git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/kernels@3385 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 2a83e62e132ce406986efa1456b5f1bca6b93691

4 files changed, 96 insertions, 32 deletions

diff --git a/src/cython/cython/kernels.pyx b/src/cython/cython/kernels.pyx
index 466917b1..0cb296ec 100644
--- a/src/cython/cython/kernels.pyx
+++ b/src/cython/cython/kernels.pyx
@@ -34,8 +34,8 @@ cdef extern from "Kernels_interface.h" namespace "Gudhi::persistence_diagram":
     vector[vector[double]] sw_matrix     (vector[vector[pair[double, double]]],  vector[vector[pair[double, double]]],  double, int)
     double                 pss           (vector[pair[double, double]],          vector[pair[double, double]],          double, int)
     vector[vector[double]] pss_matrix    (vector[vector[pair[double, double]]],  vector[vector[pair[double, double]]],  double, int)
-    double                 pwg           (vector[pair[double, double]],          vector[pair[double, double]],          double, int)
-    vector[vector[double]] pwg_matrix    (vector[vector[pair[double, double]]],  vector[vector[pair[double, double]]],  double, int)
+    double                 pwg           (vector[pair[double, double]],          vector[pair[double, double]],          double, int, double, double)
+    vector[vector[double]] pwg_matrix    (vector[vector[pair[double, double]]],  vector[vector[pair[double, double]]],  double, int, double, double)
 
 def sliced_wasserstein(diagram_1, diagram_2, sigma = 1, N = 100):
     """
@@ -65,7 +65,7 @@ def sliced_wasserstein_matrix(diagrams_1, diagrams_2, sigma = 1, N = 100):
     """
     return sw_matrix(diagrams_1, diagrams_2, sigma, N)
 
-def persistence_weighted_gaussian(diagram_1, diagram_2, sigma = 1, N = 100):
+def persistence_weighted_gaussian(diagram_1, diagram_2, sigma = 1, N = 100, C = 1, p = 1):
     """
 
     :param diagram_1: The first diagram.
@@ -74,12 +74,14 @@ def persistence_weighted_gaussian(diagram_1, diagram_2, sigma = 1, N = 100):
     :type diagram_2: vector[pair[double, double]]
     :param sigma: bandwidth of Gaussian
     :param N: number of Fourier features
+    :param C: cost of persistence weight
+    :param p: power of persistence weight
 
     :returns: the persistence weighted gaussian kernel.
     """
-    return pwg(diagram_1, diagram_2, sigma, N)
+    return pwg(diagram_1, diagram_2, sigma, N, C, p)
 
-def persistence_weighted_gaussian_matrix(diagrams_1, diagrams_2, sigma = 1, N = 100):
+def persistence_weighted_gaussian_matrix(diagrams_1, diagrams_2, sigma = 1, N = 100, C = 1, p = 1):
     """
 
     :param diagram_1: The first set of diagrams.
@@ -88,10 +90,12 @@ def persistence_weighted_gaussian_matrix(diagrams_1, diagrams_2, sigma = 1, N =
     :type diagram_2: vector[vector[pair[double, double]]]
     :param sigma: bandwidth of Gaussian
     :param N: number of Fourier features
+    :param C: cost of persistence weight
+    :param p: power of persistence weight
 
     :returns: the persistence weighted gaussian kernel matrix.
     """
-    return pwg_matrix(diagrams_1, diagrams_2, sigma, N)
+    return pwg_matrix(diagrams_1, diagrams_2, sigma, N, C, p)
 
