhwepu6Ebed0qDnAEFHXLmGuWgiZwxycY+JRILZzmazEFgWVRBCPBbFZrGsGKbrBotXiXXXxShflKAehGtY8kDINhVuhi2C9emREF+7FU8pFlTG1M+u6UsqBzRR6jZHedlQH67L5WM6aj7ylE+LF3XF9oGGh98rLNfPh4wdiMNZ14TjuBPEDctaLuxut+oF6gNng2G8syYHZ9fIT7bXwVuD+dkfqSWkQUicQ6WNlyyuocR4Op9btmdY6rTXykgklfRePt+7hsjoDZIMUQjAog00qH58zUCmSqRro5WCLG6XajNUI3tCAkdPyPdz3ogvWE3U0VIKLzE1o1SClmfSumHVyFCR7SnsMC8MGb/cDDYFaO9ZPzzqLC/04+F6g3DusV1+HxZ2NdN71Umb+d6PBECxGQH0N99+KRhjzMGDqAaAzM8wbw6N/f/C7p9wdUJXu//63qX1VkD4fw2DJ87sxR5QEB3V/jj9/IcaCf/9vXo3kIyeGMHKEGVY4LKJ9agtyRkcjzj42tLsAGUFjI+DC2rpMhXDNhBBYpt7Ik3nBT2ZGOyEmt3XKtlLrwIZwXRPH4VUpvXyFIFhMUDohLu44PP2DWNQdUqcNcgq0ELHSkO6AsCTX6DzTvZcvCAsGo1KrcaQnLCqX4YumidBEySFMF1nwC0KpLDnwSTN5HGwJxplROxhjrt/gjgfNEbk3QhwELV4HFBdUmo+PgqAdfvXjxtfXk0vUWYod+PAS+fSbg22NHAarGj+/7bxcn/khCV9ed77Vwva0erBhFC5PidUEqQdVIuka+c2nHRuCqPBxW/jhw0L65cZTCPxpLvQx+CkvXlPzR6/8m3/jR97a4K/HhS/1gJR4+3TS+uCXBX6QSEiDY4dPpdI2AV2Qb434kvn06c5zgJwzlhc+fz15uQZu3dAU+fJth48r1x54KsYtDkYpnM3oGkjVwc2i0ceDa2W1hVM66xLY3w6WmDkeuhY1WjfSUHp0jUPogBoVI04aehPjiN4mrxbpBTY1bqFjyaAq2gaWDTFlBEXawESwNdAILCIUdauw3iJruTPSAriN2EbHViGOQe8diwsxCq3Z7HKDS2/eXbeulDEwcYbLorKM6ZgUr1IKuKZqaHMR/d0I4gwbfaDJK5ZMvLPz2l13UxYvV19Km4YTYdlOao2M+LjoDoIVYlT+y3/y+zkWTP/+f26tN//Smkcl0Lq/8UsAM3K6uNlpCoaxCmd1EFbv75sQONCSWePRzMcq3l3kf5cWOPfv+jnmtQYNcLxBuLhORV3D833zM6atfT7Px3ion/OkH/xxFJIGck5cg1v1Q0pEzQ70mBocG7RafVS8LPRWkRCpxxvr5YmkmSUGr8kB1hgA1xCJ6ndTUgpeSSXzNR7nDNWUxHneKedBSJkQvTUhLwmss16eWVJEDN7e3ut2DBBVbBSSwLJeyMlDXEUyvVeWnFnWC60Wj53AGKNyXSIvlwVT5Xa787oXiim1djSs7Pvu8TZtoGE6RG31uBRxcf0wZ+T66NTayXljP+/0oWDey9fbybostNYprc3S4kEfAcOvSWc5iemCqbppIEbazHNSffT3uXat9wpjfL99mLmpZf6Oqo8p84zLOK2zhRVQDmtIH/SHo4XBDOfzNZYywYSsioWFY7+9s0bH/HNanHWqxUFNXKf5Jc11xTww8M5imToL1aqzWTZmRpU4SxZsfhceGXpz/Kczj8HCDAz1SBmn3uLUZeEH1M///e+W5uof/K1fuXFqHMQ1IgfUOujZkOaVBRbdnRakwygTiEbXNzXcmWQ4EKpO71nuSAOi20s9Hb8jZAQhiGtejrMzbIY7dk9lH6sRgmKH976JuhMuBRcGi/j1p0iid2NVt6kLHUpFru7KkiZ4hZhna5kN1jg4LCKWGMMraIYGwhqJvdC6EGrB2iCO6vqc6AJMGX7CHXkhEDjlfRPvbZBmQ3q+ZIZVjtsBOZH7yaEZGV5/c9adyxZJUfn2VvxaWkBxu+qPH1de906OhkVD+sJrO8hJyCnBUPb7nVEGTx8TaUBLnT4CzxMc6+4RDs/D0+h/E/0A/WsJfP5y8Pxx4ee3k+UlYvvgWQNfrHOz4qXWBh8/rIRX+Bf/4pUPL5Ff/XDl0887f/iv/8g/+/kbf/my8KffhOuq/PzHN75EYV19NFLujaefNs9pss7TBv/0552yLnzQwAFUE0gJTk+hx5pnZGlnuwvDDoIKJSQCkXsciAoRF/3nXp1x3HfEvMhZxoA1MpqfAENWDomMFhA50daQ4QGIYc2oCr01QoF4GdTaYTe0dFIWrFbqtvk14jw51gvaO6o267SEinnp6sypAr9gSlCiGRY9i0yaV2A0/LQdDrfix+hiaVPxknMzQnEN1jBzgXx6pLo/hPENi5OyN5DuOWnlMk+DuxCl0pZEF1im27DisRN/73/9/RS0P8CViGK1vms/ZI5kHvby0ebpfvgGIh4MyTgftj6/Q3UXKRJ9MxH5V8HVQ79i83bPBvD7qgWuaY532vuGd3nyza7X+fkq1NMZBQnvAOz7+KY7ENs2kipbhCUpaeYgqToDM7oDAZsAKk7gATAIRBqqPh6letlyiA7EDI9fYOqtzAopviARjv1ON2VdFmpx/dAw1wEtMZDz8j3tPEj/zlYlFdJ6pdZCLcd3Q0lp7rgcD6cb7n5TOktOpCi+B43B05oRVY4K9wKt7fQOGgJC4CgnmFJ6pbfDnXLFtUVtOv5CiLTRHZ/odM1JotYdoaJ6obc7e+2YLojq949sTAOEqGIEai0kVdfj9UGQ+edWvKuvn84wifpn2wffBVePn8f475EVZeBiK/wQUOu8sftzfQjMH793vZKXlXpWclwmy3gSQ3SH4tvn7yJyHyvOB6k76DZlAzYZ2zKfyxwLml+D6G0yYdUT78fEAN8LnKvPSoNMHZf4mp7By4/KG2A+B7Bv/8PvGLj6wydTgVRgvyo9BC+APE8sJWToNAL4SU1kdTFxOFFTuhh2JliFMeCn+8lZOucmWFTS+gwiJO40K5xc/ZrQ6/eSYz2r541F4ykF9uYgZaiL20f3CpcUfIPpHd+s+omo0Jdneh3ouiCjYT4ZoWcf8y1TTC21UvcDCb5KtY8Z4TFcGoFiTWh5ARXW3UehR5qHgm+DumY0dqzdfaNX4QirZ1yph1VucuEoJ+VWGUskrgu639HkF482vKMwRfVKnm7cz5MQjWDOTEgMRB1kFUZL6F6pvZFjIUriLQnXS6Leimtl98bQRH6JXpfzrHz5dvKr55UxjFurtA+B/PPgtlf0rMRFsa+OjZe/HOl/fHLvlevH7XvlT38brD9l9loIRNLHxPjSWH965viTg0/14GkL/JwDz69C5KR1I4/AKYEelZAGbyKEW+C5d8oq0CohBkoX5DxJW6LV5EXdF+Hyy53j5QoC0QbtgG3mXu317sA8JCwpI61Y8c+XiDM8IrScSfdB7DtBE2XAgnHqQMpJMAckx6LkLlR8VKmn9yO2ISzq1VAiwuuWudSdU6IHNDYjRGgWyMHdtNajn5xVaCGT6NPu785Z2YIXwQ4fcRZdyDnA6afsGo2gionQz0baTzQELAV6G6jPROgxIcNjLB7XZg1CiZ5Kr0TsJuS9Ywrl4mYAPRrLEvgv/vfPv5fgSv7dv+Mf7pi6j/Bn0ur1MS6Z+T3thGV1oPTbm9ljc7I/o0dp7R2kTV2NByj+1ts9R9+eiTDwjbPMpGt7rwRp5zvTFeYhE3Mn2xiuB5ZGb43TIhy/EJYnbJjnZo3BkgLSGi+XD6DC7X4j0sghUEbz655Bt8iaQB81SqpEff+zmSKtsqwXVCAw429UGXgXZq03VDzXKqnrePOMgunDK2tSCmCBnIx1XekSGG1gw8XkqoFeOhY8GiOlTIqeAWbdHbPrtjLKwcvzM1uulOrTqKP43j8IEIRSjLf7G0GNYcEF8+XmIMcGqMdlDOtIWBCgjQmMifRRCTp8lC/QCZSZjzdMEM3UXmnDaL3PybFHM5gm78cd5zv4GH2C6Oif7QMoYw6ywmQ9gfey5AD18PWgE0hPE8L3UTUTfOeLP/fz1QFNM8jZGVd9BjmIlyuZ4DEex22ypMPBkwD7/V2o/qizqdWfc5j3qfhjTubNDxJTK/HQIrbz/fk9qnPKPrWI8wDYm//uw+X49XcMXP3DP/zRhnorvNIItXoCelixRaAadOOiidECNYqPt0an0/3AZ4aNQAgBZsq1mDuz6lYJxV0HXUFqwSygdSbbClgQknUqTEpViUGRufnVptTRYFFsQNZGlg200NqgdcUr1vp3c04fRhw3oiTuI7qrkERcomfPWCBywl7oz0+ICHJTRryjBHJM3HJDWmOt/jxiLdzjhiQfuZVq3wX5IkqaIWrFOqF4Ka8ZHN3ICShGTJGeOlYro1SwjA1jRGFdA9cUMVG/xpfOrSkxKe10GviSlK9vO5cc2Y8GwRmJ9OTFv7lUngRuDS6XzVPB6+B8PWlmbE+JUjvP81S7dM942j5kPrws3HojBOVf/OmNEDyz7Odfdl5+feXLl4NS1ef8V+Mvpcy6BH7z6eSHH18Y0nh77e78HInRjHu5EVPA7m/ksHKOBlXIZGIOVDF0VZIIR4Ntjdy7f/lcb1ZQTVQzmjbS10pfPOpgRCN2aHipccSvFb01H21flHp09JwAZHNQE08vdk4aiNLZVehjeEWNQIyDxaCkTGuDpz64p4AFJVinzwtIb8NDFQmTRDDvhTTvYTQMC4FUp1vU+ncg1Kdmp7dOnIx+zgHG3Pu1+MhkOOAsAy+rXoRAdBfRZPK7usNSgKM3VvVrciEQpCGt0lpiUUNTpeudv/s/1d9PcPW3/445I5TmWENnanTzbJ68+QZRzz/j9EvvWpXewU7f8MIG1nxz7NU3jLxORqnxPYlax7toV5PPsfsObD5u0bnJyRzz9OGnfOlzM54bsaa50amPV4IgqnzMiWvuqEbut1fP2Eo+GtzShp0Hw6B0nwpc1s2z0ybrtmxPyNg5avGE9lFJIoi6hqj3QW07MWZGqbw8f6BUZxxq6eRlJcfI/eYjvzGqB4dGz8Jq5j2YlxQxCxylMvrxvdfP5rgvpoXLcuEoB615xcyDDOnlTu8Hy+IM3SUJOXp5tmni6+udJpHSBqXcUXWHH1YQXf06UitjdEKM1FbprZGWhdofxcXGsO4J66rQm0dH4N/roxz+mkcD8+fdTWda/kbrpyedS3AWMrtLb1kvLJL5cv/i625ZkXFgbQLxh/D7oWt6AJDv+VMPMTsOVmqZQvPJZh63d1CuMwT0wcr6M3ZLv81DwnwO3k6ukz1qYKuvu3I46tYHQJoj7mHvLO9DNi6TkWpzvceZk9XbHGerM76GP7fR36McHocVVeyX/+7P7Zr0F0LQvtLo1wtnC4y3O8ZCHApNYIi3s1vnLootlVh2FxuXSE/L95OLbAVrrr1SBRFveF/umX4eyBeBnxKLXjhPowfXlAhePAmuHZHguT4ihmYobdBSQe+R8LoTkudadYscq7CGxALsq0FRH7uUEzNY7UI5hTBTrvMmtNpI40aUTA0JlohpoHVjW34m9yeafaPdA2N5ItXuuSYqvF1+YCk7iw3evlXfQM3Qe8e2gYVMsoGeQnlSzhmqes0BekPGwT4iP4Yr957Zw+DjXnwUtin3W+WejfacGF8L+q2Rsr9PdXh1xv3bSdTmGrNVsCZ8eF54HY2X58z9F+X4IGgFu2byGHyNjaf1wttbRRUuWyJ9DLy+FX4QFxdeN7dzf/vsWqvWBx9eNu6vg6dlI57Cjz9cGG8nx9lY6sJ+a7yWgxwiv3wpiAx2HWxRqF/vPP9642/EhS/3xmeeeIrG+NwYCPcWmfJyrmvhy6eDsGRe3wZplgy/xoENow518Hmv9AWSNWdX6zJPZx5LIMSp3fLKklYvbBYoW3eiog5kGG3pWOiUGIgYoxqxC0tvnGFQxgs97Ogw1iVSQ/MgyCZYh4bXJ4Vwx8TYzsY9ge5+qFtCwzqs0WjFq3Hq2ZHgTKwckbhu3HMkRvFstG6UCsIgRUXqimFUNbBEkh0iSBXkKGgKiAq1u47Dzq+OH/tk55KztnILcA7SeidKpzLI4/r/09XmL8BPVNAVYmD2lDgI6tWZI0uTLQrvOT+MyR7M0Z/VOSLpYL/4RvNIow5hbm4zkVq80w/rvmGOBvXNbVvagN3vK69uhNB1JsROAfyYm2toU28FjDusC4h6zEgf9PTMn96Nsn/mw/NK7Y04hN4jxzB6D6QYvFcuJY4+X6MpzRq3t8+kKWwepgxbqWKMcvOGAQu02mH1MeN5nrPWxmMi2nmDERlWuW7P1ObBq0/bwllnjIomavVxVowBjSuMjqjB8G4/gK/fPk1A1+j1K61X8uJsTU4bqgsmfWLL4domcRbN+mBZNz5+/Ii1k14qJkppA5FBink6MRsxbLTkj6qqtFERGW4QwNnqgdF7QUPGGIguGK6RHR2yKjoqrIZxejhn2th7ZAyF8gshQLgkWvvGD5eEWCKtC8fhTRC1G2ILISz0sTPG4JKeaYw5QVPE4DzupHTh2/3NT1Vfv0EGpLng/HscwgRilqHfplawuwSjTxYqZv93oq4/DBFubxAfBwLz9Z8W/zvw303LZLn0fQQ4oybcGDJHjH1mRmIO3mSZbNZcz8Pm9+l0AFf+TOTE/8efvxDM1T/6mz9ZC0rl4QQAwegRwuF0tgVIPdC7MRKwBdCGHgGpJ0OEERxpBxVyCuyP0StGDMolGlUCde9oMyqdbY0zMDP4WrBBEGi3TghKWQPWBto7EpWu7voiGDkNwuGamT0bazLqIfS3Rk6+2YYY2Wsni9H2AlsiqDAskE14S86oXWicpbs7S926L6Uztsg6DAvRR3U0QmuoKXVUJFboiWYR6YZGw9ocNXTvE5SLYrEz6mCE7JlFl0Td/dS0Xi/UOiB0+q3RsxKDeFFoMS6bp+C3tx2txpnVnbhR2JJyO41YC+HjSquDiwkhD55CQlX5di/EOGh3I7XOXQOShBYG7SiEHZ5+WFivDkxIwse70LbML69v9E+V++ik50ipg8u28ul254cfF0QCx1t1MNQ6y5YZFrndK6+KA8/WQCLhPFARSsw+tkRovRGicd+FsASGBFobLDkwzDAZ2F7RJ3+PVCD0xoh+wly6UdSI1UFuy4GhygrOLkW/AIxa0EWw4UaAFIyqIFYgZXqNhFZcv9kaKkaKgY9Z+fKt0KNno51tJS4DegSMa/TuSZcaGDpcR3GIEYNwxEhMg1Fd52ISZuabfzm0DeKWGedAY0Nw55ANI1AIuzNVJn7BGklJu5Bm5pCmgNqgGkh3faFaIxfh3PwxpCumAy14x2BMjK783f/j6+8nc/Xv/KePk9zcPCabdB4uWt+egCnWFXwTypMp0Ox29XY6c3UesF7mRjZHNjaZJ1VnE/LF7ysFP/GDA6w2xyiSfCQ45nix478f53iyz1O+TJBnD5CV3l1YYYI+/M+CEaMHk4YQyck8AiEkBy0zZNIZ1gsDw0ajWSdMfRTnjRymUy4kDIEZQipiiHXvs+uFFAPDBlEG+3njsmxkFc7a2baFYZFeO2aP/CSorbAtCzEofQyCJDrFCTx8FClBiChnbfSZap6DjxuVzjUmrsvqLB2F0o2zVo7q+qnr5crtdnDUV3LeWJYX3m6fUfH34TyKZ5mN5lVGEjjryZIWavWxloVITBsavJpqUL0X0gK32ytRViwYWxZUV1attJ4ZuvHly2feeqPUN1Q8g9FidCeqCHQFCrRjjtjUxeWqvrbaXBcP4FSLM5pprjfD10KfYD/MMM92d5ZV0zsICuYa8tU7CHmYNVTnZ/JnohBGxUfa9f22/jiM1HeNIOBgqvl348FYjcmqPRi0x30J/jpbeZeaieuv7P4//m4xVxYa3ZyZGaWzXBI5KfffVC/SDN7Ufjy5DTh9g2BebBq2wFPYODrssjtwORoFF0uqCjy7Bqe8eqJs00JYI6EOjMayCKN3ahvokjhDImzG/eysAk3cKWNtkGeyqzWB3qjPYN2wdeFWG08tIT8t3G6nV/BEY/RBCZnr2ihljnNkpyJsJ4gIbbq3llEI1WhxRRZB+gHpwjiLM2xLntoBD4s0u4AO9O7C+xEzFhytW1BaiKRFic1fpyWotXNvnXB0rk14C2/kS6STkJdMbl4FM4K7ge6/3Hn+aeVcFkoYJB3csvESI3k3tqfMzSDnxGstvO0nWxRGTJTWIQj31lmOgm5PPFPpnwrt48qhiXgZvJ2VLyK0s/C8Cz9flfXTjfAkNGn8+OuF9bLw6fNBD0L+MrjXk97hwx9s/PqHjX/y1vj558JVDkrtfIhCeLly/yOjvyhL/oFLDHxbT0SEqheO4uOQyzrHp/dC7oX2FtkuieNe/ET69pVVhELC+iD0SBRhqBKTYNFBvdwqZK+TMYPLeWcPGbFOPu7QCosIteKp828ZuxZ6Pwm1EduJxeGfv3V+c0tIioQ+GA1WvTOO4k4pDbzV4F2FVRhJEYuoDmJ3nWIYp0tpKM7O9wvablxaYwxYFtiPRGyw5+RmkFYRhBqfsQg9TFZu9dNdTUJtQtqMVga2rIT9DgSiCsaF2/Mgvxa/CMfAuC+0p9ld2Ssxh//3i8Hvw4+tDqhChFKnBXzg1QXjXZgbs9OQ6kADTXC+4YxSBIvTZYX/Tj+dncrPc4yirncRc2D1cE89rO5J5wiyAmmOIQNQfNPy2bADOn1EOAR3FwamkPhhk8U36+GBnoRBHc7SiHSKLAwRb0Ui8rQotXgcQc7O4nQqSxCSRj8Yxo0qnd52xmiEIQSppJToFjiOO/f9E3uvLPED63Kh9TsgNCLleKPryutvviJaqbbBcWdLC6ixV8ixsZiR1idC+4Wz3/iwLPzqxatqpL4xJPHjx2difPacuHRwf9sJeeMP//pf4cMFlqcrx1vjn//JZ16//kLOK60IdZzU+sbLh7/K7Xby5dtJiAt1QGvGaUrbC3vp1N44zQhR6YfrdnPMlP0V0jlTNZxFlhBhHFhrniUF/jlb8TUwpmC73ifgToxQHRiXOarTMPUkaa6Ph9aqwTk1UP0xCpxOPcPv/z7XSsDXykP7pGGO+BZf471NADcZJHSu3eGP+XC8PkBam2L1xhwp7r4+H9laOjVhY+oCHwSK9WnG2J15tXmQqLvrzSTM9WpA9HG7TociTKD1O8hc/bf/3o92DkVq9yiFeiD4ptKHO5XsbB4YGoQ6fGTjFxOwEVi3QN+hrsL1iByXAq872AQcBikYYl7pICIQG2MYo7gQOHRzMX2bIsrgabg8r9ju46EYBlaL57sIiBRUzPWlv2VkCC1gMdAsEUKgiWE2Z799IFFZVTnNZ+nZIqYdMc8PkXpgU28Ro3feiQCbi8NFw8yM8WThRkFFkDMjUSmtECRCnAzaujh4vB+EbfFqFIx1Ee8gOxu3uVLb2Xh5SjQbPGXl83mnHYn4dmOxABel3Cr9grvFmqd625J4aeIVQKWzLbDb4OOa+HoYsUb6fpA2ZXtO6Nm5bpF//sdv/PBh4adr5jAjduVlS5zqgNfeGv+yFG7Dgfevk/cMclV+1MiffD1nZl0nF1h/zLwQeIvC568HP/76if1omCwcx8GCd0zKeYPoaeNRAqV0LC8e6NcNke4XgOiCVzmU2Hd31KmgZi5jUfGxqggjKjKEME9lYg6Cx3A2yKIh+An1kXLemjCiF+wKB1Z3zCIMZURFY/byXQFG4y4bS6zOVO1zZIvr3kzClOk0H3iOyqiV2AxNSiFjYngltzsGNXvxcpwXmj78/1tQRjcfQXQj6qANSKr0ADqFzqkaQ5QzCIviJeTDmwJcdziQaozVDxIyErV0/u4fffu9ZK5+9Z/8Z3Yed4qslNsnOK8utFX1aIR89c0mZ9/Q1hfgl3eRugXQDhJJaaH3naQRb84aXC6ZNW/88OFHem9wuFaytsHbfXfN4RjUc4foCeeqnq8UU2RI5GWG9z6lwdvRkZjY9xu9FsKyEUTZ90op+3dW5dv9xig3Ur5ySYpQ2XImhsT1svJ6+4Wnyw/0NrhcrtxvX2l2oHGlngdLDB4DouoEB4bEZ+g3n/C0zF5djP25n1C9fqy2To4X+nkyMFJMxNGIMdJLZYlC1IAOZ4kark997MzdQMagtjsxJ5p1hkKrhcOEMCJ9ZPjuZpsMz5jJnaPB9gzHF4iK5MXjI1pjaCfrlaMfRPWOw4B5gGdrkw3EN/38xCMMGHGQLYDYnTEGIb4AhzN1Qf06AsSwMjTT6t0Zv4ejXZV6HkjasHL81ghtApXRcMG4vGuTUn4HVDIF5jZddWMySK57eRe3hwmWuvntYY75ZI6/l9UPB3kmL0vw9R7UmbB4mU5UgftX1wXG6Jqr6Zr0cfY8hMQ82akxx43FX8/1Ml2Mk33r0+wxDT2e7A7UCejqwXezx/yx83/53RK0/4O/vVkjENkopZFmWFyWTjuaO+v68EgGFcId2lUxc7R8NeW0Gxft7FEZnQEAAB/5SURBVMm4NnhVxUZiaCTGjgjc00ZS7+cbw6D5Bqcq5E047511SdRTGPe7B30+L7ycnXpNXpsSGicBOzuXLfGtClEH1hoWlHx+Q08lZEAjZVmJkim1khajHIEYhaiFcQ7aFBUPi0w+HhEHRGmOngDf8M1YFFo3dOZsjRDm6MnDJNsQ8hpI4lklJfhrfQ7mtTpnoUggq5DDoNVG10q/NaL6plqXFb0GKA1tlbEKOVw43yp7NLTCtkXC8GLpbe2eO/ba0B8zvHZyUvZfCuGitHsnbRkbJ9sqaI3kp0jbHSB8PRtmRvwQaTfXP/SvjfqDX3iWnPlbH575/OmV9qVyXoX9NK4HfLsKmg1rxvhWqU/CERPb8I1oWQLsU0MXO6cEZPGU9LE3bIIiURfn6uiMEFiuF16Hkc4DIxJqJa2Z0ouPEY5B4A5E1z5tRt0XZEsed3FWzzl7zlNSY6CC1IGkgljE+kBjQVDSzS+UxxII6if6bkIsBzYCfV3c/XcqS7nTQ/D2+gnAe/Q8tGQe5WCzv9G+DSc4nozaOlFWN47Mjq5RB1qNlAI1Tj2Wej5QGkKRSBgFVEjFdWNnfsKS0J9PxIx1hxYCWiLreWMPMwrFBM2BxiCbovVkNB8PmMF//X+P30twJX/tP54X3TCZpgWnqMw3njQ3M6sz1Tq70D0o3F9dcDzUT+q9zVHe3ORGh/WDbyzn7X2cNwbk6Q5ss3fN4HsY42+PTuIUFPfqQZAP5sAmG/bQtyxXvy3OMY8Uf25xvgb6exikzJ0tXibYmxvfssFj6tMmcHmMQ2dIMaP6xtw6351dMbv2phbfeO2ARwO6THG0GITs76MokiIRo06QEMUBJfTvoZneEpI4jm+IuLu19gZyAVHWUYnbB45RnVHS7M65syBTLyVB6cV77LQXPlwvPCeva4spc99PpB1oWmj18FgJjdz2O3l58oYPERiDMpofpMegVeEYFTUva+88DkNQhzcrhBBn3IV/tWpr7lwsj898OFh6xBOc52Se1N/bOkFRjL8V2NnfR8LgbE8938XtKfv6OudtMb1Xz8QFWCbzMFk0plmD7I99vs2Do00AJH6oEPXfr90ZrDC/M3n1/6YMfYaIKq5XnNFG/p15gKv5vB96K3QyX9MY0sr3Fno7/7ffLXD1D//tj9baYFwH66tRkxKTxyvYMHKE81YZ2d/8oQEdhtiKKKSudDmQfkyhn9eIEFYUpS9ClYie1QMQG+SgeI7IPH1/NGpXtAZiVKzcCcH1VfdjEE6f1VocPk68rPQ6XQZALDsWja4bEaEn956kXlhNeXscSAxiOVzSsC0ch2G2kK+RpoHYmjMKwwhpsGjmtrumSAQkhdky33mpBzvFgcKj60mMJoklCMdQwqosXbw0Gp9XaxB09sb1Yaj4prrir711o1yMZBnuHcneAZjtpJnwpztwr7QtEJLy8qR8G43t1fxzegr88Hzh837zGpjF+NfWld/cTs7WsBFZronnJSKhE0X4ySJydn4OcNlhSOOnbeXbXgk/ekbYL18re2leKbQP1npSV6XIwpID1wHn2TmrYYtwWYVywH10ejdajKS6o8WwIFhKzjQPGEsiJX+fwmnI0hltEIb4HjQvJKY+8ovdrxd7vjgbVQc6Qz11NIIabV7EtDRWCk0CJkpQpefkAD/E6VKNlARhdov2DYIqaZbzfri98Y1MD/48hniGmgZDqhHq4cnyybCaIAqBwKgCOohkam+E7KxSloPa3WEr8+Jj4uyXpgUR9f4xxDcMwE43a5ACRiCNg9EHZoERFRNFcgC8osSCF1yHUllUOFX9+2zK2Dv/zb98+z0FV/+RfY9REHUxcJjBiaJ+WIpzfDIalDcHEKq+gRzzBL8tfvvx6ne8Pv+WVqa5Fio9kt1nJAPTSWhztPJgM8YA0kwj5t15KMEfs1cHKUwR8LyekZZ3HZfO0dLo/hjjnAL94Rt0TH4fOfra7A2NAR0nEMizmy5+nzBFgnRq8xwo68YSBQ0LtVTOfrDljXubtSu48DrOIt7RG9E6cJLyExoia/BSaxV3fW/rij4Ox6IEuieiz5Xp6QAJsfI9zDSFyBiex2Ui7m4MGcNowyilUFshpisLg6Gd1pkdno17Hd9HUGagMVFLpRMQ6e+dgUAUocwcrKPsHGOhtcK2ZIROAIYEZFRME4Yxunev2hiOU7pHcwwbpLT67dNNaP1glvLyPRZkPEI2wQ8A5qzRI5xzmAvTH1lrvTrYNXz9Ke/s0AO0pwl8PGzSwVXwcFenDtXZu0c/5ozjcEMG7+D+Ucb43QVrniag3UF/mCRF13fQF+Z41OboW8K7S/ABf2wAgu3/+HcLXP2jfyPaGMZ6DMoFjotXO0hR0hI49UqvgzxOUlT2kWFUcjRa6WgxqIm+4YcX6xhKjpXRp4DWAk9xUGohaOCUyDjuSBBnLGr2FPbYCefBma6eY5U6liKWEmfpxOHanEvdPcE8JUJQbhiDxqVXKIMjLOS0oKtgZZBwN2Crnd472+iMHDi6EsYNtUTYEgQXUiNg2pD7ICblWHyNh7uzMff2hsUL2qKv55hZsnLbIRzfaFrRkLBayTHSjhPE0GEu1gzOUi1LIC8uyh+rcJydYAF5LbQI48dIPpRxdNKHzEUqX+/CcTZ+nTIhKP/n8crLLshLIufA+DbIW2Pf3Rn3lz9c+ZNWkND5q5K5jUbrg99kr8AJXw6uf+3C/u3khUxW5cPHyP/1j79w+Wsb5fQL0edfGuEPFq7PgZ8/7Vx+XJCvwnp60Oq97Z5vpRFdhWpeQaT9wnE0Vgb3uDiDU06iRCpGuwixDDchiGtBqOp9YuvmYbBrpN0bYeysS+DAQRcFYtvZ6kHNG727YHyIp+ubCDp8Peol0YhuFuiV1o1+GpHOSAEtRo8N3fy0b2aewTOMWCG/fcMsMZJCiJgUD8lN8bsGObUGLTOWHWkrTRNa7oRUXbOsy7xvHzm2tJAeAXrBNYpyPwgE/w6q0EdHRbDitP55MYIOUnMwxnHH2qAsT5gK236bmtLBSJmuSjg7Kh5XUTOEZvxXf/R7GsXwV/5D87GLzvHG8R5tgBsSHGGIX/TrPJ0/bO7bNt1NxxSj9/cN4+Gq8gbld8dg3pxN6B1ZMtZO/pXcq4EzSA8B8/e9NbyDQJ3PcXScZZ+nf8F/R20+nzFDT8V/H69TCjSEANJRM8boCB6y2+ogx+yp4PMl5OgAorXKMO/Gy9GbK8wgq8w6G3Xjyeis2xO9vHGUxrpd2UJijAMsIJqok6E4zxspb4j5SJTeOXtjnY7ANvBg3+ifidrJmldCDASDvK7UsrOlSA7G/TxAN0rtVDNqPQnpwhgntTbqUHKIqLgRyQxUA6WcSPBYiI5Sm4M2z3PpaFBC9DiHo1f2qkQqUc1LtvtgSIBx0gYMM6o5E19rYYnO/tc5wvcMsBONC6KZ3u+MMc0PY471bGqtHusixHdw9QiP1Ucsx0Mkru/gKkUH+Ib//QP0PMBVncylrlMfJr/FoE6jBL9VRSPyndjlUaIo4qBsNNdnPUaWY0wmc44uZbJ0eX1n7voDVI3316keV2Jvf36C9r8Q4Orv/80nEzOEQkgzLE6gWYERsEk7m64gjeUsLrKVhTaKh4yaEkZhBEGjsJyga3An896oS/PxuE4x2zAsLlzLHXs76EkZZPqW0DXT+txQ+kBqQ9RHIBj0mImPTBB5ppfOJR8QOjV61k+rK3k1Z9e7Z0uNYfRjaqNio9eA1o6EQVMlaqSlyBaFcnc9mHCDkFnjhR5lhpkOhjkI08OFsCP5IgkaXEhv85p4KuMpfA+MpBpbFNrWWFTZ75V0SbzdKvLaPPB0ptanGWZ5xEpr5iyMCna46FKk8+Nz5BISOQVuvfH2Vqlr4mUJjCY+Pv96eIyEdWjG/dtBO72RvnUPu7xskVShlM63Vt2p+7I4mD4aKQUfo6mwn53LJVFOr9/ZWyWIA7WzdHQoH54zu/nYt5bOx5eF1y83jgGpKSl6EbWpQA6U6pq660U57gJxeKJ+dDCd2olpoI/oAISKMsjxxd162VnAcZzenVZOxjCSROIlcDZBRYjWPRhVb1AWH1FLxKKh1dz80ipdPECvXyKhGXEyrGde/X2YjIOqA5wwK6KsO6A1WRlZ0DRcV9VcgyWDGRkxvo+fpckcEfntbIt//2onLZEajVE72dwRCRBINOlYGa5xDgIkkihn75g0JPkuWYcL2VPpWPLMOIC/909/T5mrX/0HDq6mPuY7CxRd3/duO28g9f3Ubwr8lghYcY2JTlfWaH5aFwWSby7HDfbiIzzvdgHq+wn/EbA4mjMApu+bls6soDHex3GPwMjH4z/628bw+x4NePx51pSIg7A1OiRrY5CCoubrZrRKJxJ1uJidQe2Vy7Iypj5MQyRgaFw5z4M1r/R+eIFJiJS6E1EuW0bF3X1hBp1GjLxeaP10kKfquqrmTFcK4qnuj80eYFTickURUgRrzvAjgkhHUZYlkWc+XRuDLkI7/b2tbdBGc50rgVIO+vSPmRhIZometxhC5rbf2dvpAdKzyzGtGzIGo/lncS8He48s0cCENWVu58klKaMdhHRxY4Aote6oTq0mULvRrRNlEOPKWQcheD7XeOQ8jeBAH/geIAqugWp3iBvvuVVzDH3OcNHHmghzLXRmhtvy/p4+GM7HyPAxVnwkretkRFubLO0DzPX378QD+KvwXff2OKg87utRDTXrgzwDB75visPmc5uCe6eMwRp2+59/t9yCKQVq6XR9wVDak79R2j7DUOLdRd3a3qilIil42a34+EK0ILERTg/+HBapHwQOP9G//iqyfK3Eo5K3TMoBE6PGxi1cqNdnlqMRQyTJ4GxCy7C1QiiZVhXNDen/T3vnsiNJkpXhz67ukZeqvsyANAuWrJB4ABZI80QskHiE4Q2RWCAhsYHp6e6qzIxwt8s5LI55eGpmSS5Qt31Sq6urIzMj3S3cfjuX/4DXQA6g8oj3ULLVlfS6UmtDfcE1IbZq4qZA8o7e1+HcuwzrDYeLEb8Kvi20UCjNEbeNbVVcdvZc2hdSiGgo1JZGTYCD1tBdkEWJoRMkWpbddbYvhTAG+obPnfY/O95lfHC058CuDal2SixFqNevfPdp4XWBEJSCRyI0p3QnfPOjXf8vayfnQP1kTsLJJV73wmsolJeOvNmHptUrt587Uh3ts/KJR3777YXL1XHNELUTWmVrhe8/PZODjRO6BoElkJ/t9Fi+3LiWxpIDPjn2Cu3LjjwGrqURA1xLp6nioqNuQkiep4sNZ32l8PSQeK4NfWvszawIWnqiASG9kpfI9bWgBJY1sRfl6TeF1x+CPZjeTISIJPAODYJ/a7inFWmN2n6wDEq7EJOF0GVvtGUlBI/80GnNo5dOV2UPJqSebk/0cKVqIpZKyQmNjpqtWHO5vRCdo70qSZ2tM+/4tr3x2gKRlV66ObdHGxTuHahGku8E9zN7Aa4WKeshmEeOs4iIihWsKkoCuipJFIKwqzOHdka39NWR00JfRn1XrXAT2oPaWtSGiiM6G6QeEVrvxGapz2dJlGQHIZqSbnqG43+N/MXjO54n89ZHd9674uNy1ENZFMjqQ4bj9jGSREa6Yx+jTdoLI+wAGuC2WTDg5dWaNC7RBJcfgs0zWuVHqrD30UU4NrAjevXn77uP9KPz8Pp6iqkjMpCGl1HrbK9HJCFR3EjvLAHcBaRQvNoG7CqRwL4VnpcLu+ym4xQefMcFMwp9+vTZDDW9jaZJMVOaoFKsfhQserM+Uvad7gLRZ1qrPIYHdlfotbELQMCp0IZ1hA8re7WRMlXErBNER/RVWIOZdrLa87mXgm/NzIR6o9YKPuNDxqsjJc+SE7WahXugU7vwWirON0prJI2E5FjGCDMLGmV2oNZGDo4qyhrcSMebaHmrlegDrVbzzoppFOzDrVTu3acqVGdRNbYbcoyUkeGl5t5FqA6bkBhHzVuE25vddnGWyoZ34vrdB1rF0sHOm58ajMjRWPhH3vf4GjcOGt7Zeon5LJhXNaEmx9eMNd1H7ZaOf46xTr3ZYaO3M5V4dM62bq8j2r9lv6eTrRPtF9gt+Ie//6S1Cq5atMlG20CojVYFxCPZEzabgVaC+fpkjShQRfFdkOTx2lhXjwuJ/iZcF8dlswnjrikh2XDKUjvuk52cgvdc30D3Tqo3O7yFxfzQXKI1C73m5Ak+QIrcpPIYA7oVi6aop6gj3XZIgYgge8W5TswBDYlSOn6NBC+4scCcWMdWe7VRPUGA1cNqurftNzIOLdDFRi/k5ClpsU4sL2hpMGpcNFjU4aIbIo7OYl1vY4Cq25VtXVkuMmq1xgxFVbyMeqLbq0UuniINJcqDpaZypZTOUj21CuGiNBVSD5bqUovebF14+pxZlsD+pfLSG35NfB+smN8Vpe3Cj1shJU9/DrQfNx6/vbBfG00r2Sek2vf7q4eFP/7phnvwPD/lYVroSN7zUhovOfGbh4Wsha04rhch/XdluxXAI4vQUubTt46XHx2hmzliELlH9GoIhCqWQvYQUJwI+rhiPgtmnlhuHprN3HPY6wCatxl8mWC1kmLuxb27u52M38EHGfukRcrc+EArNtw4SEHxuB6tflAZ6RULYasLROdxUejV4WUDSbTLeDC0QN4rzVmrvWokBIdWq2NzPoz5gHDvjB6dhnQI3aGPat2NI4WyYs0PLyL4pta13UHWlbJBrA2fOtLU5hA3N6IpWGfgkqwjsxdLdapdu3/5r/IrjVz9Xu2kFke6ZJz+VYdFQrSNzY8NT5sVe4d01qLI2CTkXTcUjPRdttSL9DOF40c7c3BYOm8UKB/DmO8dX/382Xdx1u+b8919WxmbmHBvwz9ShYcz/JH2PHyOVKxWh25pShhpI7Hf0Y3ISVzOzdGP4nUR2+SPFXN4H2V7bVweabefycvK89Mz17dXQoiUZv5LXTt9iNHgIx0hOseSL+AqGUghsXqzMlkuiYBD1VHKlSUGkrcr0qVzSYHLsqB6AwmEeOFaNpo4Wq8sl89cby9se8W5OAxCR3e6OFSa1UKN62MlcHLf3pMfNU0hc607a77Qu5mm+jF8OYQI0eq4qoB0q9eNIVFaQ0TGfOI/u4+9DeG0jBSdO+8FnHVPd5+oURQe87vavbHmjvt7RDtlCG2Uu6FowH6G82cUa0ywsG7BsQYYworjMMHR4zXWn+McxRRsGLkOP7Z6O9//0Yn4PtrlRg3WMcHg+Kz0fkbHRNHt339ZacF//dugUhISA+4SSc3TVKDc0ORRF0GhZQfJ47Zu3nqrpcZS8JTSYW/EKIQmOG8txZsXwi6E50zfMk0beW3E6HgJVjQcgqMV65BCYHmMLAo/V89j2ckpsKmSs+dLdUAkxBfybSfsZvNgYwYiIT3Ru9CzOW+3bvYSi4eqHtcFPtmm6b3jtVtn1dP1J7w3Hxd2pbSIXwItPtB3wUeHusZDsIdwjzYOojtsQvvNhiNnH9n2jk8J8RZuJytebaGlvRJeXuFiA6e9d2i3WY4sAIp31t7vk+NWBVetruv6BKF24k2Jtw6rFddrGgIt2SnGvXYbMyQQnLPQv/eQbTZi/xzYsvLNzzZq5vPvHvjy48bnR48vypdb5fam3H4q/M3vnhiTaPgqnT/9tFkKVDyLc+RLoF4LocPmPNId5buVpz8J3cNTCNR6Y1coT+DaiqsFHyLfJs+2WfHnfgno1vAU+iUQqhkgRukggbJaTVL3C8ldQCq9dkIzcRazTbRfMLFMiOjlQt8KwTuaWt2GF5sVSe8UXYhldO2RCMlTHht529Hi8Wugt2Ho6S+01pHsiDhiVuiB3JWtv0Hr+OiRLrA+sw1zvXS94bqiBEJ2hGzPDhG1Ds3gx9gNB77Tto7bbXROzYmUPNWBb5XmM9KEx/1nostcvUAPqAYbcZSB4PG7gG9k76jVkdpunazYwQmxlPM//Wf/dYqr3/7ejuPOmcg4CmvdSO+N4e/mVQRQR8oK0FGO8N7vxx4mnMJmiKf2BqzjdSMdcrS0H4XzfThj6zjtE84Iw/vNELiPC/FHfc0oKD7CkM5zL8pnbLIyUpCHuIpDmachrlywTbENPyLZz3oe8yo5RRqcG2MYhpYxjKjEiFi44/0cm76M9CW2kTo7HNtfjsgf1vRhvk7VrkUbG/zlQpLG42VliZEclef8SAhwSZFtfyPjCIt1dteutK7sxSyx1QU8Zgy9l81KDTRQ2o2cLxQBR6K2K855fMj0XukOko8mmFwYZseZt7evPKQVEUGd0KRT1axecsqUVmxmY9lsHqO8v3aHq/8QxKWY2BaxLs52G2uB02BT2hA4/qwJLMXuy7Ee8nqKq340Loz0sVb7b9y5Bo41xBDp7jgcHIcKz2kO6sc9H5FOnK3J3u323QecL9wjT0cUKzzY73EI8cMd9hh/40e684i+qUO3f/tlias//N2jVg8g+NaBCNlmvZXiyNlz6Ru7LogqvRVyDuyt07zV6qhzhN5tMQbBieObxwfeFFor6N7pTx3JD3C1upmL6zCiCl3MU2vJgf1rxUmD0Lk8P3JLytIDTa3FtVThm+ao0kiy41Pm5oKNs0mNa3ckr0QcTyRw0ILVwrzVQqtCWDx6E6IIDSVppWlEs+B9ojiPTwtZsbSUH91+zfxNHqOFoN9ebbbcdznyGjL73tHFUZp1kC1VUamUZONbPrdMjA6/7/TVc22dtlVcjohzUDrhcbV0T99xkunRbB20dUoVYvF4r3z/aeGKoHshBiGHzE/XSnWddTWLgkVsEHB52/ECGiLP3vGUIk2VFy8UvyM/qKXrPTjxtN9EHoJnv1ZqExYCz99H4s3ztdhYn8sqtKa0l0KINoOsq0WsRa2mtn1yhJatq1irzWdMF2pVyjiY4ZSA4CTShlmnC0LMgozMeesdL+2+8RyFryKKukavDWkB5xo+2j0K0ijORuJ412yNEch4ZDG3+djM5iCoifvj+eHGfMyleLY1kddOAfQGSTo+ms+RVNBeCWuHfqHVhm+KWxTvA9fFQ1n47IUqhUUbX7kQ3RgTJdCCRTCjdlubCjoOBFZr1y2iqiARirOHpSdRLp1cPdU5vBZyd+Mz7Ohe8VHsIKqKH9dOuoLAP/9Rf53i6tt/MIO0uNjDPjj7c7kxZo2MF46I0JGec+PE4jxWi3WxzQvGhvRgYqluo529jUjXu8t8mIaq2KYG5qvlhPu8w/KumPjgaNe3N4ZFMuIZvYAz0uEEltE95jzQLMoAQOJuBdDrKIg+7BrK2ChHJMNhG/oRHVGxjTOMvxdGbY3ahjvS8qdAaON6jY7JVkYXo5xdlEeh9fgd0vC/Ck7xwWp9Y4h8vX2xn1Wxe/Sw4vrO8xJ5So+81RtNbGRXjAHvPKs2Yk7ktBLihb28sHqo2tnKxmV5GKUZN7qLtNpxwbzhcsqUckMYHnoiVBxdFe880ithuaBtQ+ohFI4RMPG8NnfhMARHzHZd9417IXkcESatsD6dAgzOay7Hg2mIdzeiVKQzeoob6TW4D4Y+RNzxtbZYx5Ic6UfXzy4+m856NmQMS4r7waG/M7g93OJ7te+VRmRXsUjoIeCH071t9vWsuwJbN26kvrug+h+/LHE1mUwmvxbcX/+jWnGvtw3ANduA8gXKbuahrdqG1t6LjWB1Kf3dRqRtFLV725fiYl8jHsigb9x9BTSeIqWrneSlWz2NhGEAiYmfvyivWt6JkTpG6QwBdxhftt08n4I7Iw9uvL6XM3X58J29x/I2BMCxIY8B1EcKi8MzK4/WfhlCdAg2ZbwujTqd21mno+EUfu12Cggf7Hc/ZtD5wN3Tywd8jKh26yJ0yYakO/PEaiogheYeQDtBKv0QLUfa1K9n+lRHKhMdAq9a+ipnXBq1tP2Nx+WB7B2qnpStHlKc4pznupkojSkPu4h0T9uJg3L7issXvHj2ZqLYh4ALidqLdQLCKbbamA25bbb25N099pwdeyGOOYAN3OPZXFGLXbMw/M+0m9B9H309ODoPjzV8RK4OkXXUdr0X/0e9n4zUsfZTJI5yDkttFluT0ofYuo2TuRufoWE8OvzG7qlBVVtDbXzPo6NQOviEll9Y5GoymUx+LVhakDOtdxhttjFAOY2RNbWf/68e6UB/nsKPyNbxCD9SePnpNOKEM5Wo0YTckSLzI1+mYmKEdkZ+CGeK7UjT9H7WVx21Yinbz10ebFM+ohJlbLz7i6Vsjq6xdDmjU2GF7SdLB4Z4eiNpg7Ra1EnH+/T+/J2O2rQ8bCjqPoyU+5kuPC5KfLKNuJuvklufcBRiypSGOYInGxx/DBwOMZDigoogUqm94V0wLaqdECOINy+4Vuw+tWFy6RJWXyHc5/X5bt2Td0f2EYHp7ayD4t11hRH9S+aZl1Y+5QulvCDvmwrUj+xa4na7gQo+RrwMd/Zu97KVbQxkriPi5M/rU62c5u4ldUQfgwdNQzAdInW395yWM1rYmomyavWthMUip0d0kHGIONbTkRIUOOf8Be5pizZqwI6u1MOkVvspxKSfa945e6/DnNgiZu1MP96vVbDDRCsmxOso9D86EruJOJUZuZpMJpPJZDL5f8mfB38nk8lkMplMJv8HpriaTCaTyWQy+UCmuJpMJpPJZDL5QKa4mkwmk8lkMvlApriaTCaTyWQy+UCmuJpMJpPJZDL5QKa4mkwmk8lkMvlApriaTCaTyWQy+UCmuJpMJpPJZDL5QKa4mkwmk8lkMvlApriaTCaTyWQy+UCmuJpMJpPJZDL5QKa4mkwmk8lkMvlApriaTCaTyWQy+UCmuJpMJpPJZDL5QKa4mkwmk8lkMvlApriaTCaTyWQy+UCmuJpMJpPJZDL5QKa4mkwmk8lkMvlApriaTCaTyWQy+UCmuJpMJpPJZDL5QKa4mkwmk8lkMvlApriaTCaTyWQy+UD+FwDHMiQCYSc3AAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 720x504 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(2, figsize=(10, 7))\n",
- "\n",
- "pl.subplot(2, 2, 1)\n",
- "pl.imshow(I1)\n",
- "pl.axis('off')\n",
- "pl.title('Im. 1')\n",
- "\n",
- "pl.subplot(2, 2, 2)\n",
- "pl.imshow(I2)\n",
- "pl.axis('off')\n",
