diff options
56 files changed, 1589 insertions, 253 deletions
diff --git a/biblio/bibliography.bib b/biblio/bibliography.bib index e56734e4..24c0f718 100644 --- a/biblio/bibliography.bib +++ b/biblio/bibliography.bib @@ -1,12 +1,12 @@ @inproceedings{gudhilibrary_ICMS14, - author = {Cl\'ement Maria and Jean-Daniel Boissonnat and + author = {Cl\'ement Maria and Jean-Daniel Boissonnat and Marc Glisse and Mariette Yvinec}, title = {The {G}udhi Library: Simplicial Complexes and Persistent Homology}, booktitle = {ICMS}, year = {2014}, } -@article{Carriere17c, +@article{Carriere17c, author = {Carri\`ere, Mathieu and Michel, Bertrand and Oudot, Steve}, title = {{Statistical Analysis and Parameter Selection for Mapper}}, journal = {CoRR}, @@ -20,7 +20,7 @@ booktitle = {Proceedings of the Twenty-ninth Annual Symposium on Computational Geometry}, year = {2013}, pages = {107--116}, -} +} @article{Carriere16, title={{Structure and Stability of the 1-Dimensional Mapper}}, @@ -70,7 +70,7 @@ language={English} publisher = {ACM Press/Addison-Wesley Publishing Co.}, address = {New York, NY, USA}, keywords = {level of detail, mutiresolution modeling, non-manifold, pair contraction, surface simplification}, -} +} @inproceedings{Lindstrom, @@ -85,7 +85,7 @@ language={English} numpages = {8}, publisher = {IEEE Computer Society Press}, address = {Los Alamitos, CA, USA}, -} +} @article{boissonnatmariacamalgorithmica, @@ -201,13 +201,13 @@ language={English}, bibsource = {DBLP, http://dblp.uni-trier.de} } @article{RS62, - author={J. B. Rosser and L. Schoenfeld}, - title={Approximate Formulas for some Functions of Prime Numbers}, - journal= {ijm}, - volume= 6, - year= 1962, - pages={64-94}, - mrnumber={25 \#1139} + author={J. B. Rosser and L. Schoenfeld}, + title={Approximate Formulas for some Functions of Prime Numbers}, + journal= {ijm}, + volume= 6, + year= 1962, + pages={64-94}, + mrnumber={25 \#1139} } @article{DBLP:journals/siamcomp/Furer09, author = {Martin F{\"u}rer}, @@ -410,7 +410,7 @@ url="http://dx.doi.org/10.1007/s00454-013-9557-2" edition = {2}, publisher = {Cambridge University Press}, address = {New York, NY, USA}, -} +} @book{DBLP:books/aw/AhoHU74, author = {Alfred V. Aho and John E. Hopcroft and @@ -508,7 +508,7 @@ proceedings{DBLP:conf/issac/1996, pages = {501--509}, numpages = {9}, keywords = {clique complexes, data structure, edge contraction, flag complexes, high dimensions, homotopy equivalence, shape reconstruction, shape simplification, simplicial complexes, vietoris-rips complexes}, -} +} @@ -571,7 +571,7 @@ note = "http://gmplib.org/", publisher = {ACM}, address = {New York, NY, USA}, keywords = {discrete and computational geometry, embedding theorem, persistent homology}, -} +} @article{Fredman:1984:SST:828.1884, author = {Fredman, Michael L. and Koml\'{o}s, J\'{a}nos and Szemer{\'e}di, Endre}, title = {Storing a Sparse Table with {O}(1) Worst Case Access Time}, @@ -589,7 +589,7 @@ note = "http://gmplib.org/", acmid = {1884}, publisher = {ACM}, address = {New York, NY, USA}, -} +} %------------------------------------------------------------------ @@ -739,7 +739,7 @@ inproceedings{DBLP:conf/soda/1997, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, keywords = {Filtration, Klein bottle, Manifold, Natural images, Persistent homology, Topology}, -} +} article{DBLP:journals/ijcv/LeePM03, author = {Ann B. Lee and @@ -767,7 +767,7 @@ pages={234115}, year={2010}, abstract={Understanding energy landscapes is a major challenge in chemistry and biology. Although a wide variety of methods have been invented and applied to this problem, very little is understood about the actual mathematical structures underlying such landscapes. Perhaps the most general assumption is the idea that energy landscapes are low-dimensional manifolds embedded in high-dimensional Euclidean space. While this is a very mild assumption, we have discovered an example of an energy landscape which is nonmanifold, demonstrating previously unknown mathematical complexity. The example occurs in the energy landscape of cyclo-octane, which was found to have the structure of a reducible algebraic variety, composed of the union of a sphere and a Klein bottle, intersecting in two rings.} } - + book{hatcher2002algebraic, title={Algebraic Topology}, @@ -803,7 +803,7 @@ book{hatcher2002algebraic, isbn = {0070131511}, edition = {2nd}, publisher = {McGraw-Hill Higher Education}, -} +} @article{DBLP:journals/focm/CarlssonS10, author = {Gunnar E. Carlsson and @@ -1017,7 +1017,7 @@ language={English} journal = {Journal of Symbolic Computation.}, year = {2016} } - + @ARTICLE{Fasy_Kim_Lecci_Maria_tda, author = {B. Fasy and J. Kim and F. Lecci and C. Maria}, title = {Introduction to the R package TDA.}, @@ -1064,13 +1064,20 @@ language={English} } @ARTICLE{Carriere_Oudot_Ovsjanikov_top_signatures_3d, - author = {M. Carrière and S. Oudot and M. Ovsjanikov}, + author = {M. Carri\`ere and S. Oudot and M. Ovsjanikov}, title = {Stable Topological Signatures for Points on 3D Shapes.}, journal = {Proc. Sympos. on Geometry Processing}, year = {2015} } - +@article{buchet16efficient, + title = {Efficient and Robust Persistent Homology for Measures}, + author = {Micka\"{e}l Buchet and Fr\'{e}d\'{e}ric Chazal and Steve Y. Oudot and Donald Sheehy}, + booktitle = {Computational Geometry: Theory and Applications}, + volume = {58}, + pages = {70--96}, + year = {2016} +} @InProceedings{pmlr-v70-carriere17a, diff --git a/biblio/how_to_cite_gudhi.bib b/biblio/how_to_cite_gudhi.bib index 5994124a..942f8d7e 100644 --- a/biblio/how_to_cite_gudhi.bib +++ b/biblio/how_to_cite_gudhi.bib @@ -97,7 +97,7 @@ } @incollection{gudhi:RipsComplex -, author = "Cl\'ement Maria, Pawel Dlotko, Vincent Rouvreau" +, author = "Cl\'ement Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse" , title = "Rips complex" , publisher = "{GUDHI Editorial Board}" , booktitle = "{GUDHI} User and Reference Manual" diff --git a/data/correlation_matrix/lower_triangular_correlation_matrix.csv b/data/correlation_matrix/lower_triangular_correlation_matrix.csv new file mode 100644 index 00000000..99ad0b5d --- /dev/null +++ b/data/correlation_matrix/lower_triangular_correlation_matrix.csv @@ -0,0 +1,6 @@ + +0.4090538938 +0.2182708406;0.5664245836 +0.9109757412;0.5234453492;0.4239008464 +0.2426856242;0.7178816327;0.4748826202;0.8254894051 +0.0908790566;0.9369574252;0.9760741671;0.5256838992;0.0653515265 diff --git a/data/points/Kl.off b/data/points/Kl.off index 911bcd23..8930a321 100644 --- a/data/points/Kl.off +++ b/data/points/Kl.off @@ -1,5 +1,5 @@ -OFF -10000 0 0 +nOFF +5 10000 0 0 0.5 0 0 0 1 0.562791 0 0.125333 0 0.998027 0.625333 0 0.24869 0 0.992115 diff --git a/src/Alpha_complex/include/gudhi/Alpha_complex.h b/src/Alpha_complex/include/gudhi/Alpha_complex.h index 63c6675c..91305032 100644 --- a/src/Alpha_complex/include/gudhi/Alpha_complex.h +++ b/src/Alpha_complex/include/gudhi/Alpha_complex.h @@ -34,6 +34,7 @@ #include <CGAL/Epick_d.h> #include <CGAL/Spatial_sort_traits_adapter_d.h> #include <CGAL/property_map.h> // for CGAL::Identity_property_map +#include <CGAL/NT_converter.h> #include <iostream> #include <vector> @@ -323,8 +324,9 @@ class Alpha_complex { if (f_simplex_dim > 0) { // squared_radius function initialization Squared_Radius squared_radius = kernel_.compute_squared_radius_d_object(); + CGAL::NT_converter<typename Geom_traits::FT, Filtration_value> cv; - alpha_complex_filtration = squared_radius(pointVector.begin(), pointVector.end()); + alpha_complex_filtration = cv(squared_radius(pointVector.begin(), pointVector.end())); } complex.assign_filtration(f_simplex, alpha_complex_filtration); #ifdef DEBUG_TRACES diff --git a/src/Alpha_complex/utilities/alphacomplex.md b/src/Alpha_complex/utilities/alphacomplex.md index aace85d3..ede749a9 100644 --- a/src/Alpha_complex/utilities/alphacomplex.md +++ b/src/Alpha_complex/utilities/alphacomplex.md @@ -1,3 +1,13 @@ +---
+layout: page
+title: "Alpha complex"
+meta_title: "Alpha complex"
+teaser: ""
+permalink: /alphacomplex/
+---
+{::comment}
+Leave the lines above as it is required by the web site generator 'Jekyll'
+{:/comment}
# Alpha complex #
diff --git a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h index 969daba6..770eb55f 100644 --- a/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h +++ b/src/Bitmap_cubical_complex/include/gudhi/Bitmap_cubical_complex.h @@ -383,7 +383,7 @@ class Bitmap_cubical_complex : public T { std::vector<std::size_t> bdry = this->get_boundary_of_a_cell(sh); if (globalDbg) { std::cerr << "std::pair<Simplex_handle, Simplex_handle> endpoints( Simplex_handle sh )\n"; - std::cerr << "bdry.size() : " << bdry.size() << std::endl; + std::cerr << "bdry.size() : " << bdry.size() << "\n"; } // this method returns two first elements from the boundary of sh. if (bdry.size() < 2) diff --git a/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md b/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md index 6e1b2578..a1bb9007 100644 --- a/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md +++ b/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md @@ -1,3 +1,13 @@ +---
+layout: page
+title: "Cubical complex"
+meta_title: "Cubical complex"
+teaser: ""
+permalink: /cubicalcomplex/
+---
+{::comment}
+Leave the lines above as it is required by the web site generator 'Jekyll'
+{:/comment}
# Cubical complex#
diff --git a/src/Bottleneck_distance/utilities/bottleneckdistance.md b/src/Bottleneck_distance/utilities/bottleneckdistance.md index 526f5822..f2749acc 100644 --- a/src/Bottleneck_distance/utilities/bottleneckdistance.md +++ b/src/Bottleneck_distance/utilities/bottleneckdistance.md @@ -1,3 +1,13 @@ +---
+layout: page
+title: "Bottleneck distance"
+meta_title: "Bottleneck distance"
+teaser: ""
+permalink: /bottleneckdistance/
+---
+{::comment}
+Leave the lines above as it is required by the web site generator 'Jekyll'
+{:/comment}
# Bottleneck distance #
diff --git a/src/GudhUI/model/Model.h b/src/GudhUI/model/Model.h index fc284cc6..072d1185 100644 --- a/src/GudhUI/model/Model.h +++ b/src/GudhUI/model/Model.h @@ -74,7 +74,7 @@ class CGAL_geometric_flag_complex_wrapper { // std::cout << "size:" << vertices.size() << std::endl; for (std::size_t i = 0; i < vertices.size(); ++i) for (std::size_t j = i + 1; j < vertices.size(); ++j) - complex_.add_edge(Vertex_handle(vertices[i]), Vertex_handle(vertices[j])); + complex_.add_edge_without_blockers(Vertex_handle(vertices[i]), Vertex_handle(vertices[j])); } } diff --git a/src/GudhUI/utils/Critical_points.h b/src/GudhUI/utils/Critical_points.h index 2a18e079..e7b9ef31 100644 --- a/src/GudhUI/utils/Critical_points.h +++ b/src/GudhUI/utils/Critical_points.h @@ -79,7 +79,7 @@ template<typename SkBlComplex> class Critical_points { unsigned pos = 0; for (Edge e : edges) { std::cout << "edge " << pos++ << "/" << edges.size() << "\n"; - auto eh = filled_complex_.add_edge(e.first, e.second); + auto eh = filled_complex_.add_edge_without_blockers(e.first, e.second); int is_contractible(is_link_reducible(eh)); switch (is_contractible) { diff --git a/src/GudhUI/utils/K_nearest_builder.h b/src/GudhUI/utils/K_nearest_builder.h index 7be0a4f4..4000a331 100644 --- a/src/GudhUI/utils/K_nearest_builder.h +++ b/src/GudhUI/utils/K_nearest_builder.h @@ -81,7 +81,7 @@ template<typename SkBlComplex> class K_nearest_builder { for (auto it = ++search.begin(); it != search.end(); ++it) { Vertex_handle q(std::get<1>(it->first)); if (p != q && complex_.contains_vertex(p) && complex_.contains_vertex(q)) - complex_.add_edge(p, q); + complex_.add_edge_without_blockers(p, q); } } } diff --git a/src/GudhUI/utils/Rips_builder.h b/src/GudhUI/utils/Rips_builder.h index b22f4db6..59b4bee2 100644 --- a/src/GudhUI/utils/Rips_builder.h +++ b/src/GudhUI/utils/Rips_builder.h @@ -60,7 +60,7 @@ template<typename SkBlComplex> class Rips_builder { std::cout.flush(); for (auto q = p; ++q != vertices.end(); /**/) if (squared_eucl_distance(complex_.point(*p), complex_.point(*q)) < 4 * alpha * alpha) - complex_.add_edge(*p, *q); + complex_.add_edge_without_blockers(*p, *q); } std::cout << std::endl; } diff --git a/src/Nerve_GIC/include/gudhi/GIC.h b/src/Nerve_GIC/include/gudhi/GIC.h index 40ff7a4a..f5b67be6 100644 --- a/src/Nerve_GIC/include/gudhi/GIC.h +++ b/src/Nerve_GIC/include/gudhi/GIC.h @@ -23,6 +23,11 @@ #ifndef GIC_H_ #define GIC_H_ +#ifdef GUDHI_USE_TBB +#include <tbb/parallel_for.h> +#include <tbb/mutex.h> +#endif + #include <gudhi/Debug_utils.h> #include <gudhi/graph_simplicial_complex.h> #include <gudhi/reader_utils.h> @@ -99,9 +104,8 @@ class Cover_complex { int data_dimension; // dimension of input data. int n; // number of points. - std::map<int, double> func; // function used to compute the output simplicial complex. - std::map<int, double> - func_color; // function used to compute the colors of the nodes of the output simplicial complex. + std::vector<double> func; // function used to compute the output simplicial complex. + std::vector<double> func_color; // function used to compute the colors of the nodes of the output simplicial complex. bool functional_cover = false; // whether we use a cover with preimages of a function or not. Graph one_skeleton_OFF; // one-skeleton given by the input OFF file (if it exists). @@ -114,8 +118,8 @@ class Cover_complex { Persistence_diagram