diff options
Diffstat (limited to 'include/gudhi')
59 files changed, 6606 insertions, 1457 deletions
diff --git a/include/gudhi/Active_witness/Active_witness.h b/include/gudhi/Active_witness/Active_witness.h new file mode 100644 index 00000000..d41a6811 --- /dev/null +++ b/include/gudhi/Active_witness/Active_witness.h @@ -0,0 +1,67 @@ +/* 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): Siargey Kachanovich + * + * Copyright (C) 2016 INRIA (France) + * + * 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 ACTIVE_WITNESS_ACTIVE_WITNESS_H_ +#define ACTIVE_WITNESS_ACTIVE_WITNESS_H_ + +#include <gudhi/Active_witness/Active_witness_iterator.h> +#include <list> + +namespace Gudhi { + +namespace witness_complex { + + /* \class Active_witness + * \brief Class representing a list of nearest neighbors to a given witness. + * \details Every element is a pair of a landmark identifier and the squared distance to it. + */ +template< typename Id_distance_pair, + typename INS_range > +class Active_witness { + public: + typedef Active_witness<Id_distance_pair, INS_range> ActiveWitness; + typedef typename INS_range::iterator INS_iterator; + typedef Active_witness_iterator< ActiveWitness, Id_distance_pair, INS_iterator > iterator; + typedef typename std::list<Id_distance_pair> Table; + + Table nearest_landmark_table_; + INS_range search_range_; + INS_iterator iterator_next_; + INS_iterator iterator_end_; + + Active_witness(const INS_range& search_range) + : search_range_(search_range), iterator_next_(search_range_.begin()), iterator_end_(search_range_.end()) { + } + + iterator begin() { + return iterator(this, nearest_landmark_table_.begin()); + } + + iterator end() { + return iterator(this); + } +}; + +} // namespace witness_complex +} // namespace Gudhi + +#endif // ACTIVE_WITNESS_ACTIVE_WITNESS_H_ diff --git a/include/gudhi/Active_witness/Active_witness_iterator.h b/include/gudhi/Active_witness/Active_witness_iterator.h new file mode 100644 index 00000000..0a05173a --- /dev/null +++ b/include/gudhi/Active_witness/Active_witness_iterator.h @@ -0,0 +1,108 @@ +/* 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): Siargey Kachanovich + * + * Copyright (C) 2016 INRIA (France) + * + * 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 ACTIVE_WITNESS_ACTIVE_WITNESS_ITERATOR_H_ +#define ACTIVE_WITNESS_ACTIVE_WITNESS_ITERATOR_H_ + +#include <boost/iterator/iterator_facade.hpp> +#include <list> + +namespace Gudhi { + +namespace witness_complex { + +/* \brief Iterator in the nearest landmark list. + * \details After the iterator reaches the end of the list, + * the list is augmented by a (nearest landmark, distance) pair if possible. + * If all the landmarks are present in the list, iterator returns the specific end value + * of the corresponding 'Active_witness' object. + */ +template< typename Active_witness, + typename Id_distance_pair, + typename INS_iterator > +class Active_witness_iterator + : public boost::iterator_facade< Active_witness_iterator <Active_witness, Id_distance_pair, INS_iterator>, + Id_distance_pair const, + boost::forward_traversal_tag, + Id_distance_pair const> { + friend class boost::iterator_core_access; + + typedef typename std::list<Id_distance_pair>::iterator Pair_iterator; + typedef typename Gudhi::witness_complex::Active_witness_iterator<Active_witness, + Id_distance_pair, + INS_iterator> Iterator; + + Active_witness *aw_; + Pair_iterator lh_; // landmark handle + bool is_end_; // true only if the pointer is end and there are no more neighbors to add + + public: + Active_witness_iterator(Active_witness* aw) + : aw_(aw), lh_(aw_->nearest_landmark_table_.end()), is_end_(true) { + } + + Active_witness_iterator(Active_witness* aw, const Pair_iterator& lh) + : aw_(aw), lh_(lh) { + is_end_ = false; + if (lh_ == aw_->nearest_landmark_table_.end()) { + if (aw_->iterator_next_ == aw_->iterator_end_) { + is_end_ = true; + } else { + aw_->nearest_landmark_table_.push_back(*aw_->iterator_next_); + lh_ = --aw_->nearest_landmark_table_.end(); + ++(aw_->iterator_next_); + } + } + } + + private : + Id_distance_pair& dereference() const { + return *lh_; + } + + bool equal(const Iterator& other) const { + return (is_end_ == other.is_end_) || (lh_ == other.lh_); + } + + void increment() { + // the neighbor search can't be at the end iterator of a list + GUDHI_CHECK(!is_end_ && lh_ != aw_->nearest_landmark_table_.end(), + std::logic_error("Wrong active witness increment.")); + // if the id of the current landmark is the same as the last one + + lh_++; + if (lh_ == aw_->nearest_landmark_table_.end()) { + if (aw_->iterator_next_ == aw_->iterator_end_) { + is_end_ = true; + } else { + aw_->nearest_landmark_table_.push_back(*aw_->iterator_next_); + lh_ = std::prev(aw_->nearest_landmark_table_.end()); + ++(aw_->iterator_next_); + } + } + } +}; + +} // namespace witness_complex +} // namespace Gudhi + +#endif // ACTIVE_WITNESS_ACTIVE_WITNESS_ITERATOR_H_ diff --git a/include/gudhi/Alpha_complex.h b/include/gudhi/Alpha_complex.h index 2c95ceb4..1ff95c3d 100644 --- a/include/gudhi/Alpha_complex.h +++ b/include/gudhi/Alpha_complex.h @@ -4,7 +4,7 @@ * * Author(s): Vincent Rouvreau * - * Copyright (C) 2015 INRIA Saclay (France) + * Copyright (C) 2015 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 @@ -23,9 +23,6 @@ #ifndef ALPHA_COMPLEX_H_ #define ALPHA_COMPLEX_H_ -// to construct a simplex_tree from Delaunay_triangulation -#include <gudhi/graph_simplicial_complex.h> -#include <gudhi/Simplex_tree.h> #include <gudhi/Debug_utils.h> // to construct Alpha_complex from a OFF file of points #include <gudhi/Points_off_io.h> @@ -36,6 +33,7 @@ #include <CGAL/Delaunay_triangulation.h> #include <CGAL/Epick_d.h> #include <CGAL/Spatial_sort_traits_adapter_d.h> +#include <CGAL/property_map.h> // for CGAL::Identity_property_map #include <iostream> #include <vector> @@ -57,9 +55,9 @@ namespace alpha_complex { * \ingroup alpha_complex * * \details - * The data structure can be constructed from a CGAL Delaunay triangulation (for more informations on CGAL Delaunay - * triangulation, please refer to the corresponding chapter in page http://doc.cgal.org/latest/Triangulation/) or from - * an OFF file (cf. Points_off_reader). + * The data structure is constructing a CGAL Delaunay triangulation (for more informations on CGAL Delaunay + * triangulation, please refer to the corresponding chapter in page http://doc.cgal.org/latest/Triangulation/) from a + * range of points or from an OFF file (cf. Points_off_reader). * * Please refer to \ref alpha_complex for examples. * @@ -74,7 +72,7 @@ namespace alpha_complex { * */ template<class Kernel = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>> -class Alpha_complex : public Simplex_tree<> { +class Alpha_complex { public: // Add an int in TDS to save point index in the structure typedef CGAL::Triangulation_data_structure<typename Kernel::Dimension, @@ -90,13 +88,6 @@ class Alpha_complex : public Simplex_tree<> { typedef Kernel Geom_traits; private: - // From Simplex_tree - // Type required to insert into a simplex_tree (with or without subfaces). - typedef std::vector<Vertex_handle> Vector_vertex; - - // Simplex_result is the type returned from simplex_tree insert function. - typedef typename std::pair<Simplex_handle, bool> Simplex_result; - typedef typename Kernel::Compute_squared_radius_d Squared_Radius; typedef typename Kernel::Side_of_bounded_sphere_d Is_Gabriel; typedef typename Kernel::Point_dimension_d Point_Dimension; @@ -111,7 +102,7 @@ class Alpha_complex : public Simplex_tree<> { typedef typename Delaunay_triangulation::size_type size_type; // Map type to switch from simplex tree vertex handle to CGAL vertex iterator. - typedef typename std::map< Vertex_handle, CGAL_vertex_iterator > Vector_vertex_iterator; + typedef typename std::map< std::size_t, CGAL_vertex_iterator > Vector_vertex_iterator; private: /** \brief Vertex iterator vector to switch from simplex tree vertex handle to CGAL vertex iterator. @@ -124,16 +115,15 @@ class Alpha_complex : public Simplex_tree<> { public: /** \brief Alpha_complex constructor from an OFF file name. - * Uses the Delaunay_triangulation_off_reader to construct the Delaunay triangulation required to initialize + * + * Uses the Points_off_reader to construct the Delaunay triangulation required to initialize * the Alpha_complex. * * Duplicate points are inserted once in the Alpha_complex. This is the reason why the vertices may be not contiguous. * * @param[in] off_file_name OFF file [path and] name. - * @param[in] max_alpha_square maximum for alpha square value. Default value is +\f$\infty\f$. */ - Alpha_complex(const std::string& off_file_name, - Filtration_value max_alpha_square = std::numeric_limits<Filtration_value>::infinity()) + Alpha_complex(const std::string& off_file_name) : triangulation_(nullptr) { Gudhi::Points_off_reader<Point_d> off_reader(off_file_name); if (!off_reader.is_valid()) { @@ -141,7 +131,7 @@ class Alpha_complex : public Simplex_tree<> { exit(-1); // ----- >> } - init_from_range(off_reader.get_point_cloud(), max_alpha_square); + init_from_range(off_reader.get_point_cloud()); } /** \brief Alpha_complex constructor from a list of points. @@ -149,23 +139,17 @@ class Alpha_complex : public Simplex_tree<> { * Duplicate points are inserted once in the Alpha_complex. This is the reason why the vertices may be not contiguous. * * @param[in] points Range of points to triangulate. Points must be in Kernel::Point_d - * @param[in] max_alpha_square maximum for alpha square value. Default value is +\f$\infty\f$. * * The type InputPointRange must be a range for which std::begin and * std::end return input iterators on a Kernel::Point_d. - * - * @post Compare num_simplices with InputPointRange points number (not the same in case of duplicate points). */ template<typename InputPointRange > - Alpha_complex(const InputPointRange& points, - Filtration_value max_alpha_square = std::numeric_limits<Filtration_value>::infinity()) + Alpha_complex(const InputPointRange& points) : triangulation_(nullptr) { - init_from_range(points, max_alpha_square); + init_from_range(points); } - /** \brief Alpha_complex destructor. - * - * @warning Deletes the Delaunay triangulation. + /** \brief Alpha_complex destructor deletes the Delaunay triangulation. */ ~Alpha_complex() { delete triangulation_; @@ -183,15 +167,24 @@ class Alpha_complex : public Simplex_tree<> { * @return The point found. * @exception std::out_of_range In case vertex is not found (cf. std::vector::at). */ - Point_d get_point(Vertex_handle vertex) const { + const Point_d& get_point(std::size_t vertex) const { return vertex_handle_to_iterator_.at(vertex)->point(); } + /** \brief number_of_vertices returns the number of vertices (same as the number of points). + * + * @return The number of vertices. + */ + const std::size_t number_of_vertices() const { + return vertex_handle_to_iterator_.size(); + } + private: template<typename InputPointRange > - void init_from_range(const InputPointRange& points, Filtration_value max_alpha_square) { + void init_from_range(const InputPointRange& points) { auto first = std::begin(points); auto last = std::end(points); + if (first != last) { // point_dimension function initialization Point_Dimension point_dimension = kernel_.point_dimension_d_object(); @@ -199,90 +192,107 @@ class Alpha_complex : public Simplex_tree<> { // Delaunay triangulation is point dimension. triangulation_ = new Delaunay_triangulation(point_dimension(*first)); - std::vector<Point_d> points(first, last); + std::vector<Point_d> point_cloud(first, last); // Creates a vector {0, 1, ..., N-1} std::vector<std::ptrdiff_t> indices(boost::counting_iterator<std::ptrdiff_t>(0), - boost::counting_iterator<std::ptrdiff_t>(points.size())); + boost::counting_iterator<std::ptrdiff_t>(point_cloud.size())); + + typedef boost::iterator_property_map<typename std::vector<Point_d>::iterator, + CGAL::Identity_property_map<std::ptrdiff_t>> Point_property_map; + typedef CGAL::Spatial_sort_traits_adapter_d<Kernel, Point_property_map> Search_traits_d; - // Sort indices considering CGAL spatial sort - typedef CGAL::Spatial_sort_traits_adapter_d<Kernel, Point_d*> Search_traits_d; - spatial_sort(indices.begin(), indices.end(), Search_traits_d(&(points[0]))); + CGAL::spatial_sort(indices.begin(), indices.end(), Search_traits_d(std::begin(point_cloud))); typename Delaunay_triangulation::Full_cell_handle hint; for (auto index : indices) { - typename Delaunay_triangulation::Vertex_handle pos = triangulation_->insert(points[index], hint); + typename Delaunay_triangulation::Vertex_handle pos = triangulation_->insert(point_cloud[index], hint); // Save index value as data to retrieve it after insertion pos->data() = index; hint = pos->full_cell(); } - init(max_alpha_square); + // -------------------------------------------------------------------------------------------- + // double map to retrieve simplex tree vertex handles from CGAL vertex iterator and vice versa + // Loop on triangulation vertices list + for (CGAL_vertex_iterator vit = triangulation_->vertices_begin(); vit != triangulation_->vertices_end(); ++vit) { + if (!triangulation_->is_infinite(*vit)) { +#ifdef DEBUG_TRACES + std::cout << "Vertex insertion - " << vit->data() << " -> " << vit->point() << std::endl; +#endif // DEBUG_TRACES + vertex_handle_to_iterator_.emplace(vit->data(), vit); + } + } + // -------------------------------------------------------------------------------------------- } } - /** \brief Initialize the Alpha_complex from the Delaunay triangulation. + public: + template <typename SimplicialComplexForAlpha> + bool create_complex(SimplicialComplexForAlpha& complex) { + typedef typename SimplicialComplexForAlpha::Filtration_value Filtration_value; + return create_complex(complex, std::numeric_limits<Filtration_value>::infinity()); + } + + /** \brief Inserts all Delaunay triangulation into the simplicial complex. + * It also computes the filtration values accordingly to the \ref createcomplexalgorithm * - * @param[in] max_alpha_square maximum for alpha square value. + * \tparam SimplicialComplexForAlpha must meet `SimplicialComplexForAlpha` concept. * - * @warning Delaunay triangulation must be already constructed with at least one vertex and dimension must be more - * than 0. + * @param[in] complex SimplicialComplexForAlpha to be created. + * @param[in] max_alpha_square maximum for alpha square value. Default value is +\f$\infty\f$. + * + * @return true if creation succeeds, false otherwise. + * + * @pre Delaunay triangulation must be already constructed with dimension strictly greater than 0. + * @pre The simplicial complex must be empty (no vertices) * * Initialization can be launched once. */ - void init(Filtration_value max_alpha_square) { + template <typename SimplicialComplexForAlpha, typename Filtration_value> + bool create_complex(SimplicialComplexForAlpha& complex, Filtration_value max_alpha_square) { + // From SimplicialComplexForAlpha type required to insert into a simplicial complex (with or without subfaces). + typedef typename SimplicialComplexForAlpha::Vertex_handle Vertex_handle; + typedef typename SimplicialComplexForAlpha::Simplex_handle Simplex_handle; + typedef std::vector<Vertex_handle> Vector_vertex; + if (triangulation_ == nullptr) { - std::cerr << "Alpha_complex init - Cannot init from a NULL triangulation\n"; - return; // ----- >> - } - if (triangulation_->number_of_vertices() < 1) { - std::cerr << "Alpha_complex init - Cannot init from a triangulation without vertices\n"; - return; // ----- >> + std::cerr << "Alpha_complex cannot create_complex from a NULL triangulation\n"; + return false; // ----- >> } if (triangulation_->maximal_dimension() < 1) { - std::cerr << "Alpha_complex init - Cannot init from a zero-dimension triangulation\n"; - return; // ----- >> + std::cerr << "Alpha_complex cannot create_complex from a zero-dimension triangulation\n"; + return false; // ----- >> } - if (num_vertices() > 0) { - std::cerr << "Alpha_complex init - Cannot init twice\n"; - return; // ----- >> + if (complex.num_vertices() > 0) { + std::cerr << "Alpha_complex create_complex - complex is not empty\n"; + return false; // ----- >> } - set_dimension(triangulation_->maximal_dimension()); - - // -------------------------------------------------------------------------------------------- - // double map to retrieve simplex tree vertex handles from CGAL vertex iterator and vice versa - // Loop on triangulation vertices list - for (CGAL_vertex_iterator vit = triangulation_->vertices_begin(); vit != triangulation_->vertices_end(); ++vit) { - if (!triangulation_->is_infinite(*vit)) { -#ifdef DEBUG_TRACES - std::cout << "Vertex insertion - " << vit->data() << " -> " << vit->point() << std::endl; -#endif // DEBUG_TRACES - vertex_handle_to_iterator_.emplace(vit->data(), vit); - } - } - // -------------------------------------------------------------------------------------------- + complex.set_dimension(triangulation_->maximal_dimension()); // -------------------------------------------------------------------------------------------- // Simplex_tree construction from loop on triangulation finite full cells list - for (auto cit = triangulation_->finite_full_cells_begin(); cit != triangulation_->finite_full_cells_end(); ++cit) { - Vector_vertex vertexVector; + if (triangulation_->number_of_vertices() > 0) { + for (auto cit = triangulation_->finite_full_cells_begin(); cit != triangulation_->finite_full_cells_end(); ++cit) { + Vector_vertex vertexVector; #ifdef DEBUG_TRACES - std::cout << "Simplex_tree insertion "; + std::cout << "Simplex_tree insertion "; #endif // DEBUG_TRACES - for (auto vit = cit->vertices_begin(); vit != cit->vertices_end(); ++vit) { - if (*vit != nullptr) { + for (auto vit = cit->vertices_begin(); vit != cit->vertices_end(); ++vit) { + if (*vit != nullptr) { #ifdef DEBUG_TRACES - std::cout << " " << (*vit)->data(); + std::cout << " " << (*vit)->data(); #endif // DEBUG_TRACES - // Vector of vertex construction for simplex_tree structure - vertexVector.push_back((*vit)->data()); + // Vector of vertex construction for simplex_tree structure + vertexVector.push_back((*vit)->data()); + } } - } #ifdef DEBUG_TRACES - std::cout << std::endl; + std::cout << std::endl; #endif // DEBUG_TRACES - // Insert each simplex and its subfaces in the simplex tree - filtration is NaN - insert_simplex_and_subfaces(vertexVector, std::numeric_limits<double>::quiet_NaN()); + // Insert each simplex and its subfaces in the simplex tree - filtration is NaN + complex.insert_simplex_and_subfaces(vertexVector, std::numeric_limits<double>::quiet_NaN()); + } } // -------------------------------------------------------------------------------------------- @@ -290,16 +300,16 @@ class Alpha_complex : public Simplex_tree<> { // Will be re-used many times Vector_of_CGAL_points pointVector; // ### For i : d -> 0 - for (int decr_dim = dimension(); decr_dim >= 0; decr_dim--) { + for (int decr_dim = triangulation_->maximal_dimension(); decr_dim >= 0; decr_dim--) { // ### Foreach Sigma of dim i - for (auto f_simplex : skeleton_simplex_range(decr_dim)) { - int f_simplex_dim = dimension(f_simplex); + for (Simplex_handle f_simplex : complex.skeleton_simplex_range(decr_dim)) { + int f_simplex_dim = complex.dimension(f_simplex); if (decr_dim == f_simplex_dim) { pointVector.clear(); #ifdef DEBUG_TRACES std::cout << "Sigma of dim " << decr_dim << " is"; #endif // DEBUG_TRACES - for (auto vertex : simplex_vertex_range(f_simplex)) { + for (auto vertex : complex.simplex_vertex_range(f_simplex)) { pointVector.push_back(get_point(vertex)); #ifdef DEBUG_TRACES std::cout << " " << vertex; @@ -309,7 +319,7 @@ class Alpha_complex : public Simplex_tree<> { std::cout << std::endl; #endif // DEBUG_TRACES // ### If filt(Sigma) is NaN : filt(Sigma) = alpha(Sigma) - if (std::isnan(filtration(f_simplex))) { + if (std::isnan(complex.filtration(f_simplex))) { Filtration_value alpha_complex_filtration = 0.0; // No need to compute squared_radius on a single point - alpha is 0.0 if (f_simplex_dim > 0) { @@ -318,12 +328,12 @@ class Alpha_complex : public Simplex_tree<> { alpha_complex_filtration = squared_radius(pointVector.begin(), pointVector.end()); } - assign_filtration(f_simplex, alpha_complex_filtration); + complex.assign_filtration(f_simplex, alpha_complex_filtration); #ifdef DEBUG_TRACES - std::cout << "filt(Sigma) is NaN : filt(Sigma) =" << filtration(f_simplex) << std::endl; + std::cout << "filt(Sigma) is NaN : filt(Sigma) =" << complex.filtration(f_simplex) << std::endl; #endif // DEBUG_TRACES } - propagate_alpha_filtration(f_simplex, decr_dim); + propagate_alpha_filtration(complex, f_simplex, decr_dim); } } } @@ -331,36 +341,41 @@ class Alpha_complex : public Simplex_tree<> { // -------------------------------------------------------------------------------------------- // As Alpha value is an approximation, we have to make filtration non decreasing while increasing the dimension - bool modified_filt = make_filtration_non_decreasing(); + complex.make_filtration_non_decreasing(); // Remove all simplices that have a filtration value greater than max_alpha_square - // Remark: prune_above_filtration does not require initialize_filtration to be done before. - modified_filt |= prune_above_filtration(max_alpha_square); - if (modified_filt) { - initialize_filtration(); - } + complex.prune_above_filtration(max_alpha_square); // -------------------------------------------------------------------------------------------- + return true; } - template<typename Simplex_handle> - void propagate_alpha_filtration(Simplex_handle f_simplex, int decr_dim) { + private: + template <typename SimplicialComplexForAlpha, typename Simplex_handle> + void propagate_alpha_filtration(SimplicialComplexForAlpha& complex, Simplex_handle f_simplex, int decr_dim) { + // From SimplicialComplexForAlpha type required to assign filtration values. + typedef typename SimplicialComplexForAlpha::Filtration_value Filtration_value; +#ifdef DEBUG_TRACES + typedef typename SimplicialComplexForAlpha::Vertex_handle Vertex_handle; +#endif // DEBUG_TRACES + // ### Foreach Tau face of Sigma - for (auto f_boundary : boundary_simplex_range(f_simplex)) { + for (auto f_boundary : complex.boundary_simplex_range(f_simplex)) { #ifdef DEBUG_TRACES std::cout << " | --------------------------------------------------\n"; std::cout << " | Tau "; - for (auto vertex : simplex_vertex_range(f_boundary)) { + for (auto vertex : complex.simplex_vertex_range(f_boundary)) { std::cout << vertex << " "; } std::cout << "is a face of