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-rw-r--r--src/cython/cython/kernels.pyx126
-rw-r--r--src/cython/cython/vectors.pyx65
-rw-r--r--src/cython/gudhi.pyx.in2
-rw-r--r--src/cython/include/Kernels_interface.h125
-rw-r--r--src/cython/include/Vectors_interface.h59
5 files changed, 377 insertions, 0 deletions
diff --git a/src/cython/cython/kernels.pyx b/src/cython/cython/kernels.pyx
new file mode 100644
index 00000000..0cb296ec
--- /dev/null
+++ b/src/cython/cython/kernels.pyx
@@ -0,0 +1,126 @@
+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 "Kernels_interface.h" namespace "Gudhi::persistence_diagram":
+ double sw (vector[pair[double, double]], vector[pair[double, double]], double, int)
+ 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, 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):
+ """
+
+ :param diagram_1: The first diagram.
+ :type diagram_1: vector[pair[double, double]]
+ :param diagram_2: The second diagram.
+ :type diagram_2: vector[pair[double, double]]
+ :param sigma: bandwidth of Gaussian
+ :param N: number of directions
+
+ :returns: the sliced wasserstein kernel.
+ """
+ return sw(diagram_1, diagram_2, sigma, N)
+
+def sliced_wasserstein_matrix(diagrams_1, diagrams_2, sigma = 1, N = 100):
+ """
+
+ :param diagram_1: The first set of diagrams.
+ :type diagram_1: vector[vector[pair[double, double]]]
+ :param diagram_2: The second set of diagrams.
+ :type diagram_2: vector[vector[pair[double, double]]]
+ :param sigma: bandwidth of Gaussian
+ :param N: number of directions
+
+ :returns: the sliced wasserstein kernel matrix.
+ """
+ return sw_matrix(diagrams_1, diagrams_2, sigma, N)
+
+def persistence_weighted_gaussian(diagram_1, diagram_2, sigma = 1, N = 100, C = 1, p = 1):
+ """
+
+ :param diagram_1: The first diagram.
+ :type diagram_1: vector[pair[double, double]]
+ :param diagram_2: The second diagram.
+ :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, C, p)
+
+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.
+ :type diagram_1: vector[vector[pair[double, double]]]
+ :param diagram_2: The second set of diagrams.
+ :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, C, p)
+
+def persistence_scale_space(diagram_1, diagram_2, sigma = 1, N = 100):
+ """
+
+ :param diagram_1: The first diagram.
+ :type diagram_1: vector[pair[double, double]]
+ :param diagram_2: The second diagram.
+ :type diagram_2: vector[pair[double, double]]
+ :param sigma: bandwidth of Gaussian
+ :param N: number of Fourier features
+
+ :returns: the persistence scale space kernel.
+ """
+ return pss(diagram_1, diagram_2, sigma, N)
+
+def persistence_scale_space_matrix(diagrams_1, diagrams_2, sigma = 1, N = 100):
+ """
+
+ :param diagram_1: The first set of diagrams.
+ :type diagram_1: vector[vector[pair[double, double]]]
+ :param diagram_2: The second set of diagrams.
+ :type diagram_2: vector[vector[pair[double, double]]]
+ :param sigma: bandwidth of Gaussian
+ :param N: number of Fourier features
+
+ :returns: the persistence scale space kernel matrix.
+ """
+ return pss_matrix(diagrams_1, diagrams_2, sigma, N)
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 b94f2251..3555bbd6 100644
--- a/src/cython/gudhi.pyx.in
+++ b/src/cython/gudhi.pyx.in
@@ -36,6 +36,8 @@ include '@CMAKE_CURRENT_SOURCE_DIR@/cython/persistence_graphical_tools.py'
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
new file mode 100644
index 00000000..dd46656f
--- /dev/null
+++ b/src/cython/include/Kernels_interface.h
@@ -0,0 +1,125 @@
+/* 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/>.