- "pl.title('Im. 2')\n",
- "\n",
- "pl.subplot(2, 2, 3)\n",
- "pl.imshow(I1t)\n",
- "pl.axis('off')\n",
- "pl.title('Mapping Im. 1')\n",
- "\n",
- "pl.subplot(2, 2, 4)\n",
- "pl.imshow(I2t)\n",
- "pl.axis('off')\n",
- "pl.title('Inverse mapping Im. 2')"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_otda_mapping.ipynb b/notebooks/plot_otda_mapping.ipynb
deleted file mode 100644
index 9a7ae04..0000000
--- a/notebooks/plot_otda_mapping.ipynb
+++ /dev/null
@@ -1,288 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# OT mapping estimation for domain adaptation\n",
- "\n",
- "\n",
- "This example presents how to use MappingTransport to estimate at the same\n",
- "time both the coupling transport and approximate the transport map with either\n",
- "a linear or a kernelized mapping as introduced in [8].\n",
- "\n",
- "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n",
- " \"Mapping estimation for discrete optimal transport\",\n",
- " Neural Information Processing Systems (NIPS), 2016.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n",
- "# Stanislas Chambon <stan.chambon@gmail.com>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pylab as pl\n",
- "import ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source_samples = 100\n",
- "n_target_samples = 100\n",
- "theta = 2 * np.pi / 20\n",
- "noise_level = 0.1\n",
- "\n",
- "Xs, ys = ot.datasets.make_data_classif(\n",
- " 'gaussrot', n_source_samples, nz=noise_level)\n",
- "Xs_new, _ = ot.datasets.make_data_classif(\n",
- " 'gaussrot', n_source_samples, nz=noise_level)\n",
- "Xt, yt = ot.datasets.make_data_classif(\n",
- " 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n",
- "\n",
- "# one of the target mode changes its variance (no linear mapping)\n",
- "Xt[yt == 2] *= 3\n",
- "Xt = Xt + 4"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot data\n",
- "---------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5,1,'Source and target distributions')"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(1, (10, 5))\n",
- "pl.clf()\n",
- "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n",
- "pl.legend(loc=0)\n",
- "pl.title('Source and target distributions')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate the different transport algorithms and fit them\n",
- "-----------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 0|4.132385e+03|0.000000e+00\n",
- " 1|4.124370e+03|-1.939427e-03\n",
- " 2|4.124043e+03|-7.928706e-05\n",
- " 3|4.123904e+03|-3.369312e-05\n",
- " 4|4.123827e+03|-1.881208e-05\n",
- " 5|4.123778e+03|-1.184435e-05\n",
- " 6|4.123764e+03|-3.358329e-06\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 0|4.143700e+02|0.000000e+00\n",
- " 1|4.111590e+02|-7.748977e-03\n",
- " 2|4.109509e+02|-5.062453e-04\n",
- " 3|4.108581e+02|-2.257162e-04\n",
- " 4|4.107918e+02|-1.614130e-04\n",
- " 5|4.107473e+02|-1.083067e-04\n",
- " 6|4.107110e+02|-8.833802e-05\n",
- " 7|4.106839e+02|-6.600463e-05\n",
- " 8|4.106615e+02|-5.455553e-05\n",
- " 9|4.106428e+02|-4.548650e-05\n",
- " 10|4.106278e+02|-3.649926e-05\n"
- ]
- }
- ],
- "source": [
- "# MappingTransport with linear kernel\n",
- "ot_mapping_linear = ot.da.MappingTransport(\n",
- " kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n",
- " max_iter=20, verbose=True)\n",
- "\n",
- "ot_mapping_linear.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "# for original source samples, transform applies barycentric mapping\n",
- "transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n",
- "\n",
- "# for out of source samples, transform applies the linear mapping\n",
- "transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n",
- "\n",
- "\n",
- "# MappingTransport with gaussian kernel\n",
- "ot_mapping_gaussian = ot.da.MappingTransport(\n",
- " kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n",
- " max_iter=10, verbose=True)\n",
- "ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "# for original source samples, transform applies barycentric mapping\n",
- "transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n",
- "\n",
- "# for out of source samples, transform applies the gaussian mapping\n",
- "transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot transported samples\n",
- "------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(2)\n",
- "pl.clf()\n",
- "pl.subplot(2, 2, 1)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=.2)\n",
- "pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n",
- " label='Mapped source samples')\n",
- "pl.title(\"Bary. mapping (linear)\")\n",
- "pl.legend(loc=0)\n",
- "\n",
- "pl.subplot(2, 2, 2)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=.2)\n",
- "pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n",
- " c=ys, marker='+', label='Learned mapping')\n",
- "pl.title(\"Estim. mapping (linear)\")\n",
- "\n",
- "pl.subplot(2, 2, 3)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=.2)\n",
- "pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n",
- " marker='+', label='barycentric mapping')\n",
- "pl.title(\"Bary. mapping (kernel)\")\n",
- "\n",
- "pl.subplot(2, 2, 4)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=.2)\n",
- "pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n",
- " marker='+', label='Learned mapping')\n",
- "pl.title(\"Estim. mapping (kernel)\")\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_otda_mapping_colors_images.ipynb b/notebooks/plot_otda_mapping_colors_images.ipynb
deleted file mode 100644
index b66640b..0000000
--- a/notebooks/plot_otda_mapping_colors_images.ipynb
+++ /dev/null
@@ -1,378 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# OT for image color adaptation with mapping estimation\n",
- "\n",
- "\n",
- "OT for domain adaptation with image color adaptation [6] with mapping\n",
- "estimation [8].\n",
- "\n",
- "[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n",
- " discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n",
- " 1853-1882.\n",
- "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n",
- " discrete optimal transport\", Neural Information Processing Systems (NIPS),\n",
- " 2016.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n",
- "# Stanislas Chambon <stan.chambon@gmail.com>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import numpy as np\n",
- "from scipy import ndimage\n",
- "import matplotlib.pylab as pl\n",
- "import ot\n",
- "\n",
- "r = np.random.RandomState(42)\n",
- "\n",
- "\n",
- "def im2mat(I):\n",
- " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n",
- " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n",
- "\n",
- "\n",
- "def mat2im(X, shape):\n",
- " \"\"\"Converts back a matrix to an image\"\"\"\n",
- " return X.reshape(shape)\n",
- "\n",
- "\n",
- "def minmax(I):\n",
- " return np.clip(I, 0, 1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n",
- "`imread` is deprecated in SciPy 1.0.0.\n",
- "Use ``matplotlib.pyplot.imread`` instead.\n",
- " \n",
- "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n",
- "`imread` is deprecated in SciPy 1.0.0.\n",
- "Use ``matplotlib.pyplot.imread`` instead.\n",
- " This is separate from the ipykernel package so we can avoid doing imports until\n"
- ]
- }
- ],
- "source": [
- "# Loading images\n",
- "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n",
- "I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n",
- "\n",
- "\n",
- "X1 = im2mat(I1)\n",
- "X2 = im2mat(I2)\n",
- "\n",
- "# training samples\n",
- "nb = 1000\n",
- "idx1 = r.randint(X1.shape[0], size=(nb,))\n",
- "idx2 = r.randint(X2.shape[0], size=(nb,))\n",
- "\n",
- "Xs = X1[idx1, :]\n",
- "Xt = X2[idx2, :]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Domain adaptation for pixel distribution transfer\n",
- "-------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 0|3.680534e+02|0.000000e+00\n",
- " 1|3.592501e+02|-2.391854e-02\n",
- " 2|3.590682e+02|-5.061555e-04\n",
- " 3|3.589745e+02|-2.610227e-04\n",
- " 4|3.589167e+02|-1.611644e-04\n",
- " 5|3.588768e+02|-1.109242e-04\n",
- " 6|3.588482e+02|-7.972733e-05\n",
- " 7|3.588261e+02|-6.166174e-05\n",
- " 8|3.588086e+02|-4.871697e-05\n",
- " 9|3.587946e+02|-3.919056e-05\n",
- " 10|3.587830e+02|-3.228124e-05\n",
- " 11|3.587731e+02|-2.744744e-05\n",
- " 12|3.587648e+02|-2.334451e-05\n",
- " 13|3.587576e+02|-1.995629e-05\n",
- " 14|3.587513e+02|-1.761058e-05\n",
- " 15|3.587457e+02|-1.542568e-05\n",
- " 16|3.587408e+02|-1.366315e-05\n",
- " 17|3.587365e+02|-1.221732e-05\n",
- " 18|3.587325e+02|-1.102488e-05\n",
- " 19|3.587303e+02|-6.062107e-06\n",
- "It. |Loss |Delta loss\n",
- "--------------------------------\n",
- " 0|3.784871e+02|0.000000e+00\n",
- " 1|3.646491e+02|-3.656142e-02\n",
- " 2|3.642975e+02|-9.642655e-04\n",
- " 3|3.641626e+02|-3.702413e-04\n",
- " 4|3.640888e+02|-2.026301e-04\n",
- " 5|3.640419e+02|-1.289607e-04\n",
- " 6|3.640097e+02|-8.831646e-05\n",
- " 7|3.639861e+02|-6.487612e-05\n",
- " 8|3.639679e+02|-4.994063e-05\n",
- " 9|3.639536e+02|-3.941436e-05\n",
- " 10|3.639419e+02|-3.209753e-05\n"
- ]
- }
- ],
- "source": [
- "# EMDTransport\n",
- "ot_emd = ot.da.EMDTransport()\n",
- "ot_emd.fit(Xs=Xs, Xt=Xt)\n",
- "transp_Xs_emd = ot_emd.transform(Xs=X1)\n",
- "Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n",
- "\n",
- "# SinkhornTransport\n",
- "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n",
- "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n",
- "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\n",
- "Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n",
- "\n",
- "ot_mapping_linear = ot.da.MappingTransport(\n",
- " mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\n",
- "ot_mapping_linear.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "X1tl = ot_mapping_linear.transform(Xs=X1)\n",
- "Image_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n",
- "\n",
- "ot_mapping_gaussian = ot.da.MappingTransport(\n",
- " mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\n",
- "ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n",
- "\n",
- "X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\n",
- "Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot original images\n",
- "--------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x216 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(1, figsize=(6.4, 3))\n",
- "pl.subplot(1, 2, 1)\n",
- "pl.imshow(I1)\n",
- "pl.axis('off')\n",
- "pl.title('Image 1')\n",
- "\n",
- "pl.subplot(1, 2, 2)\n",
- "pl.imshow(I2)\n",
- "pl.axis('off')\n",
- "pl.title('Image 2')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot pixel values distribution\n",
- "------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 460.8x360 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(2, figsize=(6.4, 5))\n",
- "\n",
- "pl.subplot(1, 2, 1)\n",
- "pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\n",
- "pl.axis([0, 1, 0, 1])\n",
- "pl.xlabel('Red')\n",
- "pl.ylabel('Blue')\n",
- "pl.title('Image 1')\n",
- "\n",
- "pl.subplot(1, 2, 2)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\n",
- "pl.axis([0, 1, 0, 1])\n",
- "pl.xlabel('Red')\n",
- "pl.ylabel('Blue')\n",
- "pl.title('Image 2')\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot transformed images\n",
- "-----------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 6 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(2, figsize=(10, 5))\n",
- "\n",
- "pl.subplot(2, 3, 1)\n",
- "pl.imshow(I1)\n",
- "pl.axis('off')\n",
- "pl.title('Im. 1')\n",
- "\n",
- "pl.subplot(2, 3, 4)\n",
- "pl.imshow(I2)\n",
- "pl.axis('off')\n",
- "pl.title('Im. 2')\n",
- "\n",
- "pl.subplot(2, 3, 2)\n",
- "pl.imshow(Image_emd)\n",
- "pl.axis('off')\n",
- "pl.title('EmdTransport')\n",
- "\n",
- "pl.subplot(2, 3, 5)\n",
- "pl.imshow(Image_sinkhorn)\n",
- "pl.axis('off')\n",
- "pl.title('SinkhornTransport')\n",
- "\n",
- "pl.subplot(2, 3, 3)\n",
- "pl.imshow(Image_mapping_linear)\n",
- "pl.axis('off')\n",
- "pl.title('MappingTransport (linear)')\n",
- "\n",
- "pl.subplot(2, 3, 6)\n",
- "pl.imshow(Image_mapping_gaussian)\n",
- "pl.axis('off')\n",
- "pl.title('MappingTransport (gaussian)')\n",
- "pl.tight_layout()\n",
- "\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_otda_semi_supervised.ipynb b/notebooks/plot_otda_semi_supervised.ipynb
deleted file mode 100644
index 484c2ee..0000000
--- a/notebooks/plot_otda_semi_supervised.ipynb
+++ /dev/null
@@ -1,294 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# OTDA unsupervised vs semi-supervised setting\n",
- "\n",
- "\n",
- "This example introduces a semi supervised domain adaptation in a 2D setting.\n",
- "It explicits the problem of semi supervised domain adaptation and introduces\n",
- "some optimal transport approaches to solve it.\n",
- "\n",
- "Quantities such as optimal couplings, greater coupling coefficients and\n",
- "transported samples are represented in order to give a visual understanding\n",
- "of what the transport methods are doing.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Authors: Remi Flamary <remi.flamary@unice.fr>\n",
- "# Stanislas Chambon <stan.chambon@gmail.com>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import matplotlib.pylab as pl\n",
- "import ot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate data\n",
- "-------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_samples_source = 150\n",
- "n_samples_target = 150\n",
- "\n",
- "Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\n",
- "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Transport source samples onto target samples\n",
- "--------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# unsupervised domain adaptation\n",
- "ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\n",
- "ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\n",
- "transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n",
- "\n",
- "# semi-supervised domain adaptation\n",
- "ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\n",
- "ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\n",
- "transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n",
- "\n",
- "# semi supervised DA uses available labaled target samples to modify the cost\n",
- "# matrix involved in the OT problem. The cost of transporting a source sample\n",
- "# of class A onto a target sample of class B != A is set to infinite, or a\n",
- "# very large value\n",
- "\n",
- "# note that in the present case we consider that all the target samples are\n",
- "# labeled. For daily applications, some target sample might not have labels,\n",
- "# in this case the element of yt corresponding to these samples should be\n",
- "# filled with -1.\n",
- "\n",
- "# Warning: we recall that -1 cannot be used as a class label"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 1 : plots source and target samples + matrix of pairwise distance\n",
- "---------------------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(1, figsize=(10, 10))\n",
- "pl.subplot(2, 2, 1)\n",
- "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.legend(loc=0)\n",
- "pl.title('Source samples')\n",
- "\n",
- "pl.subplot(2, 2, 2)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.legend(loc=0)\n",
- "pl.title('Target samples')\n",
- "\n",
- "pl.subplot(2, 2, 3)\n",
- "pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Cost matrix - unsupervised DA')\n",
- "\n",
- "pl.subplot(2, 2, 4)\n",
- "pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Cost matrix - semisupervised DA')\n",
- "\n",
- "pl.tight_layout()\n",
- "\n",
- "# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n",
- "# similar\" to the cost matrix, (block diagonal matrix)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 2 : plots optimal couplings for the different methods\n",
- "---------------------------------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(2, figsize=(8, 4))\n",
- "\n",
- "pl.subplot(1, 2, 1)\n",
- "pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Optimal coupling\\nUnsupervised DA')\n",
- "\n",
- "pl.subplot(1, 2, 2)\n",
- "pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "pl.title('Optimal coupling\\nSemi-supervised DA')\n",
- "\n",
- "pl.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fig 3 : plot transported samples\n",
- "--------------------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# display transported samples\n",
- "pl.figure(4, figsize=(8, 4))\n",
- "pl.subplot(1, 2, 1)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=0.5)\n",
- "pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n",
- " marker='+', label='Transp samples', s=30)\n",
- "pl.title('Transported samples\\nEmdTransport')\n",
- "pl.legend(loc=0)\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "\n",
- "pl.subplot(1, 2, 2)\n",
- "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
- " label='Target samples', alpha=0.5)\n",
- "pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n",
- " marker='+', label='Transp samples', s=30)\n",
- "pl.title('Transported samples\\nSinkhornTransport')\n",
- "pl.xticks([])\n",
- "pl.yticks([])\n",
- "\n",
- "pl.tight_layout()\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/plot_stochastic.ipynb b/notebooks/plot_stochastic.ipynb
deleted file mode 100644
index 0911c28..0000000
--- a/notebooks/plot_stochastic.ipynb
+++ /dev/null
@@ -1,563 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "# Stochastic examples\n",
- "\n",
- "\n",
- "This example is designed to show how to use the stochatic optimization\n",
- "algorithms for descrete and semicontinous measures from the POT library.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Author: Kilian Fatras <kilian.fatras@gmail.com>\n",
- "#\n",
- "# License: MIT License\n",
- "\n",
- "import matplotlib.pylab as pl\n",
- "import numpy as np\n",
- "import ot\n",
- "import ot.plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n",
- "############################################################################\n",
- "############################################################################\n",
- " DISCRETE CASE:\n",
- "\n",
- " Sample two discrete measures for the discrete case\n",
- " ---------------------------------------------\n",
- "\n",
- " Define 2 discrete measures a and b, the points where are defined the source\n",
- " and the target measures and finally the cost matrix c.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source = 7\n",
- "n_target = 4\n",
- "reg = 1\n",
- "numItermax = 1000\n",
- "\n",
- "a = ot.utils.unif(n_source)\n",
- "b = ot.utils.unif(n_target)\n",
- "\n",
- "rng = np.random.RandomState(0)\n",
- "X_source = rng.randn(n_source, 2)\n",
- "Y_target = rng.randn(n_target, 2)\n",
- "M = ot.dist(X_source, Y_target)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Call the \"SAG\" method to find the transportation matrix in the discrete case\n",
- "---------------------------------------------\n",
- "\n",
- "Define the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\n",
- "results.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06]\n",
- " [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03]\n",
- " [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07]\n",
- " [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04]\n",
- " [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01]\n",
- " [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01]\n",
- " [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]]\n"
- ]
- }
- ],
- "source": [
- "method = \"SAG\"\n",
- "sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n",
- " numItermax)\n",
- "print(sag_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "SEMICONTINOUS CASE:\n",
- "\n",
- "Sample one general measure a, one discrete measures b for the semicontinous\n",
- "case\n",
- "---------------------------------------------\n",
- "\n",
- "Define one general measure a, one discrete measures b, the points where\n",
- "are defined the source and the target measures and finally the cost matrix c.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source = 7\n",
- "n_target = 4\n",
- "reg = 1\n",
- "numItermax = 1000\n",
- "log = True\n",
- "\n",
- "a = ot.utils.unif(n_source)\n",
- "b = ot.utils.unif(n_target)\n",
- "\n",
- "rng = np.random.RandomState(0)\n",
- "X_source = rng.randn(n_source, 2)\n",
- "Y_target = rng.randn(n_target, 2)\n",
- "M = ot.dist(X_source, Y_target)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\n",
- "case\n",
- "---------------------------------------------\n",
- "\n",
- "Define the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\n",
- "results.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[3.88833283 7.64041833 3.93000933 2.68489048 1.42837354 3.25840738\n",
- " 2.80033951] [-2.50038759 -2.4083026 -0.96389053 5.87258072]\n",
- "[[2.49326139e-02 1.01118047e-01 1.68018025e-02 4.67918477e-06]\n",
- " [1.20543018e-01 1.29218840e-02 1.25860644e-03 8.13363473e-03]\n",
- " [3.52425849e-03 7.83826265e-02 6.09501106e-02 1.47316769e-07]\n",
- " [2.62727985e-02 3.49290291e-02 8.11998888e-02 4.55426386e-04]\n",
- " [9.00986942e-03 7.15412954e-04 9.65318348e-03 1.23478677e-01]\n",
- " [1.98446848e-02 8.60145164e-04 1.67017745e-03 1.20482135e-01]\n",
- " [4.16774129e-02 2.77550575e-02 7.07529364e-02 2.67173611e-03]]\n"
- ]
- }
- ],
- "source": [
- "method = \"ASGD\"\n",
- "asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n",
- " numItermax, log=log)\n",
- "print(log_asgd['alpha'], log_asgd['beta'])\n",
- "print(asgd_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compare the results with the Sinkhorn algorithm\n",
- "---------------------------------------------\n",
- "\n",
- "Call the Sinkhorn algorithm from POT\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]\n",
- " [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]\n",