Sigma\n"; - std::cout << " | isnan(filtration(Tau)=" << std::isnan(filtration(f_boundary)) << std::endl; + std::cout << " | isnan(complex.filtration(Tau)=" << std::isnan(complex.filtration(f_boundary)) << std::endl; #endif // DEBUG_TRACES // ### If filt(Tau) is not NaN - if (!std::isnan(filtration(f_boundary))) { + if (!std::isnan(complex.filtration(f_boundary))) { // ### filt(Tau) = fmin(filt(Tau), filt(Sigma)) - Filtration_value alpha_complex_filtration = fmin(filtration(f_boundary), filtration(f_simplex)); - assign_filtration(f_boundary, alpha_complex_filtration); + Filtration_value alpha_complex_filtration = fmin(complex.filtration(f_boundary), + complex.filtration(f_simplex)); + complex.assign_filtration(f_boundary, alpha_complex_filtration); #ifdef DEBUG_TRACES - std::cout << " | filt(Tau) = fmin(filt(Tau), filt(Sigma)) = " << filtration(f_boundary) << std::endl; + std::cout << " | filt(Tau) = fmin(filt(Tau), filt(Sigma)) = " << complex.filtration(f_boundary) << std::endl; #endif // DEBUG_TRACES // ### Else } else { @@ -372,12 +387,12 @@ class Alpha_complex : public Simplex_tree<> { #ifdef DEBUG_TRACES Vertex_handle vertexForGabriel = Vertex_handle(); #endif // DEBUG_TRACES - for (auto vertex : simplex_vertex_range(f_boundary)) { + for (auto vertex : complex.simplex_vertex_range(f_boundary)) { pointVector.push_back(get_point(vertex)); } // Retrieve the Sigma point that is not part of Tau - parameter for is_gabriel function Point_d point_for_gabriel; - for (auto vertex : simplex_vertex_range(f_simplex)) { + for (auto vertex : complex.simplex_vertex_range(f_simplex)) { point_for_gabriel = get_point(vertex); if (std::find(pointVector.begin(), pointVector.end(), point_for_gabriel) == pointVector.end()) { #ifdef DEBUG_TRACES @@ -398,10 +413,10 @@ class Alpha_complex : public Simplex_tree<> { // ### If Tau is not Gabriel of Sigma if (false == is_gab) { // ### filt(Tau) = filt(Sigma) - Filtration_value alpha_complex_filtration = filtration(f_simplex); - assign_filtration(f_boundary, alpha_complex_filtration); + Filtration_value alpha_complex_filtration = complex.filtration(f_simplex); + complex.assign_filtration(f_boundary, alpha_complex_filtration); #ifdef DEBUG_TRACES - std::cout << " | filt(Tau) = filt(Sigma) = " << filtration(f_boundary) << std::endl; + std::cout << " | filt(Tau) = filt(Sigma) = " << complex.filtration(f_boundary) << std::endl; #endif // DEBUG_TRACES } } diff --git a/include/gudhi/Alpha_complex.h~ b/include/gudhi/Alpha_complex.h~ deleted file mode 100644 index a1900cb9..00000000 --- a/include/gudhi/Alpha_complex.h~ +++ /dev/null @@ -1,417 +0,0 @@ -/* 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) 2015 INRIA Saclay (France) - * - * 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 ALPHA_COMPLEX_H_ -#define ALPHA_COMPLEX_H_ - -// to construct a simplex_tree from Delaunay_triangulation -#include <gudhi/graph_simplicial_complex.h> -#include <gudhi/Simplex_tree.h> -#include <gudhi/Debug_utils.h> -// to construct Alpha_complex from a OFF file of points -#include <gudhi/Points_off_io.h> - -#include <stdlib.h> -#include <math.h> // isnan, fmax - -#include <CGAL/Delaunay_triangulation.h> -#include <CGAL/Epick_d.h> -#include <CGAL/Spatial_sort_traits_adapter_d.h> - -#include <iostream> -#include <vector> -#include <string> -#include <limits> // NaN -#include <map> -#include <utility> // std::pair -#include <stdexcept> -#include <numeric> // for std::iota - -namespace Gudhi { - -namespace alphacomplex { - -/** - * \class Alpha_complex Alpha_complex.h gudhi/Alpha_complex.h - * \brief Alpha complex data structure. - * - * \ingroup alpha_complex - * - * \details - * The data structure can be constructed from a CGAL Delaunay triangulation (for more informations on CGAL Delaunay - * triangulation, please refer to the corresponding chapter in page http://doc.cgal.org/latest/Triangulation/) or from - * an OFF file (cf. Points_off_reader). - * - * Please refer to \ref alpha_complex for examples. - * - * The complex is a template class requiring an Epick_d <a target="_blank" - * href="http://doc.cgal.org/latest/Kernel_d/index.html#Chapter_dD_Geometry_Kernel">dD Geometry Kernel</a> - * \cite cgal:s-gkd-15b from CGAL as template, default value is <a target="_blank" - * href="http://doc.cgal.org/latest/Kernel_d/classCGAL_1_1Epick__d.html">CGAL::Epick_d</a> - * < <a target="_blank" href="http://doc.cgal.org/latest/Kernel_23/classCGAL_1_1Dynamic__dimension__tag.html"> - * CGAL::Dynamic_dimension_tag </a> > - * - * \remark When Alpha_complex is constructed with an infinite value of alpha, the complex is a Delaunay complex. - * - */ -template<class Kernel = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>> -class Alpha_complex : public Simplex_tree<> { - public: - // Add an int in TDS to save point index in the structure - typedef CGAL::Triangulation_data_structure<typename Kernel::Dimension, - CGAL::Triangulation_vertex<Kernel, std::ptrdiff_t>, - CGAL::Triangulation_full_cell<Kernel> > TDS; - /** \brief A Delaunay triangulation of a set of points in \f$ \mathbb{R}^D\f$.*/ - typedef CGAL::Delaunay_triangulation<Kernel, TDS> Delaunay_triangulation; - - /** \brief A point in Euclidean space.*/ - typedef typename Kernel::Point_d Point_d; - /** \brief Geometric traits class that provides the geometric types and predicates needed by Delaunay - * triangulations.