+ */
+
+#ifndef INCLUDE_KERNELS_INTERFACE_H_
+#define INCLUDE_KERNELS_INTERFACE_H_
+
+#include <gudhi/Sliced_Wasserstein.h>
+#include <gudhi/Persistence_weighted_gaussian.h>
+#include <gudhi/Weight_functions.h>
+
+#include <iostream>
+#include <vector>
+#include <utility> // for std::pair
+
+namespace Gudhi {
+
+namespace persistence_diagram {
+
+
+ // *******************
+ // 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, double C, double p) {
+ Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg1(diag1, sigma, N, Gudhi::Persistence_representations::arctan_weight(C,p));
+ Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg2(diag2, sigma, N, Gudhi::Persistence_representations::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) {
+ 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);
+
+ Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg1(pd1, 2*std::sqrt(sigma), N, Gudhi::Persistence_representations::pss_weight);
+ Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg2(pd2, 2*std::sqrt(sigma), N, Gudhi::Persistence_representations::pss_weight);
+
+ 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) {
+ Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg1(diag1, 2*std::sqrt(sigma), N, Gudhi::Persistence_representations::pss_weight);
+ Gudhi::Persistence_representations::Persistence_weighted_gaussian pwg2(diag2, 2*std::sqrt(sigma), N, Gudhi::Persistence_representations::pss_weight);
+
+ return pwg1.compute_scalar_product (pwg2) / (16*Gudhi::Persistence_representations::pi*sigma);
+ }
+
+
+ // ****************
+ // 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);}
+ std::vector<Gudhi::Persistence_representations::Sliced_Wasserstein> ss2;
+ int num_diag_2 = s2.size(); for(int i = 0; i < num_diag_2; i++){Gudhi::Persistence_representations::Sliced_Wasserstein sw2(s2[i], sigma, N); ss2.push_back(sw2);}
+ 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(ss1[i].compute_scalar_product(ss2[j])); matrix.push_back(ps);
+ }
+ 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, 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, 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();
+ 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);
+ ss1.push_back(pd1);
+ }
+
+ for(int i = 0; i < num_diag_2; i++){
+ std::vector<std::pair<double, double>> pd2 = s2[i]; int numpts = s2[i].size();
+ for(int j = 0; j < numpts; j++) pd2.emplace_back(s2[i][j].second,s2[i][j].first);
+ ss2.push_back(pd2);
+ }
+
+ 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(pss_sym(ss1[i], ss2[j], sigma, N)); matrix.push_back(ps);
+ }
+ return matrix;
+ }
+
+} // namespace persistence_diagram
+
+} // namespace Gudhi
+
+
+#endif // INCLUDE_KERNELS_INTERFACE_H_
diff --git a/src/cython/include/Vectors_interface.h b/src/cython/include/Vectors_interface.h
new file mode 100644
index 00000000..902ccc10
--- /dev/null
+++ b/src/cython/include/Vectors_interface.h
@@ -0,0 +1,59 @@
+/* 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/>.
+ */
+
+#ifndef INCLUDE_VECTORS_INTERFACE_H_
+#define INCLUDE_VECTORS_INTERFACE_H_
+
+#include <gudhi/Persistence_landscape_on_grid_exact.h>
+#include <gudhi/Persistence_heat_maps_exact.h>
+#include <gudhi/Weight_functions.h>
+
+#include <iostream>
+#include <vector>
+#include <utility> // for std::pair
+
+using Weight = std::function<double (std::pair<double,double>) >;
+
+namespace Gudhi {
+
+namespace persistence_diagram {
+
+ std::vector<std::vector<double> > compute_ls(const std::vector<std::pair<double, double> >& diag, int nb_ls, double min_x, double max_x, int res_x) {
+ Gudhi::Persistence_representations::Persistence_landscape_on_grid_exact L(diag, nb_ls, min_x, max_x, res_x);
+ return L.vectorize();
+ }
+
+ std::vector<std::vector<double> > compute_pim(const std::vector<std::pair<double, double> >& diag, double min_x, double max_x, int res_x, double min_y, double max_y, int res_y, std::string weight, double sigma, double C, double p) {
+ Weight weight_fn;
+ if(weight.compare("linear") == 0) weight_fn = Gudhi::Persistence_representations::linear_weight;
+ if(weight.compare("arctan") == 0) weight_fn = Gudhi::Persistence_representations::arctan_weight(C,p);
+ if(weight.compare("const") == 0) weight_fn = Gudhi::Persistence_representations::const_weight;
+ Gudhi::Persistence_representations::Persistence_heat_maps_exact P(diag, min_x, max_x, res_x, min_y, max_y, res_y, weight_fn, sigma);
+ return P.vectorize();
+ }
+
+} // namespace persistence_diagram
+
+} // namespace Gudhi
+
+
+#endif // INCLUDE_VECTORS_INTERFACE_H_
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-16 09:44:32 +0000