- " [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]\n",
- " [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]\n",
- " [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]\n",
- " [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]\n",
- " [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]\n"
- ]
- }
- ],
- "source": [
- "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n",
- "print(sinkhorn_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "PLOT TRANSPORTATION MATRIX\n",
- "#############################################################################\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot SAG results\n",
- "----------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(4, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot ASGD results\n",
- "-----------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(4, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot Sinkhorn results\n",
- "---------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(4, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n",
- "############################################################################\n",
- "############################################################################\n",
- " SEMICONTINOUS CASE:\n",
- "\n",
- " Sample one general measure a, one discrete measures b for the semicontinous\n",
- " case\n",
- " ---------------------------------------------\n",
- "\n",
- " Define one general measure a, one discrete measures b, the points where\n",
- " are defined the source and the target measures and finally the cost matrix c.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "n_source = 7\n",
- "n_target = 4\n",
- "reg = 1\n",
- "numItermax = 100000\n",
- "lr = 0.1\n",
- "batch_size = 3\n",
- "log = True\n",
- "\n",
- "a = ot.utils.unif(n_source)\n",
- "b = ot.utils.unif(n_target)\n",
- "\n",
- "rng = np.random.RandomState(0)\n",
- "X_source = rng.randn(n_source, 2)\n",
- "Y_target = rng.randn(n_target, 2)\n",
- "M = ot.dist(X_source, Y_target)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Call the \"SGD\" dual method to find the transportation matrix in the\n",
- "semicontinous case\n",
- "---------------------------------------------\n",
- "\n",
- "Call ot.solve_dual_entropic and plot the results.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0.92524245 2.75994495 1.08144666 0.02747421 0.60913832 1.8156535\n",
- " 0.11738177] [0.33905828 0.46705197 1.56941919 4.96075241]\n",
- "[[2.20327995e-02 9.26244184e-02 1.09321230e-02 9.71212784e-08]\n",
- " [1.56579562e-02 1.73985799e-03 1.20373178e-04 2.48153271e-05]\n",
- " [3.49227454e-03 8.05110304e-02 4.44694627e-02 3.42874458e-09]\n",
- " [3.15181548e-02 4.34346087e-02 7.17227024e-02 1.28326090e-05]\n",
- " [6.79336320e-02 5.59136813e-03 5.35899879e-02 2.18675752e-02]\n",
- " [8.02083959e-02 3.60364770e-03 4.97032746e-03 1.14377502e-02]\n",
- " [4.87374362e-02 3.36433325e-02 6.09190548e-02 7.33833971e-05]]\n"
- ]
- }
- ],
- "source": [
- "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n",
- " batch_size, numItermax,\n",
- " lr, log=log)\n",
- "print(log_sgd['alpha'], log_sgd['beta'])\n",
- "print(sgd_dual_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compare the results with the Sinkhorn algorithm\n",
- "---------------------------------------------\n",
- "\n",
- "Call the Sinkhorn algorithm from POT\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]\n",
- " [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]\n",
- " [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]\n",
- " [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]\n",
- " [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]\n",
- " [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]\n",
- " [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]\n"
- ]
- }
- ],
- "source": [
- "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n",
- "print(sinkhorn_pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot SGD results\n",
- "-----------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(4, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\n",
- "pl.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot Sinkhorn results\n",
- "---------------------\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 360x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pl.figure(4, figsize=(5, 5))\n",
- "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\n",
- "pl.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/ot/__init__.py b/ot/__init__.py
index 89c7936..0e6e2e2 100644
--- a/ot/__init__.py
+++ b/ot/__init__.py
@@ -1,34 +1,5 @@
"""
-This is the main module of the POT toolbox. It provides easy access to
-a number of sub-modules and functions described below.
-
-.. note::
-
-
- Here is a list of the submodules and short description of what they contain.
-
- - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems.
- - :any:`ot.bregman` contains OT solvers for the entropic OT problems using
- Bregman projections.
- - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems.
- - :any:`ot.smooth` contains OT solvers for the regularized (l2 and kl) smooth OT
- problems.
- - :any:`ot.gromov` contains solvers for Gromov-Wasserstein and Fused Gromov
- Wasserstein problems.
- - :any:`ot.optim` contains generic solvers OT based optimization problems
- - :any:`ot.da` contains classes and function related to Monge mapping
- estimation and Domain Adaptation (DA).
- - :any:`ot.gpu` contains GPU (cupy) implementation of some OT solvers
- - :any:`ot.dr` contains Dimension Reduction (DR) methods such as Wasserstein
- Discriminant Analysis.
- - :any:`ot.utils` contains utility functions such as distance computation and
- timing.
- - :any:`ot.datasets` contains toy dataset generation functions.
- - :any:`ot.plot` contains visualization functions
- - :any:`ot.stochastic` contains stochastic solvers for regularized OT.
- - :any:`ot.unbalanced` contains solvers for regularized unbalanced OT.
-
.. warning::
The list of automatically imported sub-modules is as follows:
:py:mod:`ot.lp`, :py:mod:`ot.bregman`, :py:mod:`ot.optim`
@@ -61,6 +32,7 @@ from . import gromov
from . import smooth
from . import stochastic
from . import unbalanced
+from . import partial
# OT functions
from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d
@@ -71,7 +43,7 @@ from .da import sinkhorn_lpl1_mm
# utils functions
from .utils import dist, unif, tic, toc, toq
-__version__ = "0.6.0"
+__version__ = "0.7.0"
__all__ = ['emd', 'emd2', 'emd_1d', 'sinkhorn', 'sinkhorn2', 'utils', 'datasets',
'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov',
diff --git a/ot/bregman.py b/ot/bregman.py
index 2cd832b..f1f8437 100644
--- a/ot/bregman.py
+++ b/ot/bregman.py
@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
"""
-Bregman projections for regularized OT
+Bregman projections solvers for entropic regularized OT
"""
# Author: Remi Flamary <remi.flamary@unice.fr>
@@ -8,12 +8,16 @@ Bregman projections for regularized OT
# Kilian Fatras <kilian.fatras@irisa.fr>
# Titouan Vayer <titouan.vayer@irisa.fr>
# Hicham Janati <hicham.janati@inria.fr>
+# Mokhtar Z. Alaya <mokhtarzahdi.alaya@gmail.com>
+# Alexander Tong <alexander.tong@yale.edu>
+# Ievgen Redko <ievgen.redko@univ-st-etienne.fr>
#
# License: MIT License
import numpy as np
import warnings
from .utils import unif, dist
+from scipy.optimize import fmin_l_bfgs_b
def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000,
@@ -536,12 +540,12 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False,
old_v = v[i_2]
v[i_2] = b[i_2] / (K[:, i_2].T.dot(u))
G[:, i_2] = u * K[:, i_2] * v[i_2]
- #aviol = (G@one_m - a)
- #aviol_2 = (G.T@one_n - b)
+ # aviol = (G@one_m - a)
+ # aviol_2 = (G.T@one_n - b)
viol += (-old_v + v[i_2]) * K[:, i_2] * u
viol_2[i_2] = v[i_2] * K[:, i_2].dot(u) - b[i_2]
- #print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2)))
+ # print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2)))
if stopThr_val <= stopThr:
break
@@ -905,11 +909,6 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4,
else:
alpha, beta = warmstart
- def get_K(alpha, beta):
- """log space computation"""
- return np.exp(-(M - alpha.reshape((dim_a, 1))
- - beta.reshape((1, dim_b))) / reg)
-
# print(np.min(K))
def get_reg(n): # exponential decreasing
return (epsilon0 - reg) * np.exp(-n) + reg
@@ -937,7 +936,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4,
# the 10th iterations
transp = G
err = np.linalg.norm(
- (np.sum(transp, axis=0) - b))**2 + np.linalg.norm((np.sum(transp, axis=1) - a))**2
+ (np.sum(transp, axis=0) - b)) ** 2 + np.linalg.norm((np.sum(transp, axis=1) - a)) ** 2
if log:
log['err'].append(err)
@@ -963,7 +962,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4,
def geometricBar(weights, alldistribT):
"""return the weighted geometric mean of distributions"""
- assert(len(weights) == alldistribT.shape[1])
+ assert (len(weights) == alldistribT.shape[1])
return np.exp(np.dot(np.log(alldistribT), weights.T))
@@ -1037,11 +1036,13 @@ def barycenter(A, M, reg, weights=None, method="sinkhorn", numItermax=10000,
"""
if method.lower() == 'sinkhorn':
- return barycenter_sinkhorn(A, M, reg, numItermax=numItermax,
+ return barycenter_sinkhorn(A, M, reg, weights=weights,
+ numItermax=numItermax,
stopThr=stopThr, verbose=verbose, log=log,
**kwargs)
elif method.lower() == 'sinkhorn_stabilized':
- return barycenter_stabilized(A, M, reg, numItermax=numItermax,
+ return barycenter_stabilized(A, M, reg, weights=weights,
+ numItermax=numItermax,
stopThr=stopThr, verbose=verbose,
log=log, **kwargs)
else:
@@ -1103,7 +1104,7 @@ def barycenter_sinkhorn(A, M, reg, weights=None, numItermax=1000,
if weights is None:
weights = np.ones(A.shape[1]) / A.shape[1]
else:
- assert(len(weights) == A.shape[1])
+ assert (len(weights) == A.shape[1])
if log:
log = {'err': []}
@@ -1201,7 +1202,7 @@ def barycenter_stabilized(A, M, reg, tau=1e10, weights=None, numItermax=1000,
if weights is None:
weights = np.ones(n_hists) / n_hists
else:
- assert(len(weights) == A.shape[1])
+ assert (len(weights) == A.shape[1])
if log:
log = {'err': []}
@@ -1329,7 +1330,7 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000,
if weights is None:
weights = np.ones(A.shape[0]) / A.shape[0]
else:
- assert(len(weights) == A.shape[0])
+ assert (len(weights) == A.shape[0])
if log:
log = {'err': []}
@@ -1342,12 +1343,17 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000,
err = 1
# build the convolution operator
+ # this is equivalent to blurring on horizontal then vertical directions
t = np.linspace(0, 1, A.shape[1])
[Y, X] = np.meshgrid(t, t)
- xi1 = np.exp(-(X - Y)**2 / reg)
+ xi1 = np.exp(-(X - Y) ** 2 / reg)
+
+ t = np.linspace(0, 1, A.shape[2])
+ [Y, X] = np.meshgrid(t, t)
+ xi2 = np.exp(-(X - Y) ** 2 / reg)
def K(x):
- return np.dot(np.dot(xi1, x), xi1)
+ return np.dot(np.dot(xi1, x), xi2)
while (err > stopThr and cpt < numItermax):
@@ -1492,6 +1498,164 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000,
return np.sum(K0, axis=1)
+def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100,
+ stopThr=1e-6, verbose=False, log=False, **kwargs):
+ r'''Joint OT and proportion estimation for multi-source target shift as proposed in [27]
+
+ The function solves the following optimization problem:
+
+ .. math::
+
+ \mathbf{h} = arg\min_{\mathbf{h}}\quad \sum_{k=1}^{K} \lambda_k
+ W_{reg}((\mathbf{D}_2^{(k)} \mathbf{h})^T, \mathbf{a})
+
+ s.t. \ \forall k, \mathbf{D}_1^{(k)} \gamma_k \mathbf{1}_n= \mathbf{h}
+
+ where :
+
+ - :math:`\lambda_k` is the weight of k-th source domain
+ - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn)
+ - :math:`\mathbf{D}_2^{(k)}` is a matrix of weights related to k-th source domain defined as in [p. 5, 27], its expected shape is `(n_k, C)` where `n_k` is the number of elements in the k-th source domain and `C` is the number of classes
+ - :math:`\mathbf{h}` is a vector of estimated proportions in the target domain of size C
+ - :math:`\mathbf{a}` is a uniform vector of weights in the target domain of size `n`
+ - :math:`\mathbf{D}_1^{(k)}` is a matrix of class assignments defined as in [p. 5, 27], its expected shape is `(n_k, C)`
+
+ The problem consist in solving a Wasserstein barycenter problem to estimate the proportions :math:`\mathbf{h}` in the target domain.
+
+ The algorithm used for solving the problem is the Iterative Bregman projections algorithm
+ with two sets of marginal constraints related to the unknown vector :math:`\mathbf{h}` and uniform target distribution.
+
+ Parameters
+ ----------
+ Xs : list of K np.ndarray(nsk,d)
+ features of all source domains' samples
+ Ys : list of K np.ndarray(nsk,)
+ labels of all source domains' samples
+ Xt : np.ndarray (nt,d)
+ samples in the target domain
+ reg : float
+ Regularization term > 0
+ metric : string, optional (default="sqeuclidean")
+ The ground metric for the Wasserstein problem
+ numItermax : int, optional
+ Max number of iterations
+ stopThr : float, optional
+ Stop threshold on relative change in the barycenter (>0)
+ log : bool, optional
+ record log if True
+ verbose : bool, optional (default=False)
+ Controls the verbosity of the optimization algorithm
+
+ Returns
+ -------
+ h : (C,) ndarray
+ proportion estimation in the target domain
+ log : dict
+ log dictionary return only if log==True in parameters
+
+
+ References
+ ----------
+
+ .. [27] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia
+ "Optimal transport for multi-source domain adaptation under target shift",
+ International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
+
+ '''
+ nbclasses = len(np.unique(Ys[0]))
+ nbdomains = len(Xs)
+
+ # log dictionary
+ if log:
+ log = {'niter': 0, 'err': [], 'M': [], 'D1': [], 'D2': [], 'gamma': []}
+
+ K = []
+ M = []
+ D1 = []
+ D2 = []
+
+ # For each source domain, build cost matrices M, Gibbs kernels K and corresponding matrices D_1 and D_2
+ for d in range(nbdomains):
+ dom = {}
+ nsk = Xs[d].shape[0] # get number of elements for this domain
+ dom['nbelem'] = nsk
+ classes = np.unique(Ys[d]) # get number of classes for this domain
+
+ # format classes to start from 0 for convenience
+ if np.min(classes) != 0:
+ Ys[d] = Ys[d] - np.min(classes)
+ classes = np.unique(Ys[d])
+
+ # build the corresponding D_1 and D_2 matrices
+ Dtmp1 = np.zeros((nbclasses, nsk))
+ Dtmp2 = np.zeros((nbclasses, nsk))
+
+ for c in classes:
+ nbelemperclass = np.sum(Ys[d] == c)
+ if nbelemperclass != 0:
+ Dtmp1[int(c), Ys[d] == c] = 1.
+ Dtmp2[int(c), Ys[d] == c] = 1. / (nbelemperclass)
+ D1.append(Dtmp1)
+ D2.append(Dtmp2)
+
+ # build the cost matrix and the Gibbs kernel
+ Mtmp = dist(Xs[d], Xt, metric=metric)
+ M.append(Mtmp)
+
+ Ktmp = np.empty(Mtmp.shape, dtype=Mtmp.dtype)
+ np.divide(Mtmp, -reg, out=Ktmp)
+ np.exp(Ktmp, out=Ktmp)
+ K.append(Ktmp)
+
+ # uniform target distribution
+ a = unif(np.shape(Xt)[0])
+
+ cpt = 0 # iterations count
+ err = 1
+ old_bary = np.ones((nbclasses))
+
+ while (err > stopThr and cpt < numItermax):
+
+ bary = np.zeros((nbclasses))
+
+ # update coupling matrices for marginal constraints w.r.t. uniform target distribution
+ for d in range(nbdomains):
+ K[d] = projC(K[d], a)
+ other = np.sum(K[d], axis=1)
+ bary = bary + np.log(np.dot(D1[d], other)) / nbdomains
+
+ bary = np.exp(bary)
+
+ # update coupling matrices for marginal constraints w.r.t. unknown proportions based on [Prop 4., 27]
+ for d in range(nbdomains):
+ new = np.dot(D2[d].T, bary)
+ K[d] = projR(K[d], new)
+
+ err = np.linalg.norm(bary - old_bary)
+ cpt = cpt + 1
+ old_bary = bary
+
+ if log:
+ log['err'].append(err)
+
+ if verbose:
+ if cpt % 200 == 0:
+ print('{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
+ print('{:5d}|{:8e}|'.format(cpt, err))
+
+ bary = bary / np.sum(bary)
+
+ if log:
+ log['niter'] = cpt
+ log['M'] = M
+ log['D1'] = D1
+ log['D2'] = D2
+ log['gamma'] = K
+ return bary, log
+ else:
+ return bary
+
+
def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean',
numIterMax=10000, stopThr=1e-9, verbose=False,
log=False, **kwargs):
@@ -1583,7 +1747,8 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean',
return pi
-def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
+def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9,
+ verbose=False, log=False, **kwargs):
r'''
Solve the entropic regularization optimal transport problem from empirical
data and return the OT loss
@@ -1665,14 +1830,17 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num
M = dist(X_s, X_t, metric=metric)
if log:
- sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs)
+ sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log,
+ **kwargs)
return sinkhorn_loss, log
else:
- sinkhorn_loss = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs)
+ sinkhorn_loss = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log,
+ **kwargs)
return sinkhorn_loss
-def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
+def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9,
+ verbose=False, log=False, **kwargs):
r'''
Compute the sinkhorn divergence loss from empirical data
@@ -1758,11 +1926,14 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli
.. [23] Aude Genevay, Gabriel Peyré, Marco Cuturi, Learning Generative Models with Sinkhorn Divergences, Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018
'''
if log:
- sinkhorn_loss_ab, log_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+ sinkhorn_loss_ab, log_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax,
+ stopThr=1e-9, verbose=verbose, log=log, **kwargs)
- sinkhorn_loss_a, log_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+ sinkhorn_loss_a, log_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax,
+ stopThr=1e-9, verbose=verbose, log=log, **kwargs)
- sinkhorn_loss_b, log_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+ sinkhorn_loss_b, log_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax,
+ stopThr=1e-9, verbose=verbose, log=log, **kwargs)
sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b)
@@ -1777,11 +1948,354 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli
return max(0, sinkhorn_div), log
else:
- sinkhorn_loss_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+ sinkhorn_loss_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9,
+ verbose=verbose, log=log, **kwargs)
- sinkhorn_loss_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+ sinkhorn_loss_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9,
+ verbose=verbose, log=log, **kwargs)
- sinkhorn_loss_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+ sinkhorn_loss_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9,
+ verbose=verbose, log=log, **kwargs)
sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b)
return max(0, sinkhorn_div)
+
+
+def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, restricted=True,
+ maxiter=10000, maxfun=10000, pgtol=1e-09, verbose=False, log=False):
+ r""""
+ Screening Sinkhorn Algorithm for Regularized Optimal Transport
+
+ The function solves an approximate dual of Sinkhorn divergence [2] which is written as the following optimization problem:
+
+ ..math::
+ (u, v) = \argmin_{u, v} 1_{ns}^T B(u,v) 1_{nt} - <\kappa u, a> - <v/\kappa, b>
+
+ where B(u,v) = \diag(e^u) K \diag(e^v), with K = e^{-M/reg} and
+
+ s.t. e^{u_i} \geq \epsilon / \kappa, for all i \in {1, ..., ns}
+
+ e^{v_j} \geq \epsilon \kappa, for all j \in {1, ..., nt}
+
+ The parameters \kappa and \epsilon are determined w.r.t the couple number budget of points (ns_budget, nt_budget), see Equation (5) in [26]
+
+
+ Parameters
+ ----------
+ a : `numpy.ndarray`, shape=(ns,)
+ samples weights in the source domain
+
+ b : `numpy.ndarray`, shape=(nt,)
+ samples weights in the target domain
+
+ M : `numpy.ndarray`, shape=(ns, nt)
+ Cost matrix
+
+ reg : `float`
+ Level of the entropy regularisation
+
+ ns_budget : `int`, deafult=None
+ Number budget of points to be keeped in the source domain
+ If it is None then 50% of the source sample points will be keeped
+
+ nt_budget : `int`, deafult=None
+ Number budget of points to be keeped in the target domain
+ If it is None then 50% of the target sample points will be keeped
+
+ uniform : `bool`, default=False
+ If `True`, the source and target distribution are supposed to be uniform, i.e., a_i = 1 / ns and b_j = 1 / nt
+
+ restricted : `bool`, default=True
+ If `True`, a warm-start initialization for the L-BFGS-B solver
+ using a restricted Sinkhorn algorithm with at most 5 iterations
+
+ maxiter : `int`, default=10000
+ Maximum number of iterations in LBFGS solver
+
+ maxfun : `int`, default=10000
+ Maximum number of function evaluations in LBFGS solver
+
+ pgtol : `float`, default=1e-09
+ Final objective function accuracy in LBFGS solver
+
+ verbose : `bool`, default=False
+ If `True`, dispaly informations about the cardinals of the active sets and the paramerters kappa
+ and epsilon
+
+ Dependency
+ ----------
+ To gain more efficiency, screenkhorn needs to call the "Bottleneck" package (https://pypi.org/project/Bottleneck/)
+ in the screening pre-processing step. If Bottleneck isn't installed, the following error message appears:
+ "Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/"
+
+
+ Returns
+ -------
+ gamma : `numpy.ndarray`, shape=(ns, nt)
+ Screened optimal transportation matrix for the given parameters
+
+ log : `dict`, default=False
+ Log dictionary return only if log==True in parameters
+
+
+ References
+ -----------
+ .. [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). Screening Sinkhorn Algorithm for Regularized Optimal Transport (NIPS) 33, 2019
+
+ """
+ # check if bottleneck module exists
+ try:
+ import bottleneck
+ except ImportError:
+ warnings.warn(
+ "Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.")