*/ - typedef Kernel Geom_traits; - - private: - // From Simplex_tree - // Type required to insert into a simplex_tree (with or without subfaces). - typedef std::vector<Vertex_handle> Vector_vertex; - - // Simplex_result is the type returned from simplex_tree insert function. - typedef typename std::pair<Simplex_handle, bool> Simplex_result; - - typedef typename Kernel::Compute_squared_radius_d Squared_Radius; - typedef typename Kernel::Side_of_bounded_sphere_d Is_Gabriel; - typedef typename Kernel::Point_dimension_d Point_Dimension; - - // Type required to compute squared radius, or side of bounded sphere on a vector of points. - typedef typename std::vector<Point_d> Vector_of_CGAL_points; - - // Vertex_iterator type from CGAL. - typedef typename Delaunay_triangulation::Vertex_iterator CGAL_vertex_iterator; - - // size_type type from CGAL. - typedef typename Delaunay_triangulation::size_type size_type; - - // Map type to switch from simplex tree vertex handle to CGAL vertex iterator. - typedef typename std::map< Vertex_handle, CGAL_vertex_iterator > Vector_vertex_iterator; - - private: - /** \brief Vertex iterator vector to switch from simplex tree vertex handle to CGAL vertex iterator. - * Vertex handles are inserted sequentially, starting at 0.*/ - Vector_vertex_iterator vertex_handle_to_iterator_; - /** \brief Pointer on the CGAL Delaunay triangulation.*/ - Delaunay_triangulation* triangulation_; - /** \brief Kernel for triangulation_ functions access.*/ - Kernel kernel_; - - public: - /** \brief Alpha_complex constructor from an OFF file name. - * Uses the Delaunay_triangulation_off_reader to construct the Delaunay triangulation required to initialize - * the Alpha_complex. - * - * Duplicate points are inserted once in the Alpha_complex. This is the reason why the vertices may be not contiguous. - * - * @param[in] off_file_name OFF file [path and] name. - * @param[in] max_alpha_square maximum for alpha square value. Default value is +\f$\infty\f$. - */ - Alpha_complex(const std::string& off_file_name, - Filtration_value max_alpha_square = std::numeric_limits<Filtration_value>::infinity()) - : triangulation_(nullptr) { - Gudhi::Points_off_reader<Point_d> off_reader(off_file_name); - if (!off_reader.is_valid()) { - std::cerr << "Alpha_complex - Unable to read file " << off_file_name << "\n"; - exit(-1); // ----- >> - } - - init_from_range(off_reader.get_point_cloud(), max_alpha_square); - } - - /** \brief Alpha_complex constructor from a list of points. - * - * Duplicate points are inserted once in the Alpha_complex. This is the reason why the vertices may be not contiguous. - * - * @param[in] points Range of points to triangulate. Points must be in Kernel::Point_d - * @param[in] max_alpha_square maximum for alpha square value. Default value is +\f$\infty\f$. - * - * The type InputPointRange must be a range for which std::begin and - * std::end return input iterators on a Kernel::Point_d. - * - * @post Compare num_simplices with InputPointRange points number (not the same in case of duplicate points). - */ - template<typename InputPointRange > - Alpha_complex(const InputPointRange& points, - Filtration_value max_alpha_square = std::numeric_limits<Filtration_value>::infinity()) - : triangulation_(nullptr) { - init_from_range(points, max_alpha_square); - } - - /** \brief Alpha_complex destructor. - * - * @warning Deletes the Delaunay triangulation. - */ - ~Alpha_complex() { - delete triangulation_; - } - - // Forbid copy/move constructor/assignment operator - Alpha_complex(const Alpha_complex& other) = delete; - Alpha_complex& operator= (const Alpha_complex& other) = delete; - Alpha_complex (Alpha_complex&& other) = delete; - Alpha_complex& operator= (Alpha_complex&& other) = delete; - - /** \brief get_point returns the point corresponding to the vertex given as parameter. - * - * @param[in] vertex Vertex handle of the point to retrieve. - * @return The point found. - * @exception std::out_of_range In case vertex is not found (cf. std::vector::at). - */ - Point_d get_point(Vertex_handle vertex) const { - return vertex_handle_to_iterator_.at(vertex)->point(); - } - - private: - template<typename InputPointRange > - void init_from_range(const InputPointRange& points, Filtration_value max_alpha_square) { - auto first = std::begin(points); - auto last = std::end(points); - if (first != last) { - // point_dimension function initialization - Point_Dimension point_dimension = kernel_.point_dimension_d_object(); - - // Delaunay triangulation is point dimension. - triangulation_ = new Delaunay_triangulation(point_dimension(*first)); - - std::vector<Point_d> points(first, last); - - // Creates a vector {0, 1, ..., N-1} - std::vector<std::ptrdiff_t> indices(boost::counting_iterator<std::ptrdiff_t>(0), - boost::counting_iterator<std::ptrdiff_t>(points.size())); - - // Sort indices considering CGAL spatial sort - typedef CGAL::Spatial_sort_traits_adapter_d<Kernel, Point_d*> Search_traits_d; - spatial_sort(indices.begin(), indices.end(), Search_traits_d(&(points[0]))); - - typename Delaunay_triangulation::Full_cell_handle hint; - for (auto index : indices) { - typename Delaunay_triangulation::Vertex_handle pos = triangulation_->insert(points[index], hint); - // Save index value as data to retrieve it after insertion - pos->data() = index; - hint = pos->full_cell(); - } - init(max_alpha_square); - } - } - - /** \brief Initialize the Alpha_complex from the Delaunay triangulation. - * - * @param[in] max_alpha_square maximum for alpha square value. - * - * @warning Delaunay triangulation must be already constructed with at least one vertex and dimension must be more - * than 0. - * - * Initialization can be launched once. - */ - void init(Filtration_value max_alpha_square) { - if (triangulation_ == nullptr) { - std::cerr << "Alpha_complex init - Cannot init from a NULL triangulation\n"; - return; // ----- >> - } - if (triangulation_->number_of_vertices() < 