 def persistence_scale_space(diagram_1, diagram_2, sigma = 1, N = 100):
     """
diff --git a/src/cython/cython/vectors.pyx b/src/cython/cython/vectors.pyx
new file mode 100644
index 00000000..42390ae6
--- /dev/null
+++ b/src/cython/cython/vectors.pyx
@@ -0,0 +1,65 @@
+from cython cimport numeric
+from libcpp.vector cimport vector
+from libcpp.utility cimport pair
+import os
+
+"""This file is part of the Gudhi Library. The Gudhi library
+   (Geometric Understanding in Higher Dimensions) is a generic C++
+   library for computational topology.
+
+   Author(s):       Mathieu Carriere
+
+   Copyright (C) 2018 INRIA
+
+   This program is free software: you can redistribute it and/or modify
+   it under the terms of the GNU General Public License as published by
+   the Free Software Foundation, either version 3 of the License, or
+   (at your option) any later version.
+
+   This program is distributed in the hope that it will be useful,
+   but WITHOUT ANY WARRANTY; without even the implied warranty of
+   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+   GNU General Public License for more details.
+
+   You should have received a copy of the GNU General Public License
+   along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+__author__ = "Mathieu Carriere"
+__copyright__ = "Copyright (C) 2018 INRIA"
+__license__ = "GPL v3"
+
+cdef extern from "Vectors_interface.h" namespace "Gudhi::persistence_diagram":
+    vector[vector[double]] compute_ls    (vector[pair[double, double]], int, double, double, int)
+    vector[vector[double]] compute_pim   (vector[pair[double, double]], double, double, int, double, double, int, string, double, double, double)
+
+def landscape(diagram, nb_ls = 10, min_x = 0.0, max_x = 1.0, res_x = 100):
+    """
+
+    :param diagram: The diagram
+    :type diagram: vector[pair[double, double]]
+    :param nb_ls: Number of landscapes
+    :param min_x: Minimum abscissa
+    :param max_x: Maximum abscissa
+    :param res_x: Number of samples 
+
+    :returns: the landscape
+    """
+    return compute_ls(diagram, nb_ls, min_x, max_x, res_x)
+
+def persistence_image(diagram, min_x = 0.0, max_x = 1.0, res_x = 10, min_y = 0.0, max_y = 1.0, res_y = 10, weight = "linear", sigma = 1.0, C = 1.0, p = 1.0):
+    """
+
+    :param diagram: The diagram
+    :type diagram: vector[vector[pair[double, double]]]
+    :param min_x: Minimum abscissa
+    :param max_x: Maximum abscissa
+    :param res_x: Number of abscissa pixels 
+    :param min_x: Minimum ordinate
+    :param max_x: Maximum ordinate
+    :param res_x: Number of ordinate pixels 
+    :param sigma: bandwidth of Gaussian
+
+    :returns: the persistence image
+    """
+    return compute_pim(diagram, min_x, max_x, res_x, min_y, max_y, res_y, weight, sigma, C, p)
diff --git a/src/cython/gudhi.pyx.in b/src/cython/gudhi.pyx.in
index 7f42968d..3ff68085 100644
--- a/src/cython/gudhi.pyx.in
+++ b/src/cython/gudhi.pyx.in
@@ -37,6 +37,7 @@ include '@CMAKE_CURRENT_SOURCE_DIR@/cython/reader_utils.pyx'
 include '@CMAKE_CURRENT_SOURCE_DIR@/cython/witness_complex.pyx'
 include '@CMAKE_CURRENT_SOURCE_DIR@/cython/strong_witness_complex.pyx'
 include '@CMAKE_CURRENT_SOURCE_DIR@/cython/kernels.pyx'
+include '@CMAKE_CURRENT_SOURCE_DIR@/cython/vectors.pyx'
 @GUDHI_CYTHON_ALPHA_COMPLEX@
 @GUDHI_CYTHON_EUCLIDEAN_WITNESS_COMPLEX@
 @GUDHI_CYTHON_SUBSAMPLING@
diff --git a/src/cython/include/Kernels_interface.h b/src/cython/include/Kernels_interface.h
index 1742d016..0da28245 100644
--- a/src/cython/include/Kernels_interface.h
+++ b/src/cython/include/Kernels_interface.h
@@ -34,25 +34,24 @@ namespace Gudhi {
 
 namespace persistence_diagram {
 
-  double sw(const std::vector<std::pair<double, double>>& diag1,
-            const std::vector<std::pair<double, double>>& diag2,
-            double sigma, int N) {
+
+  // *******************
+  // Kernel evaluations.
+  // *******************
+
+  double sw(const std::vector<std::pair<double, double>>& diag1, const std::vector<std::pair<double, double>>& diag2, double sigma, int N) {
     Gudhi::Persistence_representations::Sliced_Wasserstein sw1(diag1, sigma, N);
     Gudhi::Persistence_representations::Sliced_Wasserstein sw2(diag2, sigma, N);
     return sw1.compute_scalar_product(sw2);
   }
 
-  double pwg(const std::vector<std::pair<double, double>>& diag1,
-             const std::vector<std::pair<double, double>>& diag2,
-             double sigma, int N) {
-    Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg1(diag1, sigma, N, Gudhi::Persistence_representations::Persistence_weighted_gaussian::arctan_weight);
-    Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg2(diag2, sigma, N, Gudhi::Persistence_representations::Persistence_weighted_gaussian::arctan_weight);
+  double pwg(const std::vector<std::pair<double, double>>& diag1, const std::vector<std::pair<double, double>>& diag2, double sigma, int N, double C, double p) {
+    Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg1(diag1, sigma, N, Gudhi::Persistence_representations::Persistence_weighted_gaussian::arctan_weight(C,p));
+    Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg2(diag2, sigma, N, Gudhi::Persistence_representations::Persistence_weighted_gaussian::arctan_weight(C,p));
     return pwg1.compute_scalar_product(pwg2);
   }
 