+ bottleneck = np
+
+ a = np.asarray(a, dtype=np.float64)
+ b = np.asarray(b, dtype=np.float64)
+ M = np.asarray(M, dtype=np.float64)
+ ns, nt = M.shape
+
+ # by default, we keep only 50% of the sample data points
+ if ns_budget is None:
+ ns_budget = int(np.floor(0.5 * ns))
+ if nt_budget is None:
+ nt_budget = int(np.floor(0.5 * nt))
+
+ # calculate the Gibbs kernel
+ K = np.empty_like(M)
+ np.divide(M, -reg, out=K)
+ np.exp(K, out=K)
+
+ def projection(u, epsilon):
+ u[u <= epsilon] = epsilon
+ return u
+
+ # ----------------------------------------------------------------------------------------------------------------#
+ # Step 1: Screening pre-processing #
+ # ----------------------------------------------------------------------------------------------------------------#
+
+ if ns_budget == ns and nt_budget == nt:
+ # full number of budget points (ns, nt) = (ns_budget, nt_budget)
+ Isel = np.ones(ns, dtype=bool)
+ Jsel = np.ones(nt, dtype=bool)
+ epsilon = 0.0
+ kappa = 1.0
+
+ cst_u = 0.
+ cst_v = 0.
+
+ bounds_u = [(0.0, np.inf)] * ns
+ bounds_v = [(0.0, np.inf)] * nt
+
+ a_I = a
+ b_J = b
+ K_IJ = K
+ K_IJc = []
+ K_IcJ = []
+
+ vec_eps_IJc = np.zeros(nt)
+ vec_eps_IcJ = np.zeros(ns)
+
+ else:
+ # sum of rows and columns of K
+ K_sum_cols = K.sum(axis=1)
+ K_sum_rows = K.sum(axis=0)
+
+ if uniform:
+ if ns / ns_budget < 4:
+ aK_sort = np.sort(K_sum_cols)
+ epsilon_u_square = a[0] / aK_sort[ns_budget - 1]
+ else:
+ aK_sort = bottleneck.partition(K_sum_cols, ns_budget - 1)[ns_budget - 1]
+ epsilon_u_square = a[0] / aK_sort
+
+ if nt / nt_budget < 4:
+ bK_sort = np.sort(K_sum_rows)
+ epsilon_v_square = b[0] / bK_sort[nt_budget - 1]
+ else:
+ bK_sort = bottleneck.partition(K_sum_rows, nt_budget - 1)[nt_budget - 1]
+ epsilon_v_square = b[0] / bK_sort
+ else:
+ aK = a / K_sum_cols
+ bK = b / K_sum_rows
+
+ aK_sort = np.sort(aK)[::-1]
+ epsilon_u_square = aK_sort[ns_budget - 1]
+
+ bK_sort = np.sort(bK)[::-1]
+ epsilon_v_square = bK_sort[nt_budget - 1]
+
+ # active sets I and J (see Lemma 1 in [26])
+ Isel = a >= epsilon_u_square * K_sum_cols
+ Jsel = b >= epsilon_v_square * K_sum_rows
+
+ if sum(Isel) != ns_budget:
+ if uniform:
+ aK = a / K_sum_cols
+ aK_sort = np.sort(aK)[::-1]
+ epsilon_u_square = aK_sort[ns_budget - 1:ns_budget + 1].mean()
+ Isel = a >= epsilon_u_square * K_sum_cols
+ ns_budget = sum(Isel)
+
+ if sum(Jsel) != nt_budget:
+ if uniform:
+ bK = b / K_sum_rows
+ bK_sort = np.sort(bK)[::-1]
+ epsilon_v_square = bK_sort[nt_budget - 1:nt_budget + 1].mean()
+ Jsel = b >= epsilon_v_square * K_sum_rows
+ nt_budget = sum(Jsel)
+
+ epsilon = (epsilon_u_square * epsilon_v_square) ** (1 / 4)
+ kappa = (epsilon_v_square / epsilon_u_square) ** (1 / 2)
+
+ if verbose:
+ print("epsilon = %s\n" % epsilon)
+ print("kappa = %s\n" % kappa)
+ print('Cardinality of selected points: |Isel| = %s \t |Jsel| = %s \n' % (sum(Isel), sum(Jsel)))
+
+ # Ic, Jc: complementary of the active sets I and J
+ Ic = ~Isel
+ Jc = ~Jsel
+
+ K_IJ = K[np.ix_(Isel, Jsel)]
+ K_IcJ = K[np.ix_(Ic, Jsel)]
+ K_IJc = K[np.ix_(Isel, Jc)]
+
+ K_min = K_IJ.min()
+ if K_min == 0:
+ K_min = np.finfo(float).tiny
+
+ # a_I, b_J, a_Ic, b_Jc
+ a_I = a[Isel]
+ b_J = b[Jsel]
+ if not uniform:
+ a_I_min = a_I.min()
+ a_I_max = a_I.max()
+ b_J_max = b_J.max()
+ b_J_min = b_J.min()
+ else:
+ a_I_min = a_I[0]
+ a_I_max = a_I[0]
+ b_J_max = b_J[0]
+ b_J_min = b_J[0]
+
+ # box constraints in L-BFGS-B (see Proposition 1 in [26])
+ bounds_u = [(max(a_I_min / ((nt - nt_budget) * epsilon + nt_budget * (b_J_max / (
+ ns * epsilon * kappa * K_min))), epsilon / kappa), a_I_max / (nt * epsilon * K_min))] * ns_budget
+
+ bounds_v = [(
+ max(b_J_min / ((ns - ns_budget) * epsilon + ns_budget * (kappa * a_I_max / (nt * epsilon * K_min))),
+ epsilon * kappa), b_J_max / (ns * epsilon * K_min))] * nt_budget
+
+ # pre-calculated constants for the objective
+ vec_eps_IJc = epsilon * kappa * (K_IJc * np.ones(nt - nt_budget).reshape((1, -1))).sum(axis=1)
+ vec_eps_IcJ = (epsilon / kappa) * (np.ones(ns - ns_budget).reshape((-1, 1)) * K_IcJ).sum(axis=0)
+
+ # initialisation
+ u0 = np.full(ns_budget, (1. / ns_budget) + epsilon / kappa)
+ v0 = np.full(nt_budget, (1. / nt_budget) + epsilon * kappa)
+
+ # pre-calculed constants for Restricted Sinkhorn (see Algorithm 1 in supplementary of [26])
+ if restricted:
+ if ns_budget != ns or nt_budget != nt:
+ cst_u = kappa * epsilon * K_IJc.sum(axis=1)
+ cst_v = epsilon * K_IcJ.sum(axis=0) / kappa
+
+ cpt = 1
+ while cpt < 5: # 5 iterations
+ K_IJ_v = np.dot(K_IJ.T, u0) + cst_v
+ v0 = b_J / (kappa * K_IJ_v)
+ KIJ_u = np.dot(K_IJ, v0) + cst_u
+ u0 = (kappa * a_I) / KIJ_u
+ cpt += 1
+
+ u0 = projection(u0, epsilon / kappa)
+ v0 = projection(v0, epsilon * kappa)
+
+ else:
+ u0 = u0
+ v0 = v0
+
+ def restricted_sinkhorn(usc, vsc, max_iter=5):
+ """
+ Restricted Sinkhorn Algorithm as a warm-start initialized point for L-BFGS-B (see Algorithm 1 in supplementary of [26])
+ """
+ cpt = 1
+ while cpt < max_iter:
+ K_IJ_v = np.dot(K_IJ.T, usc) + cst_v
+ vsc = b_J / (kappa * K_IJ_v)
+ KIJ_u = np.dot(K_IJ, vsc) + cst_u
+ usc = (kappa * a_I) / KIJ_u
+ cpt += 1
+
+ usc = projection(usc, epsilon / kappa)
+ vsc = projection(vsc, epsilon * kappa)
+
+ return usc, vsc
+
+ def screened_obj(usc, vsc):
+ part_IJ = np.dot(np.dot(usc, K_IJ), vsc) - kappa * np.dot(a_I, np.log(usc)) - (1. / kappa) * np.dot(b_J,
+ np.log(vsc))
+ part_IJc = np.dot(usc, vec_eps_IJc)
+ part_IcJ = np.dot(vec_eps_IcJ, vsc)
+ psi_epsilon = part_IJ + part_IJc + part_IcJ
+ return psi_epsilon
+
+ def screened_grad(usc, vsc):
+ # gradients of Psi_(kappa,epsilon) w.r.t u and v
+ grad_u = np.dot(K_IJ, vsc) + vec_eps_IJc - kappa * a_I / usc
+ grad_v = np.dot(K_IJ.T, usc) + vec_eps_IcJ - (1. / kappa) * b_J / vsc
+ return grad_u, grad_v
+
+ def bfgspost(theta):
+ u = theta[:ns_budget]
+ v = theta[ns_budget:]
+ # objective
+ f = screened_obj(u, v)
+ # gradient
+ g_u, g_v = screened_grad(u, v)
+ g = np.hstack([g_u, g_v])
+ return f, g
+
+ # ----------------------------------------------------------------------------------------------------------------#
+ # Step 2: L-BFGS-B solver #
+ # ----------------------------------------------------------------------------------------------------------------#
+
+ u0, v0 = restricted_sinkhorn(u0, v0)
+ theta0 = np.hstack([u0, v0])
+
+ bounds = bounds_u + bounds_v # constraint bounds
+
+ def obj(theta):
+ return bfgspost(theta)
+
+ theta, _, _ = fmin_l_bfgs_b(func=obj,
+ x0=theta0,
+ bounds=bounds,
+ maxfun=maxfun,
+ pgtol=pgtol,
+ maxiter=maxiter)
+
+ usc = theta[:ns_budget]
+ vsc = theta[ns_budget:]
+
+ usc_full = np.full(ns, epsilon / kappa)
+ vsc_full = np.full(nt, epsilon * kappa)
+ usc_full[Isel] = usc
+ vsc_full[Jsel] = vsc
+
+ if log:
+ log = {}
+ log['u'] = usc_full
+ log['v'] = vsc_full
+ log['Isel'] = Isel
+ log['Jsel'] = Jsel
+
+ gamma = usc_full[:, None] * K * vsc_full[None, :]
+ gamma = gamma / gamma.sum()
+
+ if log:
+ return gamma, log
+ else:
+ return gamma
diff --git a/ot/da.py b/ot/da.py
index 108a38d..b881a8b 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -7,15 +7,16 @@ Domain adaptation with optimal transport
# Nicolas Courty <ncourty@irisa.fr>
# Michael Perrot <michael.perrot@univ-st-etienne.fr>
# Nathalie Gayraud <nat.gayraud@gmail.com>
+# Ievgen Redko <ievgen.redko@univ-st-etienne.fr>
#
# License: MIT License
import numpy as np
import scipy.linalg as linalg
-from .bregman import sinkhorn
+from .bregman import sinkhorn, jcpot_barycenter
from .lp import emd
-from .utils import unif, dist, kernel, cost_normalization
+from .utils import unif, dist, kernel, cost_normalization, label_normalization, laplacian, dots
from .utils import check_params, BaseEstimator
from .unbalanced import sinkhorn_unbalanced
from .optim import cg
@@ -127,7 +128,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
W = np.ones(M.shape)
for (i, c) in enumerate(classes):
majs = np.sum(transp[indices_labels[i]], axis=0)
- majs = p * ((majs + epsilon)**(p - 1))
+ majs = p * ((majs + epsilon) ** (p - 1))
W[indices_labels[i]] = majs
return transp
@@ -359,8 +360,8 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False,
def loss(L, G):
"""Compute full loss"""
- return np.sum((xs1.dot(L) - ns * G.dot(xt))**2) + mu * \
- np.sum(G * M) + eta * np.sum(sel(L - I0)**2)
+ return np.sum((xs1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \
+ np.sum(G * M) + eta * np.sum(sel(L - I0) ** 2)
def solve_L(G):
""" solve L problem with fixed G (least square)"""
@@ -372,10 +373,11 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False,
xsi = xs1.dot(L)
def f(G):
- return np.sum((xsi - ns * G.dot(xt))**2)
+ return np.sum((xsi - ns * G.dot(xt)) ** 2)
def df(G):
return -2 * ns * (xsi - ns * G.dot(xt)).dot(xt.T)
+
G = cg(a, b, M, 1.0 / mu, f, df, G0=G0,
numItermax=numInnerItermax, stopThr=stopInnerThr)
return G
@@ -562,7 +564,7 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian',
def loss(L, G):
"""Compute full loss"""
- return np.sum((K1.dot(L) - ns * G.dot(xt))**2) + mu * \
+ return np.sum((K1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \
np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L))
def solve_L_nobias(G):
@@ -580,10 +582,11 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian',
xsi = K1.dot(L)
def f(G):
- return np.sum((xsi - ns * G.dot(xt))**2)
+ return np.sum((xsi - ns * G.dot(xt)) ** 2)
def df(G):
return -2 * ns * (xsi - ns * G.dot(xt)).dot(xt.T)
+
G = cg(a, b, M, 1.0 / mu, f, df, G0=G0,
numItermax=numInnerItermax, stopThr=stopInnerThr)
return G
@@ -745,6 +748,139 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None,
return A, b
+def emd_laplace(a, b, xs, xt, M, sim='knn', sim_param=None, reg='pos', eta=1, alpha=.5,
+ numItermax=100, stopThr=1e-9, numInnerItermax=100000,
+ stopInnerThr=1e-9, log=False, verbose=False):
+ r"""Solve the optimal transport problem (OT) with Laplacian regularization
+
+ .. math::
+ \gamma = arg\min_\gamma <\gamma,M>_F + eta\Omega_\alpha(\gamma)
+
+ s.t.\ \gamma 1 = a
+
+ \gamma^T 1= b
+
+ \gamma\geq 0
+
+ where:
+
+ - a and b are source and target weights (sum to 1)
+ - xs and xt are source and target samples
+ - M is the (ns,nt) metric cost matrix
+ - :math:`\Omega_\alpha` is the Laplacian regularization term
+ :math:`\Omega_\alpha = (1-\alpha)/n_s^2\sum_{i,j}S^s_{i,j}\|T(\mathbf{x}^s_i)-T(\mathbf{x}^s_j)\|^2+\alpha/n_t^2\sum_{i,j}S^t_{i,j}^'\|T(\mathbf{x}^t_i)-T(\mathbf{x}^t_j)\|^2`
+ with :math:`S^s_{i,j}, S^t_{i,j}` denoting source and target similarity matrices and :math:`T(\cdot)` being a barycentric mapping
+
+ The algorithm used for solving the problem is the conditional gradient algorithm as proposed in [5].
+
+ Parameters
+ ----------
+ a : np.ndarray (ns,)
+ samples weights in the source domain
+ b : np.ndarray (nt,)
+ samples weights in the target domain
+ xs : np.ndarray (ns,d)
+ samples in the source domain
+ xt : np.ndarray (nt,d)
+ samples in the target domain
+ M : np.ndarray (ns,nt)
+ loss matrix
+ sim : string, optional
+ Type of similarity ('knn' or 'gauss') used to construct the Laplacian.
+ sim_param : int or float, optional
+ Parameter (number of the nearest neighbors for sim='knn'
+ or bandwidth for sim='gauss') used to compute the Laplacian.
+ reg : string
+ Type of Laplacian regularization
+ eta : float
+ Regularization term for Laplacian regularization
+ alpha : float
+ Regularization term for source domain's importance in regularization
+ numItermax : int, optional
+ Max number of iterations
+ stopThr : float, optional
+ Stop threshold on error (inner emd solver) (>0)
+ numInnerItermax : int, optional
+ Max number of iterations (inner CG solver)
+ stopInnerThr : float, optional
+ Stop threshold on error (inner CG solver) (>0)
+ verbose : bool, optional
+ Print information along iterations
+ log : bool, optional
+ record log if True
+
+ Returns
+ -------
+ gamma : (ns x nt) ndarray
+ Optimal transportation matrix for the given parameters
+ log : dict
+ log dictionary return only if log==True in parameters
+
+
+ References
+ ----------
+
+ .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy,
+ "Optimal Transport for Domain Adaptation," in IEEE
+ Transactions on Pattern Analysis and Machine Intelligence ,
+ vol.PP, no.99, pp.1-1
+ .. [30] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy,
+ "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching,"
+ in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014.
+
+ See Also
+ --------
+ ot.lp.emd : Unregularized OT
+ ot.optim.cg : General regularized OT
+
+ """
+ if not isinstance(sim_param, (int, float, type(None))):
+ raise ValueError(
+ 'Similarity parameter should be an int or a float. Got {type} instead.'.format(type=type(sim_param).__name__))
+
+ if sim == 'gauss':
+ if sim_param is None:
+ sim_param = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2))
+ sS = kernel(xs, xs, method=sim, sigma=sim_param)
+ sT = kernel(xt, xt, method=sim, sigma=sim_param)
+
+ elif sim == 'knn':
+ if sim_param is None:
+ sim_param = 3
+
+ from sklearn.neighbors import kneighbors_graph
+
+ sS = kneighbors_graph(X=xs, n_neighbors=int(sim_param)).toarray()
+ sS = (sS + sS.T) / 2
+ sT = kneighbors_graph(xt, n_neighbors=int(sim_param)).toarray()
+ sT = (sT + sT.T) / 2
+ else:
+ raise ValueError('Unknown similarity type {sim}. Currently supported similarity types are "knn" and "gauss".'.format(sim=sim))
+
+ lS = laplacian(sS)
+ lT = laplacian(sT)
+
+ def f(G):
+ return alpha * np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \
+ + (1 - alpha) * np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs)))))
+
+ ls2 = lS + lS.T
+ lt2 = lT + lT.T
+ xt2 = np.dot(xt, xt.T)
+
+ if reg == 'disp':
+ Cs = -eta * alpha / xs.shape[0] * dots(ls2, xs, xt.T)
+ Ct = -eta * (1 - alpha) / xt.shape[0] * dots(xs, xt.T, lt2)
+ M = M + Cs + Ct
+
+ def df(G):
+ return alpha * np.dot(ls2, np.dot(G, xt2))\
+ + (1 - alpha) * np.dot(xs, np.dot(xs.T, np.dot(G, lt2)))
+
+ return cg(a, b, M, reg=eta, f=f, df=df, G0=None, numItermax=numItermax, numItermaxEmd=numInnerItermax,
+ stopThr=stopThr, stopThr2=stopInnerThr, verbose=verbose, log=log)
+
+
def distribution_estimation_uniform(X):
"""estimates a uniform distribution from an array of samples X
@@ -772,7 +908,8 @@ class BaseTransport(BaseEstimator):
at the class level in their ``__init__`` as explicit keyword
arguments (no ``*args`` or ``**kwargs``).
- fit method should:
+ the fit method should:
+
- estimate a cost matrix and store it in a `cost_` attribute
- estimate a coupling matrix and store it in a `coupling_`
attribute
@@ -783,6 +920,9 @@ class BaseTransport(BaseEstimator):
transform method should always get as input a Xs parameter
inverse_transform method should always get as input a Xt parameter
+
+ transform_labels method should always get as input a ys parameter
+ inverse_transform_labels method should always get as input a yt parameter
"""
def fit(self, Xs=None, ys=None, Xt=None, yt=None):
@@ -794,7 +934,7 @@ class BaseTransport(BaseEstimator):
Xs : array-like, shape (n_source_samples, n_features)
The training input samples.
ys : array-like, shape (n_source_samples,)
- The class labels
+ The training class labels
Xt : array-like, shape (n_target_samples, n_features)
The training input samples.
yt : array-like, shape (n_target_samples,)
@@ -855,7 +995,7 @@ class BaseTransport(BaseEstimator):
Xs : array-like, shape (n_source_samples, n_features)
The training input samples.
ys : array-like, shape (n_source_samples,)
- The class labels
+ The class labels for training samples
Xt : array-like, shape (n_target_samples, n_features)
The training input samples.
yt : array-like, shape (n_target_samples,)
@@ -879,13 +1019,13 @@ class BaseTransport(BaseEstimator):
Parameters
----------
Xs : array-like, shape (n_source_samples, n_features)
- The training input samples.
+ The source input samples.
ys : array-like, shape (n_source_samples,)
- The class labels
+ The class labels for source samples
Xt : array-like, shape (n_target_samples, n_features)
- The training input samples.
+ The target input samples.
yt : array-like, shape (n_target_samples,)
- The class labels. If some target samples are unlabeled, fill the
+ The class labels for target. If some target samples are unlabeled, fill the
yt's elements with -1.
Warning: Note that, due to this convention -1 cannot be used as a
@@ -921,7 +1061,6 @@ class BaseTransport(BaseEstimator):
transp_Xs = []
for bi in batch_ind:
-
# get the nearest neighbor in the source domain
D0 = dist(Xs[bi], self.xs_)
idx = np.argmin(D0, axis=1)
@@ -941,20 +1080,64 @@ class BaseTransport(BaseEstimator):
return transp_Xs
+ def transform_labels(self, ys=None):
+ """Propagate source labels ys to obtain estimated target labels as in [27]
+
+ Parameters
+ ----------
+ ys : array-like, shape (n_source_samples,)
+ The source class labels
+
+ Returns
+ -------
+ transp_ys : array-like, shape (n_target_samples, nb_classes)
+ Estimated soft target labels.
+
+ References
+ ----------
+
+ .. [27] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia
+ "Optimal transport for multi-source domain adaptation under target shift",
+ International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
+
+ """
+
+ # check the necessary inputs parameters are here
+ if check_params(ys=ys):
+
+ ysTemp = label_normalization(np.copy(ys))
+ classes = np.unique(ysTemp)
+ n = len(classes)
+ D1 = np.zeros((n, len(ysTemp)))
+
+ # perform label propagation
+ transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None]
+
+ # set nans to 0
+ transp[~ np.isfinite(transp)] = 0
+
+ for c in classes:
+ D1[int(c), ysTemp == c] = 1
+
+ # compute propagated labels
+ transp_ys = np.dot(D1, transp)
+
+ return transp_ys.T
+
def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None,
batch_size=128):
- """Transports target samples Xt onto target samples Xs
+ """Transports target samples Xt onto source samples Xs
Parameters
----------
Xs : array-like, shape (n_source_samples, n_features)
- The training input samples.
+ The source input samples.
ys : array-like, shape (n_source_samples,)
- The class labels
+ The source class labels
Xt : array-like, shape (n_target_samples, n_features)
- The training input samples.
+ The target input samples.
yt : array-like, shape (n_target_samples,)
- The class labels. If some target samples are unlabeled, fill the
+ The target class labels. If some target samples are unlabeled, fill the
yt's elements with -1.
Warning: Note that, due to this convention -1 cannot be used as a
@@ -990,7 +1173,6 @@ class BaseTransport(BaseEstimator):
transp_Xt = []
for bi in batch_ind:
-
D0 = dist(Xt[bi], self.xt_)
idx = np.argmin(D0, axis=1)
@@ -1009,6 +1191,41 @@ class BaseTransport(BaseEstimator):
return transp_Xt
+ def inverse_transform_labels(self, yt=None):
+ """Propagate target labels yt to obtain estimated source labels ys
+
+ Parameters
+ ----------
+ yt : array-like, shape (n_target_samples,)
+
+ Returns
+ -------
+ transp_ys : array-like, shape (n_source_samples, nb_classes)
+ Estimated soft source labels.
+ """
+
+ # check the necessary inputs parameters are here
+ if check_params(yt=yt):
+
+ ytTemp = label_normalization(np.copy(yt))
+ classes = np.unique(ytTemp)
+ n = len(classes)
+ D1 = np.zeros((n, len(ytTemp)))
+
+ # perform label propagation
+ transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None]
+
+ # set nans to 0
+ transp[~ np.isfinite(transp)] = 0
+
+ for c in classes:
+ D1[int(c), ytTemp == c] = 1
+
+ # compute propagated samples
+ transp_ys = np.dot(D1, transp.T)
+
+ return transp_ys.T
+
class LinearTransport(BaseTransport):
""" OT linear operator between empirical distributions
@@ -1055,7 +1272,6 @@ class LinearTransport(BaseTransport):
def __init__(self, reg=1e-8, bias=True, log=False,
distribution_estimation=distribution_estimation_uniform):
-
self.bias = bias
self.log = log
self.reg = reg
@@ -1136,7 +1352,6 @@ class LinearTransport(BaseTransport):
# check the necessary inputs parameters are here
if check_params(Xs=Xs):
-
transp_Xs = Xs.dot(self.A_) + self.B_
return transp_Xs
@@ -1170,7 +1385,6 @@ class LinearTransport(BaseTransport):
# check the necessary inputs parameters are here
if check_params(Xt=Xt):
-
transp_Xt = Xt.dot(self.A1_) + self.B1_
return transp_Xt
@@ -1224,6 +1438,9 @@ class SinkhornTransport(BaseTransport):
.. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal
Transport, Advances in Neural Information Processing Systems (NIPS)
26, 2013
+ .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
+ Regularized discrete optimal transport. SIAM Journal on Imaging
+ Sciences, 7(3), 1853-1882.
"""
def __init__(self, reg_e=1., max_iter=1000,
@@ -1231,7 +1448,6 @@ class SinkhornTransport(BaseTransport):
metric="sqeuclidean", norm=None,
distribution_estimation=distribution_estimation_uniform,
out_of_sample_map='ferradans', limit_max=np.infty):
-
self.reg_e = reg_e
self.max_iter = max_iter
self.tol = tol
@@ -1323,13 +1539,15 @@ class EMDTransport(BaseTransport):
.. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy,
"Optimal Transport for Domain Adaptation," in IEEE Transactions
on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
+ .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
+ Regularized discrete optimal transport. SIAM Journal on Imaging
+ Sciences, 7(3), 1853-1882.
"""
def __init__(self, metric="sqeuclidean", norm=None, log=False,
distribution_estimation=distribution_estimation_uniform,
out_of_sample_map='ferradans', limit_max=10,
max_iter=100000):
-
self.metric = metric
self.norm = norm
self.log = log
@@ -1431,7 +1649,9 @@ class SinkhornLpl1Transport(BaseTransport):
.. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015).
Generalized conditional gradient: analysis of convergence
and applications. arXiv preprint arXiv:1510.06567.
-
+ .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
+ Regularized discrete optimal transport. SIAM Journal on Imaging
+ Sciences, 7(3), 1853-1882.
"""
def __init__(self, reg_e=1., reg_cl=0.1,
@@ -1440,7 +1660,6 @@ class SinkhornLpl1Transport(BaseTransport):
metric="sqeuclidean", norm=None,
distribution_estimation=distribution_estimation_uniform,
out_of_sample_map='ferradans', limit_max=np.infty):
-
self.reg_e = reg_e
self.reg_cl = reg_cl
self.max_iter = max_iter
@@ -1481,7 +1700,6 @@ class SinkhornLpl1Transport(BaseTransport):
# check the necessary inputs parameters are here
if check_params(Xs=Xs, Xt=Xt, ys=ys):
-
super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt)
returned_ = sinkhorn_lpl1_mm(
@@ -1499,6 +1717,127 @@ class SinkhornLpl1Transport(BaseTransport):
return self
+class EMDLaplaceTransport(BaseTransport):
+
+ """Domain Adapatation OT method based on Earth Mover's Distance with Laplacian regularization
+
+ Parameters
+ ----------
+ reg_type : string optional (default='pos')
+ Type of the regularization term: 'pos' and 'disp' for
+ regularization term defined in [2] and [6], respectively.
+ reg_lap : float, optional (default=1)
+ Laplacian regularization parameter
+ reg_src : float, optional (default=0.5)
+ Source relative importance in regularization
+ metric : string, optional (default="sqeuclidean")
+ The ground metric for the Wasserstein problem
+ norm : string, optional (default=None)
+ If given, normalize the ground metric to avoid numerical errors that
+ can occur with large metric values.
+ similarity : string, optional (default="knn")
+ The similarity to use either knn or gaussian
+ similarity_param : int or float, optional (default=None)
+ Parameter for the similarity: number of nearest neighbors or bandwidth
+ if similarity="knn" or "gaussian", respectively. If None is provided,
+ it is set to 3 or the average pairwise squared Euclidean distance, respectively.
+ max_iter : int, optional (default=100)
+ Max number of BCD iterations
+ tol : float, optional (default=1e-5)
+ Stop threshold on relative loss decrease (>0)
+ max_inner_iter : int, optional (default=10)
+ Max number of iterations (inner CG solver)
+ inner_tol : float, optional (default=1e-6)
+ Stop threshold on error (inner CG solver) (>0)
+ log : int, optional (default=False)
+ Controls the logs of the optimization algorithm
+ distribution_estimation : callable, optional (defaults to the uniform)
+ The kind of distribution estimation to employ
+ out_of_sample_map : string, optional (default="ferradans")
+ The kind of out of sample mapping to apply to transport samples
+ from a domain into another one. Currently the only possible option is
+ "ferradans" which uses the method proposed in [6].
+
+ Attributes
+ ----------
+ coupling_ : array-like, shape (n_source_samples, n_target_samples)
+ The optimal coupling
+
+ References
+ ----------
+ .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy,
+ "Optimal Transport for Domain Adaptation," in IEEE Transactions
+ on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
+ .. [2] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy,
+ "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching,"
+ in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014.
+ .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
+ Regularized discrete optimal transport. SIAM Journal on Imaging
+ Sciences, 7(3), 1853-1882.