PD; std::vector<double> distribution; - std::map<int, std::vector<int> > - cover; // function associating to each data point its vectors of cover elements to which it belongs. + std::vector<std::vector<int> > + cover; // function associating to each data point the vector of cover elements to which it belongs. std::map<int, std::vector<int> > cover_back; // inverse of cover, in order to get the data points associated to a specific cover element. std::map<int, double> cover_std; // standard function (induced by func) used to compute the extended persistence @@ -140,8 +144,8 @@ class Cover_complex { // Point comparator struct Less { - Less(std::map<int, double> func) { Fct = func; } - std::map<int, double> Fct; + Less(std::vector<double> func) { Fct = func; } + std::vector<double> Fct; bool operator()(int a, int b) { if (Fct[a] == Fct[b]) return a < b; @@ -276,6 +280,7 @@ class Cover_complex { point_cloud.emplace_back(point.begin(), point.begin() + data_dimension); boost::add_vertex(one_skeleton_OFF); vertices.push_back(boost::add_vertex(one_skeleton)); + std::vector<int> dummy; dummy.clear(); cover.push_back(dummy); i++; } } @@ -430,17 +435,29 @@ class Cover_complex { if (distances.size() == 0) compute_pairwise_distances(distance); - // #pragma omp parallel for - for (int i = 0; i < N; i++) { - SampleWithoutReplacement(n, m, samples); - double hausdorff_dist = 0; - for (int j = 0; j < n; j++) { - double mj = distances[j][samples[0]]; - for (int k = 1; k < m; k++) mj = std::min(mj, distances[j][samples[k]]); - hausdorff_dist = std::max(hausdorff_dist, mj); + #ifdef GUDHI_USE_TBB + tbb::parallel_for(0, N, [&](int i){ + SampleWithoutReplacement(n, m, samples); + double hausdorff_dist = 0; + for (int j = 0; j < n; j++) { + double mj = distances[j][samples[0]]; + for (int k = 1; k < m; k++) mj = std::min(mj, distances[j][samples[k]]); + hausdorff_dist = std::max(hausdorff_dist, mj); + } + delta += hausdorff_dist / N; + }); + #else + for (int i = 0; i < N; i++) { + SampleWithoutReplacement(n, m, samples); + double hausdorff_dist = 0; + for (int j = 0; j < n; j++) { + double mj = distances[j][samples[0]]; + for (int k = 1; k < m; k++) mj = std::min(mj, distances[j][samples[k]]); + hausdorff_dist = std::max(hausdorff_dist, mj); + } + delta += hausdorff_dist / N; } - delta += hausdorff_dist / N; - } + #endif if (verbose) std::cout << "delta = " << delta << std::endl; set_graph_from_rips(delta, distance); @@ -465,7 +482,7 @@ class Cover_complex { while (std::getline(input, line)) { std::stringstream stream(line); stream >> f; - func.emplace(i, f); + func.push_back(f); i++; } functional_cover = true; @@ -479,7 +496,7 @@ class Cover_complex { * */ void set_function_from_coordinate(int k) { - for (int i = 0; i < n; i++) func.emplace(i, point_cloud[i][k]); + for (int i = 0; i < n; i++) func.push_back(point_cloud[i][k]); functional_cover = true; cover_name = "coordinate " + std::to_string(k); } @@ -492,7 +509,7 @@ class Cover_complex { */ template <class InputRange> void set_function_from_range(InputRange const& function) { - for (int i = 0; i < n; i++) func.emplace(i, function[i]); + for (int i = 0; i < n; i++) func.push_back(function[i]); functional_cover = true; } @@ -710,37 +727,69 @@ class Cover_complex { funcstd[i] = 0.5 * (u + v); } - if (verbose) std::cout << "Computing connected components..." << std::endl; - // #pragma omp parallel for - for (int i = 0; i < res; i++) { - // Compute connected components - Graph G = one_skeleton.create_subgraph(); - int num = preimages[i].size(); - std::vector<int> component(num); - for (int j = 0; j < num; j++) boost::add_vertex(index[vertices[preimages[i][j]]], G); - boost::connected_components(G, &component[0]); - int max = 0; - - // For each point in preimage - for (int j = 0; j < num; j++) { - // Update number of components in preimage - if (component[j] > max) max = component[j]; - - // Identify component with Cantor polynomial N^2 -> N - int identifier = (std::pow(i + component[j], 2) + 3 * i + component[j]) / 2; - - // Update covers - cover[preimages[i][j]].push_back(identifier); - cover_back[identifier].push_back(preimages[i][j]); - cover_fct[identifier] = i; - cover_std[identifier] = funcstd[i]; - cover_color[identifier].second += func_color[preimages[i][j]]; - cover_color[identifier].first += 1; - } + #ifdef GUDHI_USE_TBB + if (verbose) std::cout << "Computing connected components (parallelized)..." << std::endl; + tbb::parallel_for(0, res, [&](int i){ + // Compute connected components + Graph G = one_skeleton.create_subgraph(); + int num = preimages[i].size(); + std::vector<int> component(num); + for (int j = 0; j < num; j++) boost::add_vertex(index[vertices[preimages[i][j]]], G); + boost::connected_components(G, &component[0]); + int max = 0; + + // For each point in preimage + for (int j = 0; j < num; j++) { + // Update number of components in preimage + if (component[j] > max) max = component[j]; + + // Identify component with Cantor polynomial N^2 -> N + int identifier = ((i + component[j])*(i + component[j]) + 3 * i + component[j]) / 2; + + // Update covers + cover[preimages[i][j]].push_back(identifier); + cover_back[identifier].push_back(preimages[i][j]); + cover_fct[identifier] = i; + cover_std[identifier] = funcstd[i]; + cover_color[identifier].second += func_color[preimages[i][j]]; + cover_color[identifier].first += 1; + } - // Maximal dimension is total number of connected components - id += max + 1; - } + // Maximal dimension is total number of connected components + id += max + 1; + }); + #else + if (verbose) std::cout << "Computing connected components..." << std::endl; + for (int i = 0; i < res; i++) { + // Compute connected components + Graph G = one_skeleton.create_subgraph(); + int num = preimages[i].size(); + std::vector<int> component(num); + for (int j = 0; j < num; j++) boost::add_vertex(index[vertices[preimages[i][j]]], G); + boost::connected_components(G, &component[0]); + int max = 0; + + // For each point in preimage + for (int j = 0; j < num; j++) { + // Update number of components in preimage + if (component[j] > max) max = component[j]; + + // Identify component with Cantor polynomial N^2 -> N + int identifier = (std::pow(i + component[j], 2) + 3 * i + component[j]) / 2; + + // Update covers + cover[preimages[i][j]].push_back(identifier); + cover_back[identifier].push_back(preimages[i][j]); + cover_fct[identifier] = i; + cover_std[identifier] = funcstd[i]; + cover_color[identifier].second += func_color[preimages[i][j]]; + cover_color[identifier].first += 1; + } + + // Maximal dimension is total number of connected components + id += max + 1; + } + #endif maximal_dim = id - 1; for (std::map<int, std::pair<int, double> >::iterator iit = cover_color.begin(); iit != cover_color.end(); iit++) @@ -803,24 +852,46 @@ class Cover_complex { for (int j = 0; j < n; j++) mindist[j] = std::numeric_limits<double>::max(); // Compute the geodesic distances to subsamples with Dijkstra - // #pragma omp parallel for - for (int i = 0; i < m; i++) { - if (verbose) std::cout << "Computing geodesic distances to seed " << i << "..." << std::endl; - int seed = voronoi_subsamples[i]; - std::vector<double> dmap(n); - boost::dijkstra_shortest_paths( - one_skeleton, vertices[seed], - boost::weight_map(weight).distance_map(boost::make_iterator_property_map(dmap.begin(), index))); - - for (int j = 0; j < n; j++) - if (mindist[j] > dmap[j]) { - mindist[j] = dmap[j]; - if (cover[j].size() == 0) - cover[j].push_back(i); - else - cover[j][0] = i; - } - } + #ifdef GUDHI_USE_TBB + if (verbose) std::cout << "Computing geodesic distances (parallelized)..." << std::endl; + tbb::mutex coverMutex; tbb::mutex mindistMutex; + tbb::parallel_for(0, m, [&](int i){ + int seed = voronoi_subsamples[i]; + std::vector<double> dmap(n); + boost::dijkstra_shortest_paths( + one_skeleton, vertices[seed], + boost::weight_map(weight).distance_map(boost::make_iterator_property_map(dmap.begin(), index))); + + coverMutex.lock(); mindistMutex.lock(); + for (int j = 0; j < n; j++) + if (mindist[j] > dmap[j]) { + mindist[j] = dmap[j]; + if (cover[j].size() == 0) + cover[j].push_back(i); + else + cover[j][0] = i; + } + coverMutex.unlock(); mindistMutex.unlock(); + }); + #else + for (int i = 0; i < m; i++) { + if (verbose) std::cout << "Computing geodesic distances to seed " << i << "..." << std::endl; + int seed = voronoi_subsamples[i]; + std::vector<double> dmap(n); + boost::dijkstra_shortest_paths( + one_skeleton, vertices[seed], + boost::weight_map(weight).distance_map(boost::make_iterator_property_map(dmap.begin(), index))); + + for (int j = 0; j < n; j++) + if (mindist[j] > dmap[j]) { + mindist[j] = dmap[j]; + if (cover[j].size() == 0) + cover[j].push_back(i); + else + cover[j][0] = i; + } + } + #endif for (int i = 0; i < n; i++) { cover_back[cover[i][0]].push_back(i); @@ -860,7 +931,7 @@ class Cover_complex { while (std::getline(input, line)) { std::stringstream stream(line); stream >> f; - func_color.emplace(i, f); + func_color.push_back(f); i++; } color_name = color_file_name; @@ -873,7 +944,7 @@ class Cover_complex { * */ void set_color_from_coordinate(int k = 0) { - for (int i = 0; i < n; i++) func_color[i] = point_cloud[i][k]; + for (int i = 0; i < n; i++) func_color.push_back(point_cloud[i][k]); color_name = "coordinate "; color_name.append(std::to_string(k)); } @@ -885,7 +956,7 @@ class Cover_complex { * */ void set_color_from_vector(std::vector<double> color) { - for (unsigned int i = 0; i < color.size(); i++) func_color[i] = color[i]; + for (unsigned int i = 0; i < color.size(); i++) func_color.push_back(color[i]); } public: // Create a .dot file that can be compiled with neato to produce a .pdf file. @@ -1039,45 +1110,29 @@ class Cover_complex { minf = std::min(minf, it->second); } + // Build filtration for (auto const& simplex : simplices) { - // Add a simplex and a cone on it - std::vector<int> splx = simplex; - splx.push_back(-2); - st.insert_simplex_and_subfaces(splx); + std::vector<int> splx = simplex; splx.push_back(-2); + st.insert_simplex_and_subfaces(splx, -3); } - // Build filtration - for (auto simplex : st.complex_simplex_range()) { - double filta = std::numeric_limits<double>::lowest(); - double filts = filta; - bool ascending = true; - for (auto vertex : st.simplex_vertex_range(simplex)) { - if (vertex == -2) { - ascending = false; - continue; - } - filta = std::max(-2 + (cover_std[vertex] - minf) / (maxf - minf), filta); - filts = std::max(2 - (cover_std[vertex] - minf) / (maxf - minf), filts); - } - if (ascending) - st.assign_filtration(simplex, filta); - else - st.assign_filtration(simplex, filts); + for (std::map<int, double>::iterator it = cover_std.begin(); it != cover_std.end(); it++) { + int vertex = it->first; float val = it->second; + int vert[] = {vertex}; int edge[] = {vertex, -2}; + st.assign_filtration(st.find(vert), -2 + (val - minf)/(maxf - minf)); + st.assign_filtration(st.find(edge), 2 - (val - minf)/(maxf - minf)); } - int magic[] = {-2}; - st.assign_filtration(st.find(magic), -3); + st.make_filtration_non_decreasing(); // Compute PD - st.initialize_filtration(); - Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Gudhi::persistent_cohomology::Field_Zp> pcoh(st); - pcoh.init_coefficients(2); + Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Gudhi::persistent_cohomology::Field_Zp> pcoh(st); pcoh.init_coefficients(2); pcoh.compute_persistent_cohomology(); // Output PD int max_dim = st.dimension(); for (int i = 0; i < max_dim; i++) { std::vector<std::pair<double, double> > bars = pcoh.intervals_in_dimension(i); - int num_bars = bars.size(); + int num_bars = bars.size(); if(i == 0) num_bars -= 1; if(verbose) std::cout << num_bars << " interval(s) in dimension " << i << ":" << std::endl; for (int j = 0; j < num_bars; j++) { double birth = bars[j].first; @@ -1206,8 +1261,7 @@ class Cover_complex { } if (type == "Nerve") { - for(auto& simplex : cover) - simplices.push_back(simplex.second); + for(int i = 0; i < n; i++) simplices.push_back(cover[i]); std::sort(simplices.begin(), simplices.end()); std::vector<std::vector<int> >::iterator it = std::unique(simplices.begin(), simplices.end()); simplices.resize(std::distance(simplices.begin(), it)); diff --git a/src/Nerve_GIC/test/test_GIC.cpp b/src/Nerve_GIC/test/test_GIC.cpp index d633753c..e3067d35 100644 --- a/src/Nerve_GIC/test/test_GIC.cpp +++ b/src/Nerve_GIC/test/test_GIC.cpp @@ -39,6 +39,7 @@ BOOST_AUTO_TEST_CASE(check_nerve) { N.set_type("Nerve"); std::string cloud_file_name("data/cloud"); N.read_point_cloud(cloud_file_name); + N.set_color_from_coordinate(); std::string graph_file_name("data/graph"); N.set_graph_from_file(graph_file_name); std::string cover_file_name("data/cover"); @@ -58,6 +59,7 @@ BOOST_AUTO_TEST_CASE(check_GIC) { GIC.set_type("GIC"); std::string cloud_file_name("data/cloud"); GIC.read_point_cloud(cloud_file_name); + GIC.set_color_from_coordinate(); std::string graph_file_name("data/graph"); GIC.set_graph_from_file(graph_file_name); std::string cover_file_name("data/cover"); @@ -77,6 +79,7 @@ BOOST_AUTO_TEST_CASE(check_voronoiGIC) { GIC.set_type("GIC"); std::string cloud_file_name("data/cloud"); GIC.read_point_cloud(cloud_file_name); + GIC.set_color_from_coordinate(); std::string graph_file_name("data/graph"); GIC.set_graph_from_file(graph_file_name); GIC.set_cover_from_Voronoi(Gudhi::Euclidean_distance(), 