-  double pss(const std::vector<std::pair<double, double>>& diag1,
-             const std::vector<std::pair<double, double>>& diag2,
-             double sigma, int N) {
+  double pss(const std::vector<std::pair<double, double>>& diag1, const std::vector<std::pair<double, double>>& diag2, double sigma, int N) {
     std::vector<std::pair<double, double>> pd1 = diag1; int numpts = diag1.size();    for(int i = 0; i < numpts; i++)  pd1.emplace_back(diag1[i].second,diag1[i].first);
     std::vector<std::pair<double, double>> pd2 = diag2;     numpts = diag2.size();    for(int i = 0; i < numpts; i++)  pd2.emplace_back(diag2[i].second,diag2[i].first);
 
@@ -62,9 +61,7 @@ namespace persistence_diagram {
     return pwg1.compute_scalar_product  (pwg2) / (16*Gudhi::Persistence_representations::pi*sigma);
   }
 
-  double pss_sym(const std::vector<std::pair<double, double>>& diag1,
-             const std::vector<std::pair<double, double>>& diag2,
-             double sigma, int N) {
+  double pss_sym(const std::vector<std::pair<double, double>>& diag1, const std::vector<std::pair<double, double>>& diag2, double sigma, int N) {
     Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg1(diag1, 2*std::sqrt(sigma), N, Gudhi::Persistence_representations::Persistence_weighted_gaussian::pss_weight);
     Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg2(diag2, 2*std::sqrt(sigma), N, Gudhi::Persistence_representations::Persistence_weighted_gaussian::pss_weight);
 
@@ -72,9 +69,11 @@ namespace persistence_diagram {
   }
 
 
-  std::vector<std::vector<double> > sw_matrix(const std::vector<std::vector<std::pair<double, double> > >& s1,
-                                              const std::vector<std::vector<std::pair<double, double> > >& s2,
-                                              double sigma, int N){
+  // ****************
+  // Kernel matrices.
+  // ****************
+
+  std::vector<std::vector<double> > sw_matrix(const std::vector<std::vector<std::pair<double, double> > >& s1, const std::vector<std::vector<std::pair<double, double> > >& s2, double sigma, int N){
     std::vector<std::vector<double> > matrix;
     std::vector<Gudhi::Persistence_representations::Sliced_Wasserstein> ss1;
     int num_diag_1 = s1.size(); for(int i = 0; i < num_diag_1; i++){Gudhi::Persistence_representations::Sliced_Wasserstein sw1(s1[i], sigma, N); ss1.push_back(sw1);}
@@ -87,22 +86,17 @@ namespace persistence_diagram {
     return matrix;
   }
 
-  std::vector<std::vector<double> > pwg_matrix(const std::vector<std::vector<std::pair<double, double> > >& s1,
-                                               const std::vector<std::vector<std::pair<double, double> > >& s2,
-                                               double sigma, int N){
+  std::vector<std::vector<double> > pwg_matrix(const std::vector<std::vector<std::pair<double, double> > >& s1, const std::vector<std::vector<std::pair<double, double> > >& s2, double sigma, int N, double C, double p){
     std::vector<std::vector<double> > matrix; int num_diag_1 = s1.size(); int num_diag_2 = s2.size();
     for(int i = 0; i < num_diag_1; i++){
       std::cout << 100.0*i/num_diag_1 << " %" << std::endl;
-      std::vector<double> ps; for(int j = 0; j < num_diag_2; j++) ps.push_back(pwg(s1[i], s2[j], sigma, N)); matrix.push_back(ps);
+      std::vector<double> ps; for(int j = 0; j < num_diag_2; j++) ps.push_back(pwg(s1[i], s2[j], sigma, N, C, p)); matrix.push_back(ps);
     }
     return matrix;
   }
 
-  std::vector<std::vector<double> > pss_matrix(const std::vector<std::vector<std::pair<double, double> > >& s1,
-                                               const std::vector<std::vector<std::pair<double, double> > >& s2,
-                                               double sigma, int N){
-    std::vector<std::vector<std::pair<double, double> > > ss1, ss2;
-    std::vector<std::vector<double> > matrix; int num_diag_1 = s1.size(); int num_diag_2 = s2.size();
+  std::vector<std::vector<double> > pss_matrix(const std::vector<std::vector<std::pair<double, double> > >& s1, const std::vector<std::vector<std::pair<double, double> > >& s2, double sigma, int N){
+    std::vector<std::vector<std::pair<double, double> > > ss1, ss2; std::vector<std::vector<double> > matrix; int num_diag_1 = s1.size(); int num_diag_2 = s2.size();
     for(int i = 0; i < num_diag_1; i++){
       std::vector<std::pair<double, double>> pd1 = s1[i]; int numpts = s1[i].size();    
       for(int j = 0; j < numpts; j++)  pd1.emplace_back(s1[i][j].second,s1[i][j].first);