+ """
+
+ def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., metric="sqeuclidean",
+ norm=None, similarity="knn", similarity_param=None, max_iter=100, tol=1e-9,
+ max_inner_iter=100000, inner_tol=1e-9, log=False, verbose=False,
+ distribution_estimation=distribution_estimation_uniform,
+ out_of_sample_map='ferradans'):
+ self.reg = reg_type
+ self.reg_lap = reg_lap
+ self.reg_src = reg_src
+ self.metric = metric
+ self.norm = norm
+ self.similarity = similarity
+ self.sim_param = similarity_param
+ self.max_iter = max_iter
+ self.tol = tol
+ self.max_inner_iter = max_inner_iter
+ self.inner_tol = inner_tol
+ self.log = log
+ self.verbose = verbose
+ self.distribution_estimation = distribution_estimation
+ self.out_of_sample_map = out_of_sample_map
+
+ def fit(self, Xs, ys=None, Xt=None, yt=None):
+ """Build a coupling matrix from source and target sets of samples
+ (Xs, ys) and (Xt, yt)
+
+ Parameters
+ ----------
+ Xs : array-like, shape (n_source_samples, n_features)
+ The training input samples.
+ ys : array-like, shape (n_source_samples,)
+ The class labels
+ Xt : array-like, shape (n_target_samples, n_features)
+ The training input samples.
+ yt : array-like, shape (n_target_samples,)
+ The class labels. If some target samples are unlabeled, fill the
+ yt's elements with -1.
+
+ Warning: Note that, due to this convention -1 cannot be used as a
+ class label
+
+ Returns
+ -------
+ self : object
+ Returns self.
+ """
+
+ super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt)
+
+ returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_,
+ xt=self.xt_, M=self.cost_, sim=self.similarity, sim_param=self.sim_param, reg=self.reg, eta=self.reg_lap,
+ alpha=self.reg_src, numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter,
+ stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose)
+
+ # coupling estimation
+ if self.log:
+ self.coupling_, self.log_ = returned_
+ else:
+ self.coupling_ = returned_
+ self.log_ = dict()
+ return self
+
+
class SinkhornL1l2Transport(BaseTransport):
"""Domain Adapatation OT method based on sinkhorn algorithm +
@@ -1554,7 +1893,9 @@ class SinkhornL1l2Transport(BaseTransport):
.. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015).
Generalized conditional gradient: analysis of convergence
and applications. arXiv preprint arXiv:1510.06567.
-
+ .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
+ Regularized discrete optimal transport. SIAM Journal on Imaging
+ Sciences, 7(3), 1853-1882.
"""
def __init__(self, reg_e=1., reg_cl=0.1,
@@ -1563,7 +1904,6 @@ class SinkhornL1l2Transport(BaseTransport):
metric="sqeuclidean", norm=None,
distribution_estimation=distribution_estimation_uniform,
out_of_sample_map='ferradans', limit_max=10):
-
self.reg_e = reg_e
self.reg_cl = reg_cl
self.max_iter = max_iter
@@ -1685,7 +2025,6 @@ class MappingTransport(BaseEstimator):
norm=None, kernel="linear", sigma=1, max_iter=100, tol=1e-5,
max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False,
verbose2=False):
-
self.metric = metric
self.norm = norm
self.mu = mu
@@ -1848,7 +2187,9 @@ class UnbalancedSinkhornTransport(BaseTransport):
.. [1] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016).
Scaling algorithms for unbalanced transport problems. arXiv preprint
arXiv:1607.05816.
-
+ .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
+ Regularized discrete optimal transport. SIAM Journal on Imaging
+ Sciences, 7(3), 1853-1882.
"""
def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn',
@@ -1856,7 +2197,6 @@ class UnbalancedSinkhornTransport(BaseTransport):
metric="sqeuclidean", norm=None,
distribution_estimation=distribution_estimation_uniform,
out_of_sample_map='ferradans', limit_max=10):
-
self.reg_e = reg_e
self.reg_m = reg_m
self.method = method
@@ -1914,3 +2254,267 @@ class UnbalancedSinkhornTransport(BaseTransport):
self.log_ = dict()
return self
+
+
+class JCPOTTransport(BaseTransport):
+
+ """Domain Adapatation OT method for multi-source target shift based on Wasserstein barycenter algorithm.
+
+ Parameters
+ ----------
+ reg_e : float, optional (default=1)
+ Entropic regularization parameter
+ max_iter : int, float, optional (default=10)
+ The minimum number of iteration before stopping the optimization
+ algorithm if no it has not converged
+ tol : float, optional (default=10e-9)
+ Stop threshold on error (inner sinkhorn solver) (>0)
+ verbose : bool, optional (default=False)
+ Controls the verbosity of the optimization algorithm
+ log : bool, optional (default=False)
+ Controls the logs of the optimization algorithm
+ metric : string, optional (default="sqeuclidean")
+ The ground metric for the Wasserstein problem
+ norm : string, optional (default=None)
+ If given, normalize the ground metric to avoid numerical errors that
+ can occur with large metric values.
+ distribution_estimation : callable, optional (defaults to the uniform)
+ The kind of distribution estimation to employ
+ out_of_sample_map : string, optional (default="ferradans")
+ The kind of out of sample mapping to apply to transport samples
+ from a domain into another one. Currently the only possible option is
+ "ferradans" which uses the method proposed in [6].
+
+ Attributes
+ ----------
+ coupling_ : list of array-like objects, shape K x (n_source_samples, n_target_samples)
+ A set of optimal couplings between each source domain and the target domain
+ proportions_ : array-like, shape (n_classes,)
+ Estimated class proportions in the target domain
+ log_ : dictionary
+ The dictionary of log, empty dic if parameter log is not True
+
+ References
+ ----------
+
+ .. [1] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia
+ "Optimal transport for multi-source domain adaptation under target shift",
+ International Conference on Artificial Intelligence and Statistics (AISTATS),
+ vol. 89, p.849-858, 2019.
+
+ .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
+ Regularized discrete optimal transport. SIAM Journal on Imaging
+ Sciences, 7(3), 1853-1882.
+
+
+ """
+
+ def __init__(self, reg_e=.1, max_iter=10,
+ tol=10e-9, verbose=False, log=False,
+ metric="sqeuclidean",
+ out_of_sample_map='ferradans'):
+ self.reg_e = reg_e
+ self.max_iter = max_iter
+ self.tol = tol
+ self.verbose = verbose
+ self.log = log
+ self.metric = metric
+ self.out_of_sample_map = out_of_sample_map
+
+ def fit(self, Xs, ys=None, Xt=None, yt=None):
+ """Building coupling matrices from a list of source and target sets of samples
+ (Xs, ys) and (Xt, yt)
+
+ Parameters
+ ----------
+ Xs : list of K array-like objects, shape K x (nk_source_samples, n_features)
+ A list of the training input samples.
+ ys : list of K array-like objects, shape K x (nk_source_samples,)
+ A list of the class labels
+ Xt : array-like, shape (n_target_samples, n_features)
+ The training input samples.
+ yt : array-like, shape (n_target_samples,)
+ The class labels. If some target samples are unlabeled, fill the
+ yt's elements with -1.
+
+ Warning: Note that, due to this convention -1 cannot be used as a
+ class label
+
+ Returns
+ -------
+ self : object
+ Returns self.
+ """
+
+ # check the necessary inputs parameters are here
+ if check_params(Xs=Xs, Xt=Xt, ys=ys):
+
+ self.xs_ = Xs
+ self.xt_ = Xt
+
+ returned_ = jcpot_barycenter(Xs=Xs, Ys=ys, Xt=Xt, reg=self.reg_e,
+ metric=self.metric, distrinumItermax=self.max_iter, stopThr=self.tol,
+ verbose=self.verbose, log=True)
+
+ self.coupling_ = returned_[1]['gamma']
+
+ # deal with the value of log
+ if self.log:
+ self.proportions_, self.log_ = returned_
+ else:
+ self.proportions_ = returned_
+ self.log_ = dict()
+
+ return self
+
+ def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128):
+ """Transports source samples Xs onto target ones Xt
+
+ Parameters
+ ----------
+ Xs : list of K array-like objects, shape K x (nk_source_samples, n_features)
+ A list of the training input samples.
+ ys : list of K array-like objects, shape K x (nk_source_samples,)
+ A list of the class labels
+ Xt : array-like, shape (n_target_samples, n_features)
+ The training input samples.
+ yt : array-like, shape (n_target_samples,)
+ The class labels. If some target samples are unlabeled, fill the
+ yt's elements with -1.
+
+ Warning: Note that, due to this convention -1 cannot be used as a
+ class label
+ batch_size : int, optional (default=128)
+ The batch size for out of sample inverse transform
+ """
+
+ transp_Xs = []
+
+ # check the necessary inputs parameters are here
+ if check_params(Xs=Xs):
+
+ if all([np.allclose(x, y) for x, y in zip(self.xs_, Xs)]):
+
+ # perform standard barycentric mapping for each source domain
+
+ for coupling in self.coupling_:
+ transp = coupling / np.sum(coupling, 1)[:, None]
+
+ # set nans to 0
+ transp[~ np.isfinite(transp)] = 0
+
+ # compute transported samples
+ transp_Xs.append(np.dot(transp, self.xt_))
+ else:
+
+ # perform out of sample mapping
+ indices = np.arange(Xs.shape[0])
+ batch_ind = [
+ indices[i:i + batch_size]
+ for i in range(0, len(indices), batch_size)]
+
+ transp_Xs = []
+
+ for bi in batch_ind:
+ transp_Xs_ = []
+
+ # get the nearest neighbor in the sources domains
+ xs = np.concatenate(self.xs_, axis=0)
+ idx = np.argmin(dist(Xs[bi], xs), axis=1)
+
+ # transport the source samples
+ for coupling in self.coupling_:
+ transp = coupling / np.sum(
+ coupling, 1)[:, None]
+ transp[~ np.isfinite(transp)] = 0
+ transp_Xs_.append(np.dot(transp, self.xt_))
+
+ transp_Xs_ = np.concatenate(transp_Xs_, axis=0)
+
+ # define the transported points
+ transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - xs[idx, :]
+ transp_Xs.append(transp_Xs_)
+
+ transp_Xs = np.concatenate(transp_Xs, axis=0)
+
+ return transp_Xs
+
+ def transform_labels(self, ys=None):
+ """Propagate source labels ys to obtain target labels as in [27]
+
+ Parameters
+ ----------
+ ys : list of K array-like objects, shape K x (nk_source_samples,)
+ A list of the class labels
+
+ Returns
+ -------
+ yt : array-like, shape (n_target_samples, nb_classes)
+ Estimated soft target labels.
+ """
+
+ # check the necessary inputs parameters are here
+ if check_params(ys=ys):
+ yt = np.zeros((len(np.unique(np.concatenate(ys))), self.xt_.shape[0]))
+ for i in range(len(ys)):
+ ysTemp = label_normalization(np.copy(ys[i]))
+ classes = np.unique(ysTemp)
+ n = len(classes)
+ ns = len(ysTemp)
+
+ # perform label propagation
+ transp = self.coupling_[i] / np.sum(self.coupling_[i], 1)[:, None]
+
+ # set nans to 0
+ transp[~ np.isfinite(transp)] = 0
+
+ if self.log:
+ D1 = self.log_['D1'][i]
+ else:
+ D1 = np.zeros((n, ns))
+
+ for c in classes:
+ D1[int(c), ysTemp == c] = 1
+
+ # compute propagated labels
+ yt = yt + np.dot(D1, transp) / len(ys)
+
+ return yt.T
+
+ def inverse_transform_labels(self, yt=None):
+ """Propagate source labels ys to obtain target labels
+
+ Parameters
+ ----------
+ yt : array-like, shape (n_source_samples,)
+ The target class labels
+
+ Returns
+ -------
+ transp_ys : list of K array-like objects, shape K x (nk_source_samples, nb_classes)
+ A list of estimated soft source labels
+ """
+
+ # check the necessary inputs parameters are here
+ if check_params(yt=yt):
+ transp_ys = []
+ ytTemp = label_normalization(np.copy(yt))
+ classes = np.unique(ytTemp)
+ n = len(classes)
+ D1 = np.zeros((n, len(ytTemp)))
+
+ for c in classes:
+ D1[int(c), ytTemp == c] = 1
+
+ for i in range(len(self.xs_)):
+
+ # perform label propagation
+ transp = self.coupling_[i] / np.sum(self.coupling_[i], 1)[:, None]
+
+ # set nans to 0
+ transp[~ np.isfinite(transp)] = 0
+
+ # compute propagated labels
+ transp_ys.append(np.dot(D1, transp.T).T)
+
+ return transp_ys
diff --git a/ot/datasets.py b/ot/datasets.py
index ba0cfd9..b86ef3b 100644
--- a/ot/datasets.py
+++ b/ot/datasets.py
@@ -1,5 +1,5 @@
"""
-Simple example datasets for OT
+Simple example datasets
"""
# Author: Remi Flamary <remi.flamary@unice.fr>
@@ -30,7 +30,7 @@ def make_1D_gauss(n, m, s):
1D histogram for a gaussian distribution
"""
x = np.arange(n, dtype=np.float64)
- h = np.exp(-(x - m)**2 / (2 * s**2))
+ h = np.exp(-(x - m) ** 2 / (2 * s ** 2))
return h / h.sum()
@@ -80,7 +80,7 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None):
return make_2D_samples_gauss(n, m, sigma, random_state=None)
-def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs):
+def make_data_classif(dataset, n, nz=.5, theta=0, p=.5, random_state=None, **kwargs):
"""Dataset generation for classification problems
Parameters
@@ -91,6 +91,8 @@ def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs):
number of training samples
nz : float
noise level (>0)
+ p : float
+ proportion of one class in the binary setting
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
@@ -145,11 +147,22 @@ def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs):
n2 = np.sum(y == 2)
x = np.zeros((n, 2))
- x[y == 1, :] = get_2D_samples_gauss(n1, m1, nz, random_state=generator)
- x[y == 2, :] = get_2D_samples_gauss(n2, m2, nz, random_state=generator)
+ x[y == 1, :] = make_2D_samples_gauss(n1, m1, nz, random_state=generator)
+ x[y == 2, :] = make_2D_samples_gauss(n2, m2, nz, random_state=generator)
x = x.dot(rot)
+ elif dataset.lower() == '2gauss_prop':
+
+ y = np.concatenate((np.ones(int(p * n)), np.zeros(int((1 - p) * n))))
+ x = np.hstack((0 * y[:, None] - 0, 1 - 2 * y[:, None])) + nz * np.random.randn(len(y), 2)
+
+ if ('bias' not in kwargs) and ('b' not in kwargs):
+ kwargs['bias'] = np.array([0, 2])
+
+ x[:, 0] += kwargs['bias'][0]
+ x[:, 1] += kwargs['bias'][1]
+
else:
x = np.array(0)
y = np.array(0)
diff --git a/ot/dr.py b/ot/dr.py
index 680dabf..11d2e10 100644
--- a/ot/dr.py
+++ b/ot/dr.py
@@ -1,10 +1,10 @@
# -*- coding: utf-8 -*-
"""
-Dimension reduction with optimal transport
+Dimension reduction with OT
.. warning::
- Note that by default the module is not import in :mod:`ot`. In order to
+ Note that by default the module is not imported in :mod:`ot`. In order to
use it you need to explicitely import :mod:`ot.dr`
"""
diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py
index 1ab95bb..7478fb9 100644
--- a/ot/gpu/__init__.py
+++ b/ot/gpu/__init__.py
@@ -1,8 +1,9 @@
# -*- coding: utf-8 -*-
"""
+GPU implementation for several OT solvers and utility
+functions.
-This module provides GPU implementation for several OT solvers and utility
-functions. The GPU backend in handled by `cupy
+The GPU backend in handled by `cupy
<https://cupy.chainer.org/>`_.
.. warning::
diff --git a/ot/gromov.py b/ot/gromov.py
index 699ae4c..4427a96 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
"""
-Gromov-Wasserstein transport method
+Gromov-Wasserstein and Fused-Gromov-Wasserstein solvers
"""
# Author: Erwan Vautier <erwan.vautier@gmail.com>
@@ -276,7 +276,6 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs
- p : distribution in the source space
- q : distribution in the target space
- L : loss function to account for the misfit between the similarity matrices
- - H : entropy
Parameters
----------
@@ -343,6 +342,83 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs
return cg(p, q, 0, 1, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
+def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs):
+ """
+ Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q)
+
+ The function solves the following optimization problem:
+
+ .. math::
+ GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
+
+ Where :
+ - C1 : Metric cost matrix in the source space
+ - C2 : Metric cost matrix in the target space
+ - p : distribution in the source space
+ - q : distribution in the target space
+ - L : loss function to account for the misfit between the similarity matrices
+
+ Parameters
+ ----------
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric cost matrix in the target space
+ p : ndarray, shape (ns,)
+ Distribution in the source space.
+ q : ndarray, shape (nt,)
+ Distribution in the target space.
+ loss_fun : str
+ loss function used for the solver either 'square_loss' or 'kl_loss'
+ max_iter : int, optional
+ Max number of iterations
+ tol : float, optional
+ Stop threshold on error (>0)
+ verbose : bool, optional
+ Print information along iterations
+ log : bool, optional
+ record log if True
+ armijo : bool, optional
+ If True the steps of the line-search is found via an armijo research. Else closed form is used.
+ If there is convergence issues use False.
+
+ Returns
+ -------
+ gw_dist : float
+ Gromov-Wasserstein distance
+ log : dict
+ convergence information and Coupling marix
+
+ References
+ ----------
+ .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
+
+ .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the
+ metric approach to object matching. Foundations of computational
+ mathematics 11.4 (2011): 417-487.
+
+ """
+
+ constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun)
+
+ G0 = p[:, None] * q[None, :]
+
+ def f(G):
+ return gwloss(constC, hC1, hC2, G)
+
+ def df(G):
+ return gwggrad(constC, hC1, hC2, G)
+ res, log_gw = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
+ log_gw['gw_dist'] = gwloss(constC, hC1, hC2, res)
+ log_gw['T'] = res
+ if log:
+ return log_gw['gw_dist'], log_gw
+ else:
+ return log_gw['gw_dist']
+
+
def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs):
"""
Computes the FGW transport between two graphs see [24]
@@ -357,8 +433,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
where :
- M is the (ns,nt) metric cost matrix
- - :math:`f` is the regularization term ( and df is its gradient)
- - a and b are source and target weights (sum to 1)
+ - p and q are source and target weights (sum to 1)
- L is a loss function to account for the misfit between the similarity matrices
The algorithm used for solving the problem is conditional gradient as discussed in [24]_
@@ -377,17 +452,13 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
Distribution in the target space
loss_fun : str, optional
Loss function used for the solver
- max_iter : int, optional
- Max number of iterations
- tol : float, optional
- Stop threshold on error (>0)
- verbose : bool, optional
- Print information along iterations
- log : bool, optional
- record log if True
+ alpha : float, optional
+ Trade-off parameter (0 < alpha < 1)
armijo : bool, optional
If True the steps of the line-search is found via an armijo research. Else closed form is used.
If there is convergence issues use False.
+ log : bool, optional
+ record log if True
**kwargs : dict
parameters can be directly passed to the ot.optim.cg solver
@@ -417,11 +488,11 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
return gwggrad(constC, hC1, hC2, G)
if log:
- res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)
+ res, log = cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)
log['fgw_dist'] = log['loss'][::-1][0]
return res, log
else:
- return cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
+ return cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs):
@@ -439,8 +510,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
where :
- M is the (ns,nt) metric cost matrix
- - :math:`f` is the regularization term ( and df is its gradient)
- - a and b are source and target weights (sum to 1)
+ - p and q are source and target weights (sum to 1)
- L is a loss function to account for the misfit between the similarity matrices
The algorithm used for solving the problem is conditional gradient as discussed in [1]_
@@ -458,17 +528,13 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
Distribution in the target space.
loss_fun : str, optional
Loss function used for the solver.
- max_iter : int, optional
- Max number of iterations
- tol : float, optional
- Stop threshold on error (>0)
- verbose : bool, optional
- Print information along iterations
- log : bool, optional
- Record log if True.
+ alpha : float, optional
+ Trade-off parameter (0 < alpha < 1)
armijo : bool, optional
If True the steps of the line-search is found via an armijo research.
Else closed form is used. If there is convergence issues use False.
+ log : bool, optional
+ Record log if True.
**kwargs : dict
Parameters can be directly pased to the ot.optim.cg solver.
@@ -497,7 +563,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
def df(G):
return gwggrad(constC, hC1, hC2, G)
- res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)
+ res, log = cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)
if log:
log['fgw_dist'] = log['loss'][::-1][0]
log['T'] = res
@@ -506,84 +572,6 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
return log['fgw_dist']
-def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs):
- """
- Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q)
-
- The function solves the following optimization problem:
-
- .. math::
- GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
-
- Where :
- - C1 : Metric cost matrix in the source space
- - C2 : Metric cost matrix in the target space
- - p : distribution in the source space
- - q : distribution in the target space
- - L : loss function to account for the misfit between the similarity matrices
- - H : entropy
-
- Parameters
- ----------
- C1 : ndarray, shape (ns, ns)
- Metric cost matrix in the source space
- C2 : ndarray, shape (nt, nt)
- Metric cost matrix in the target space
- p : ndarray, shape (ns,)
- Distribution in the source space.
- q : ndarray, shape (nt,)
- Distribution in the target space.
- loss_fun : str
- loss function used for the solver either 'square_loss' or 'kl_loss'
- max_iter : int, optional
- Max number of iterations
- tol : float, optional
- Stop threshold on error (>0)
- verbose : bool, optional
- Print information along iterations
- log : bool, optional
- record log if True
- armijo : bool, optional
- If True the steps of the line-search is found via an armijo research. Else closed form is used.
- If there is convergence issues use False.
-
- Returns
- -------
- gw_dist : float
- Gromov-Wasserstein distance
- log : dict
- convergence information and Coupling marix
-
- References
- ----------
- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
- "Gromov-Wasserstein averaging of kernel and distance matrices."
- International Conference on Machine Learning (ICML). 2016.
-
- .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the
- metric approach to object matching. Foundations of computational
- mathematics 11.4 (2011): 417-487.
-
- """
-
- constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun)
-
- G0 = p[:, None] * q[None, :]
-
- def f(G):
- return gwloss(constC, hC1, hC2, G)
-
- def df(G):
- return gwggrad(constC, hC1, hC2, G)
- res, log = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
- log['gw_dist'] = gwloss(constC, hC1, hC2, res)
- log['T'] = res
- if log:
- return log['gw_dist'], log
- else:
- return log['gw_dist']
-
-
def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon,
max_iter=1000, tol=1e-9, verbose=False, log=False):
"""
@@ -996,6 +984,16 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_
Whether to fix the structure of the barycenter during the updates
fixed_features : bool
Whether to fix the feature of the barycenter during the updates
+ loss_fun : str
+ Loss function used for the solver either 'square_loss' or 'kl_loss'
+ max_iter : int, optional
+ Max number of iterations
+ tol : float, optional
+ Stop threshol on error (>0).
+ verbose : bool, optional
+ Print information along iterations.
+ log : bool, optional
+ Record log if True.
init_C : ndarray, shape (N,N), optional
Initialization for the barycenters' structure matrix. If not set
a random init is used.
@@ -1084,7 +1082,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_
T_temp = [t.T for t in T]
C = update_sructure_matrix(p, lambdas, T_temp, Cs)
- T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha,
+ T = [fused_gromov_wasserstein(Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha,
numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)]
# T is N,ns
diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h
index f42e222..c0fe7a3 100644
--- a/ot/lp/EMD.h
+++ b/ot/lp/EMD.h
@@ -32,4 +32,6 @@ enum ProblemType {
int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter);
+
+
#endif
diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp
index fc7ca63..bc873ed 100644
--- a/ot/lp/EMD_wrapper.cpp
+++ b/ot/lp/EMD_wrapper.cpp
@@ -17,13 +17,13 @@
int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G,
double* alpha, double* beta, double *cost, int maxIter) {
-// beware M and C anre strored in row major C style!!!
- int n, m, i, cur;
+ // beware M and C anre strored in row major C style!!!
+ int n, m, i, cur;
typedef FullBipartiteDigraph Digraph;
- DIGRAPH_TYPEDEFS(FullBipartiteDigraph);
+ DIGRAPH_TYPEDEFS(FullBipartiteDigraph);
- // Get the number of non zero coordinates for r and c
+ // Get the number of non zero coordinates for r and c
n=0;
for (int i=0; i<n1; i++) {
double val=*(X+i);
@@ -105,3 +105,4 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G,
return ret;
}
+
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index 0c92810..514a607 100644
--- a/ot/lp/__init__.py
+++ b/ot/lp/__init__.py
@@ -2,8 +2,6 @@
"""
Solvers for the original linear program OT problem
-
-
"""
# Author: Remi Flamary <remi.flamary@unice.fr>
@@ -12,22 +10,169 @@ Solvers for the original linear program OT problem
import multiprocessing
import sys
+
import numpy as np
from scipy.sparse import coo_matrix
-from .import cvx
-
+from . import cvx
+from .cvx import barycenter
# import compiled emd
from .emd_wrap import emd_c, check_result, emd_1d_sorted
-from ..utils import parmap
-from .cvx import barycenter
from ..utils import dist
+from ..utils import parmap
+
+__all__ = ['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx',
+ 'emd_1d', 'emd2_1d', 'wasserstein_1d']
+
+
+def center_ot_dual(alpha0, beta0, a=None, b=None):
+ r"""Center dual OT potentials w.r.t. theirs weights
+
+ The main idea of this function is to find unique dual potentials
+ that ensure some kind of centering/fairness. The main idea is to find dual potentials that lead to the same final objective value for both source and targets (see below for more details). It will help having
+ stability when multiple calling of the OT solver with small changes.
+
+ Basically we add another constraint to the potential that will not
+ change the objective value but will ensure unicity. The constraint
+ is the following:
+
+ .. math::
+ \alpha^T a= \beta^T b
+
+ in addition to the OT problem constraints.
+
+ since :math:`\sum_i a_i=\sum_j b_j` this can be solved by adding/removing
+ a constant from both :math:`\alpha_0` and :math:`\beta_0`.
+
+ .. math::
+ c=\frac{\beta0^T b-\alpha_0^T a}{1^Tb+1^Ta}
+
+ \alpha=\alpha_0+c
+
+ \beta=\beta0+c
+
+ Parameters
+ ----------
+ alpha0 : (ns,) numpy.ndarray, float64
+ Source dual potential
+ beta0 : (nt,) numpy.ndarray, float64
+ Target dual potential
+ a : (ns,) numpy.ndarray, float64
+ Source histogram (uniform weight if empty list)
+ b : (nt,) numpy.ndarray, float64
+ Target histogram (uniform weight if empty list)
+
+ Returns
+ -------
+ alpha : (ns,) numpy.ndarray, float64
+ Source centered dual potential
+ beta : (nt,) numpy.ndarray, float64
+ Target centered dual potential
+
+ """
+ # if no weights are provided, use uniform
+ if a is None:
+ a = np.ones(alpha0.shape[0]) / alpha0.shape[0]
+ if b is None:
+ b = np.ones(beta0.shape[0]) / beta0.shape[0]
+
+ # compute constant that balances the weighted sums of the duals
+ c = (b.dot(beta0) - a.dot(alpha0)) / (a.sum() + b.sum())
+
+ # update duals
+ alpha = alpha0 + c
+ beta = beta0 - c
+
+ return alpha, beta
+
+
+def estimate_dual_null_weights(alpha0, beta0, a, b, M):
+ r"""Estimate feasible values for 0-weighted dual potentials
+
+ The feasible values are computed efficiently but rather coarsely.
+
+ .. warning::
+ This function is necessary because the C++ solver in emd_c
+ discards all samples in the distributions with
+ zeros weights. This means that while the primal variable (transport
+ matrix) is exact, the solver only returns feasible dual potentials
+ on the samples with weights different from zero.
+
+ First we compute the constraints violations:
+
+ .. math::
+ V=\alpha+\beta^T-M
+
+ Next we compute the max amount of violation per row (alpha) and
+ columns (beta)
-__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx',
- 'emd_1d', 'emd2_1d', 'wasserstein_1d']
+ .. math::
+ v^a_i=\max_j V_{i,j}
+
+ v^b_j=\max_i V_{i,j}
+
+ Finally we update the dual potential with 0 weights if a
+ constraint is violated
+
+ .. math::
+ \alpha_i = \alpha_i -v^a_i \quad \text{ if } a_i=0 \text{ and } v^a_i>0
+
+ \beta_j = \beta_j -v^b_j \quad \text{ if } b_j=0 \text{ and } v^b_j>0
+
+ In the end the dual potentials are centered using function
+ :ref:`center_ot_dual`.
+
+ Note that all those updates do not change the objective value of the
+ solution but provide dual potentials that do not violate the constraints.
+
+ Parameters
+ ----------
+ alpha0 : (ns,) numpy.ndarray, float64
+ Source dual potential
+ beta0 : (nt,) numpy.ndarray, float64
+ Target dual potential
+ alpha0 : (ns,) numpy.ndarray, float64
+ Source dual potential
+ beta0 : (nt,) numpy.ndarray, float64
+ Target dual potential
+ a : (ns,) numpy.ndarray, float64
+ Source distribution (uniform weights if empty list)
+ b : (nt,) numpy.ndarray, float64
+ Target distribution (uniform weights if empty list)
+ M : (ns,nt) numpy.ndarray, float64
+ Loss matrix (c-order array with type float64)
+
+ Returns
+ -------
+ alpha : (ns,) numpy.ndarray, float64
+ Source corrected dual potential
+ beta : (nt,) numpy.ndarray, float64
+ Target corrected dual potential
+
+ """
+
+ # binary indexing of non-zeros weights
+ asel = a != 0
+ bsel = b != 0
+
+ # compute dual constraints violation
+ constraint_violation = alpha0[:, None] + beta0[None, :] - M
+
+ # Compute largest violation per line and columns
+ aviol = np.max(constraint_violation, 1)
+ bviol = np.max(constraint_violation, 0)
+ # update corrects violation of
+ alpha_up = -1 * ~asel * np.maximum(aviol, 0)
+ beta_up = -1 * ~bsel * np.maximum(bviol, 0)
-def emd(a, b, M, numItermax=100000, log=False):
+ alpha = alpha0 + alpha_up
+ beta = beta0 + beta_up
+
+ return center_ot_dual(alpha, beta, a, b)
+
+
+def emd(a, b, M, numItermax=100000, log=False, center_dual=True):
r"""Solves the Earth Movers distance problem and returns the OT matrix
@@ -35,7 +180,9 @@ def emd(a, b, M, numItermax=100000, log=False):
\gamma = arg\min_\gamma <\gamma,M>_F
s.t. \gamma 1 = a
+
\gamma^T 1= b
+
\gamma\geq 0
where :
@@ -43,7 +190,7 @@ def emd(a, b, M, numItermax=100000, log=False):
- a and b are the sample weights
.. warning::
- Note that the M matrix needs to be a C-order numpy.array in float64
+ Note that the M matrix needs to be a C-order numpy.array in float64
format.