2); diff --git a/src/Nerve_GIC/utilities/covercomplex.md b/src/Nerve_GIC/utilities/covercomplex.md index f33cb2e0..6d16d16f 100644 --- a/src/Nerve_GIC/utilities/covercomplex.md +++ b/src/Nerve_GIC/utilities/covercomplex.md @@ -1,3 +1,13 @@ +--- +layout: page +title: "Cover complex" +meta_title: "Cover complex" +teaser: "" +permalink: /covercomplex/ +--- +{::comment} +Leave the lines above as it is required by the web site generator 'Jekyll' +{:/comment} # Cover complex # diff --git a/src/Persistence_representations/test/CMakeLists.txt b/src/Persistence_representations/test/CMakeLists.txt index 335a71ef..ca1713c3 100644 --- a/src/Persistence_representations/test/CMakeLists.txt +++ b/src/Persistence_representations/test/CMakeLists.txt @@ -35,6 +35,11 @@ target_link_libraries(Read_persistence_from_file_test_unit ${Boost_UNIT_TEST_FRA gudhi_add_coverage_test(Read_persistence_from_file_test_unit) +add_executable ( kernels_unit kernels.cpp ) +target_link_libraries(kernels_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY}) + +gudhi_add_coverage_test(kernels_unit) + if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1) add_executable (Persistence_intervals_with_distances_test_unit persistence_intervals_with_distances_test.cpp ) target_link_libraries(Persistence_intervals_with_distances_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY}) diff --git a/src/Persistence_representations/test/kernels.cpp b/src/Persistence_representations/test/kernels.cpp new file mode 100644 index 00000000..9db19123 --- /dev/null +++ b/src/Persistence_representations/test/kernels.cpp @@ -0,0 +1,54 @@ +/* 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 Carrière + * + * 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/>. + */ + +#define BOOST_TEST_DYN_LINK +#define BOOST_TEST_MODULE "kernel" + +#include <boost/test/unit_test.hpp> +#include <cmath> // float comparison +#include <limits> +#include <string> +#include <vector> +#include <algorithm> // std::max +#include <gudhi/common_persistence_representations.h> +#include <gudhi/Weight_functions.h> +#include <gudhi/Persistence_weighted_gaussian.h> +#include <gudhi/Sliced_Wasserstein.h> +#include <gudhi/distance_functions.h> +#include <gudhi/reader_utils.h> + +using SW = Gudhi::Persistence_representations::Sliced_Wasserstein; +using PWG = Gudhi::Persistence_representations::Persistence_weighted_gaussian; + +BOOST_AUTO_TEST_CASE(check_PWG) { + Persistence_diagram v1, v2; v1.emplace_back(0,1); v2.emplace_back(0,2); + PWG pwg1(v1, 1.0, 1000, Gudhi::Persistence_representations::arctan_weight(1,1)); PWG pwgex1(v1, 1.0, -1, Gudhi::Persistence_representations::arctan_weight(1,1)); + PWG pwg2(v2, 1.0, 1000, Gudhi::Persistence_representations::arctan_weight(1,1)); PWG pwgex2(v2, 1.0, -1, Gudhi::Persistence_representations::arctan_weight(1,1)); + BOOST_CHECK(std::abs(pwg1.compute_scalar_product(pwg2) - pwgex1.compute_scalar_product(pwgex2)) <= 1e-1); +} + +BOOST_AUTO_TEST_CASE(check_SW) { + Persistence_diagram v1, v2; v1.emplace_back(0,1); v2.emplace_back(0,2); + SW sw1(v1, 1.0, 100); SW swex1(v1, 1.0, -1); + SW sw2(v2, 1.0, 100); SW swex2(v2, 1.0, -1); + BOOST_CHECK(std::abs(sw1.compute_scalar_product(sw2) - swex1.compute_scalar_product(swex2)) <= 1e-1); +} diff --git a/src/Persistent_cohomology/concept/FilteredComplex.h b/src/Persistent_cohomology/concept/FilteredComplex.h index c19698df..d6b662e9 100644 --- a/src/Persistent_cohomology/concept/FilteredComplex.h +++ b/src/Persistent_cohomology/concept/FilteredComplex.h @@ -31,7 +31,7 @@ struct FilteredComplex typedef unspecified Simplex_handle; /** \brief Key associated to each simplex. * - * Must be a signed integer type. */ + * Must be an integer type. */ typedef unspecified Simplex_key; /** \brief Type for the value of the filtration function. * @@ -67,8 +67,8 @@ struct FilteredComplex Simplex_key key ( Simplex_handle sh ); /** \brief Returns the simplex that has index idx in the filtration. * - * This is never called on null_key(). */ - Simplex_handle simplex ( Simplex_key idx ); + * This is only called on valid indices. */ + Simplex_handle simplex ( size_t idx ); /** \brief Assign a key to a simplex. */ void assign_key(Simplex_handle sh, Simplex_key key); diff --git a/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h b/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h index 4dbe82c7..3113a22c 100644 --- a/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h +++ b/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h @@ -162,6 +162,19 @@ persistence diagram with a family of field coefficients. Rips_complex/rips_distance_matrix_persistence.cpp</a> computes the Rips complex of a distance matrix and outputs its persistence diagram. +The file should contain square or lower triangular distance matrix with semicolons as separators. +The code do not check if it is dealing with a distance matrix. It is the user responsibility to provide a valid input. +Please refer to data/distance_matrix/lower_triangular_distance_matrix.csv for an example of a file. + +\li <a href="_rips_complex_2rips_correlation_matrix_persistence_8cpp-example.html"> +Rips_complex/rips_correlation_matrix_persistence.cpp</a> +computes the Rips complex of a correlation matrix and outputs its persistence diagram. + +Note that no check is performed if the matrix given as the input is a correlation matrix. +It is the user responsibility to ensure that this is the case. The input is to be given either as a square or a lower +triangular matrix. +Please refer to data/correlation_matrix/lower_triangular_correlation_matrix.csv for an example of a file. + \li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html"> Alpha_complex/alpha_complex_3d_persistence.cpp</a> computes the persistent homology with \f$\mathbb{Z}/2\mathbb{Z}\f$ coefficients of the alpha complex on points sampling from an OFF file. diff --git a/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h b/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h index e0a147b3..a8c9afa3 100644 --- a/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h +++ b/src/Persistent_cohomology/include/gudhi/Persistent_cohomology.h @@ -285,7 +285,7 @@ class Persistent_cohomology { } } cpx_->assign_key(sigma, cpx_->null_key()); - } else { // If ku == kv, same connected component: create a 1-cocycle class. + } else if (dim_max_ > 1) { // If ku == kv, same connected component: create a 1-cocycle class. create_cocycle(sigma, coeff_field_.multiplicative_identity(), coeff_field_.characteristic()); } } diff --git a/src/Rips_complex/doc/Intro_rips_complex.h b/src/Rips_complex/doc/Intro_rips_complex.h index 8c517516..5a551e60 100644 --- a/src/Rips_complex/doc/Intro_rips_complex.h +++ b/src/Rips_complex/doc/Intro_rips_complex.h @@ -29,28 +29,41 @@ namespace rips_complex { /** \defgroup rips_complex Rips complex * - * \author Clément Maria, Pawel Dlotko, Vincent Rouvreau + * \author Clément Maria, Pawel Dlotko, Vincent Rouvreau, Marc Glisse * * @{ * * \section ripsdefinition Rips complex definition * - * Rips_complex - * <a target="_blank" href="https://en.wikipedia.org/wiki/Vietoris%E2%80%93Rips_complex">(Wikipedia)</a> is a - * one skeleton graph that allows to construct a - * <a target="_blank" href="https://en.wikipedia.org/wiki/Simplicial_complex">simplicial complex</a> - * from it. - * The input can be a point cloud with a given distance function, or a distance matrix. - * - * The filtration value of each edge is computed from a user-given distance function, or directly from the distance - * matrix. + * The Vietoris-Rips complex + * <a target="_blank" href="https://en.wikipedia.org/wiki/Vietoris%E2%80%93Rips_complex">(Wikipedia)</a> + * is an abstract simplicial complex + * defined on a finite metric space, where each simplex corresponds to a subset + * of point whose diameter is smaller that some given threshold. + * Varying the threshold, we can also see the Rips complex as a filtration of + * the \f$(n-1)-\f$dimensional simplex, where the filtration value of each + * simplex is the diameter of the corresponding subset of points. + * + * This filtered complex is most often used as an approximation of the + * ÄŒech complex. After rescaling (Rips using the length of the edges and ÄŒech + * the half-length), they share the same 1-skeleton and are multiplicatively + * 2-interleaved or better. While it is slightly bigger, it is also much + * easier to compute. + * + * The number of simplices in the full Rips complex is exponential in the + * number of vertices, it is thus usually restricted, by excluding all the + * simplices with filtration value larger than some threshold, and keeping only + * the dim_max-skeleton. + * + * In order to build this complex, the algorithm first builds the graph. + * The filtration value of each edge is computed from a user-given distance + * function, or directly read from the distance matrix. + * In a second step, this graph is inserted in a simplicial complex, which then + * gets expanded to a flag complex. * - * All edges that have a filtration value strictly greater than a given threshold value are not inserted into - * the complex. + * The input can be given as a range of points and a distance function, or as a + * distance matrix. * - * When creating a simplicial complex from this one skeleton graph, Rips inserts the one skeleton graph into the data - * structure, and then expands the simplicial complex when required. - * * Vertex name correspond to the index of the point in the given range (aka. the point cloud). * * \image html "rips_complex_representation.png" "Rips-complex one skeleton graph representation" @@ -61,7 +74,36 @@ namespace rips_complex { * * If the Rips_complex interfaces are not detailed enough for your need, please refer to * <a href="_persistent_cohomology_2rips_persistence_step_by_step_8cpp-example.html"> - * rips_persistence_step_by_step.cpp</a> example, where the graph construction over the Simplex_tree is more detailed. + * rips_persistence_step_by_step.cpp</a> example, where the constructions of the graph and + * the Simplex_tree are more detailed. + * + * \section sparserips Sparse Rips complex + * + * Even truncated in filtration value and dimension, the Rips complex remains + * quite large. However, it is possible to approximate it by a much smaller + * filtered simplicial complex (linear size, with constants that depend on + * ε and the doubling dimension of the space) that is + * \f$(1+O(\epsilon))-\f$interleaved with it (in particular, their persistence + * diagrams are at log-bottleneck distance at most \f$O(\epsilon)\f$). + * + * The sparse Rips filtration was introduced by Don Sheehy + * \cite sheehy13linear. We are using the version described in + * \cite buchet16efficient (except that we multiply all filtration values + * by 2, to match the usual Rips complex), which proves a + * \f$\frac{1+\epsilon}{1-\epsilon}\f$-interleaving, although in practice the + * error is usually smaller. + * A more intuitive presentation of the idea is available in + * \cite cavanna15geometric, and in a video \cite cavanna15visualizing. + * + * The interface of `Sparse_rips_complex` is similar to the one for the usual + * `Rips_complex`, except that one has to specify the approximation factor, and + * there is no option to limit the maximum filtration value (the way the + * approximation is done means that larger filtration values are much cheaper + * to handle than low filtration values, so the gain would be too small). + * + * Theoretical guarantees are only available for \f$\epsilon<1\f$. The + * construction accepts larger values of ε, and the size of the complex + * keeps decreasing, but there is no guarantee on the quality of the result. * * \section ripspointsdistance Point cloud and distance function * @@ -104,6 +146,24 @@ namespace rips_complex { * * \include Rips_complex/full_skeleton_rips_for_doc.txt * + * + * \subsection sparseripspointscloudexample Example of a sparse Rips from a point cloud + * + * This example builds the full sparse Rips of a set of 2D Euclidean points, then prints some minimal + * information about the complex. + * + * \include Rips_complex/example_sparse_rips.cpp + * + * When launching: + * + * \code $> ./Rips_complex_example_sparse + * \endcode + * + * the program output may be (the exact output varies from one run to the next): + * + * \code Sparse Rips complex is of dimension 2 - 19 simplices - 7 vertices. + * \endcode + * * * * \section ripsdistancematrix Distance matrix @@ -146,6 +206,33 @@ namespace rips_complex { * * \include Rips_complex/full_skeleton_rips_for_doc.txt * + * + * \section ripscorrelationematrix Correlation matrix + * + * Analogously to the case of distance matrix, Rips complexes can be also constructed based on correlation matrix. + * Given a correlation matrix M, comportment-wise 1-M is a distance matrix. + * This example builds the one skeleton graph from the given corelation matrix and threshold value. + * Then it creates a `Simplex_tree` with it. + * + * Then, it is asked to display information about the simplicial complex. + * + * \include Rips_complex/example_one_skeleton_rips_from_correlation_matrix.cpp + * + * When launching: + * + * \code $> ./example_one_skeleton_from_correlation_matrix + * \endcode + * + * the program output is: + * + * \include Rips_complex/one_skeleton_rips_from_correlation_matrix_for_doc.txt + * + * All the other constructions discussed for Rips complex for distance matrix can be also performed for Rips complexes + * construction from correlation matrices. + * + * @warning As persistence diagrams points will be under the diagonal, bottleneck distance and persistence graphical + * tool will not work properly, this is a known issue. + * */ /** @} */ // end defgroup rips_complex diff --git a/src/Rips_complex/example/CMakeLists.txt b/src/Rips_complex/example/CMakeLists.txt index 2940f164..af86636b 100644 --- a/src/Rips_complex/example/CMakeLists.txt +++ b/src/Rips_complex/example/CMakeLists.txt @@ -11,17 +11,29 @@ add_executable ( Rips_complex_example_one_skeleton_from_distance_matrix example_ add_executable ( Rips_complex_example_from_csv_distance_matrix example_rips_complex_from_csv_distance_matrix_file.cpp ) +# Sparse rips from points +add_executable ( Rips_complex_example_sparse example_sparse_rips.cpp ) + +# Correlation matrix +add_executable ( Rips_complex_example_one_skeleton_rips_from_correlation_matrix example_one_skeleton_rips_from_correlation_matrix.cpp ) + if (TBB_FOUND) target_link_libraries(Rips_complex_example_from_off ${TBB_LIBRARIES}) target_link_libraries(Rips_complex_example_one_skeleton_from_points ${TBB_LIBRARIES}) target_link_libraries(Rips_complex_example_one_skeleton_from_distance_matrix ${TBB_LIBRARIES}) target_link_libraries(Rips_complex_example_from_csv_distance_matrix ${TBB_LIBRARIES}) + target_link_libraries(Rips_complex_example_sparse ${TBB_LIBRARIES}) + target_link_libraries(Rips_complex_example_one_skeleton_rips_from_correlation_matrix ${TBB_LIBRARIES}) endif() add_test(NAME Rips_complex_example_one_skeleton_from_points COMMAND $<TARGET_FILE:Rips_complex_example_one_skeleton_from_points>) add_test(NAME Rips_complex_example_one_skeleton_from_distance_matrix COMMAND $<TARGET_FILE:Rips_complex_example_one_skeleton_from_distance_matrix>) +add_test(NAME Rips_complex_example_sparse + COMMAND $<TARGET_FILE:Rips_complex_example_sparse>) +add_test(NAME Rips_complex_example_one_skeleton_rips_from_correlation_matrix + COMMAND $<TARGET_FILE:Rips_complex_example_one_skeleton_rips_from_correlation_matrix>) add_test(NAME Rips_complex_example_from_off_doc_12_1 COMMAND $<TARGET_FILE:Rips_complex_example_from_off> "${CMAKE_SOURCE_DIR}/data/points/alphacomplexdoc.off" "12.0" "1" "${CMAKE_CURRENT_BINARY_DIR}/ripsoffreader_result_12_1.txt") @@ -57,3 +69,4 @@ install(TARGETS Rips_complex_example_from_off DESTINATION bin) install(TARGETS Rips_complex_example_one_skeleton_from_points DESTINATION bin) install(TARGETS Rips_complex_example_one_skeleton_from_distance_matrix DESTINATION bin) install(TARGETS Rips_complex_example_from_csv_distance_matrix DESTINATION bin) +install(TARGETS Rips_complex_example_one_skeleton_rips_from_correlation_matrix DESTINATION bin) diff --git a/src/Rips_complex/example/example_one_skeleton_rips_from_correlation_matrix.cpp b/src/Rips_complex/example/example_one_skeleton_rips_from_correlation_matrix.cpp new file mode 100644 index 00000000..1343f24d --- /dev/null +++ b/src/Rips_complex/example/example_one_skeleton_rips_from_correlation_matrix.cpp @@ -0,0 +1,81 @@ +#include <gudhi/Rips_complex.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/distance_functions.h> + +#include <iostream> +#include <string> +#include <vector> +#include <limits> // for std::numeric_limits + +int main() { + // Type definitions + using Simplex_tree = Gudhi::Simplex_tree<>; + using Filtration_value = Simplex_tree::Filtration_value; + using Rips_complex = Gudhi::rips_complex::Rips_complex<Filtration_value>; + using Distance_matrix = std::vector<std::vector<Filtration_value>>; + + // User defined correlation matrix is: + // |1 0.06 0.23 0.01 0.89| + // |0.06 1 0.74 0.01 0.61| + // |0.23 0.74 1 0.72 0.03| + // |0.01 0.01 0.72 1 0.7 | + // |0.89 0.61 0.03 0.7 1 | + + Distance_matrix correlations; + correlations.push_back({}); + correlations.push_back({0.06}); + correlations.push_back({0.23, 0.74}); + correlations.push_back({0.01, 0.01, 0.72}); + correlations.push_back({0.89, 0.61, 0.03, 0.7}); + + // ---------------------------------------------------------------------------- + // Convert correlation matrix to a distance matrix: + // ---------------------------------------------------------------------------- + double threshold = 0; + for (size_t i = 0; i != correlations.size(); ++i) { + for (size_t j = 0; j != correlations[i].size(); ++j) { + // Here we check if our data comes from corelation matrix. + if ((correlations[i][j] < -1) || (correlations[i][j] > 1)) { + std::cerr << "The input matrix is not a correlation matrix. The program will now terminate.\n"; + throw "The input matrix is not a correlation matrix. The program will now terminate.\n"; + } + correlations[i][j] = 1 - correlations[i][j]; + // Here we make sure that we will get the treshold value equal to maximal + // distance in the matrix. + if (correlations[i][j] > threshold) threshold = correlations[i][j]; + } + } + + //----------------------------------------------------------------------------- + // Now the correlation matrix is a distance matrix and can be processed further. + //----------------------------------------------------------------------------- + Distance_matrix distances = correlations; + + Rips_complex rips_complex_from_points(distances, threshold); + + Simplex_tree stree; + rips_complex_from_points.create_complex(stree, 1); + // ---------------------------------------------------------------------------- + // Display information about the one skeleton Rips complex. Note that + // the filtration displayed here comes from the distance matrix computed + // above, which is 1 - initial correlation matrix. Only this way, we obtain + // a complex with filtration. If a correlation matrix is used instead, we would + // have a reverse filtration (i.e. filtration of boundary of each simplex S + // is greater or equal to the filtration of S). + // ---------------------------------------------------------------------------- + std::cout << "Rips complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - " + << stree.num_vertices() << " vertices." << std::endl; + + std::cout << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; + for (auto f_simplex : stree.filtration_simplex_range()) { + std::cout << " ( "; + for (auto vertex : stree.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << ") -> " + << "[" << stree.filtration(f_simplex) << "] "; + std::cout << std::endl; + } + + return 0; +} diff --git a/src/Rips_complex/example/example_sparse_rips.cpp b/src/Rips_complex/example/example_sparse_rips.cpp new file mode 100644 index 00000000..1c95b48c --- /dev/null +++ b/src/Rips_complex/example/example_sparse_rips.cpp @@ -0,0 +1,30 @@ +#include <gudhi/Sparse_rips_complex.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/distance_functions.h> + +#include <iostream> +#include <vector> + +int main() { + using Point = std::vector<double>; + using Simplex_tree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; + using Filtration_value = Simplex_tree::Filtration_value; + using Sparse_rips = Gudhi::rips_complex::Sparse_rips_complex<Filtration_value>; + + Point points[] = {{1.0, 1.0}, {7.0, 0.0}, {4.0, 6.0}, {9.0, 6.0}, {0.0, 14.0}, {2.0, 19.0}, {9.0, 17.0}}; + + // ---------------------------------------------------------------------------- + // Init from Euclidean points + // ---------------------------------------------------------------------------- + double epsilon = 2; // very rough, no guarantees + Sparse_rips sparse_rips(points, Gudhi::Euclidean_distance(), epsilon); + + Simplex_tree stree; + sparse_rips.create_complex(stree, 10); + + // ---------------------------------------------------------------------------- + // Display information about the complex + // ---------------------------------------------------------------------------- + std::cout << "Sparse Rips complex is of dimension " << stree.dimension() << " - " << stree.num_simplices() + << " simplices - " << stree.num_vertices() << " vertices." << std::endl; +} diff --git a/src/Rips_complex/example/one_skeleton_rips_from_correlation_matrix_for_doc.txt b/src/Rips_complex/example/one_skeleton_rips_from_correlation_matrix_for_doc.txt new file mode 100644 index 00000000..640d7083 --- /dev/null +++ b/src/Rips_complex/example/one_skeleton_rips_from_correlation_matrix_for_doc.txt @@ -0,0 +1,17 @@ +Rips complex is of dimension 1 - 15 simplices - 5 vertices. +Iterator on Rips complex simplices in the filtration order, with [filtration value]: + ( 0 ) -> [0] + ( 1 ) -> [0] + ( 2 ) -> [0] + ( 3 ) -> [0] + ( 4 ) -> [0] + ( 4 0 ) -> [0.11] + ( 2 1 ) -> [0.26] + ( 3 2 ) -> [0.28] + ( 4 3 ) -> [0.3] + ( 4 1 ) -> [0.39] + ( 2 0 ) -> [0.77] + ( 1 0 ) -> [0.94] + ( 4 2 ) -> [0.97] + ( 3 0 ) -> [0.99] + ( 3 1 ) -> [0.99] diff --git a/src/Rips_complex/include/gudhi/Sparse_rips_complex.h b/src/Rips_complex/include/gudhi/Sparse_rips_complex.h new file mode 100644 index 00000000..1a9d6ebb --- /dev/null +++ b/src/Rips_complex/include/gudhi/Sparse_rips_complex.h @@ -0,0 +1,175 @@ +/* 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): Marc Glisse + * + * 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 SPARSE_RIPS_COMPLEX_H_ +#define SPARSE_RIPS_COMPLEX_H_ + +#include <gudhi/Debug_utils.h> +#include <gudhi/graph_simplicial_complex.h> +#include <gudhi/choose_n_farthest_points.h> + +#include <boost/graph/adjacency_list.hpp> +#include <boost/range/metafunctions.hpp> + +#include <vector> + +namespace Gudhi { + +namespace rips_complex { + +// The whole interface is copied on Rips_complex. A redesign should be discussed with all complex creation classes in +// mind. + +/** + * \class Sparse_rips_complex + * \brief Sparse Rips complex data structure. + * + * \ingroup rips_complex + * + * \details + * This class is used to construct a sparse \f$(1+O(\epsilon))\f$-approximation of `Rips_complex`, i.e. a filtered + * simplicial complex that is multiplicatively \f$(1+O(\epsilon))\f$-interleaved with the Rips filtration. + * + * \tparam Filtration_value is the type used to store the filtration values of the simplicial complex. + */ +template <typename Filtration_value> +class Sparse_rips_complex { + private: + // TODO: use a different graph where we know we can safely insert in parallel. + typedef typename boost::adjacency_list<boost::vecS, boost::vecS, boost::undirectedS, + boost::property<vertex_filtration_t, Filtration_value>, + boost::property<edge_filtration_t, Filtration_value>> + Graph; + + typedef int Vertex_handle; + + public: + /** \brief Sparse_rips_complex constructor from a list of points. + * + * @param[in] points Range of points. + * @param[in] distance Distance function that returns a `Filtration_value` from 2 given points. + * @param[in] epsilon Approximation parameter. epsilon must be positive. + * + */ + template <typename RandomAccessPointRange, typename Distance> + Sparse_rips_complex(const RandomAccessPointRange& points, Distance distance, double epsilon) { + GUDHI_CHECK(epsilon > 0, "epsilon must be positive"); + std::vector<Vertex_handle> sorted_points; + std::vector<Filtration_value> params; + auto dist_fun = [&](Vertex_handle i, Vertex_handle j) { return distance(points[i], points[j]); }; + Ker<decltype(dist_fun)> kernel(dist_fun); + subsampling::choose_n_farthest_points(kernel, boost::irange<Vertex_handle>(0, boost::size(points)), -1, -1, + std::back_inserter(sorted_points), std::back_inserter(params)); + compute_sparse_graph(sorted_points, params, dist_fun, epsilon); + } + + /** \brief Sparse_rips_complex constructor from a distance matrix. + * + * @param[in] distance_matrix Range of range of distances. + * `distance_matrix[i][j]` returns the distance between points \f$i\f$ and + * \f$j\f$ as long as \f$ 0 \leqslant i < j \leqslant + * distance\_matrix.size().\f$ + * @param[in] epsilon Approximation parameter. epsilon must be positive. + */ + template <typename DistanceMatrix> + Sparse_rips_complex(const DistanceMatrix& distance_matrix, double epsilon) + : Sparse_rips_complex(boost::irange<Vertex_handle>(0, boost::size(distance_matrix)), + [&](Vertex_handle i, Vertex_handle j) { return distance_matrix[j][i]; }, epsilon) {} + + /** \brief Fills the simplicial complex with the sparse Rips graph and + * expands it with all the cliques, stopping at a given maximal dimension. + * + * \tparam SimplicialComplexForRips must meet `SimplicialComplexForRips` concept. + * + * @param[in] complex the complex to fill + * @param[in] dim_max maximal dimension of the simplicial complex. + * @exception std::invalid_argument In debug mode, if `complex.num_vertices()` does not return 0. + * + */ + template <typename SimplicialComplexForRips> + void create_complex(SimplicialComplexForRips& complex, int dim_max) { + GUDHI_CHECK(complex.num_vertices() == 0, + std::invalid_argument("Sparse_rips_complex::create_complex - simplicial complex is not empty")); + + complex.insert_graph(graph_); + complex.expansion(dim_max); + } + + private: + // choose_n_farthest_points wants the distance function in this form... + template <class Distance> + struct Ker { + typedef std::size_t Point_d; // index into point range + Ker(Distance& d) : dist(d) {} + // Despite the name, this is not squared... + typedef Distance Squared_distance_d; + Squared_distance_d& squared_distance_d_object() const { return dist; } + Distance& dist; + }; + + // PointRange must be random access. + template <typename PointRange, typename ParamRange, typename Distance> + void compute_sparse_graph(const PointRange& points, const ParamRange& params, Distance& dist, double epsilon) { + const int n = boost::size(points); + graph_.