1) { - std::cerr << "Alpha_complex init - Cannot init from a triangulation without vertices\n"; - return; // ----- >> - } - if (triangulation_->maximal_dimension() < 1) { - std::cerr << "Alpha_complex init - Cannot init from a zero-dimension triangulation\n"; - return; // ----- >> - } - if (num_vertices() > 0) { - std::cerr << "Alpha_complex init - Cannot init twice\n"; - return; // ----- >> - } - - set_dimension(triangulation_->maximal_dimension()); - - // -------------------------------------------------------------------------------------------- - // double map to retrieve simplex tree vertex handles from CGAL vertex iterator and vice versa - // Loop on triangulation vertices list - for (CGAL_vertex_iterator vit = triangulation_->vertices_begin(); vit != triangulation_->vertices_end(); ++vit) { - if (!triangulation_->is_infinite(*vit)) { -#ifdef DEBUG_TRACES - std::cout << "Vertex insertion - " << vit->data() << " -> " << vit->point() << std::endl; -#endif // DEBUG_TRACES - vertex_handle_to_iterator_.emplace(vit->data(), vit); - } - } - // -------------------------------------------------------------------------------------------- - - // -------------------------------------------------------------------------------------------- - // Simplex_tree construction from loop on triangulation finite full cells list - for (auto cit = triangulation_->finite_full_cells_begin(); cit != triangulation_->finite_full_cells_end(); ++cit) { - Vector_vertex vertexVector; -#ifdef DEBUG_TRACES - std::cout << "Simplex_tree insertion "; -#endif // DEBUG_TRACES - for (auto vit = cit->vertices_begin(); vit != cit->vertices_end(); ++vit) { - if (*vit != nullptr) { -#ifdef DEBUG_TRACES - std::cout << " " << (*vit)->data(); -#endif // DEBUG_TRACES - // Vector of vertex construction for simplex_tree structure - vertexVector.push_back((*vit)->data()); - } - } -#ifdef DEBUG_TRACES - std::cout << std::endl; -#endif // DEBUG_TRACES - // Insert each simplex and its subfaces in the simplex tree - filtration is NaN - insert_simplex_and_subfaces(vertexVector, std::numeric_limits<double>::quiet_NaN()); - } - // -------------------------------------------------------------------------------------------- - - // -------------------------------------------------------------------------------------------- - // Will be re-used many times - Vector_of_CGAL_points pointVector; - // ### For i : d -> 0 - for (int decr_dim = dimension(); decr_dim >= 0; decr_dim--) { - // ### Foreach Sigma of dim i - for (auto f_simplex : skeleton_simplex_range(decr_dim)) { - int f_simplex_dim = dimension(f_simplex); - if (decr_dim == f_simplex_dim) { - pointVector.clear(); -#ifdef DEBUG_TRACES - std::cout << "Sigma of dim " << decr_dim << " is"; -#endif // DEBUG_TRACES - for (auto vertex : simplex_vertex_range(f_simplex)) { - pointVector.push_back(get_point(vertex)); -#ifdef DEBUG_TRACES - std::cout << " " << vertex; -#endif // DEBUG_TRACES - } -#ifdef DEBUG_TRACES - std::cout << std::endl; -#endif // DEBUG_TRACES - // ### If filt(Sigma) is NaN : filt(Sigma) = alpha(Sigma) - if (isnan(filtration(f_simplex))) { - Filtration_value alpha_complex_filtration = 0.0; - // No need to compute squared_radius on a single point - alpha is 0.0 - if (f_simplex_dim > 0) { - // squared_radius function initialization - Squared_Radius squared_radius = kernel_.compute_squared_radius_d_object(); - - alpha_complex_filtration = squared_radius(pointVector.begin(), pointVector.end()); - } - assign_filtration(f_simplex, alpha_complex_filtration); -#ifdef DEBUG_TRACES - std::cout << "filt(Sigma) is NaN : filt(Sigma) =" << filtration(f_simplex) << std::endl; -#endif // DEBUG_TRACES - } - propagate_alpha_filtration(f_simplex, decr_dim); - } - } - } - // -------------------------------------------------------------------------------------------- - - // -------------------------------------------------------------------------------------------- - // As Alpha value is an approximation, we have to make filtration non decreasing while increasing the dimension - bool modified_filt = make_filtration_non_decreasing(); - // Remove all simplices that have a filtration value greater than max_alpha_square - // Remark: prune_above_filtration does not require initialize_filtration to be done before. - modified_filt |= prune_above_filtration(max_alpha_square); - if (modified_filt) { - initialize_filtration(); - } - // -------------------------------------------------------------------------------------------- - } - - template<typename Simplex_handle> - void propagate_alpha_filtration(Simplex_handle f_simplex, int decr_dim) { - // ### Foreach Tau face of Sigma - for (auto f_boundary : boundary_simplex_range(f_simplex)) { -#ifdef DEBUG_TRACES - std::cout << " | --------------------------------------------------\n"; - std::cout << " | Tau "; - for (auto vertex : simplex_vertex_range(f_boundary)) { - std::cout << vertex << " "; - } - std::cout << "is a face of Sigma\n"; - std::cout << " | isnan(filtration(Tau)=" << isnan(filtration(f_boundary)) << std::endl; -#endif // DEBUG_TRACES - // ### If filt(Tau) is not NaN - if (!isnan(filtration(f_boundary))) { - // ### filt(Tau) = fmin(filt(Tau), filt(Sigma)) - Filtration_value alpha_complex_filtration = fmin(filtration(f_boundary), filtration(f_simplex)); - assign_filtration(f_boundary, alpha_complex_filtration); -#ifdef DEBUG_TRACES - std::cout << " | filt(Tau) = fmin(filt(Tau), filt(Sigma)) = " << filtration(f_boundary) << std::endl; -#endif // DEBUG_TRACES - // ### Else - } else { - // No need to compute is_gabriel for dimension <= 2 - // i.e. : Sigma = (3,1) => Tau = 1 - if (decr_dim > 1) { - // insert the