Uses the algorithm proposed in [1]_
@@ -62,6 +209,9 @@ def emd(a, b, M, numItermax=100000, log=False):
log: bool, optional (default=False)
If True, returns a dictionary containing the cost and dual
variables. Otherwise returns only the optimal transportation matrix.
+ center_dual: boolean, optional (default=True)
+ If True, centers the dual potential using function
+ :ref:`center_ot_dual`.
Returns
-------
@@ -109,7 +259,20 @@ def emd(a, b, M, numItermax=100000, log=False):
if len(b) == 0:
b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
+ assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \
+ "Dimension mismatch, check dimensions of M with a and b"
+
+ asel = a != 0
+ bsel = b != 0
+
G, cost, u, v, result_code = emd_c(a, b, M, numItermax)
+
+ if center_dual:
+ u, v = center_ot_dual(u, v, a, b)
+
+ if np.any(~asel) or np.any(~bsel):
+ u, v = estimate_dual_null_weights(u, v, a, b, M)
+
result_code_string = check_result(result_code)
if log:
log = {}
@@ -123,14 +286,17 @@ def emd(a, b, M, numItermax=100000, log=False):
def emd2(a, b, M, processes=multiprocessing.cpu_count(),
- numItermax=100000, log=False, return_matrix=False):
+ numItermax=100000, log=False, return_matrix=False,
+ center_dual=True):
r"""Solves the Earth Movers distance problem and returns the loss
.. math::
- \gamma = arg\min_\gamma <\gamma,M>_F
+ \min_\gamma <\gamma,M>_F
s.t. \gamma 1 = a
+
\gamma^T 1= b
+
\gamma\geq 0
where :
@@ -138,7 +304,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(),
- a and b are the sample weights
.. warning::
- Note that the M matrix needs to be a C-order numpy.array in float64
+ Note that the M matrix needs to be a C-order numpy.array in float64
format.
Uses the algorithm proposed in [1]_
@@ -161,6 +327,9 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(),
variables. Otherwise returns only the optimal transportation cost.
return_matrix: boolean, optional (default=False)
If True, returns the optimal transportation matrix in the log.
+ center_dual: boolean, optional (default=True)
+ If True, centers the dual potential using function
+ :ref:`center_ot_dual`.
Returns
-------
@@ -204,7 +373,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(),
# problem with pikling Forks
if sys.platform.endswith('win32'):
- processes=1
+ processes = 1
# if empty array given then use uniform distributions
if len(a) == 0:
@@ -212,21 +381,43 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(),
if len(b) == 0:
b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
+ assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \
+ "Dimension mismatch, check dimensions of M with a and b"
+
+ asel = a != 0
+
if log or return_matrix:
def f(b):
- G, cost, u, v, resultCode = emd_c(a, b, M, numItermax)
- result_code_string = check_result(resultCode)
+ bsel = b != 0
+
+ G, cost, u, v, result_code = emd_c(a, b, M, numItermax)
+
+ if center_dual:
+ u, v = center_ot_dual(u, v, a, b)
+
+ if np.any(~asel) or np.any(~bsel):
+ u, v = estimate_dual_null_weights(u, v, a, b, M)
+
+ result_code_string = check_result(result_code)
log = {}
if return_matrix:
log['G'] = G
log['u'] = u
log['v'] = v
log['warning'] = result_code_string
- log['result_code'] = resultCode
+ log['result_code'] = result_code
return [cost, log]
else:
def f(b):
+ bsel = b != 0
G, cost, u, v, result_code = emd_c(a, b, M, numItermax)
+
+ if center_dual:
+ u, v = center_ot_dual(u, v, a, b)
+
+ if np.any(~asel) or np.any(~bsel):
+ u, v = estimate_dual_null_weights(u, v, a, b, M)
+
check_result(result_code)
return cost
@@ -234,7 +425,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(),
return f(b)
nb = b.shape[1]
- if processes>1:
+ if processes > 1:
res = parmap(f, [b[:, i] for i in range(nb)], processes)
else:
res = list(map(f, [b[:, i].copy() for i in range(nb)]))
@@ -242,8 +433,8 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(),
return res
-
-def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100, stopThr=1e-7, verbose=False, log=None):
+def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100,
+ stopThr=1e-7, verbose=False, log=None):
"""
Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance)
@@ -295,7 +486,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None
k = X_init.shape[0]
d = X_init.shape[1]
if b is None:
- b = np.ones((k,))/k
+ b = np.ones((k,)) / k
if weights is None:
weights = np.ones((N,)) / N
@@ -306,17 +497,17 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None
displacement_square_norm = stopThr + 1.
- while ( displacement_square_norm > stopThr and iter_count < numItermax ):
+ while (displacement_square_norm > stopThr and iter_count < numItermax):
T_sum = np.zeros((k, d))
- for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()):
-
+ for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights,
+ weights.tolist()):
M_i = dist(X, measure_locations_i)
T_i = emd(b, measure_weights_i, M_i)
T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i)
- displacement_square_norm = np.sum(np.square(T_sum-X))
+ displacement_square_norm = np.sum(np.square(T_sum - X))
if log:
displacement_square_norms.append(displacement_square_norm)
@@ -436,12 +627,12 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True,
if b.ndim == 0 or len(b) == 0:
b = np.ones((x_b.shape[0],), dtype=np.float64) / x_b.shape[0]
- x_a_1d = x_a.reshape((-1, ))
- x_b_1d = x_b.reshape((-1, ))
+ x_a_1d = x_a.reshape((-1,))
+ x_b_1d = x_b.reshape((-1,))
perm_a = np.argsort(x_a_1d)
perm_b = np.argsort(x_b_1d)
- G_sorted, indices, cost = emd_1d_sorted(a, b,
+ G_sorted, indices, cost = emd_1d_sorted(a[perm_a], b[perm_b],
x_a_1d[perm_a], x_b_1d[perm_b],
metric=metric, p=p)
G = coo_matrix((G_sorted, (perm_a[indices[:, 0]], perm_b[indices[:, 1]])),
diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx
index 2b6c495..c167964 100644
--- a/ot/lp/emd_wrap.pyx
+++ b/ot/lp/emd_wrap.pyx
@@ -19,7 +19,7 @@ import warnings
cdef extern from "EMD.h":
- int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter)
+ int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) nogil
cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED
@@ -35,8 +35,7 @@ def check_result(result_code):
message = "numItermax reached before optimality. Try to increase numItermax."
warnings.warn(message)
return message
-
-
+
@cython.boundscheck(False)
@cython.wraparound(False)
def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter):
@@ -61,6 +60,12 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod
.. warning::
Note that the M matrix needs to be a C-order :py.cls:`numpy.array`
+ .. warning::
+ The C++ solver discards all samples in the distributions with
+ zeros weights. This means that while the primal variable (transport
+ matrix) is exact, the solver only returns feasible dual potentials
+ on the samples with weights different from zero.
+
Parameters
----------
a : (ns,) numpy.ndarray, float64
@@ -73,7 +78,6 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod
The maximum number of iterations before stopping the optimization
algorithm if it has not converged.
-
Returns
-------
gamma: (ns x nt) numpy.ndarray
@@ -82,12 +86,19 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod
"""
cdef int n1= M.shape[0]
cdef int n2= M.shape[1]
+ cdef int nmax=n1+n2-1
+ cdef int result_code = 0
+ cdef int nG=0
cdef double cost=0
- cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2])
cdef np.ndarray[double, ndim=1, mode="c"] alpha=np.zeros(n1)
cdef np.ndarray[double, ndim=1, mode="c"] beta=np.zeros(n2)
+ cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([0, 0])
+
+ cdef np.ndarray[double, ndim=1, mode="c"] Gv=np.zeros(0)
+ cdef np.ndarray[long, ndim=1, mode="c"] iG=np.zeros(0,dtype=np.int)
+ cdef np.ndarray[long, ndim=1, mode="c"] jG=np.zeros(0,dtype=np.int)
if not len(a):
a=np.ones((n1,))/n1
@@ -95,8 +106,12 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod
if not len(b):
b=np.ones((n2,))/n2
+ # init OT matrix
+ G=np.zeros([n1, n2])
+
# calling the function
- cdef int result_code = EMD_wrap(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <double*> G.data, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter)
+ with nogil:
+ result_code = EMD_wrap(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <double*> G.data, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter)
return G, cost, alpha, beta, result_code
diff --git a/ot/lp/network_simplex_simple.h b/ot/lp/network_simplex_simple.h
index 7c6a4ce..5d93040 100644
--- a/ot/lp/network_simplex_simple.h
+++ b/ot/lp/network_simplex_simple.h
@@ -686,7 +686,7 @@ namespace lemon {
/// \see resetParams(), reset()
ProblemType run() {
#if DEBUG_LVL>0
- std::cout << "OPTIMAL = " << OPTIMAL << "\nINFEASIBLE = " << INFEASIBLE << "\nUNBOUNDED = " << UNBOUNDED << "\nMAX_ITER_REACHED" << MAX_ITER_REACHED\n";
+ std::cout << "OPTIMAL = " << OPTIMAL << "\nINFEASIBLE = " << INFEASIBLE << "\nUNBOUNDED = " << UNBOUNDED << "\nMAX_ITER_REACHED" << MAX_ITER_REACHED << "\n" ;
#endif
if (!init()) return INFEASIBLE;
@@ -875,7 +875,7 @@ namespace lemon {
c += Number(it->second) * Number(_cost[it->first]);
return c;*/
- for (int i=0; i<_flow.size(); i++)
+ for (unsigned long i=0; i<_flow.size(); i++)
c += _flow[i] * Number(_cost[i]);
return c;
@@ -1257,7 +1257,7 @@ namespace lemon {
u = w;
}
_pred[u_in] = in_arc;
- _forward[u_in] = (u_in == _source[in_arc]);
+ _forward[u_in] = ((unsigned int)u_in == _source[in_arc]);
_succ_num[u_in] = old_succ_num;
// Set limits for updating _last_succ form v_in and v_out
@@ -1418,7 +1418,6 @@ namespace lemon {
template <typename PivotRuleImpl>
ProblemType start() {
PivotRuleImpl pivot(*this);
- double prevCost=-1;
ProblemType retVal = OPTIMAL;
// Perform heuristic initial pivots
diff --git a/ot/optim.py b/ot/optim.py
index 0abd9e9..b9ca891 100644
--- a/ot/optim.py
+++ b/ot/optim.py
@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
"""
-Optimization algorithms for OT
+Generic solvers for regularized OT
"""
# Author: Remi Flamary <remi.flamary@unice.fr>
@@ -134,7 +134,7 @@ def solve_linesearch(cost, G, deltaG, Mi, f_val,
return alpha, fc, f_val
-def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
+def cg(a, b, M, reg, f, df, G0=None, numItermax=200, numItermaxEmd=100000,
stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False, **kwargs):
"""
Solve the general regularized OT problem with conditional gradient
@@ -172,6 +172,8 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
initial guess (default is indep joint density)
numItermax : int, optional
Max number of iterations
+ numItermaxEmd : int, optional
+ Max number of iterations for emd
stopThr : float, optional
Stop threshol on the relative variation (>0)
stopThr2 : float, optional
@@ -238,7 +240,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
Mi += Mi.min()
# solve linear program
- Gc = emd(a, b, Mi)
+ Gc = emd(a, b, Mi, numItermax=numItermaxEmd)
deltaG = Gc - G
diff --git a/ot/partial.py b/ot/partial.py
new file mode 100755
index 0000000..eb707d8
--- /dev/null
+++ b/ot/partial.py
@@ -0,0 +1,1062 @@
+# -*- coding: utf-8 -*-
+"""
+Partial OT solvers
+"""
+
+# Author: Laetitia Chapel <laetitia.chapel@irisa.fr>
+# License: MIT License
+
+import numpy as np
+
+from .lp import emd
+
+
+def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False,
+ **kwargs):
+ r"""
+ Solves the partial optimal transport problem for the quadratic cost
+ and returns the OT plan
+
+ The function considers the following problem:
+
+ .. math::
+ \gamma = \arg\min_\gamma <\gamma,(M-\lambda)>_F
+
+ s.t.
+ \gamma\geq 0 \\
+ \gamma 1 \leq a\\
+ \gamma^T 1 \leq b\\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\}
+
+
+ or equivalently (see Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X.
+ (2018). An interpolating distance between optimal transport and Fisher–Rao
+ metrics. Foundations of Computational Mathematics, 18(1), 1-44.)
+
+ .. math::
+ \gamma = \arg\min_\gamma <\gamma,M>_F + \sqrt(\lambda/2)
+ (\|\gamma 1 - a\|_1 + \|\gamma^T 1 - b\|_1)
+
+ s.t.
+ \gamma\geq 0 \\
+
+
+ where :
+
+ - M is the metric cost matrix
+ - a and b are source and target unbalanced distributions
+ - :math:`\lambda` is the lagragian cost. Tuning its value allows attaining
+ a given mass to be transported m
+
+ The formulation of the problem has been proposed in [28]_
+
+
+ Parameters
+ ----------
+ a : np.ndarray (dim_a,)
+ Unnormalized histogram of dimension dim_a
+ b : np.ndarray (dim_b,)
+ Unnormalized histograms of dimension dim_b
+ M : np.ndarray (dim_a, dim_b)
+ cost matrix for the quadratic cost
+ reg_m : float, optional
+ Lagragian cost
+ nb_dummies : int, optional, default:1
+ number of reservoir points to be added (to avoid numerical
+ instabilities, increase its value if an error is raised)
+ log : bool, optional
+ record log if True
+ **kwargs : dict
+ parameters can be directly passed to the emd solver
+
+ .. warning::
+ When dealing with a large number of points, the EMD solver may face
+ some instabilities, especially when the mass associated to the dummy
+ point is large. To avoid them, increase the number of dummy points
+ (allows a smoother repartition of the mass over the points).
+
+ Returns
+ -------
+ gamma : (dim_a x dim_b) ndarray
+ Optimal transportation matrix for the given parameters
+ log : dict
+ log dictionary returned only if `log` is `True`
+
+
+ Examples
+ --------
+
+ >>> import ot
+ >>> a = [.1, .2]
+ >>> b = [.1, .1]
+ >>> M = [[0., 1.], [2., 3.]]
+ >>> np.round(partial_wasserstein_lagrange(a,b,M), 2)
+ array([[0.1, 0. ],
+ [0. , 0.1]])
+ >>> np.round(partial_wasserstein_lagrange(a,b,M,reg_m=2), 2)
+ array([[0.1, 0. ],
+ [0. , 0. ]])
+
+ References
+ ----------
+
+ .. [28] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in
+ optimal transport and Monge-Ampere obstacle problems. Annals of
+ mathematics, 673-730.
+
+ See Also
+ --------
+ ot.partial.partial_wasserstein : Partial Wasserstein with fixed mass
+ """
+
+ if np.sum(a) > 1 or np.sum(b) > 1:
+ raise ValueError("Problem infeasible. Check that a and b are in the "
+ "simplex")
+
+ if reg_m is None:
+ reg_m = np.max(M) + 1
+ if reg_m < -np.max(M):
+ return np.zeros((len(a), len(b)))
+
+ eps = 1e-20
+ M = np.asarray(M, dtype=np.float64)
+ b = np.asarray(b, dtype=np.float64)
+ a = np.asarray(a, dtype=np.float64)
+
+ M_star = M - reg_m # modified cost matrix
+
+ # trick to fasten the computation: select only the subset of columns/lines
+ # that can have marginals greater than 0 (that is to say M < 0)
+ idx_x = np.where(np.min(M_star, axis=1) < eps)[0]
+ idx_y = np.where(np.min(M_star, axis=0) < eps)[0]
+
+ # extend a, b, M with "reservoir" or "dummy" points
+ M_extended = np.zeros((len(idx_x) + nb_dummies, len(idx_y) + nb_dummies))
+ M_extended[:len(idx_x), :len(idx_y)] = M_star[np.ix_(idx_x, idx_y)]
+
+ a_extended = np.append(a[idx_x], [(np.sum(a) - np.sum(a[idx_x]) +
+ np.sum(b)) / nb_dummies] * nb_dummies)
+ b_extended = np.append(b[idx_y], [(np.sum(b) - np.sum(b[idx_y]) +
+ np.sum(a)) / nb_dummies] * nb_dummies)
+
+ gamma_extended, log_emd = emd(a_extended, b_extended, M_extended, log=True,
+ **kwargs)
+ gamma = np.zeros((len(a), len(b)))
+ gamma[np.ix_(idx_x, idx_y)] = gamma_extended[:-nb_dummies, :-nb_dummies]
+
+ if log_emd['warning'] is not None:
+ raise ValueError("Error in the EMD resolution: try to increase the"
+ " number of dummy points")
+ log_emd['cost'] = np.sum(gamma * M)
+ if log:
+ return gamma, log_emd
+ else:
+ return gamma
+
+
+def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs):
+ r"""
+ Solves the partial optimal transport problem for the quadratic cost
+ and returns the OT plan
+
+ The function considers the following problem:
+
+ .. math::
+ \gamma = \arg\min_\gamma <\gamma,M>_F
+
+ s.t.
+ \gamma\geq 0 \\
+ \gamma 1 \leq a\\
+ \gamma^T 1 \leq b\\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\}
+
+
+ where :
+
+ - M is the metric cost matrix
+ - a and b are source and target unbalanced distributions
+ - m is the amount of mass to be transported
+
+ Parameters
+ ----------
+ a : np.ndarray (dim_a,)
+ Unnormalized histogram of dimension dim_a
+ b : np.ndarray (dim_b,)
+ Unnormalized histograms of dimension dim_b
+ M : np.ndarray (dim_a, dim_b)
+ cost matrix for the quadratic cost
+ m : float, optional
+ amount of mass to be transported
+ nb_dummies : int, optional, default:1
+ number of reservoir points to be added (to avoid numerical
+ instabilities, increase its value if an error is raised)
+ log : bool, optional
+ record log if True
+ **kwargs : dict
+ parameters can be directly passed to the emd solver
+
+
+ .. warning::
+ When dealing with a large number of points, the EMD solver may face
+ some instabilities, especially when the mass associated to the dummy
+ point is large. To avoid them, increase the number of dummy points
+ (allows a smoother repartition of the mass over the points).
+
+
+ Returns
+ -------
+ :math:`gamma` : (dim_a x dim_b) ndarray
+ Optimal transportation matrix for the given parameters
+ log : dict
+ log dictionary returned only if `log` is `True`
+
+
+ Examples
+ --------
+
+ >>> import ot
+ >>> a = [.1, .2]
+ >>> b = [.1, .1]
+ >>> M = [[0., 1.], [2., 3.]]
+ >>> np.round(partial_wasserstein(a,b,M), 2)
+ array([[0.1, 0. ],
+ [0. , 0.1]])
+ >>> np.round(partial_wasserstein(a,b,M,m=0.1), 2)
+ array([[0.1, 0. ],
+ [0. , 0. ]])
+
+ References
+ ----------
+ .. [28] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in
+ optimal transport and Monge-Ampere obstacle problems. Annals of
+ mathematics, 673-730.
+ .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov-
+ Wasserstein with Applications on Positive-Unlabeled Learning".
+ arXiv preprint arXiv:2002.08276.
+
+ See Also
+ --------
+ ot.partial.partial_wasserstein_lagrange: Partial Wasserstein with
+ regularization on the marginals
+ ot.partial.entropic_partial_wasserstein: Partial Wasserstein with a
+ entropic regularization parameter
+ """
+
+ if m is None:
+ return partial_wasserstein_lagrange(a, b, M, log=log, **kwargs)
+ elif m < 0:
+ raise ValueError("Problem infeasible. Parameter m should be greater"
+ " than 0.")
+ elif m > np.min((np.sum(a), np.sum(b))):
+ raise ValueError("Problem infeasible. Parameter m should lower or"
+ " equal than min(|a|_1, |b|_1).")
+
+ b_extended = np.append(b, [(np.sum(a) - m) / nb_dummies] * nb_dummies)
+ a_extended = np.append(a, [(np.sum(b) - m) / nb_dummies] * nb_dummies)
+ M_extended = np.zeros((len(a_extended), len(b_extended)))
+ M_extended[-1, -1] = np.max(M) * 1e5
+ M_extended[:len(a), :len(b)] = M
+
+ gamma, log_emd = emd(a_extended, b_extended, M_extended, log=True,
+ **kwargs)
+ if log_emd['warning'] is not None:
+ raise ValueError("Error in the EMD resolution: try to increase the"
+ " number of dummy points")
+ log_emd['partial_w_dist'] = np.sum(M * gamma[:len(a), :len(b)])
+
+ if log:
+ return gamma[:len(a), :len(b)], log_emd
+ else:
+ return gamma[:len(a), :len(b)]
+
+
+def partial_wasserstein2(a, b, M, m=None, nb_dummies=1, log=False, **kwargs):
+ r"""
+ Solves the partial optimal transport problem for the quadratic cost
+ and returns the partial GW discrepancy
+
+ The function considers the following problem:
+
+ .. math::
+ \gamma = \arg\min_\gamma <\gamma,M>_F
+
+ s.t.
+ \gamma\geq 0 \\
+ \gamma 1 \leq a\\
+ \gamma^T 1 \leq b\\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\}
+
+
+ where :
+
+ - M is the metric cost matrix
+ - a and b are source and target unbalanced distributions
+ - m is the amount of mass to be transported
+
+ Parameters
+ ----------
+ a : np.ndarray (dim_a,)
+ Unnormalized histogram of dimension dim_a
+ b : np.ndarray (dim_b,)
+ Unnormalized histograms of dimension dim_b
+ M : np.ndarray (dim_a, dim_b)
+ cost matrix for the quadratic cost
+ m : float, optional
+ amount of mass to be transported
+ nb_dummies : int, optional, default:1
+ number of reservoir points to be added (to avoid numerical
+ instabilities, increase its value if an error is raised)
+ log : bool, optional
+ record log if True
+ **kwargs : dict
+ parameters can be directly passed to the emd solver
+
+
+ .. warning::
+ When dealing with a large number of points, the EMD solver may face
+ some instabilities, especially when the mass associated to the dummy
+ point is large. To avoid them, increase the number of dummy points
+ (allows a smoother repartition of the mass over the points).
+
+
+ Returns
+ -------
+ :math:`gamma` : (dim_a x dim_b) ndarray
+ Optimal transportation matrix for the given parameters
+ log : dict
+ log dictionary returned only if `log` is `True`
+
+
+ Examples
+ --------
+
+ >>> import ot
+ >>> a=[.1, .2]
+ >>> b=[.1, .1]
+ >>> M=[[0., 1.], [2., 3.]]
+ >>> np.round(partial_wasserstein2(a, b, M), 1)
+ 0.3
+ >>> np.round(partial_wasserstein2(a,b,M,m=0.1), 1)
+ 0.0
+
+ References
+ ----------
+ .. [28] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in
+ optimal transport and Monge-Ampere obstacle problems. Annals of
+ mathematics, 673-730.
+ .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov-
+ Wasserstein with Applications on Positive-Unlabeled Learning".
+ arXiv preprint arXiv:2002.08276.
+ """
+
+ partial_gw, log_w = partial_wasserstein(a, b, M, m, nb_dummies, log=True,
+ **kwargs)
+
+ log_w['T'] = partial_gw
+
+ if log:
+ return np.sum(partial_gw * M), log_w
+ else:
+ return np.sum(partial_gw * M)
+
+
+def gwgrad_partial(C1, C2, T):
+ """Compute the GW gradient. Note: we can not use the trick in [12]_ as
+ the marginals may not sum to 1.
+
+ Parameters
+ ----------
+ C1: array of shape (n_p,n_p)
+ intra-source (P) cost matrix
+
+ C2: array of shape (n_u,n_u)
+ intra-target (U) cost matrix
+
+ T : array of shape(n_p+nb_dummies, n_u) (default: None)
+ Transport matrix
+
+ Returns
+ -------
+ numpy.array of shape (n_p+nb_dummies, n_u)
+ gradient
+
+ References
+ ----------
+ .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
+ """
+ cC1 = np.dot(C1 ** 2 / 2, np.dot(T, np.ones(C2.shape[0]).reshape(-1, 1)))
+ cC2 = np.dot(np.dot(np.ones(C1.shape[0]).reshape(1, -1), T), C2 ** 2 / 2)
+ constC = cC1 + cC2
+ A = -np.dot(C1, T).dot(C2.T)
+ tens = constC + A
+ return tens * 2
+
+
+def gwloss_partial(C1, C2, T):
+ """Compute the GW loss.
+
+ Parameters
+ ----------
+ C1: array of shape (n_p,n_p)
+ intra-source (P) cost matrix
+
+ C2: array of shape (n_u,n_u)
+ intra-target (U) cost matrix
+
+ T : array of shape(n_p+nb_dummies, n_u) (default: None)
+ Transport matrix
+
+ Returns
+ -------
+ GW loss
+ """
+ g = gwgrad_partial(C1, C2, T) * 0.5
+ return np.sum(g * T)
+
+
+def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None,
+ thres=1, numItermax=1000, tol=1e-7,
+ log=False, verbose=False, **kwargs):
+ r"""
+ Solves the partial optimal transport problem
+ and returns the OT plan
+
+ The function considers the following problem:
+
+ .. math::
+ \gamma = arg\min_\gamma <\gamma,M>_F
+
+ s.t. \gamma 1 \leq a \\
+ \gamma^T 1 \leq b \\
+ \gamma\geq 0 \\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} \\
+
+ where :
+
+ - M is the metric cost matrix
+ - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)
+ =\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
+ - a and b are the sample weights
+ - m is the amount of mass to be transported
+
+ The formulation of the problem has been proposed in [29]_
+
+
+ Parameters
+ ----------
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric costfr matrix in the target space
+ p : ndarray, shape (ns,)
+ Distribution in the source space
+ q : ndarray, shape (nt,)
+ Distribution in the target space
+ m : float, optional
+ Amount of mass to be transported (default: min (|p|_1, |q|_1))
+ nb_dummies : int, optional
+ Number of dummy points to add (avoid instabilities in the EMD solver)
+ G0 : ndarray, shape (ns, nt), optional
+ Initialisation of the transportation matrix
+ thres : float, optional
+ quantile of the gradient matrix to populate the cost matrix when 0
+ (default: 1)
+ numItermax : int, optional
+ Max number of iterations
+ tol : float, optional
+ tolerance for stopping iterations
+ log : bool, optional
+ return log if True
+ verbose : bool, optional
+ Print information along iterations
+ **kwargs : dict
+ parameters can be directly passed to the emd solver
+
+
+ Returns
+ -------
+ gamma : (dim_a x dim_b) ndarray
+ Optimal transportation matrix for the given parameters
+ log : dict
+ log dictionary returned only if `log` is `True`
+
+
+ Examples
+ --------
+ >>> import ot
+ >>> import scipy as sp
+ >>> a = np.array([0.25] * 4)
+ >>> b = np.array([0.25] * 4)
+ >>> x = np.array([1,2,100,200]).reshape((-1,1))
+ >>> y = np.array([3,2,98,199]).reshape((-1,1))
+ >>> C1 = sp.spatial.distance.cdist(x, x)
+ >>> C2 = sp.spatial.distance.cdist(y, y)
+ >>> np.round(partial_gromov_wasserstein(C1, C2, a, b),2)
+ array([[0. , 0.25, 0. , 0. ],
+ [0.25, 0. , 0. , 0. ],
+ [0. , 0. , 0.25, 0. ],
+ [0. , 0. , 0. , 0.25]])
+ >>> np.round(partial_gromov_wasserstein(C1, C2, a, b, m=0.25),2)
+ array([[0. , 0. , 0. , 0. ],
+ [0. , 0. , 0. , 0. ],
+ [0. , 0. , 0. , 0. ],
+ [0. , 0. , 0. , 0.25]])
+
+ References
+ ----------
+ .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov-
+ Wasserstein with Applications on Positive-Unlabeled Learning".
+ arXiv preprint arXiv:2002.08276.
+
+ """
+
+ if m is None:
+ m = np.min((np.sum(p), np.sum(q)))
+ elif m < 0:
+ raise ValueError("Problem infeasible. Parameter m should be greater"
+ " than 0.")
+ elif m > np.min((np.sum(p), np.sum(q))):
+ raise ValueError("Problem infeasible. Parameter m should lower or"
+ " equal than min(|a|_1, |b|_1).")