~Graph(); + new (&graph_) Graph(n); + // for(auto v : vertices(g)) // doesn't work :-( + typename boost::graph_traits<Graph>::vertex_iterator v_i, v_e; + for (std::tie(v_i, v_e) = vertices(graph_); v_i != v_e; ++v_i) { + auto v = *v_i; + // This whole loop might not be necessary, leave it until someone investigates if it is safe to remove. + put(vertex_filtration_t(), graph_, v, 0); + } + + // TODO: + // - make it parallel + // - only test near-enough neighbors + for (int i = 0; i < n; ++i) + for (int j = i + 1; j < n; ++j) { + auto&& pi = points[i]; + auto&& pj = points[j]; + auto d = dist(pi, pj); + auto li = params[i]; + auto lj = params[j]; + GUDHI_CHECK(lj <= li, "Bad furthest point sorting"); + Filtration_value alpha; + + // The paper has d/2 and d-lj/e to match the Cech, but we use doubles to match the Rips + if (d * epsilon <= 2 * lj) + alpha = d; + else if (d * epsilon <= li + lj && (epsilon >= 1 || d * epsilon <= lj * (1 + 1 / (1 - epsilon)))) + alpha = (d - lj / epsilon) * 2; + else + continue; + + add_edge(pi, pj, alpha, graph_); + } + } + + Graph graph_; +}; + +} // namespace rips_complex + +} // namespace Gudhi + +#endif // SPARSE_RIPS_COMPLEX_H_ diff --git a/src/Rips_complex/test/test_rips_complex.cpp b/src/Rips_complex/test/test_rips_complex.cpp index 89afbc25..4e7b79d2 100644 --- a/src/Rips_complex/test/test_rips_complex.cpp +++ b/src/Rips_complex/test/test_rips_complex.cpp @@ -31,6 +31,7 @@ #include <algorithm> // std::max #include <gudhi/Rips_complex.h> +#include <gudhi/Sparse_rips_complex.h> // to construct Rips_complex from a OFF file of points #include <gudhi/Points_off_io.h> #include <gudhi/Simplex_tree.h> @@ -43,6 +44,7 @@ using Point = std::vector<double>; using Simplex_tree = Gudhi::Simplex_tree<>; using Filtration_value = Simplex_tree::Filtration_value; using Rips_complex = Gudhi::rips_complex::Rips_complex<Simplex_tree::Filtration_value>; +using Sparse_rips_complex = Gudhi::rips_complex::Sparse_rips_complex<Simplex_tree::Filtration_value>; using Distance_matrix = std::vector<std::vector<Filtration_value>>; BOOST_AUTO_TEST_CASE(RIPS_DOC_OFF_file) { @@ -230,6 +232,71 @@ BOOST_AUTO_TEST_CASE(Rips_complex_from_points) { } } +BOOST_AUTO_TEST_CASE(Sparse_rips_complex_from_points) { + // This is a clone of the test above + // ---------------------------------------------------------------------------- + // Init of a list of points + // ---------------------------------------------------------------------------- + Vector_of_points points; + std::vector<double> coords = { 0.0, 0.0, 0.0, 1.0 }; + points.push_back(Point(coords.begin(), coords.end())); + coords = { 0.0, 0.0, 1.0, 0.0 }; + points.push_back(Point(coords.begin(), coords.end())); + coords = { 0.0, 1.0, 0.0, 0.0 }; + points.push_back(Point(coords.begin(), coords.end())); + coords = { 1.0, 0.0, 0.0, 0.0 }; + points.push_back(Point(coords.begin(), coords.end())); + + // ---------------------------------------------------------------------------- + // Init of a Rips complex from the list of points + // ---------------------------------------------------------------------------- + // .001 is small enough that we get a deterministic result matching the exact Rips + Sparse_rips_complex sparse_rips(points, Custom_square_euclidean_distance(), .001); + + std::cout << "========== Sparse_rips_complex_from_points ==========" << std::endl; + Simplex_tree st; + const int DIMENSION = 3; + sparse_rips.create_complex(st, DIMENSION); + + // Another way to check num_simplices + std::cout << "Iterator on Rips complex simplices in the filtration order, with [filtration value]:" << std::endl; + int num_simplices = 0; + for (auto f_simplex : st.filtration_simplex_range()) { + num_simplices++; + std::cout << " ( "; + for (auto vertex : st.simplex_vertex_range(f_simplex)) { + std::cout << vertex << " "; + } + std::cout << ") -> " << "[" << st.filtration(f_simplex) << "] "; + std::cout << std::endl; + } + BOOST_CHECK(num_simplices == 15); + std::cout << "st.num_simplices()=" << st.num_simplices() << std::endl; + BOOST_CHECK(st.num_simplices() == 15); + + std::cout << "st.dimension()=" << st.dimension() << std::endl; + BOOST_CHECK(st.dimension() == DIMENSION); + std::cout << "st.num_vertices()=" << st.num_vertices() << std::endl; + BOOST_CHECK(st.num_vertices() == 4); + + for (auto f_simplex : st.filtration_simplex_range()) { + std::cout << "dimension(" << st.dimension(f_simplex) << ") - f = " << st.filtration(f_simplex) << std::endl; + switch (st.dimension(f_simplex)) { + case 0: + GUDHI_TEST_FLOAT_EQUALITY_CHECK(st.filtration(f_simplex), 0.0); + break; + case 1: + case 2: + case 3: + GUDHI_TEST_FLOAT_EQUALITY_CHECK(st.filtration(f_simplex), 2.0); + break; + default: + BOOST_CHECK(false); // Shall not happen + break; + } + } +} + BOOST_AUTO_TEST_CASE(Rips_doc_csv_file) { // ---------------------------------------------------------------------------- // diff --git a/src/Rips_complex/utilities/CMakeLists.txt b/src/Rips_complex/utilities/CMakeLists.txt index baa571fa..deb73ff0 100644 --- a/src/Rips_complex/utilities/CMakeLists.txt +++ b/src/Rips_complex/utilities/CMakeLists.txt @@ -7,15 +7,29 @@ target_link_libraries(rips_distance_matrix_persistence ${Boost_PROGRAM_OPTIONS_L add_executable(rips_persistence rips_persistence.cpp) target_link_libraries(rips_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) +add_executable(rips_correlation_matrix_persistence rips_correlation_matrix_persistence.cpp) +target_link_libraries(rips_correlation_matrix_persistence ${Boost_SYSTEM_LIBRARY} ${Boost_PROGRAM_OPTIONS_LIBRARY}) + +add_executable(sparse_rips_persistence sparse_rips_persistence.cpp) +target_link_libraries(sparse_rips_persistence ${Boost_PROGRAM_OPTIONS_LIBRARY}) + if (TBB_FOUND) target_link_libraries(rips_distance_matrix_persistence ${TBB_LIBRARIES}) target_link_libraries(rips_persistence ${TBB_LIBRARIES}) + target_link_libraries(rips_correlation_matrix_persistence ${TBB_LIBRARIES}) + target_link_libraries(sparse_rips_persistence ${TBB_LIBRARIES}) endif() add_test(NAME Rips_complex_utility_from_rips_distance_matrix COMMAND $<TARGET_FILE:rips_distance_matrix_persistence> "${CMAKE_SOURCE_DIR}/data/distance_matrix/full_square_distance_matrix.csv" "-r" "1.0" "-d" "3" "-p" "3" "-m" "0") add_test(NAME Rips_complex_utility_from_rips_on_tore_3D COMMAND $<TARGET_FILE:rips_persistence> "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "-r" "0.25" "-m" "0.5" "-d" "3" "-p" "3") +add_test(NAME Rips_complex_utility_from_rips_correlation_matrix COMMAND $<TARGET_FILE:rips_correlation_matrix_persistence> + "${CMAKE_SOURCE_DIR}/data/correlation_matrix/lower_triangular_correlation_matrix.csv" "-c" "0.3" "-d" "3" "-p" "3" "-m" "0") +add_test(NAME Sparse_rips_complex_utility_on_tore_3D COMMAND $<TARGET_FILE:sparse_rips_persistence> + "${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off" "-e" "0.5" "-m" "0.2" "-d" "3" "-p" "2") install(TARGETS rips_distance_matrix_persistence DESTINATION bin) install(TARGETS rips_persistence DESTINATION bin) +install(TARGETS rips_correlation_matrix_persistence DESTINATION bin) +install(TARGETS sparse_rips_persistence DESTINATION bin) diff --git a/src/Rips_complex/utilities/rips_correlation_matrix_persistence.cpp b/src/Rips_complex/utilities/rips_correlation_matrix_persistence.cpp new file mode 100644 index 00000000..c2082fae --- /dev/null +++ b/src/Rips_complex/utilities/rips_correlation_matrix_persistence.cpp @@ -0,0 +1,170 @@ +/* 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): Pawel Dlotko, Vincent Rouvreau + * + * Copyright (C) 2016 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/>. + */ + +#include <gudhi/Rips_complex.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/Persistent_cohomology.h> +#include <gudhi/reader_utils.h> +#include <gudhi/writing_persistence_to_file.h> + +#include <boost/program_options.hpp> + +#include <string> +#include <vector> +#include <limits> // infinity + +// Types definition +using Simplex_tree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; +using Filtration_value = Simplex_tree::Filtration_value; +using Rips_complex = Gudhi::rips_complex::Rips_complex<Filtration_value>; +using Field_Zp = Gudhi::persistent_cohomology::Field_Zp; +using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Field_Zp>; +using Correlation_matrix = std::vector<std::vector<Filtration_value>>; +using intervals_common = Gudhi::Persistence_interval_common<double, int>; + +void program_options(int argc, char* argv[], std::string& csv_matrix_file, std::string& filediag, + Filtration_value& correlation_min, int& dim_max, int& p, Filtration_value& min_persistence); + +int main(int argc, char* argv[]) { + std::string csv_matrix_file; + std::string filediag; + Filtration_value correlation_min; + int dim_max; + int p; + Filtration_value min_persistence; + + program_options(argc, argv, csv_matrix_file, filediag, correlation_min, dim_max, p, min_persistence); + + Correlation_matrix correlations = + Gudhi::read_lower_triangular_matrix_from_csv_file<Filtration_value>(csv_matrix_file); + + Filtration_value threshold = 0; + + // Given a correlation matrix M, we compute component-wise M'[i,j] = 1-M[i,j] to get a distance matrix: + for (size_t i = 0; i != correlations.size(); ++i) { + for (size_t j = 0; j != correlations[i].size(); ++j) { + correlations[i][j] = 1 - correlations[i][j]; + // Here we make sure that the values of corelations lie between -1 and 1. + // If not, we throw an exception. + if ((correlations[i][j] < -1) || (correlations[i][j] > 1)) { + std::cerr << "The input matrix is not a correlation matrix. The program will now terminate. \n"; + throw "The input matrix is not a correlation matrix. The program will now terminate. \n"; + } + if (correlations[i][j] > threshold) threshold = correlations[i][j]; + } + } + + Rips_complex rips_complex_from_file(correlations, threshold); + + // Construct the Rips complex in a Simplex Tree + Simplex_tree simplex_tree; + + rips_complex_from_file.create_complex(simplex_tree, dim_max); + std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; + + // Sort the simplices in the order of the filtration + simplex_tree.initialize_filtration(); + + // Compute the persistence diagram of the complex + Persistent_cohomology pcoh(simplex_tree); + // initializes the coefficient field for homology + pcoh.init_coefficients(p); + // compute persistence + pcoh.compute_persistent_cohomology(min_persistence); + + // invert the persistence diagram. The reason for this procedure is the following: + // The input to the program is a corelation matrix M. When processing it, it is + // turned into 1-M and the obtained persistence intervals are in '1-M' units. + // Below we reverse every (birth,death) pair into (1-birth, 1-death) pair + // so that the input and the output to the program is expressed in the same + // units. + auto pairs = pcoh.get_persistent_pairs(); + std::vector<intervals_common> processed_persistence_intervals; + processed_persistence_intervals.reserve(pairs.size()); + for (auto pair : pairs) { + double birth = 1 - simplex_tree.filtration(get<0>(pair)); + double death = 1 - simplex_tree.filtration(get<1>(pair)); + unsigned dimension = (unsigned)simplex_tree.dimension(get<0>(pair)); + int field = get<2>(pair); + processed_persistence_intervals.push_back(intervals_common(birth, death, dimension, field)); + } + + // sort the processed intervals: + std::sort(processed_persistence_intervals.begin(), processed_persistence_intervals.end()); + + // and write them to a file + if (filediag.empty()) { + write_persistence_intervals_to_stream(processed_persistence_intervals); + } else { + std::ofstream out(filediag); + write_persistence_intervals_to_stream(processed_persistence_intervals, out); + } + return 0; +} + +void program_options(int argc, char* argv[], std::string& csv_matrix_file, std::string& filediag, + Filtration_value& correlation_min, int& dim_max, int& p, Filtration_value& min_persistence) { + namespace po = boost::program_options; + po::options_description hidden("Hidden options"); + hidden.add_options()( + "input-file", po::value<std::string>(&csv_matrix_file), + "Name of file containing a corelation matrix. Can be square or lower triangular matrix. Separator is ';'."); + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "min-edge-corelation,c", po::value<Filtration_value>(&correlation_min)->default_value(0), + "Minimal corelation of an edge for the Rips complex construction.")( + "cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), + "Maximal dimension of the Rips complex we want to compute.")( + "field-charac,p", po::value<int>(&p)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map vm; + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); + po::notify(vm); + + if (vm.count("help") || !vm.count("input-file")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a Rips complex defined on a corelation matrix.