+
+ if G0 is None:
+ G0 = np.outer(p, q)
+
+ dim_G_extended = (len(p) + nb_dummies, len(q) + nb_dummies)
+ q_extended = np.append(q, [(np.sum(p) - m) / nb_dummies] * nb_dummies)
+ p_extended = np.append(p, [(np.sum(q) - m) / nb_dummies] * nb_dummies)
+
+ cpt = 0
+ err = 1
+ eps = 1e-20
+ if log:
+ log = {'err': []}
+
+ while (err > tol and cpt < numItermax):
+
+ Gprev = G0
+
+ M = gwgrad_partial(C1, C2, G0)
+ M[M < eps] = np.quantile(M, thres)
+
+ M_emd = np.zeros(dim_G_extended)
+ M_emd[:len(p), :len(q)] = M
+ M_emd[-nb_dummies:, -nb_dummies:] = np.max(M) * 1e5
+ M_emd = np.asarray(M_emd, dtype=np.float64)
+
+ Gc, logemd = emd(p_extended, q_extended, M_emd, log=True, **kwargs)
+
+ if logemd['warning'] is not None:
+ raise ValueError("Error in the EMD resolution: try to increase the"
+ " number of dummy points")
+
+ G0 = Gc[:len(p), :len(q)]
+
+ if cpt % 10 == 0: # to speed up the computations
+ err = np.linalg.norm(G0 - Gprev)
+ if log:
+ log['err'].append(err)
+ if verbose:
+ if cpt % 200 == 0:
+ print('{:5s}|{:12s}|{:12s}'.format(
+ 'It.', 'Err', 'Loss') + '\n' + '-' * 31)
+ print('{:5d}|{:8e}|{:8e}'.format(cpt, err,
+ gwloss_partial(C1, C2, G0)))
+
+ cpt += 1
+
+ if log:
+ log['partial_gw_dist'] = gwloss_partial(C1, C2, G0)
+ return G0[:len(p), :len(q)], log
+ else:
+ return G0[:len(p), :len(q)]
+
+
+def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None,
+ thres=1, numItermax=1000, tol=1e-7,
+ log=False, verbose=False, **kwargs):
+ r"""
+ Solves the partial optimal transport problem
+ and returns the partial Gromov-Wasserstein discrepancy
+
+ The function considers the following problem:
+
+ .. math::
+ \gamma = arg\min_\gamma <\gamma,M>_F
+
+ s.t. \gamma 1 \leq a \\
+ \gamma^T 1 \leq b \\
+ \gamma\geq 0 \\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} \\
+
+ where :
+
+ - M is the metric cost matrix
+ - :math:`\Omega` is the entropic regularization term
+ :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
+ - a and b are the sample weights
+ - m is the amount of mass to be transported
+
+ The formulation of the problem has been proposed in [29]_
+
+
+ Parameters
+ ----------
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric costfr matrix in the target space
+ p : ndarray, shape (ns,)
+ Distribution in the source space
+ q : ndarray, shape (nt,)
+ Distribution in the target space
+ m : float, optional
+ Amount of mass to be transported (default: min (|p|_1, |q|_1))
+ nb_dummies : int, optional
+ Number of dummy points to add (avoid instabilities in the EMD solver)
+ G0 : ndarray, shape (ns, nt), optional
+ Initialisation of the transportation matrix
+ thres : float, optional
+ quantile of the gradient matrix to populate the cost matrix when 0
+ (default: 1)
+ numItermax : int, optional
+ Max number of iterations
+ tol : float, optional
+ tolerance for stopping iterations
+ log : bool, optional
+ return log if True
+ verbose : bool, optional
+ Print information along iterations
+ **kwargs : dict
+ parameters can be directly passed to the emd solver
+
+
+ .. warning::
+ When dealing with a large number of points, the EMD solver may face
+ some instabilities, especially when the mass associated to the dummy
+ point is large. To avoid them, increase the number of dummy points
+ (allows a smoother repartition of the mass over the points).
+
+
+ Returns
+ -------
+ partial_gw_dist : (dim_a x dim_b) ndarray
+ partial GW discrepancy
+ log : dict
+ log dictionary returned only if `log` is `True`
+
+
+ Examples
+ --------
+ >>> import ot
+ >>> import scipy as sp
+ >>> a = np.array([0.25] * 4)
+ >>> b = np.array([0.25] * 4)
+ >>> x = np.array([1,2,100,200]).reshape((-1,1))
+ >>> y = np.array([3,2,98,199]).reshape((-1,1))
+ >>> C1 = sp.spatial.distance.cdist(x, x)
+ >>> C2 = sp.spatial.distance.cdist(y, y)
+ >>> np.round(partial_gromov_wasserstein2(C1, C2, a, b),2)
+ 1.69
+ >>> np.round(partial_gromov_wasserstein2(C1, C2, a, b, m=0.25),2)
+ 0.0
+
+ References
+ ----------
+ .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov-
+ Wasserstein with Applications on Positive-Unlabeled Learning".
+ arXiv preprint arXiv:2002.08276.
+
+ """
+
+ partial_gw, log_gw = partial_gromov_wasserstein(C1, C2, p, q, m,
+ nb_dummies, G0, thres,
+ numItermax, tol, True,
+ verbose, **kwargs)
+
+ log_gw['T'] = partial_gw
+
+ if log:
+ return log_gw['partial_gw_dist'], log_gw
+ else:
+ return log_gw['partial_gw_dist']
+
+
+def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000,
+ stopThr=1e-100, verbose=False, log=False):
+ r"""
+ Solves the partial optimal transport problem
+ and returns the OT plan
+
+ The function considers the following problem:
+
+ .. math::
+ \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma)
+
+ s.t. \gamma 1 \leq a \\
+ \gamma^T 1 \leq b \\
+ \gamma\geq 0 \\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} \\
+
+ where :
+
+ - M is the metric cost matrix
+ - :math:`\Omega` is the entropic regularization term
+ :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
+ - a and b are the sample weights
+ - m is the amount of mass to be transported
+
+ The formulation of the problem has been proposed in [3]_ (prop. 5)
+
+
+ Parameters
+ ----------
+ a : np.ndarray (dim_a,)
+ Unnormalized histogram of dimension dim_a
+ b : np.ndarray (dim_b,)
+ Unnormalized histograms of dimension dim_b
+ M : np.ndarray (dim_a, dim_b)
+ cost matrix
+ reg : float
+ Regularization term > 0
+ m : float, optional
+ Amount of mass to be transported
+ numItermax : int, optional
+ Max number of iterations
+ stopThr : float, optional
+ Stop threshold on error (>0)
+ verbose : bool, optional
+ Print information along iterations
+ log : bool, optional
+ record log if True
+
+
+ Returns
+ -------
+ gamma : (dim_a x dim_b) ndarray
+ Optimal transportation matrix for the given parameters
+ log : dict
+ log dictionary returned only if `log` is `True`
+
+
+ Examples
+ --------
+ >>> import ot
+ >>> a = [.1, .2]
+ >>> b = [.1, .1]
+ >>> M = [[0., 1.], [2., 3.]]
+ >>> np.round(entropic_partial_wasserstein(a, b, M, 1, 0.1), 2)
+ array([[0.06, 0.02],
+ [0.01, 0. ]])
+
+ References
+ ----------
+ .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G.
+ (2015). Iterative Bregman projections for regularized transportation
+ problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
+
+ See Also
+ --------
+ ot.partial.partial_wasserstein: exact Partial Wasserstein
+ """
+
+ a = np.asarray(a, dtype=np.float64)
+ b = np.asarray(b, dtype=np.float64)
+ M = np.asarray(M, dtype=np.float64)
+
+ dim_a, dim_b = M.shape
+ dx = np.ones(dim_a, dtype=np.float64)
+ dy = np.ones(dim_b, dtype=np.float64)
+
+ if len(a) == 0:
+ a = np.ones(dim_a, dtype=np.float64) / dim_a
+ if len(b) == 0:
+ b = np.ones(dim_b, dtype=np.float64) / dim_b
+
+ if m is None:
+ m = np.min((np.sum(a), np.sum(b))) * 1.0
+ if m < 0:
+ raise ValueError("Problem infeasible. Parameter m should be greater"
+ " than 0.")
+ if m > np.min((np.sum(a), np.sum(b))):
+ raise ValueError("Problem infeasible. Parameter m should lower or"
+ " equal than min(|a|_1, |b|_1).")
+
+ log_e = {'err': []}
+
+ # Next 3 lines equivalent to K=np.exp(-M/reg), but faster to compute
+ K = np.empty(M.shape, dtype=M.dtype)
+ np.divide(M, -reg, out=K)
+ np.exp(K, out=K)
+ np.multiply(K, m / np.sum(K), out=K)
+
+ err, cpt = 1, 0
+
+ while (err > stopThr and cpt < numItermax):
+ Kprev = K
+ K1 = np.dot(np.diag(np.minimum(a / np.sum(K, axis=1), dx)), K)
+ K2 = np.dot(K1, np.diag(np.minimum(b / np.sum(K1, axis=0), dy)))
+ K = K2 * (m / np.sum(K2))
+
+ if np.any(np.isnan(K)) or np.any(np.isinf(K)):
+ print('Warning: numerical errors at iteration', cpt)
+ break
+ if cpt % 10 == 0:
+ err = np.linalg.norm(Kprev - K)
+ if log:
+ log_e['err'].append(err)
+ if verbose:
+ if cpt % 200 == 0:
+ print(
+ '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 11)
+ print('{:5d}|{:8e}|'.format(cpt, err))
+
+ cpt = cpt + 1
+ log_e['partial_w_dist'] = np.sum(M * K)
+ if log:
+ return K, log_e
+ else:
+ return K
+
+
+def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None,
+ numItermax=1000, tol=1e-7, log=False,
+ verbose=False):
+ r"""
+ Returns the partial Gromov-Wasserstein transport between (C1,p) and (C2,q)
+
+ The function solves the following optimization problem:
+
+ .. math::
+ GW = \arg\min_{\gamma} \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})\cdot
+ \gamma_{i,j}\cdot\gamma_{k,l} + reg\cdot\Omega(\gamma)
+
+ s.t.
+ \gamma\geq 0 \\
+ \gamma 1 \leq a\\
+ \gamma^T 1 \leq b\\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\}
+
+ where :
+
+ - C1 is the metric cost matrix in the source space
+ - C2 is the metric cost matrix in the target space
+ - p and q are the sample weights
+ - L : quadratic loss function
+ - :math:`\Omega` is the entropic regularization term
+ :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
+ - m is the amount of mass to be transported
+
+ The formulation of the GW problem has been proposed in [12]_ and the
+ partial GW in [29]_.
+
+ Parameters
+ ----------
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric costfr matrix in the target space
+ p : ndarray, shape (ns,)
+ Distribution in the source space
+ q : ndarray, shape (nt,)
+ Distribution in the target space
+ reg: float
+ entropic regularization parameter
+ m : float, optional
+ Amount of mass to be transported (default: min (|p|_1, |q|_1))
+ G0 : ndarray, shape (ns, nt), optional
+ Initialisation of the transportation matrix
+ numItermax : int, optional
+ Max number of iterations
+ tol : float, optional
+ Stop threshold on error (>0)
+ log : bool, optional
+ return log if True
+ verbose : bool, optional
+ Print information along iterations
+
+ Examples
+ --------
+ >>> import ot
+ >>> import scipy as sp
+ >>> a = np.array([0.25] * 4)
+ >>> b = np.array([0.25] * 4)
+ >>> x = np.array([1,2,100,200]).reshape((-1,1))
+ >>> y = np.array([3,2,98,199]).reshape((-1,1))
+ >>> C1 = sp.spatial.distance.cdist(x, x)
+ >>> C2 = sp.spatial.distance.cdist(y, y)
+ >>> np.round(entropic_partial_gromov_wasserstein(C1, C2, a, b,50), 2)
+ array([[0.12, 0.13, 0. , 0. ],
+ [0.13, 0.12, 0. , 0. ],
+ [0. , 0. , 0.25, 0. ],
+ [0. , 0. , 0. , 0.25]])
+ >>> np.round(entropic_partial_gromov_wasserstein(C1, C2, a, b, 50, m=0.25), 2)
+ array([[0.02, 0.03, 0. , 0.03],
+ [0.03, 0.03, 0. , 0.03],
+ [0. , 0. , 0.03, 0. ],
+ [0.02, 0.02, 0. , 0.03]])
+
+ Returns
+ -------
+ :math: `gamma` : (dim_a x dim_b) ndarray
+ Optimal transportation matrix for the given parameters
+ log : dict
+ log dictionary returned only if `log` is `True`
+
+ References
+ ----------
+ .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
+ .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov-
+ Wasserstein with Applications on Positive-Unlabeled Learning".
+ arXiv preprint arXiv:2002.08276.
+
+ See Also
+ --------
+ ot.partial.partial_gromov_wasserstein: exact Partial Gromov-Wasserstein
+ """
+
+ if G0 is None:
+ G0 = np.outer(p, q)
+
+ if m is None:
+ m = np.min((np.sum(p), np.sum(q)))
+ elif m < 0:
+ raise ValueError("Problem infeasible. Parameter m should be greater"
+ " than 0.")
+ elif m > np.min((np.sum(p), np.sum(q))):
+ raise ValueError("Problem infeasible. Parameter m should lower or"
+ " equal than min(|a|_1, |b|_1).")
+
+ cpt = 0
+ err = 1
+
+ loge = {'err': []}
+
+ while (err > tol and cpt < numItermax):
+ Gprev = G0
+ M_entr = gwgrad_partial(C1, C2, G0)
+ G0 = entropic_partial_wasserstein(p, q, M_entr, reg, m)
+ if cpt % 10 == 0: # to speed up the computations
+ err = np.linalg.norm(G0 - Gprev)
+ if log:
+ loge['err'].append(err)
+ if verbose:
+ if cpt % 200 == 0:
+ print('{:5s}|{:12s}|{:12s}'.format(
+ 'It.', 'Err', 'Loss') + '\n' + '-' * 31)
+ print('{:5d}|{:8e}|{:8e}'.format(cpt, err,
+ gwloss_partial(C1, C2, G0)))
+
+ cpt += 1
+
+ if log:
+ loge['partial_gw_dist'] = gwloss_partial(C1, C2, G0)
+ return G0, loge
+ else:
+ return G0
+
+
+def entropic_partial_gromov_wasserstein2(C1, C2, p, q, reg, m=None, G0=None,
+ numItermax=1000, tol=1e-7, log=False,
+ verbose=False):
+ r"""
+ Returns the partial Gromov-Wasserstein discrepancy between (C1,p) and
+ (C2,q)
+
+ The function solves the following optimization problem:
+
+ .. math::
+ GW = \arg\min_{\gamma} \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})\cdot
+ \gamma_{i,j}\cdot\gamma_{k,l} + reg\cdot\Omega(\gamma)
+
+ s.t.
+ \gamma\geq 0 \\
+ \gamma 1 \leq a\\
+ \gamma^T 1 \leq b\\
+ 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\}
+
+ where :
+
+ - C1 is the metric cost matrix in the source space
+ - C2 is the metric cost matrix in the target space
+ - p and q are the sample weights
+ - L : quadratic loss function
+ - :math:`\Omega` is the entropic regularization term
+ :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
+ - m is the amount of mass to be transported
+
+ The formulation of the GW problem has been proposed in [12]_ and the
+ partial GW in [29]_.
+
+
+ Parameters
+ ----------
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric costfr matrix in the target space
+ p : ndarray, shape (ns,)
+ Distribution in the source space
+ q : ndarray, shape (nt,)
+ Distribution in the target space
+ reg: float
+ entropic regularization parameter
+ m : float, optional
+ Amount of mass to be transported (default: min (|p|_1, |q|_1))
+ G0 : ndarray, shape (ns, nt), optional
+ Initialisation of the transportation matrix
+ numItermax : int, optional
+ Max number of iterations
+ tol : float, optional
+ Stop threshold on error (>0)
+ log : bool, optional
+ return log if True
+ verbose : bool, optional
+ Print information along iterations
+
+
+ Returns
+ -------
+ partial_gw_dist: float
+ Gromov-Wasserstein distance
+ log : dict
+ log dictionary returned only if `log` is `True`
+
+ Examples
+ --------
+ >>> import ot
+ >>> import scipy as sp
+ >>> a = np.array([0.25] * 4)
+ >>> b = np.array([0.25] * 4)
+ >>> x = np.array([1,2,100,200]).reshape((-1,1))
+ >>> y = np.array([3,2,98,199]).reshape((-1,1))
+ >>> C1 = sp.spatial.distance.cdist(x, x)
+ >>> C2 = sp.spatial.distance.cdist(y, y)
+ >>> np.round(entropic_partial_gromov_wasserstein2(C1, C2, a, b,50), 2)
+ 1.87
+
+ References
+ ----------
+ .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
+ .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov-
+ Wasserstein with Applications on Positive-Unlabeled Learning".
+ arXiv preprint arXiv:2002.08276.
+ """
+
+ partial_gw, log_gw = entropic_partial_gromov_wasserstein(C1, C2, p, q, reg,
+ m, G0, numItermax,
+ tol, True,
+ verbose)
+
+ log_gw['T'] = partial_gw
+
+ if log:
+ return log_gw['partial_gw_dist'], log_gw
+ else:
+ return log_gw['partial_gw_dist']
diff --git a/ot/plot.py b/ot/plot.py
index f403e98..ad436b4 100644
--- a/ot/plot.py
+++ b/ot/plot.py
@@ -78,9 +78,10 @@ def plot2D_samples_mat(xs, xt, G, thr=1e-8, **kwargs):
thr : float, optional
threshold above which the line is drawn
**kwargs : dict
- paameters given to the plot functions (default color is black if
+ parameters given to the plot functions (default color is black if
nothing given)
"""
+
if ('color' not in kwargs) and ('c' not in kwargs):
kwargs['color'] = 'k'
mx = G.max()
diff --git a/ot/smooth.py b/ot/smooth.py
index 5a8e4b5..81f6a3e 100644
--- a/ot/smooth.py
+++ b/ot/smooth.py
@@ -26,7 +26,9 @@
# Remi Flamary <remi.flamary@unice.fr>
"""
-Implementation of
+Smooth and Sparse Optimal Transport solvers (KL an L2 reg.)
+
+Implementation of :
Smooth and Sparse Optimal Transport.
Mathieu Blondel, Vivien Seguy, Antoine Rolet.
In Proc. of AISTATS 2018.
diff --git a/ot/unbalanced.py b/ot/unbalanced.py
index d516dfc..e37f10c 100644
--- a/ot/unbalanced.py
+++ b/ot/unbalanced.py
@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
"""
-Regularized Unbalanced OT
+Regularized Unbalanced OT solvers
"""
# Author: Hicham Janati <hicham.janati@inria.fr>
@@ -384,10 +384,9 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000,
fi = reg_m / (reg_m + reg)
- cpt = 0
err = 1.
- while (err > stopThr and cpt < numItermax):
+ for i in range(numItermax):
uprev = u
vprev = v
@@ -401,28 +400,27 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000,
or np.any(np.isinf(u)) or np.any(np.isinf(v))):
# we have reached the machine precision
# come back to previous solution and quit loop
- warnings.warn('Numerical errors at iteration %s' % cpt)
+ warnings.warn('Numerical errors at iteration %s' % i)
u = uprev
v = vprev
break
- if cpt % 10 == 0:
- # we can speed up the process by checking for the error only all
- # the 10th iterations
- err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.)
- err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.)
- err = 0.5 * (err_u + err_v)
- if log:
- log['err'].append(err)
+
+ err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.)
+ err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.)
+ err = 0.5 * (err_u + err_v)
+ if log:
+ log['err'].append(err)
if verbose:
- if cpt % 200 == 0:
+ if i % 50 == 0:
print(
'{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
- print('{:5d}|{:8e}|'.format(cpt, err))
- cpt += 1
+ print('{:5d}|{:8e}|'.format(i, err))
+ if err < stopThr:
+ break
if log:
- log['logu'] = np.log(u + 1e-16)
- log['logv'] = np.log(v + 1e-16)
+ log['logu'] = np.log(u + 1e-300)
+ log['logv'] = np.log(v + 1e-300)
if n_hists: # return only loss
res = np.einsum('ik,ij,jk,ij->k', u, K, v, M)
@@ -747,8 +745,8 @@ def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3,
alpha = np.zeros(dim)
beta = np.zeros(dim)
q = np.ones(dim) / dim
- while (err > stopThr and cpt < numItermax):
- qprev = q
+ for i in range(numItermax):
+ qprev = q.copy()
Kv = K.dot(v)
f_alpha = np.exp(- alpha / (reg + reg_m))
f_beta = np.exp(- beta / (reg + reg_m))
@@ -777,7 +775,7 @@ def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3,
warnings.warn('Numerical errors at iteration %s' % cpt)
q = qprev
break
- if (cpt % 10 == 0 and not absorbing) or cpt == 0:
+ if (i % 10 == 0 and not absorbing) or i == 0:
# we can speed up the process by checking for the error only all
# the 10th iterations
err = abs(q - qprev).max() / max(abs(q).max(),
@@ -785,20 +783,21 @@ def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3,
if log:
log['err'].append(err)
if verbose:
- if cpt % 50 == 0:
+ if i % 50 == 0:
print(
'{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
- print('{:5d}|{:8e}|'.format(cpt, err))
+ print('{:5d}|{:8e}|'.format(i, err))
+ if err < stopThr:
+ break
- cpt += 1
if err > stopThr:
warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." +
"Try a larger entropy `reg` or a lower mass `reg_m`." +
"Or a larger absorption threshold `tau`.")
if log:
- log['niter'] = cpt
- log['logu'] = np.log(u + 1e-16)
- log['logv'] = np.log(v + 1e-16)
+ log['niter'] = i
+ log['logu'] = np.log(u + 1e-300)
+ log['logv'] = np.log(v + 1e-300)
return q, log
else:
return q
@@ -882,15 +881,15 @@ def barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, weights=None,
fi = reg_m / (reg_m + reg)
- v = np.ones((dim, n_hists)) / dim
- u = np.ones((dim, 1)) / dim
-
- cpt = 0
+ v = np.ones((dim, n_hists))
+ u = np.ones((dim, 1))
+ q = np.ones(dim)
err = 1.
- while (err > stopThr and cpt < numItermax):
- uprev = u
- vprev = v
+ for i in range(numItermax):
+ uprev = u.copy()
+ vprev = v.copy()
+ qprev = q.copy()
Kv = K.dot(v)
u = (A / Kv) ** fi
@@ -905,31 +904,30 @@ def barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, weights=None,
or np.any(np.isinf(u)) or np.any(np.isinf(v))):
# we have reached the machine precision
# come back to previous solution and quit loop
- warnings.warn('Numerical errors at iteration %s' % cpt)
+ warnings.warn('Numerical errors at iteration %s' % i)
u = uprev
v = vprev
+ q = qprev
break
- if cpt % 10 == 0:
- # we can speed up the process by checking for the error only all
- # the 10th iterations
- err_u = abs(u - uprev).max()
- err_u /= max(abs(u).max(), abs(uprev).max(), 1.)
- err_v = abs(v - vprev).max()
- err_v /= max(abs(v).max(), abs(vprev).max(), 1.)
- err = 0.5 * (err_u + err_v)
- if log:
- log['err'].append(err)
- if verbose:
- if cpt % 50 == 0:
- print(
- '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
- print('{:5d}|{:8e}|'.format(cpt, err))
+ # compute change in barycenter
+ err = abs(q - qprev).max()
+ err /= max(abs(q).max(), abs(qprev).max(), 1.)
+ if log:
+ log['err'].append(err)
+ # if barycenter did not change + at least 10 iterations - stop
+ if err < stopThr and i > 10:
+ break
+
+ if verbose:
+ if i % 10 == 0:
+ print(
+ '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
+ print('{:5d}|{:8e}|'.format(i, err))
- cpt += 1
if log:
- log['niter'] = cpt
- log['logu'] = np.log(u + 1e-16)
- log['logv'] = np.log(v + 1e-16)
+ log['niter'] = i
+ log['logu'] = np.log(u + 1e-300)
+ log['logv'] = np.log(v + 1e-300)
return q, log
else:
return q
@@ -1002,12 +1000,14 @@ def barycenter_unbalanced(A, M, reg, reg_m, method="sinkhorn", weights=None,
if method.lower() == 'sinkhorn':
return barycenter_unbalanced_sinkhorn(A, M, reg, reg_m,
+ weights=weights,
numItermax=numItermax,
stopThr=stopThr, verbose=verbose,
log=log, **kwargs)
elif method.lower() == 'sinkhorn_stabilized':
return barycenter_unbalanced_stabilized(A, M, reg, reg_m,
+ weights=weights,
numItermax=numItermax,
stopThr=stopThr,
verbose=verbose,
@@ -1015,6 +1015,7 @@ def barycenter_unbalanced(A, M, reg, reg_m, method="sinkhorn", weights=None,
elif method.lower() in ['sinkhorn_reg_scaling']:
warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp')
return barycenter_unbalanced(A, M, reg, reg_m,
+ weights=weights,
numItermax=numItermax,
stopThr=stopThr, verbose=verbose,
log=log, **kwargs)
diff --git a/ot/utils.py b/ot/utils.py
index b71458b..f9911a1 100644
--- a/ot/utils.py
+++ b/ot/utils.py
@@ -49,6 +49,12 @@ def kernel(x1, x2, method='gaussian', sigma=1, **kwargs):
return K
+def laplacian(x):
+ """Compute Laplacian matrix"""
+ L = np.diag(np.sum(x, axis=0)) - x
+ return L
+
+
def unif(n):
""" return a uniform histogram of length n (simplex)
@@ -200,6 +206,28 @@ def dots(*args):
return reduce(np.dot, args)
+def label_normalization(y, start=0):
+ """ Transform labels to start at a given value
+
+ Parameters
+ ----------
+ y : array-like, shape (n, )
+ The vector of labels to be normalized.
+ start : int
+ Desired value for the smallest label in y (default=0)
+
+ Returns
+ -------
+ y : array-like, shape (n1, )
+ The input vector of labels normalized according to given start value.
+ """
+
+ diff = np.min(np.unique(y)) - start
+ if diff != 0:
+ y -= diff
+ return y
+
+
def fun(f, q_in, q_out):
""" Utility function for parmap with no serializing problems """
while True:
diff --git a/requirements.txt b/requirements.txt
index 5a3432b..331dd57 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,9 +1,10 @@
numpy
-scipy>=1.0
+scipy>=1.3
cython
matplotlib
-sphinx-gallery
autograd
-pymanopt
+pymanopt==0.2.4; python_version <'3'
+pymanopt; python_version >= '3'
cvxopt
-pytest
+scikit-learn
+pytest \ No newline at end of file
diff --git a/setup.py b/setup.py
index c08e3e0..91c24d9 100755
--- a/setup.py
+++ b/setup.py
@@ -8,9 +8,15 @@ from Cython.Build import cythonize
import numpy
import re
import os
+import sys
+import subprocess
here = path.abspath(path.dirname(__file__))
+
+os.environ["CC"] = "g++"
+os.environ["CXX"] = "g++"
+
# dirty but working
__version__ = re.search(
r'__version__\s*=\s*[\'"]([^\'"]*)[\'"]', # It excludes inline comment too
@@ -24,60 +30,68 @@ ROOT = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(ROOT, 'README.md'), encoding="utf-8") as f:
README = f.read()
+opt_arg = ["-O3"]
+
+# clean cython output is clean is called
+if 'clean' in sys.argv[1:]:
+ if os.path.isfile('ot/lp/emd_wrap.cpp'):
+ os.remove('ot/lp/emd_wrap.cpp')
+
+
# add platform dependant optional compilation argument
-opt_arg=["-O3"]
-import platform
-if platform.system()=='Darwin':
- if platform.release()=='18.0.0':
- opt_arg.append("-stdlib=libc++") # correspond to a compilation problem with Mojave and XCode 10
+if sys.platform.startswith('darwin'):
+ opt_arg.append("-stdlib=libc++")
+ sdk_path = subprocess.check_output(['xcrun', '--show-sdk-path'])
+ os.environ['CFLAGS'] = '-isysroot "{}"'.format(sdk_path.rstrip().decode("utf-8"))
setup(name='POT',
version=__version__,
description='Python Optimal Transport Library',
long_description=README,
- long_description_content_type='text/markdown',
+ long_description_content_type='text/markdown',
author=u'Remi Flamary, Nicolas Courty',
author_email='remi.flamary@gmail.com, ncourty@gmail.com',
- url='https://github.com/rflamary/POT',
+ url='https://github.com/PythonOT/POT',
packages=find_packages(),
- ext_modules = cythonize(Extension(
- "ot.lp.emd_wrap", # the extension name
- sources=["ot/lp/emd_wrap.pyx", "ot/lp/EMD_wrapper.cpp"], # the Cython source and
- # additional C++ source files
- language="c++", # generate and compile C++ code,
- include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')],
- extra_compile_args=opt_arg
- )),
- platforms=['linux','macosx','windows'],
- download_url='https://github.com/rflamary/POT/archive/{}.tar.gz'.format(__version__),
- license = 'MIT',
+ ext_modules=cythonize(Extension(
+ "ot.lp.emd_wrap", # the extension name
+ sources=["ot/lp/emd_wrap.pyx", "ot/lp/EMD_wrapper.cpp"], # the Cython source and
+ # additional C++ source files
+ language="c++", # generate and compile C++ code,
+ include_dirs=[numpy.get_include(), os.path.join(ROOT, 'ot/lp')],
+ extra_compile_args=opt_arg
+ )),
+ platforms=['linux', 'macosx', 'windows'],
+ download_url='https://github.com/PythonOT/POT/archive/{}.tar.gz'.format(__version__),
+ license='MIT',
scripts=[],
data_files=[],
- requires=["numpy","scipy","cython"],
- install_requires=["numpy","scipy","cython"],
+ requires=["numpy", "scipy", "cython"],
+ setup_requires=["numpy>=1.16", "scipy>=1.0", "cython>=0.23"],
+ install_requires=["numpy>=1.16", "scipy>=1.0", "cython>=0.23"],
classifiers=[
- 'Development Status :: 5 - Production/Stable',
- 'Intended Audience :: Developers',
- 'Intended Audience :: Education',
- 'Intended Audience :: Science/Research',
- 'License :: OSI Approved :: MIT License',
- 'Environment :: Console',
- 'Operating System :: OS Independent',
- 'Operating System :: MacOS',
- 'Operating System :: POSIX',
- 'Programming Language :: Python',
- 'Programming Language :: C++',
- 'Programming Language :: C',
- 'Programming Language :: Cython',
- 'Topic :: Utilities',
- 'Topic :: Scientific/Engineering :: Artificial Intelligence',
- 'Topic :: Scientific/Engineering :: Mathematics',
- 'Topic :: Scientific/Engineering :: Information Analysis',
- 'Programming Language :: Python :: 2',
- 'Programming Language :: Python :: 2.7',
- 'Programming Language :: Python :: 3',
- 'Programming Language :: Python :: 3.4',
- 'Programming Language :: Python :: 3.5',
- 'Programming Language :: Python :: 3.6',
- ]
- )
+ 'Development Status :: 5 - Production/Stable',
+ 'Intended Audience :: Developers',
+ 'Intended Audience :: Education',
+ 'Intended Audience :: Science/Research',
+ 'License :: OSI Approved :: MIT License',
+ 'Environment :: Console',
+ 'Operating System :: OS Independent',
+ 'Operating System :: MacOS',
+ 'Operating System :: POSIX',
+ 'Programming Language :: Python',
+ 'Programming Language :: C++',
+ 'Programming Language :: C',
+ 'Programming Language :: Cython',
+ 'Topic :: Utilities',
+ 'Topic :: Scientific/Engineering :: Artificial Intelligence',
+ 'Topic :: Scientific/Engineering :: Mathematics',
+ 'Topic :: Scientific/Engineering :: Information Analysis',
+ 'Programming Language :: Python :: 2',
+ 'Programming Language :: Python :: 2.7',
+ 'Programming Language :: Python :: 3',
+ 'Programming Language :: Python :: 3.4',
+ 'Programming Language :: Python :: 3.5',
+ 'Programming Language :: Python :: 3.6',
+ ]
+ )
diff --git a/test/test_bregman.py b/test/test_bregman.py
index f70df10..6aa4e08 100644
--- a/test/test_bregman.py
+++ b/test/test_bregman.py
@@ -57,6 +57,9 @@ def test_sinkhorn_empty():
np.testing.assert_allclose(u, G.sum(1), atol=1e-05)
np.testing.assert_allclose(u, G.sum(0), atol=1e-05)
+ # test empty weights greenkhorn
+ ot.sinkhorn([], [], M, 1, method='greenkhorn', stopThr=1e-10, log=True)
+
def test_sinkhorn_variants():
# test sinkhorn
@@ -106,7 +109,6 @@ def test_sinkhorn_variants_log():
@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"])
def test_barycenter(method):
-
n_bins = 100 # nb bins
# Gaussian distributions
@@ -125,7 +127,7 @@ def test_barycenter(method):
# wasserstein
reg = 1e-2
- bary_wass = ot.bregman.barycenter(A, M, reg, weights, method=method)
+ bary_wass, log = ot.bregman.barycenter(A, M, reg, weights, method=method, log=True)
np.testing.assert_allclose(1, np.sum(bary_wass))
@@ -133,7 +135,6 @@ def test_barycenter(method):
def test_barycenter_stabilization():
-
n_bins = 100 # nb bins
# Gaussian distributions
@@ -154,14 +155,13 @@ def test_barycenter_stabilization():
reg = 1e-2
bar_stable = ot.bregman.barycenter(A, M, reg, weights,
method="sinkhorn_stabilized",
- stopThr=1e-8)
+ stopThr=1e-8, verbose=True)
bar = ot.bregman.barycenter(A, M, reg, weights, method="sinkhorn",
- stopThr=1e-8)
+ stopThr=1e-8, verbose=True)
np.testing.assert_allclose(bar, bar_stable)
def test_wasserstein_bary_2d():
-
size = 100 # size of a square image
a1 = np.random.randn(size, size)
a1 += a1.min()
@@ -185,7 +185,6 @@ def test_wasserstein_bary_2d():
def test_unmix():
-
n_bins = 50 # nb bins
# Gaussian distributions
@@ -207,7 +206,7 @@ def test_unmix():
# wasserstein
reg = 1e-3
- um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01,)
+ um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01, )
np.testing.assert_allclose(1, np.sum(um), rtol=1e-03, atol=1e-03)
np.testing.assert_allclose([0.5, 0.5], um, rtol=1e-03, atol=1e-03)
@@ -256,7 +255,7 @@ def test_empirical_sinkhorn():
def test_empirical_sinkhorn_divergence():
- #Test sinkhorn divergence
+ # Test sinkhorn divergence
n = 10
a = ot.unif(n)
b = ot.unif(n)
@@ -337,3 +336,28 @@ def test_implemented_methods():
ot.bregman.sinkhorn(a, b, M, epsilon, method=method)
with pytest.raises(ValueError):
ot.bregman.sinkhorn2(a, b, M, epsilon, method=method)
+
+
+def test_screenkhorn():
+ # test screenkhorn
+ rng = np.random.RandomState(0)
+ n = 100
+ a = ot.unif(n)
+ b = ot.unif(n)
+
+ x = rng.randn(n, 2)
+ M = ot.dist(x, x)
+ # sinkhorn
+ G_sink = ot.sinkhorn(a, b, M, 1e-03)
+ # screenkhorn
+ G_screen = ot.bregman.screenkhorn(a, b, M, 1e-03, uniform=True, verbose=True)
+ # check marginals
+ np.testing.assert_allclose(G_sink.sum(0), G_screen.sum(0), atol=1e-02)
+ np.testing.assert_allclose(G_sink.sum(1), G_screen.sum(1), atol=1e-02)
+
+
+def test_convolutional_barycenter_non_square():
+ # test for image with height not equal width
+ A = np.ones((2, 2, 3)) / (2 * 3)
+ b = ot.bregman.convolutional_barycenter2d(A, 1e-03)
+ np.testing.assert_allclose(np.ones((2, 3)) / (2 * 3), b, atol=1e-02)
diff --git a/test/test_da.py b/test/test_da.py
index 2a5e50e..3b28119 100644
--- a/test/test_da.py
+++ b/test/test_da.py
@@ -5,7 +5,7 @@
# License: MIT License
import numpy as np
-from numpy.testing.utils import assert_allclose, assert_equal
+from numpy.testing import assert_allclose, assert_equal
import ot
from ot.datasets import make_data_classif
@@ -65,6 +65,16 @@ def test_sinkhorn_lpl1_transport_class():
transp_Xs = otda.fit_transform(Xs=Xs, ys=ys, Xt=Xt)
assert_equal(transp_Xs.shape, Xs.shape)
+ # check label propagation
+ transp_yt = otda.transform_labels(ys)
+ assert_equal(transp_yt.shape[0], yt.shape[0])
+ assert_equal(transp_yt.shape[1], len(np.unique(ys)))
+
+ # check inverse label propagation
+ transp_ys = otda.inverse_transform_labels(yt)
+ assert_equal(transp_ys.shape[0], ys.shape[0])
+ assert_equal(transp_ys.shape[1], len(np.unique(yt)))
+
# test unsupervised vs semi-supervised mode
otda_unsup = ot.da.SinkhornLpl1Transport()
otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt)
@@ -129,6 +139,16 @@ def test_sinkhorn_l1l2_transport_class():
transp_Xt = otda.inverse_transform(Xt=Xt)
assert_equal(transp_Xt.shape, Xt.shape)
+ # check label propagation
+ transp_yt = otda.transform_labels(ys)
+ assert_equal(transp_yt.shape[0], yt.shape[0])
+ assert_equal(transp_yt.shape[1], len(np.unique(ys)))
+
+ # check inverse label propagation
+ transp_ys = otda.inverse_transform_labels(yt)
+ assert_equal(transp_ys.shape[0], ys.shape[0])
+ assert_equal(transp_ys.shape[1], len(np.unique(yt)))
+
Xt_new, _ = make_data_classif('3gauss2', nt + 1)
transp_Xt_new = otda.inverse_transform(Xt=Xt_new)
@@ -210,6 +230,16 @@ def test_sinkhorn_transport_class():
transp_Xt = otda.inverse_transform(Xt=Xt)
assert_equal(transp_Xt.shape, Xt.shape)
+ # check label propagation
+ transp_yt = otda.transform_labels(ys)
+ assert_equal(transp_yt.shape[0], yt.shape[0])
+ assert_equal(transp_yt.shape[1], len(np.unique(ys)))
+
+ # check inverse label propagation
+ transp_ys = otda.inverse_transform_labels(yt)
+ assert_equal(transp_ys.shape[0], ys.shape[0])
+ assert_equal(transp_ys.shape[1], len(np.unique(yt)))
+
Xt_new, _ = make_data_classif('3gauss2', nt + 1)
transp_Xt_new = otda.inverse_transform(Xt=Xt_new)
@@ -271,6 +301,16 @@ def test_unbalanced_sinkhorn_transport_class():
transp_Xs = otda.transform(Xs=Xs)
assert_equal(transp_Xs.shape, Xs.shape)
+ # check label propagation
+ transp_yt = otda.transform_labels(ys)
+ assert_equal(transp_yt.shape[0], yt.shape[0])
+ assert_equal(transp_yt.shape[1], len(np.unique(ys)))
+
+ # check inverse label propagation
+ transp_ys = otda.inverse_transform_labels(yt)
+ assert_equal(transp_ys.shape[0], ys.shape[0])
+ assert_equal(transp_ys.shape[1], len(np.unique(yt)))
+
Xs_new, _ = make_data_classif('3gauss', ns + 1)
transp_Xs_new = otda.transform(Xs_new)
@@ -353,6 +393,16 @@ def test_emd_transport_class():
transp_Xt = otda.inverse_transform(Xt=Xt)
assert_equal(transp_Xt.shape, Xt.shape)
+ # check label propagation
+ transp_yt = otda.transform_labels(ys)
+ assert_equal(transp_yt.shape[0], yt.shape[0])
+ assert_equal(transp_yt.shape[1], len(np.unique(ys)))
+
+ # check inverse label propagation
+ transp_ys = otda.inverse_transform_labels(yt)
+ assert_equal(transp_ys.shape[0], ys.shape[0])
+ assert_equal(transp_ys.shape[1], len(np.unique(yt)))
+
Xt_new, _ = make_data_classif('3gauss2', nt + 1)
transp_Xt_new = otda.inverse_transform(Xt=Xt_new)
@@ -510,7 +560,6 @@ def test_mapping_transport_class():
def test_linear_mapping():
-
ns = 150
nt = 200
@@ -528,7 +577,6 @@ def test_linear_mapping():
def test_linear_mapping_class():
-
ns = 150
nt = 200
@@ -549,3 +597,159 @@ def test_linear_mapping_class():
Cst = np.cov(Xst.T)
np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2)
+
+
+def test_jcpot_transport_class():
+ """test_jcpot_transport
+ """
+
+ ns1 = 150
+ ns2 = 150
+ nt = 200
+
+ Xs1, ys1 = make_data_classif('3gauss', ns1)
+ Xs2, ys2 = make_data_classif('3gauss', ns2)
+
+ Xt, yt = make_data_classif('3gauss2', nt)
+
+ Xs = [Xs1, Xs2]
+ ys = [ys1, ys2]
+
+ otda = ot.da.JCPOTTransport(reg_e=1, max_iter=10000, tol=1e-9, verbose=True, log=True)
+
+ # test its computed
+ otda.fit(Xs=Xs, ys=ys, Xt=Xt)
+
+ assert hasattr(otda, "coupling_")
+ assert hasattr(otda, "proportions_")
+ assert hasattr(otda, "log_")
+
+ # test dimensions of coupling
+ for i, xs in enumerate(Xs):
+ assert_equal(otda.coupling_[i].shape, ((xs.shape[0], Xt.shape[0])))
+
+ # test all margin constraints
+ mu_t = unif(nt)
+
+ for i in range(len(Xs)):
+ # test margin constraints w.r.t. uniform target weights for each coupling matrix
+ assert_allclose(
+ np.sum(otda.coupling_[i], axis=0), mu_t, rtol=1e-3, atol=1e-3)
+
+ # test margin constraints w.r.t. modified source weights for each source domain
+
+ assert_allclose(
+ np.dot(otda.log_['D1'][i], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3,
+ atol=1e-3)
+
+ # test transform
+ transp_Xs = otda.transform(Xs=Xs)
+ [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)]
+
+ Xs_new, _ = make_data_classif('3gauss', ns1 + 1)
+ transp_Xs_new = otda.transform(Xs_new)
+
+ # check that the oos method is working
+ assert_equal(transp_Xs_new.shape, Xs_new.shape)
+
+ # check label propagation
+ transp_yt = otda.transform_labels(ys)
+ assert_equal(transp_yt.shape[0], yt.shape[0])
+ assert_equal(transp_yt.shape[1], len(np.unique(ys)))
+
+ # check inverse label propagation
+ transp_ys = otda.inverse_transform_labels(yt)
+ [assert_equal(x.shape[0], y.shape[0]) for x, y in zip(transp_ys, ys)]
+ [assert_equal(x.shape[1], len(np.unique(y))) for x, y in zip(transp_ys, ys)]
+
+
+def test_jcpot_barycenter():
+ """test_jcpot_barycenter
+ """
+
+ ns1 = 150
+ ns2 = 150
+ nt = 200
+
+ sigma = 0.1
+ np.random.seed(1985)
+
+ ps1 = .2
+ ps2 = .9
+ pt = .4
+
+ Xs1, ys1 = make_data_classif('2gauss_prop', ns1, nz=sigma, p=ps1)
+ Xs2, ys2 = make_data_classif('2gauss_prop', ns2, nz=sigma, p=ps2)
+ Xt, yt = make_data_classif('2gauss_prop', nt, nz=sigma, p=pt)
+
+ Xs = [Xs1, Xs2]
+ ys = [ys1, ys2]
+
+ prop = ot.bregman.jcpot_barycenter(Xs, ys, Xt, reg=.5, metric='sqeuclidean',
+ numItermax=10000, stopThr=1e-9, verbose=False, log=False)
+
+ np.testing.assert_allclose(prop, [1 - pt, pt], rtol=1e-3, atol=1e-3)
+
+
+def test_emd_laplace_class():
+ """test_emd_laplace_transport
+ """
+ ns = 150
+ nt = 200
+
+ Xs, ys = make_data_classif('3gauss', ns)
+ Xt, yt = make_data_classif('3gauss2', nt)
+
+ otda = ot.da.EMDLaplaceTransport(reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True)
+
+ # test its computed
+ otda.fit(Xs=Xs, ys=ys, Xt=Xt)
+
+ assert hasattr(otda, "coupling_")
+ assert hasattr(otda, "log_")
+
+ # test dimensions of coupling
+ assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+
+ # test all margin constraints
+ mu_s = unif(ns)
+ mu_t = unif(nt)
+
+ assert_allclose(
+ np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3)
+ assert_allclose(
+ np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3)
+
+ # test transform
+ transp_Xs = otda.transform(Xs=Xs)
+ [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)]
+
+ Xs_new, _ = make_data_classif('3gauss', ns + 1)
+ transp_Xs_new = otda.transform(Xs_new)
+
+ # check that the oos method is working
+ assert_equal(transp_Xs_new.shape, Xs_new.shape)
+
+ # test inverse transform
+ transp_Xt = otda.inverse_transform(Xt=Xt)
+ assert_equal(transp_Xt.shape, Xt.shape)
+
+ Xt_new, _ = make_data_classif('3gauss2', nt + 1)
+ transp_Xt_new = otda.inverse_transform(Xt=Xt_new)
+
+ # check that the oos method is working
+ assert_equal(transp_Xt_new.shape, Xt_new.shape)
+
+ # test fit_transform
+ transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt)
+ assert_equal(transp_Xs.shape, Xs.shape)
+
+ # check label propagation
+ transp_yt = otda.transform_labels(ys)
+ assert_equal(transp_yt.shape[0], yt.shape[0])
+ assert_equal(transp_yt.shape[1], len(np.unique(ys)))
+
+ # check inverse label propagation
+ transp_ys = otda.inverse_transform_labels(yt)
+ assert_equal(transp_ys.shape[0], ys.shape[0])
+ assert_equal(transp_ys.shape[1], len(np.unique(yt)))
diff --git a/test/test_gromov.py b/test/test_gromov.py
index 70fa83f..43da9fc 100644
--- a/test/test_gromov.py
+++ b/test/test_gromov.py
@@ -44,10 +44,14 @@ def test_gromov():
gw, log = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=True)
+ gw_val = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=False)
+
G = log['T']
np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e-1)
+ np.testing.assert_allclose(gw, gw_val, atol=1e-1, rtol=1e-1) # cf log=False
+
# check constratints
np.testing.assert_allclose(
p, G.sum(1), atol=1e-04) # cf convergence gromov
diff --git a/test/test_optim.py b/test/test_optim.py
index ae31e1f..87b0268 100644
--- a/test/test_optim.py
+++ b/test/test_optim.py
@@ -37,6 +37,39 @@ def test_conditional_gradient():
np.testing.assert_allclose(b, G.sum(0))
+def test_conditional_gradient2():
+ n = 1000 # nb samples
+
+ mu_s = np.array([0, 0])
+ cov_s = np.array([[1, 0], [0, 1]])
+
+ mu_t = np.array([4, 4])
+ cov_t = np.array([[1, -.8], [-.8, 1]])
+
+ xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)
+ xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)
+
+ a, b = np.ones((n,)) / n, np.ones((n,)) / n
+
+ # loss matrix
+ M = ot.dist(xs, xt)
+ M /= M.max()
+
+ def f(G):
+ return 0.5 * np.sum(G**2)
+
+ def df(G):
+ return G
+
+ reg = 1e-1
+
+ G, log = ot.optim.cg(a, b, M, reg, f, df, numItermaxEmd=200000,
+ verbose=True, log=True)
+
+ np.testing.assert_allclose(a, G.sum(1))
+ np.testing.assert_allclose(b, G.sum(0))
+
+
def test_generalized_conditional_gradient():
n_bins = 100 # nb bins
diff --git a/test/test_ot.py b/test/test_ot.py
index dacae0a..b7306f6 100644
--- a/test/test_ot.py
+++ b/test/test_ot.py
@@ -7,11 +7,27 @@
import warnings
import numpy as np
+import pytest
from scipy.stats import wasserstein_distance
import ot
from ot.datasets import make_1D_gauss as gauss
-import pytest
+
+
+def test_emd_dimension_mismatch():
+ # test emd and emd2 for dimension mismatch
+ n_samples = 100
+ n_features = 2
+ rng = np.random.RandomState(0)
+
+ x = rng.randn(n_samples, n_features)
+ a = ot.utils.unif(n_samples + 1)
+
+ M = ot.dist(x, x)
+
+ np.testing.assert_raises(AssertionError, ot.emd, a, a, M)
+
+ np.testing.assert_raises(AssertionError, ot.emd2, a, a, M)
def test_emd_emd2():
@@ -59,12 +75,12 @@ def test_emd_1d_emd2_1d():
np.testing.assert_allclose(wass, wass1d_emd2)
# check loss is similar to scipy's implementation for Euclidean metric
- wass_sp = wasserstein_distance(u.reshape((-1, )), v.reshape((-1, )))
+ wass_sp = wasserstein_distance(u.reshape((-1,)), v.reshape((-1,)))
np.testing.assert_allclose(wass_sp, wass1d_euc)
# check constraints
- np.testing.assert_allclose(np.ones((n, )) / n, G.sum(1))
- np.testing.assert_allclose(np.ones((m, )) / m, G.sum(0))
+ np.testing.assert_allclose(np.ones((n,)) / n, G.sum(1))
+ np.testing.assert_allclose(np.ones((m,)) / m, G.sum(0))
# check G is similar
np.testing.assert_allclose(G, G_1d)
@@ -76,6 +92,42 @@ def test_emd_1d_emd2_1d():
ot.emd_1d(u, v, [], [])
+def test_emd_1d_emd2_1d_with_weights():
+ # test emd1d gives similar results as emd
+ n = 20
+ m = 30
+ rng = np.random.RandomState(0)
+ u = rng.randn(n, 1)
+ v = rng.randn(m, 1)
+
+ w_u = rng.uniform(0., 1., n)
+ w_u = w_u / w_u.sum()
+
+ w_v = rng.uniform(0., 1., m)
+ w_v = w_v / w_v.sum()
+
+ M = ot.dist(u, v, metric='sqeuclidean')
+
+ G, log = ot.emd(w_u, w_v, M, log=True)
+ wass = log["cost"]
+ G_1d, log = ot.emd_1d(u, v, w_u, w_v, metric='sqeuclidean', log=True)
+ wass1d = log["cost"]
+ wass1d_emd2 = ot.emd2_1d(u, v, w_u, w_v, metric='sqeuclidean', log=False)
+ wass1d_euc = ot.emd2_1d(u, v, w_u, w_v, metric='euclidean', log=False)
+
+ # check loss is similar
+ np.testing.assert_allclose(wass, wass1d)
+ np.testing.assert_allclose(wass, wass1d_emd2)
+
+ # check loss is similar to scipy's implementation for Euclidean metric
+ wass_sp = wasserstein_distance(u.reshape((-1,)), v.reshape((-1,)), w_u, w_v)
+ np.testing.assert_allclose(wass_sp, wass1d_euc)
+
+ # check constraints
+ np.testing.assert_allclose(w_u, G.sum(1))
+ np.testing.assert_allclose(w_v, G.sum(0))
+
+
def test_wass_1d():
# test emd1d gives similar results as emd
n = 20
@@ -168,7 +220,6 @@ def test_emd2_multi():
def test_lp_barycenter():
-
a1 = np.array([1.0, 0, 0])[:, None]
a2 = np.array([0, 0, 1.0])[:, None]
@@ -185,7 +236,6 @@ def test_lp_barycenter():
def test_free_support_barycenter():
-
measures_locations = [np.array([-1.]).reshape((1, 1)), np.array([1.]).reshape((1, 1))]
measures_weights = [np.array([1.]), np.array([1.])]
@@ -201,7 +251,6 @@ def test_free_support_barycenter():
@pytest.mark.skipif(not ot.lp.cvx.cvxopt, reason="No cvxopt available")
def test_lp_barycenter_cvxopt():
-
a1 = np.array([1.0, 0, 0])[:, None]
a2 = np.array([0, 0, 1.0])[:, None]
@@ -295,6 +344,10 @@ def test_dual_variables():
np.testing.assert_almost_equal(cost1, log['cost'])
check_duality_gap(a, b, M, G, log['u'], log['v'], log['cost'])
+ constraint_violation = log['u'][:, None] + log['v'][None, :] - M
+
+ assert constraint_violation.max() < 1e-8
+
def check_duality_gap(a, b, M, G, u, v, cost):
cost_dual = np.vdot(a, u) + np.vdot(b, v)
diff --git a/test/test_partial.py b/test/test_partial.py
new file mode 100755
index 0000000..510e081
--- /dev/null
+++ b/test/test_partial.py
@@ -0,0 +1,208 @@
+"""Tests for module partial """
+
+# Author:
+# Laetitia Chapel <laetitia.chapel@irisa.fr>
+#
+# License: MIT License
+
+import numpy as np
+import scipy as sp
+import ot
+import pytest
+
+
+def test_raise_errors():
+
+ n_samples = 20 # nb samples (gaussian)
+ n_noise = 20 # nb of samples (noise)
+
+ mu = np.array([0, 0])
+ cov = np.array([[1, 0], [0, 2]])
+
+ xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+ xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))
+ xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+ xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))
+
+ M = ot.dist(xs, xt)
+
+ p = ot.unif(n_samples + n_noise)
+ q = ot.unif(n_samples + n_noise)
+
+ with pytest.raises(ValueError):
+ ot.partial.partial_wasserstein_lagrange(p + 1, q, M, 1, log=True)
+
+ with pytest.raises(ValueError):
+ ot.partial.partial_wasserstein(p, q, M, m=2, log=True)
+
+ with pytest.raises(ValueError):
+ ot.partial.partial_wasserstein(p, q, M, m=-1, log=True)
+
+ with pytest.raises(ValueError):
+ ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=2, log=True)
+
+ with pytest.raises(ValueError):
+ ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=-1, log=True)
+
+ with pytest.raises(ValueError):
+ ot.partial.partial_gromov_wasserstein(M, M, p, q, m=2, log=True)
+
+ with pytest.raises(ValueError):
+ ot.partial.partial_gromov_wasserstein(M, M, p, q, m=-1, log=True)
+
+ with pytest.raises(ValueError):
+ ot.partial.entropic_partial_gromov_wasserstein(M, M, p, q, reg=1, m=2, log=True)
+
+ with pytest.raises(ValueError):
+ ot.partial.entropic_partial_gromov_wasserstein(M, M, p, q, reg=1, m=-1, log=True)
+
+
+def test_partial_wasserstein_lagrange():
+
+ n_samples = 20 # nb samples (gaussian)
+ n_noise = 20 # nb of samples (noise)
+
+ mu = np.array([0, 0])
+ cov = np.array([[1, 0], [0, 2]])
+
+ xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+ xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))
+ xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+ xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))
+
+ M = ot.dist(xs, xt)
+
+ p = ot.unif(n_samples + n_noise)
+ q = ot.unif(n_samples + n_noise)
+
+ w0, log0 = ot.partial.partial_wasserstein_lagrange(p, q, M, 1, log=True)
+
+
+def test_partial_wasserstein():
+
+ n_samples = 20 # nb samples (gaussian)
+ n_noise = 20 # nb of samples (noise)
+
+ mu = np.array([0, 0])
+ cov = np.array([[1, 0], [0, 2]])
+
+ xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+ xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))
+ xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)
+ xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))
+
+ M = ot.dist(xs, xt)
+
+ p = ot.unif(n_samples + n_noise)
+ q = ot.unif(n_samples + n_noise)
+
+ m = 0.5
+
+ w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=m, log=True)
+ w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=m,
+ log=True, verbose=True)
+
+ # check constratints
+ np.testing.assert_equal(
+ w0.sum(1) - p <= 1e-5, [True] * len(p)) # cf convergence wasserstein
+ np.testing.assert_equal(
+ w0.sum(0) - q <= 1e-5, [True] * len(q)) # cf convergence wasserstein
+ np.testing.assert_equal(
+ w.sum(1) - p <= 1e-5, [True] * len(p)) # cf convergence wasserstein
+ np.testing.assert_equal(
+ w.sum(0) - q <= 1e-5, [True] * len(q)) # cf convergence wasserstein
+
+ # check transported mass
+ np.testing.assert_allclose(
+ np.sum(w0), m, atol=1e-04)
+ np.testing.assert_allclose(
+ np.sum(w), m, atol=1e-04)
+
+ w0, log0 = ot.partial.partial_wasserstein2(p, q, M, m=m, log=True)
+ w0_val = ot.partial.partial_wasserstein2(p, q, M, m=m, log=False)
+
+ G = log0['T']
+
+ np.testing.assert_allclose(w0, w0_val, atol=1e-1, rtol=1e-1)
+
+ # check constratints
+ np.testing.assert_equal(
+ G.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein
+ np.testing.assert_equal(
+ G.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein
+ np.testing.assert_allclose(
+ np.sum(G), m, atol=1e-04)
+
+
+def test_partial_gromov_wasserstein():
+ n_samples = 20 # nb samples
+ n_noise = 10 # nb of samples (noise)
+
+ p = ot.unif(n_samples + n_noise)
+ q = ot.unif(n_samples + n_noise)
+
+ mu_s = np.array([0, 0])
+ cov_s = np.array([[1, 0], [0, 1]])
+
+ mu_t = np.array([0, 0, 0])
+ cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+
+ xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
+ xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)
+ P = sp.linalg.sqrtm(cov_t)
+ xt = np.random.randn(n_samples, 3).dot(P) + mu_t
+ xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)
+ xt2 = xs[::-1].copy()
+
+ C1 = ot.dist(xs, xs)
+ C2 = ot.dist(xt, xt)
+ C3 = ot.dist(xt2, xt2)
+
+ m = 2 / 3
+ res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C3, p, q, m=m,
+ log=True, verbose=True)
+ np.testing.assert_allclose(res0, 0, atol=1e-1, rtol=1e-1)
+
+ C1 = sp.spatial.distance.cdist(xs, xs)
+ C2 = sp.spatial.distance.cdist(xt, xt)
+
+ m = 1
+ res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m,
+ log=True)
+ G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss')
+ np.testing.assert_allclose(G, res0, atol=1e-04)
+
+ res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,
+ m=m, log=True)
+ G = ot.gromov.entropic_gromov_wasserstein(
+ C1, C2, p, q, 'square_loss', epsilon=10)
+ np.testing.assert_allclose(G, res, atol=1e-02)
+
+ w0, log0 = ot.partial.partial_gromov_wasserstein2(C1, C2, p, q, m=m,
+ log=True)
+ w0_val = ot.partial.partial_gromov_wasserstein2(C1, C2, p, q, m=m,
+ log=False)
+ G = log0['T']
+ np.testing.assert_allclose(w0, w0_val, atol=1e-1, rtol=1e-1)
+
+ m = 2 / 3
+ res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m,
+ log=True)
+ res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q,
+ 100, m=m,
+ log=True)
+
+ # check constratints
+ np.testing.assert_equal(
+ res0.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein
+ np.testing.assert_equal(
+ res0.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein
+ np.testing.assert_allclose(
+ np.sum(res0), m, atol=1e-04)
+
+ np.testing.assert_equal(
+ res.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein
+ np.testing.assert_equal(
+ res.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein
+ np.testing.assert_allclose(
+ np.sum(res), m, atol=1e-04)
diff --git a/test/test_stochastic.py b/test/test_stochastic.py
index f0f3fc8..155622c 100644
--- a/test/test_stochastic.py
+++ b/test/test_stochastic.py
@@ -70,8 +70,8 @@ def test_stochastic_asgd():
M = ot.dist(x, x)
- G = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd",
- numItermax=numItermax)
+ G, log = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd",
+ numItermax=numItermax, log=True)
# check constratints
np.testing.assert_allclose(
@@ -145,8 +145,8 @@ def test_stochastic_dual_sgd():
M = ot.dist(x, x)
- G = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size,
- numItermax=numItermax)
+ G, log = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size,
+ numItermax=numItermax, log=True)
# check constratints
np.testing.assert_allclose(
diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py
index ca1efba..dfeaad9 100644
--- a/test/test_unbalanced.py
+++ b/test/test_unbalanced.py
@@ -31,9 +31,11 @@ def test_unbalanced_convergence(method):
G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon,
reg_m=reg_m,
method=method,
- log=True)
+ log=True,
+ verbose=True)
loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m,
- method=method)
+ method=method,
+ verbose=True)
# check fixed point equations
# in log-domain
fi = reg_m / (reg_m + epsilon)
@@ -73,7 +75,8 @@ def test_unbalanced_multiple_inputs(method):
loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon,
reg_m=reg_m,
method=method,
- log=True)
+ log=True,
+ verbose=True)
# check fixed point equations
# in log-domain
fi = reg_m / (reg_m + epsilon)
diff --git a/test/test_utils.py b/test/test_utils.py
index 640598d..db9cda6 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -36,10 +36,10 @@ def test_tic_toc():
t2 = ot.toq()
# test timing
- np.testing.assert_allclose(0.5, t, rtol=1e-2, atol=1e-2)
+ np.testing.assert_allclose(0.5, t, rtol=1e-1, atol=1e-1)
# test toc vs toq
- np.testing.assert_allclose(t, t2, rtol=1e-2, atol=1e-2)
+ np.testing.assert_allclose(t, t2, rtol=1e-1, atol=1e-1)
def test_kernel():