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/src/Rips_complex/utilities/ripscomplex.md b/src/Rips_complex/utilities/ripscomplex.md index 4291fae7..84f40cc1 100644 --- a/src/Rips_complex/utilities/ripscomplex.md +++ b/src/Rips_complex/utilities/ripscomplex.md @@ -1,3 +1,13 @@ +--- +layout: page +title: "Rips complex" +meta_title: "Rips complex" +teaser: "" +permalink: /ripscomplex/ +--- +{::comment} +Leave the lines above as it is required by the web site generator 'Jekyll' +{:/comment} # Rips complex # @@ -39,11 +49,63 @@ Same as `rips_persistence` but taking a distance matrix as input. **Usage** -`rips_persistence [options] <CSV input file>` +`rips_distance_matrix_persistence [options] <CSV input file>` where `<CSV input file>` is the path to the file containing a distance matrix. Can be square or lower triangular matrix. Separator is ';'. +The code do not check if it is dealing with a distance matrix. It is the user responsibility to provide a valid input. +Please refer to data/distance_matrix/lower_triangular_distance_matrix.csv for an example of a file. **Example** `rips_distance_matrix_persistence data/distance_matrix/full_square_distance_matrix.csv -r 15 -d 3 -p 3 -m 0` + + +## rips_correlation_matrix_persistence ## + +Same as `rips_distance_matrix_persistence` but taking a correlation matrix as input. + +**Usage** + +`rips_correlation_matrix_persistence [options] <CSV input file>` + +where +`<CSV input file>` is the path to the file containing a correlation matrix. Can be square or lower triangular matrix. Separator is ';'. +Note that no check is performed if the matrix given as the input is a correlation matrix. +It is the user responsibility to ensure that this is the case. +Please refer to data/correlation_matrix/lower_triangular_correlation_matrix.csv for an example of a file. + +**Example** + +`rips_correlation_matrix_persistence data/distance_matrix/full_square_distance_matrix.csv -r 15 -d 3 -p 3 -m 0` + +**Warning** + +As persistence diagrams points will be under the diagonal, bottleneck distance and persistence graphical tool will not work +properly, this is a known issue. + + +## sparse_rips_persistence ## +This program computes the persistent homology with coefficient field *Z/pZ* +of a sparse (1+epsilon)-approximation of the Rips complex defined on a set of input Euclidean points. The output diagram contains one bar per line, written with the convention: + +`p dim birth death` + +where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number). + +**Usage** + +`sparse_rips_persistence [options] <OFF input file>` + +**Allowed options** + +* `-h [ --help ]` Produce help message +* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output. +* `-e [ --approximation ]` (default = .5) Epsilon, where the sparse Rips complex is a (1+epsilon)-approximation of the Rips complex. +* `-d [ --cpx-dimension ]` (default = 1) Maximal dimension of the Rips complex we want to compute. +* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology. +* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals. + +**Example with Z/2Z coefficients** + +`sparse_rips_persistence ../../data/points/tore3D_1307.off -e .5 -m .2 -d 3 -p 2` diff --git a/src/Rips_complex/utilities/sparse_rips_persistence.cpp b/src/Rips_complex/utilities/sparse_rips_persistence.cpp new file mode 100644 index 00000000..d4bae3ba --- /dev/null +++ b/src/Rips_complex/utilities/sparse_rips_persistence.cpp @@ -0,0 +1,133 @@ +/* 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): Marc Glisse, Clément Maria + * + * 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/>. + */ + +#include <gudhi/Sparse_rips_complex.h> +#include <gudhi/distance_functions.h> +#include <gudhi/Simplex_tree.h> +#include <gudhi/Persistent_cohomology.h> +#include <gudhi/Points_off_io.h> + +#include <boost/program_options.hpp> + +#include <string> +#include <vector> + +// Types definition +using Simplex_tree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; +using Filtration_value = Simplex_tree::Filtration_value; +using Sparse_rips = Gudhi::rips_complex::Sparse_rips_complex<Filtration_value>; +using Field_Zp = Gudhi::persistent_cohomology::Field_Zp; +using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Field_Zp>; +using Point = std::vector<double>; +using Points_off_reader = Gudhi::Points_off_reader<Point>; + +void program_options(int argc, char* argv[], std::string& off_file_points, std::string& filediag, double& epsilon, + int& dim_max, int& p, Filtration_value& min_persistence); + +int main(int argc, char* argv[]) { + std::string off_file_points; + std::string filediag; + double epsilon; + int dim_max; + int p; + Filtration_value min_persistence; + + program_options(argc, argv, off_file_points, filediag, epsilon, dim_max, p, min_persistence); + + Points_off_reader off_reader(off_file_points); + Sparse_rips sparse_rips(off_reader.get_point_cloud(), Gudhi::Euclidean_distance(), epsilon); + + // Construct the Rips complex in a Simplex Tree + Simplex_tree simplex_tree; + + sparse_rips.create_complex(simplex_tree, dim_max); + std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n"; + std::cout << " and has dimension " << simplex_tree.dimension() << " \n"; + + // Sort the simplices in the order of the filtration + simplex_tree.initialize_filtration(); + + // Compute the persistence diagram of the complex + Persistent_cohomology pcoh(simplex_tree); + // initializes the coefficient field for homology + pcoh.init_coefficients(p); + + pcoh.compute_persistent_cohomology(min_persistence); + + // Output the diagram in filediag + if (filediag.empty()) { + pcoh.output_diagram(); + } else { + std::ofstream out(filediag); + pcoh.output_diagram(out); + out.close(); + } + + return 0; +} + +void program_options(int argc, char* argv[], std::string& off_file_points, std::string& filediag, double& epsilon, + int& dim_max, int& p, Filtration_value& min_persistence) { + namespace po = boost::program_options; + po::options_description hidden("Hidden options"); + hidden.add_options()("input-file", po::value<std::string>(&off_file_points), + "Name of an OFF file containing a point set.\n"); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()), + "Name of file in which the persistence diagram is written. Default print in std::cout")( + "approximation,e", po::value<double>(&epsilon)->default_value(.5), + "Epsilon, where the sparse Rips complex is a (1+epsilon)-approximation of the Rips complex.")( + "cpx-dimension,d", po::value<int>(&dim_max)->default_value(1), + "Maximal dimension of the Rips complex we want to compute.")( + "field-charac,p", po::value<int>(&p)->default_value(11), + "Characteristic p of the coefficient field Z/pZ for computing homology.")( + "min-persistence,m", po::value<Filtration_value>(&min_persistence), + "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length " + "intervals"); + + po::positional_options_description pos; + pos.add("input-file", 1); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map vm; + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); + po::notify(vm); + + if (vm.count("help") || !vm.count("input-file")) { + std::cout << std::endl; + std::cout << "Compute the persistent homology with coefficient field Z/pZ \n"; + std::cout << "of a sparse (1+epsilon)-approximation of the Rips complex \ndefined on a set of input points.\n \n"; + std::cout << "The output diagram contains one bar per line, written with the convention: \n"; + std::cout << " p dim b d \n"; + std::cout << "where dim is the dimension of the homological feature,\n"; + std::cout << "b and d are respectively the birth and death of the feature and \n"; + std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl; + + std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} diff --git a/src/Witness_complex/utilities/witnesscomplex.md b/src/Witness_complex/utilities/witnesscomplex.md index 2341759b..3be9bc55 100644 --- a/src/Witness_complex/utilities/witnesscomplex.md +++ b/src/Witness_complex/utilities/witnesscomplex.md @@ -1,3 +1,13 @@ +---
+layout: page
+title: "Witness complex"
+meta_title: "Witness complex"
+teaser: ""
+permalink: /witnesscomplex/
+---
+{::comment}
+Leave the lines above as it is required by the web site generator 'Jekyll'
+{:/comment}
# Witness complex #
diff --git a/src/common/doc/header.html b/src/common/doc/header.html index d69b28fa..2f54e68d 100644 --- a/src/common/doc/header.html +++ b/src/common/doc/header.html @@ -9,7 +9,7 @@ <!--BEGIN PROJECT_NAME--><title>$projectname: $title</title><!--END PROJECT_NAME--> <!--BEGIN !PROJECT_NAME--><title>$title</title><!--END !PROJECT_NAME--> <!-- GUDHI website css for header BEGIN --> -<link rel="stylesheet" type="text/css" href="http://pages.saclay.inria.fr/vincent.rouvreau/gudhi/gudhi-doc-2.0.0/assets/css/styles_feeling_responsive.css" /> +<link rel="stylesheet" type="text/css" href="http://gudhi.gforge.inria.fr/assets/css/styles_feeling_responsive.css" /> <!-- GUDHI website css for header END --> <link href="$relpath^tabs.css" rel="stylesheet" type="text/css"/> <script type="text/javascript" src="$relpath^jquery.js"></script> diff --git a/src/common/doc/offline_header.html b/src/common/doc/offline_header.html new file mode 100644 index 00000000..6a02a895 --- /dev/null +++ b/src/common/doc/offline_header.html @@ -0,0 +1,41 @@ +<!-- HTML header for doxygen 1.8.6--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<!-- GUDHI website : class="no-js" lang="en" is necessary --> +<html xmlns="http://www.w3.org/1999/xhtml" class="no-js" lang="en"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen $doxygenversion"/> +<!--BEGIN PROJECT_NAME--><title>$projectname: $title</title><!--END PROJECT_NAME--> +<!--BEGIN !PROJECT_NAME--><title>$title</title><!--END !PROJECT_NAME--> +<!-- GUDHI website css for header END --> +<link href="$relpath^tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="$relpath^jquery.js"></script> +<script type="text/javascript" src="$relpath^dynsections.js"></script> +$treeview +$search +$mathjax +<link href="$relpath^$stylesheet" rel="stylesheet" type="text/css" /> +$extrastylesheet +</head> +<body> + + +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> + +<!--BEGIN TITLEAREA--> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 30px;"> + <!--BEGIN DISABLE_INDEX--> + <!--BEGIN SEARCHENGINE--> + <td>$searchbox</td> + <!--END SEARCHENGINE--> + <!--END DISABLE_INDEX--> + </tr> + </tbody> +</table> +</div> +<!--END TITLEAREA--> +<!-- end header part --> diff --git a/src/common/example/CMakeLists.txt b/src/common/example/CMakeLists.txt index afe865d4..1273c699 100644 --- a/src/common/example/CMakeLists.txt +++ b/src/common/example/CMakeLists.txt @@ -3,11 +3,20 @@ project(Common_examples) add_executable ( vector_double_off_reader example_vector_double_points_off_reader.cpp ) target_link_libraries(vector_double_off_reader ${CGAL_LIBRARY}) +file(COPY "${CMAKE_SOURCE_DIR}/data/points/alphacomplexdoc.off" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) add_test(NAME Common_example_vector_double_off_reader COMMAND $<TARGET_FILE:vector_double_off_reader> - "${CMAKE_SOURCE_DIR}/data/points/SO3_10000.off") + "alphacomplexdoc.off") install(TARGETS vector_double_off_reader DESTINATION bin) +if (DIFF_PATH) + # Do not forget to copy test results files in current binary dir + file(COPY "vectordoubleoffreader_result.txt" DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) + + add_test(Common_example_vector_double_off_reader_diff_files ${DIFF_PATH} + ${CMAKE_CURRENT_BINARY_DIR}/vectordoubleoffreader_result.txt ${CMAKE_CURRENT_BINARY_DIR}/alphacomplexdoc.off.txt) +endif() + if(CGAL_FOUND) add_executable ( cgal_3D_off_reader example_CGAL_3D_points_off_reader.cpp ) target_link_libraries(cgal_3D_off_reader ${CGAL_LIBRARY}) diff --git a/src/common/example/example_CGAL_3D_points_off_reader.cpp b/src/common/example/example_CGAL_3D_points_off_reader.cpp index 665b7a29..4658d8d5 100644 --- a/src/common/example/example_CGAL_3D_points_off_reader.cpp +++ b/src/common/example/example_CGAL_3D_points_off_reader.cpp @@ -20,12 +20,12 @@ int main(int argc, char **argv) { usage(argv[0]); } - std::string offInputFile(argv[1]); + std::string off_input_file(argv[1]); // Read the OFF file (input file name given as parameter) and triangulate points - Gudhi::Points_3D_off_reader<Point_3> off_reader(offInputFile); + Gudhi::Points_3D_off_reader<Point_3> off_reader(off_input_file); // Check the read operation was correct if (!off_reader.is_valid()) { - std::cerr << "Unable to read file " << offInputFile << std::endl; + std::cerr << "Unable to read file " << off_input_file << std::endl; usage(argv[0]); } diff --git a/src/common/example/example_CGAL_points_off_reader.cpp b/src/common/example/example_CGAL_points_off_reader.cpp index 8c6a6b54..f45683a5 100644 --- a/src/common/example/example_CGAL_points_off_reader.cpp +++ b/src/common/example/example_CGAL_points_off_reader.cpp @@ -22,12 +22,12 @@ int main(int argc, char **argv) { usage(argv[0]); } - std::string offInputFile(argv[1]); + std::string off_input_file(argv[1]); // Read the OFF file (input file name given as parameter) and triangulate points - Gudhi::Points_off_reader<Point_d> off_reader(offInputFile); + Gudhi::Points_off_reader<Point_d> off_reader(off_input_file); // Check the read operation was correct if (!off_reader.is_valid()) { - std::cerr << "Unable to read file " << offInputFile << std::endl; + std::cerr << "Unable to read file " << off_input_file << std::endl; usage(argv[0]); } diff --git a/src/common/example/example_vector_double_points_off_reader.cpp b/src/common/example/example_vector_double_points_off_reader.cpp index 8aecb26e..5093da85 100644 --- a/src/common/example/example_vector_double_points_off_reader.cpp +++ b/src/common/example/example_vector_double_points_off_reader.cpp @@ -17,25 +17,27 @@ int main(int argc, char **argv) { usage(argv[0]); } - std::string offInputFile(argv[1]); + std::string off_input_file(argv[1]); // Read the OFF file (input file name given as parameter) and triangulate points - Gudhi::Points_off_reader<Point_d> off_reader(offInputFile); + Gudhi::Points_off_reader<Point_d> off_reader(off_input_file); // Check the read operation was correct if (!off_reader.is_valid()) { - std::cerr << "Unable to read file " << offInputFile << std::endl; + std::cerr << "Unable to read file " << off_input_file << std::endl; usage(argv[0]); } // Retrieve the triangulation std::vector<Point_d> point_cloud = off_reader.get_point_cloud(); + std::ofstream output_file(off_input_file + ".txt"); int n {0}; for (auto point : point_cloud) { - std::cout << "Point[" << n << "] = "; + output_file << "Point[" << n << "] = "; for (std::size_t i {0}; i < point.size(); i++) - std::cout << point[i] << " "; - std::cout << "\n"; + output_file << point[i] << " "; + output_file << "\n"; ++n; } + output_file.close(); return 0; } diff --git a/src/common/example/cgaloffreader_result.txt b/src/common/example/vectordoubleoffreader_result.txt index 1deb8dbd..1deb8dbd 100644 --- a/src/common/example/cgaloffreader_result.txt +++ b/src/common/example/vectordoubleoffreader_result.txt diff --git a/src/common/include/gudhi/Off_reader.h b/src/common/include/gudhi/Off_reader.h index 4fcd2af2..32320e4d 100644 --- a/src/common/include/gudhi/Off_reader.h +++ b/src/common/include/gudhi/Off_reader.h @@ -105,25 +105,26 @@ class Off_reader { bool is_off_file = (line.find("OFF") != std::string::npos); bool is_noff_file = (line.find("nOFF") != std::string::npos); + + if (!is_off_file && !is_noff_file) { std::cerr << line << std::endl; std::cerr << "missing off header\n"; return false; } + if (is_noff_file) { + // Should be on a separate line, but we accept it on the same line as the number of vertices + stream_ >> off_info_.dim; + } else { + off_info_.dim = 3; + } + if (!goto_next_uncomment_line(line)) return false; std::istringstream iss(line); - if ((is_off_file) && (!is_noff_file)) { - off_info_.dim = 3; - if (!(iss >> off_info_.num_vertices >> off_info_.num_faces >> off_info_.num_edges)) { - std::cerr << "incorrect number of vertices/faces/edges\n"; - return false; - } - } else { - if (!(iss >> off_info_.dim >> off_info_.num_vertices >> off_info_.num_faces >> off_info_.num_edges)) { + if (!(iss >> off_info_.num_vertices >> off_info_.num_faces >> off_info_.num_edges)) { std::cerr << "incorrect number of vertices/faces/edges\n"; return false; - } } off_visitor.init(off_info_.dim, off_info_.num_vertices, off_info_.num_faces, off_info_.num_edges); @@ -131,10 +132,12 @@ class Off_reader { } bool goto_next_uncomment_line(std::string& uncomment_line) { - uncomment_line.clear(); - do - std::getline(stream_, uncomment_line); while (uncomment_line[0] == '%'); - return (uncomment_line.size() > 0 && uncomment_line[0] != '%'); + do { + // skip whitespace, including empty lines + if (!std::ifstream::sentry(stream_)) return false; + std::getline(stream_, uncomment_line); + } while (uncomment_line[0] == '#'); + return (bool)stream_; } template<typename OffVisitor> diff --git a/src/common/include/gudhi/Points_off_io.h b/src/common/include/gudhi/Points_off_io.h index 29af8a8a..08f324c6 100644 --- a/src/common/include/gudhi/Points_off_io.h +++ b/src/common/include/gudhi/Points_off_io.h @@ -126,9 +126,9 @@ class Points_off_visitor_reader { * \code $> ./vector_double_off_reader ../../data/points/alphacomplexdoc.off * \endcode * - * the program output is: + * the program outputs a file ../../data/points/alphacomplexdoc.off.txt: * - * \include common/cgaloffreader_result.txt + * \include common/vectordoubleoffreader_result.txt */ template<typename Point_d> class Points_off_reader { diff --git a/src/common/include/gudhi/writing_persistence_to_file.h b/src/common/include/gudhi/writing_persistence_to_file.h new file mode 100644 index 00000000..4c5ce918 --- /dev/null +++ b/src/common/include/gudhi/writing_persistence_to_file.h @@ -0,0 +1,117 @@ +/* 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): Pawel Dlotko + * + * Copyright (C) 2017 Swansea University, UK + * + * 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 WRITING_PERSISTENCE_TO_FILE_H +#define WRITING_PERSISTENCE_TO_FILE_H + +#include <iostream> +#include <string> +#include <limits> + +namespace Gudhi { + +/** +* This is a class to store persistence intervals. Its main purpose is to +* exchange data in between different packages and provide unified way +* of writing a collection of persistence intervals to file. +**/ +template <typename Filtration_type, typename Coefficient_field> +class Persistence_interval_common { + public: + /** + * Constructor taking as an input birth and death of the pair. + **/ + Persistence_interval_common(Filtration_type birth, Filtration_type death) + : birth_(birth), + death_(death), + dimension_(std::numeric_limits<unsigned>::max()), + arith_element_(std::numeric_limits<Coefficient_field>::max()) {} + + /** + * Constructor taking as an input birth, death and dimension of the pair. + **/ + Persistence_interval_common(Filtration_type birth, Filtration_type death, unsigned dim) + : birth_(birth), death_(death), dimension_(dim), arith_element_(std::numeric_limits<Coefficient_field>::max()) {} + + /** +* Constructor taking as an input birth, death, dimension of the pair as well +* as the number p such that this interval is present over Z_p field. +**/ + Persistence_interval_common(Filtration_type birth, Filtration_type death, unsigned dim, Coefficient_field field) + : birth_(birth), death_(death), dimension_(dim), arith_element_(field) {} + + /** + * Operator to compare two persistence pairs. During the comparision all the + * fields: birth, death, dimensiona and arith_element_ are taken into account + * and they all have to be equal for two pairs to be equal. + **/ + bool operator==(const Persistence_interval_common& i2) const { + return ((this->birth_ == i2.birth_) && (this->death_ == i2.death_) && (this->dimension_ == i2.dimension_) && + (this->arith_element_ == i2.arith_element_)); + } + + /** + * Check if two persistence paris are not equal. + **/ + bool operator!=(const Persistence_interval_common& i2) const { return (!((*this) == i2)); } + + /** + * Operator to compare objects of a type Persistence_interval_common. + * One intervals is smaller than the other if it has lower persistence. + * Note that this operator do not take Arith_element into account when doing comparisions. + **/ + bool operator<(const Persistence_interval_common& i2) const { + return fabs(this->death_ - this->birth_) < fabs(i2.death_ - i2.birth_); + } + + friend std::ostream& operator<<(std::ostream& out, const Persistence_interval_common& it) { + if (it.arith_element_ != std::numeric_limits<Coefficient_field>::max()) { + out << it.arith_element_ << " "; + } + if (it.dimension_ != std::numeric_limits<unsigned>::max()) { + out << it.dimension_ << " "; + } + out << it.birth_ << " " << it.death_ << " "; + return out; + } + + private: + Filtration_type birth_; + Filtration_type death_; + unsigned dimension_; + Coefficient_field arith_element_; +}; + +/** + * This function write a vector<Persistence_interval_common> to a stream +**/ +template <typename Persistence_interval_range> +void write_persistence_intervals_to_stream(const Persistence_interval_range& intervals, + std::ostream& out = std::cout) { + for (auto interval : intervals) { + out << interval << "\n"; + } +} + +} + +#endif // WRITING_PERSISTENCE_TO_FILE_H diff --git a/src/common/utilities/pointsetgenerator.md b/src/common/utilities/pointsetgenerator.md index 284715d4..3b23e668 100644 --- a/src/common/utilities/pointsetgenerator.md +++ b/src/common/utilities/pointsetgenerator.md @@ -1,6 +1,16 @@ - - -# common # +--- +layout: page +title: "OFF point set generator" +meta_title: "OFF point set generator" +teaser: "" +permalink: /pointsetgenerator/ +--- +{::comment} +Leave the lines above as it is required by the web site generator 'Jekyll' +{:/comment} + + +# Miscellaneous # ## off_file_from_shape_generator ## diff --git a/src/cython/cython/off_reader.pyx b/src/cython/cython/off_reader.pyx index b6e107ef..266dae2c 100644 --- a/src/cython/cython/off_reader.pyx +++ b/src/cython/cython/off_reader.pyx @@ -46,4 +46,5 @@ def read_off(off_file=''): return read_points_from_OFF_file(str.encode(off_file)) else: print("file " + off_file + " not found.") + return [] diff --git a/src/cython/cython/rips_complex.pyx b/src/cython/cython/rips_complex.pyx index ad9b0a4d..73b154b8 100644 --- a/src/cython/cython/rips_complex.pyx +++ b/src/cython/cython/rips_complex.pyx @@ -34,8 +34,6 @@ __license__ = "GPL v3" cdef extern from "Rips_complex_interface.h" namespace "Gudhi": cdef cppclass Rips_complex_interface "Gudhi::rips_complex::Rips_complex_interface": Rips_complex_interface(vector[vector[double]] values, double threshold, bool euclidean) - # bool from_file is a workaround for cython to find the correct signature - Rips_complex_interface(string file_name, double threshold, bool euclidean, bool from_file) void create_simplex_tree(Simplex_tree_interface_full_featured* simplex_tree, int dim_max) # RipsComplex python interface @@ -49,7 +47,7 @@ cdef class RipsComplex: cdef Rips_complex_interface * thisptr # Fake constructor that does nothing but documenting the constructor - def __init__(self, points=None, off_file='', distance_matrix=None, csv_file='', max_edge_length=float('inf')): + def __init__(self, points=None, distance_matrix=None, max_edge_length=float('inf')): """RipsComplex constructor. :param max_edge_length: Rips value. @@ -60,41 +58,14 @@ cdef class RipsComplex: Or - :param off_file: An OFF file style name. - :type off_file: string - - Or - :param distance_matrix: A distance matrix (full square or lower triangular). :type points: list of list of double - - Or - - :param csv_file: A csv file style name containing a full square or a - lower triangular distance matrix. - :type csv_file: string """ # The real cython constructor - def __cinit__(self, points=None, off_file='', distance_matrix=None, csv_file='', max_edge_length=float('inf')): - if off_file is not '': - if os.path.isfile(off_file): - self.thisptr = new Rips_complex_interface(str.encode(off_file), - max_edge_length, - True, - True) - else: - print("file " + off_file + " not found.") - elif csv_file is not '': - if os.path.isfile(csv_file): - self.thisptr = new Rips_complex_interface(str.encode(csv_file), - max_edge_length, - False, - True) - else: - print("file " + csv_file + " not found.") - elif distance_matrix is not None: + def __cinit__(self, points=None, distance_matrix=None, max_edge_length=float('inf')): + if distance_matrix is not None: self.thisptr = new Rips_complex_interface(distance_matrix, max_edge_length, False) else: if points is None: diff --git a/src/cython/doc/cubical_complex_user.rst b/src/cython/doc/cubical_complex_user.rst index 34598f02..dd82ad93 100644 --- a/src/cython/doc/cubical_complex_user.rst +++ b/src/cython/doc/cubical_complex_user.rst @@ -152,6 +152,6 @@ End user programs are available in cython/example/ folder. Bibliography ============ -.. bibliography:: ../../bibliography.bib +.. bibliography:: ../../biblio/bibliography.bib :filter: docnames :style: unsrt diff --git a/src/cython/doc/persistence_graphical_tools_user.rst b/src/cython/doc/persistence_graphical_tools_user.rst index 9033331f..a5523d23 100644 --- a/src/cython/doc/persistence_graphical_tools_user.rst +++ b/src/cython/doc/persistence_graphical_tools_user.rst @@ -58,8 +58,8 @@ This function can display the persistence result as a diagram: import gudhi - rips_complex = gudhi.RipsComplex(off_file=gudhi.__root_source_dir__ + \ - '/data/points/tore3D_1307.off', max_edge_length=0.2) + point_cloud = gudhi.read_off(off_file=gudhi.__root_source_dir__ + '/data/points/tore3D_1307.off') + rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=0.2) simplex_tree = rips_complex.create_simplex_tree(max_dimension=3) diag = simplex_tree.persistence() plt = gudhi.plot_persistence_diagram(diag, band_boot=0.13) @@ -69,8 +69,8 @@ This function can display the persistence result as a diagram: import gudhi - rips_complex = gudhi.RipsComplex(off_file=gudhi.__root_source_dir__ + \ - '/data/points/tore3D_1307.off', max_edge_length=0.2) + point_cloud = gudhi.read_off(off_file=gudhi.__root_source_dir__ + '/data/points/tore3D_1307.off') + rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=0.2) simplex_tree = rips_complex.create_simplex_tree(max_dimension=3) diag = simplex_tree.persistence() plt = gudhi.plot_persistence_diagram(diag, band_boot=0.13) diff --git a/src/cython/doc/pyplots/diagram_persistence.py b/src/cython/doc/pyplots/diagram_persistence.py index c2fbf801..ac20bf47 100755 --- a/src/cython/doc/pyplots/diagram_persistence.py +++ b/src/cython/doc/pyplots/diagram_persistence.py @@ -1,7 +1,8 @@ import gudhi -rips_complex = gudhi.RipsComplex(off_file=gudhi.__root_source_dir__ + \ - '/data/points/tore3D_1307.off', max_edge_length=0.2) +point_cloud = gudhi.read_off(off_file=gudhi.__root_source_dir__ + \ + '/data/points/tore3D_1307.off') +rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=0.2) simplex_tree = rips_complex.create_simplex_tree(max_dimension=3) diag = simplex_tree.persistence() plt = gudhi.plot_persistence_diagram(diag, band_boot=0.13) diff --git a/src/cython/doc/rips_complex_user.rst b/src/cython/doc/rips_complex_user.rst index 96ba9944..7738aef0 100644 --- a/src/cython/doc/rips_complex_user.rst +++ b/src/cython/doc/rips_complex_user.rst @@ -101,8 +101,8 @@ Finally, it is asked to display information about the Rips complex. .. testcode:: import gudhi - rips_complex = gudhi.RipsComplex(off_file=gudhi.__root_source_dir__ + \ - '/data/points/alphacomplexdoc.off', max_edge_length=12.0) + point_cloud = gudhi.read_off(off_file=gudhi.__root_source_dir__ + '/data/points/alphacomplexdoc.off') + rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=12.0) simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ repr(simplex_tree.num_simplices()) + ' simplices - ' + \ @@ -197,7 +197,7 @@ Example from csv file ^^^^^^^^^^^^^^^^^^^^^ This example builds the :doc:`Rips_complex <rips_complex_ref>` from the given -points in an OFF file, and max_edge_length value. +distance matrix in a csv file, and max_edge_length value. Then it creates a :doc:`Simplex_tree <simplex_tree_ref>` with it. Finally, it is asked to display information about the Rips complex. @@ -206,8 +206,9 @@ Finally, it is asked to display information about the Rips complex. .. testcode:: import gudhi - rips_complex = gudhi.RipsComplex(csv_file=gudhi.__root_source_dir__ + \ - '/data/distance_matrix/full_square_distance_matrix.csv', max_edge_length=12.0) + distance_matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file=gudhi.__root_source_dir__ + \ + '/data/distance_matrix/full_square_distance_matrix.csv') + rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, max_edge_length=12.0) simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ repr(simplex_tree.num_simplices()) + ' simplices - ' + \ @@ -240,3 +241,72 @@ the program output is: [0, 3] -> 9.43 [4, 6] -> 9.49 [3, 6] -> 11.00 + +Correlation matrix +--------------- + +Example from a correlation matrix +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Analogously to the case of distance matrix, Rips complexes can be also constructed based on correlation matrix. +Given a correlation matrix M, comportment-wise 1-M is a distance matrix. +This example builds the one skeleton graph from the given corelation matrix and threshold value. +Then it creates a :doc:`Simplex_tree <simplex_tree_ref>` with it. + +Finally, it is asked to display information about the simplicial complex. + +.. testcode:: + + import gudhi + import numpy as np + + # User defined correlation matrix is: + # |1 0.06 0.23 0.01 0.89| + # |0.06 1 0.74 0.01 0.61| + # |0.23 0.74 1 0.72 0.03| + # |0.01 0.01 0.72 1 0.7 | + # |0.89 0.61 0.03 0.7 1 | + correlation_matrix=np.array([[1., 0.06, 0.23, 0.01, 0.89], + [0.06, 1., 0.74, 0.01, 0.61], + [0.23, 0.74, 1., 0.72, 0.03], + [0.01, 0.01, 0.72, 1., 0.7], + [0.89, 0.61, 0.03, 0.7, 1.]], float) + + distance_matrix = np.ones((correlation_matrix.shape),float) - correlation_matrix + rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, max_edge_length=1.0) + + simplex_tree = rips_complex.create_simplex_tree(max_dimension=1) + result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \ + repr(simplex_tree.num_simplices()) + ' simplices - ' + \ + repr(simplex_tree.num_vertices()) + ' vertices.' + print(result_str) + fmt = '%s -> %.2f' + for filtered_value in simplex_tree.get_filtration(): + print(fmt % tuple(filtered_value)) + +When launching (Rips maximal distance between 2 points is 12.0, is expanded +until dimension 1 - one skeleton graph in other words), the output is: + +.. testoutput:: + + Rips complex is of dimension 1 - 15 simplices - 5 vertices. + [0] -> 0.00 + [1] -> 0.00 + [2] -> 0.00 + [3] -> 0.00 + [4] -> 0.00 + [0, 4] -> 0.11 + [1, 2] -> 0.26 + [2, 3] -> 0.28 + [3, 4] -> 0.30 + [1, 4] -> 0.39 + [0, 2] -> 0.77 + [0, 1] -> 0.94 + [2, 4] -> 0.97 + [0, 3] -> 0.99 + [1, 3] -> 0.99 + +.. note:: + As persistence diagrams points will be under the diagonal, + bottleneck distance and persistence graphical tool will not work properly, + this is a known issue. diff --git a/src/cython/example/alpha_rips_persistence_bottleneck_distance.py b/src/cython/example/alpha_rips_persistence_bottleneck_distance.py index ab5fc1e9..386f8457 100755 --- a/src/cython/example/alpha_rips_persistence_bottleneck_distance.py +++ b/src/cython/example/alpha_rips_persistence_bottleneck_distance.py @@ -45,13 +45,14 @@ args = parser.parse_args() with open(args.file, 'r') as f: first_line = f.readline() if (first_line == 'OFF\n') or (first_line == 'nOFF\n'): + point_cloud = gudhi.read_off(off_file=args.file) print("#####################################################################") print("RipsComplex creation from points read in a OFF file") message = "RipsComplex with max_edge_length=" + repr(args.threshold) print(message) - rips_complex = gudhi.RipsComplex(off_file=args.file, + rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=args.threshold) rips_stree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) @@ -67,7 +68,7 @@ with open(args.file, 'r') as f: message = "AlphaComplex with max_edge_length=" + repr(args.threshold) print(message) - alpha_complex = gudhi.AlphaComplex(off_file=args.file) + alpha_complex = gudhi.AlphaComplex(points=point_cloud) alpha_stree = alpha_complex.create_simplex_tree(max_alpha_square=(args.threshold * args.threshold)) message = "Number of simplices=" + repr(alpha_stree.num_simplices()) diff --git a/src/cython/example/bottleneck_basic_example.py b/src/cython/example/bottleneck_basic_example.py index 31cecb29..a7fa01c1 100755 --- a/src/cython/example/bottleneck_basic_example.py +++ b/src/cython/example/bottleneck_basic_example.py @@ -28,8 +28,6 @@ __author__ = "Francois Godi, Vincent Rouvreau" __copyright__ = "Copyright (C) 2016 INRIA" __license__ = "GPL v3" -import gudhi - diag1 = [[2.7, 3.7],[9.6, 14.],[34.2, 34.974], [3.,float('Inf')]] diag2 = [[2.8, 4.45],[9.5, 14.1],[3.2,float('Inf')]] diff --git a/src/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py b/src/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py new file mode 100755 index 00000000..aa82ef71 --- /dev/null +++ b/src/cython/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python + +import gudhi +import sys +import argparse + +"""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): Vincent Rouvreau + + Copyright (C) 2017 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__ = "Vincent Rouvreau" +__copyright__ = "Copyright (C) 2017 INRIA" +__license__ = "GPL v3" + +parser = argparse.ArgumentParser(description='RipsComplex creation from ' + 'a correlation matrix read in a csv file.', + epilog='Example: ' + 'example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py ' + '-f ../data/correlation_matrix/lower_triangular_correlation_matrix.csv -e 12.0 -d 3' + '- Constructs a Rips complex with the ' + 'correlation matrix from the given csv file.') +parser.add_argument("-f", "--file", type=str, required=True) +parser.add_argument("-c", "--min_edge_correlation", type=float, default=0.5) +parser.add_argument("-d", "--max_dimension", type=int, default=1) +parser.add_argument("-b", "--band_boot", type=float, default=0.) +parser.add_argument('--no-diagram', default=False, action='store_true' , help='Flag for not to display the diagrams') + +args = parser.parse_args() + +if not (-1. < args.min_edge_correlation < 1.): + print("Wrong value of the treshold corelation (should be between -1 and 1).") + sys.exit(1) + +print("#####################################################################") +print("Caution: as persistence diagrams points will be under the diagonal,") +print("bottleneck distance and persistence graphical tool will not work") +print("properly, this is a known issue.") + +print("#####################################################################") +print("RipsComplex creation from correlation matrix read in a csv file") + +message = "RipsComplex with min_edge_correlation=" + repr(args.min_edge_correlation) +print(message) + +correlation_matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file=args.file) +# Given a correlation matrix M, we compute component-wise M'[i,j] = 1-M[i,j] to get a distance matrix: +distance_matrix = [[1.-correlation_matrix[i][j] for j in range(len(correlation_matrix[i]))] for i in range(len(correlation_matrix))] + +rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, + max_edge_length=1.-args.min_edge_correlation) +simplex_tree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) + +message = "Number of simplices=" + repr(simplex_tree.num_simplices()) +print(message) + +diag = simplex_tree.persistence() + +print("betti_numbers()=") +print(simplex_tree.betti_numbers()) + +# invert the persistence diagram +invert_diag = [(diag[pers][0],(1.-diag[pers][1][0], 1.-diag[pers][1][1])) for pers in range(len(diag))] + +if args.no_diagram == False: + pplot = gudhi.plot_persistence_diagram(invert_diag, band_boot=args.band_boot) + pplot.show() diff --git a/src/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py b/src/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py index 3baebd17..c8aac240 100755 --- a/src/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py +++ b/src/cython/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py @@ -30,12 +30,12 @@ __copyright__ = "Copyright (C) 2016 INRIA" __license__ = "GPL v3" parser = argparse.ArgumentParser(description='RipsComplex creation from ' - 'a distance matrix read in a OFF file.', + 'a distance matrix read in a csv file.', epilog='Example: ' 'example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py ' '-f ../data/distance_matrix/lower_triangular_distance_matrix.csv -e 12.0 -d 3' '- Constructs a Rips complex with the ' - 'points from the given OFF file.') + 'distance matrix from the given csv file.') parser.add_argument("-f", "--file", type=str, required=True) parser.add_argument("-e", "--max_edge_length", type=float, default=0.5) parser.add_argument("-d", "--max_dimension", type=int, default=1) @@ -50,7 +50,8 @@ print("RipsComplex creation from distance matrix read in a csv file") message = "RipsComplex with max_edge_length=" + repr(args.max_edge_length) print(message) -rips_complex = gudhi.RipsComplex(csv_file=args.file, max_edge_length=args.max_edge_length) +distance_matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file=args.file) +rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, max_edge_length=args.max_edge_length) simplex_tree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) message = "Number of simplices=" + repr(simplex_tree.num_simplices()) diff --git a/src/cython/example/rips_complex_diagram_persistence_from_off_file_example.py b/src/cython/example/rips_complex_diagram_persistence_from_off_file_example.py index 5951eedf..544b68c9 100755 --- a/src/cython/example/rips_complex_diagram_persistence_from_off_file_example.py +++ b/src/cython/example/rips_complex_diagram_persistence_from_off_file_example.py @@ -53,7 +53,8 @@ with open(args.file, 'r') as f: message = "RipsComplex with max_edge_length=" + repr(args.max_edge_length) print(message) - rips_complex = gudhi.RipsComplex(off_file=args.file, max_edge_length=args.max_edge_length) + point_cloud = gudhi.read_off(off_file=args.file) + rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=args.max_edge_length) simplex_tree = rips_complex.create_simplex_tree(max_dimension=args.max_dimension) message = "Number of simplices=" + repr(simplex_tree.num_simplices()) diff --git a/src/cython/include/Rips_complex_interface.h b/src/cython/include/Rips_complex_interface.h index 02985727..f26befbc 100644 --- a/src/cython/include/Rips_complex_interface.h +++ b/src/cython/include/Rips_complex_interface.h @@ -25,9 +25,7 @@ #include <gudhi/Simplex_tree.h> #include <gudhi/Rips_complex.h> -#include <gudhi/Points_off_io.h> #include <gudhi/distance_functions.h> -#include <gudhi/reader_utils.h> #include "Simplex_tree_interface.h" @@ -56,21 +54,6 @@ class Rips_complex_interface { } } - Rips_complex_interface(const std::string& file_name, double threshold, bool euclidean, bool from_file = true) { - if (euclidean) { - // Rips construction where file_name is an OFF file - Gudhi::Points_off_reader<Point_d> off_reader(file_name); - rips_complex_ = new Rips_complex<Simplex_tree_interface<>::Filtration_value>(off_reader.get_point_cloud(), - threshold, - Gudhi::Euclidean_distance()); - } else { - // Rips construction where values is a distance matrix - Distance_matrix distances = - Gudhi::read_lower_triangular_matrix_from_csv_file<Simplex_tree_interface<>::Filtration_value>(file_name); - rips_complex_ = new Rips_complex<Simplex_tree_interface<>::Filtration_value>(distances, threshold); - } - } - ~Rips_complex_interface() { delete rips_complex_; } |