Tau points in a vector for is_gabriel function - Vector_of_CGAL_points pointVector; -#ifdef DEBUG_TRACES - Vertex_handle vertexForGabriel = Vertex_handle(); -#endif // DEBUG_TRACES - for (auto vertex : simplex_vertex_range(f_boundary)) { - pointVector.push_back(get_point(vertex)); - } - // Retrieve the Sigma point that is not part of Tau - parameter for is_gabriel function - Point_d point_for_gabriel; - for (auto vertex : simplex_vertex_range(f_simplex)) { - point_for_gabriel = get_point(vertex); - if (std::find(pointVector.begin(), pointVector.end(), point_for_gabriel) == pointVector.end()) { -#ifdef DEBUG_TRACES - // vertex is not found in Tau - vertexForGabriel = vertex; -#endif // DEBUG_TRACES - // No need to continue loop - break; - } - } - // is_gabriel function initialization - Is_Gabriel is_gabriel = kernel_.side_of_bounded_sphere_d_object(); - bool is_gab = is_gabriel(pointVector.begin(), pointVector.end(), point_for_gabriel) - != CGAL::ON_BOUNDED_SIDE; -#ifdef DEBUG_TRACES - std::cout << " | Tau is_gabriel(Sigma)=" << is_gab << " - vertexForGabriel=" << vertexForGabriel << std::endl; -#endif // DEBUG_TRACES - // ### If Tau is not Gabriel of Sigma - if (false == is_gab) { - // ### filt(Tau) = filt(Sigma) - Filtration_value alpha_complex_filtration = filtration(f_simplex); - assign_filtration(f_boundary, alpha_complex_filtration); -#ifdef DEBUG_TRACES - std::cout << " | filt(Tau) = filt(Sigma) = " << filtration(f_boundary) << std::endl; -#endif // DEBUG_TRACES - } - } - } - } - } -}; - -} // namespace alphacomplex - -} // namespace Gudhi - -#endif // ALPHA_COMPLEX_H_ diff --git a/include/gudhi/Bottleneck.h b/include/gudhi/Bottleneck.h new file mode 100644 index 00000000..b90a0ee0 --- /dev/null +++ b/include/gudhi/Bottleneck.h @@ -0,0 +1,115 @@ +/* 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: Francois Godi + * + * Copyright (C) 2015 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 BOTTLENECK_H_ +#define BOTTLENECK_H_ + +#include <gudhi/Graph_matching.h> + +#include <vector> +#include <algorithm> // for max +#include <limits> // for numeric_limits + +#include <cmath> + +namespace Gudhi { + +namespace persistence_diagram { + +double bottleneck_distance_approx(Persistence_graph& g, double e) { + double b_lower_bound = 0.; + double b_upper_bound = g.diameter_bound(); + const double alpha = std::pow(g.size(), 1. / 5.); + Graph_matching m(g); + Graph_matching biggest_unperfect(g); + while (b_upper_bound - b_lower_bound > 2 * e) { + double step = b_lower_bound + (b_upper_bound - b_lower_bound) / alpha; + if (step <= b_lower_bound || step >= b_upper_bound) // Avoid precision problem + break; + m.set_r(step); + while (m.multi_augment()) {}; // compute a maximum matching (in the graph corresponding to the current r) + if (m.perfect()) { + m = biggest_unperfect; + b_upper_bound = step; + } else { + biggest_unperfect = m; + b_lower_bound = step; + } + } + return (b_lower_bound + b_upper_bound) / 2.; +} + +double bottleneck_distance_exact(Persistence_graph& g) { + std::vector<double> sd = g.sorted_distances(); + long lower_bound_i = 0; + long upper_bound_i = sd.size() - 1; + const double alpha = std::pow(g.size(), 1. / 5.); + Graph_matching m(g); + Graph_matching biggest_unperfect(g); + while (lower_bound_i != upper_bound_i) { + long step = lower_bound_i + static_cast<long> ((upper_bound_i - lower_bound_i - 1) / alpha); + m.set_r(sd.at(step)); + while (m.multi_augment()) {}; // compute a maximum matching (in the graph corresponding to the current r) + if (m.perfect()) { + m = biggest_unperfect; + upper_bound_i = step; + } else { + biggest_unperfect = m; + lower_bound_i = step + 1; + } + } + return sd.at(lower_bound_i); +} + +/** \brief Function to compute the Bottleneck distance between two persistence diagrams. + * + * \tparam Persistence_diagram1,Persistence_diagram2 + * models of the concept `PersistenceDiagram`. + * \param[in] e + * \parblock + * If `e` is 0, this uses an expensive algorithm to compute the exact distance. + * + * If `e` is not 0, it asks for an additive `e`-approximation, and currently + * also allows a small multiplicative error (the last 2 or 3 bits of the + * mantissa may be wrong). This version of the algorithm takes advantage of the + * limited precision of `double` and is usually a lot faster to compute, + * whatever the value of `e`. + * + * Thus, by default, `e` is the smallest positive double. + * \endparblock + * + * \ingroup bottleneck_distance + */ +template<typename Persistence_diagram1, typename Persistence_diagram2> +double bottleneck_distance(const Persistence_diagram1 &diag1, const Persistence_diagram2 &diag2, + double e = std::numeric_limits<double>::min()) { + Persistence_graph g(diag1, diag2, e); + if (g.bottleneck_alive() == std::numeric_limits<double>::infinity()) + return std::numeric_limits<double>::infinity(); + return std::max(g.bottleneck_alive(), e == 0. ? bottleneck_distance_exact(g) : bottleneck_distance_approx(g, e)); +} + +} // namespace persistence_diagram + +} // namespace Gudhi + +#endif // BOTTLENECK_H_ diff --git a/include/gudhi/Clock.h b/include/gudhi/Clock.h index 04c6ffb9..77f196ca 100644 --- a/include/gudhi/Clock.h +++ b/include/gudhi/Clock.h @@ -27,47 +27,55 @@ #include <string> +namespace Gudhi { + class Clock { public: - Clock() : end_called(false) { - startTime = boost::posix_time::microsec_clock::local_time(); - } - - Clock(const std::string& msg_) { - end_called = false; - begin(); - msg = msg_; - } + // Construct and start the timer + Clock(const std::string& msg_ = std::string()) + : startTime(boost::posix_time::microsec_clock::local_time()), + end_called(false), + msg(msg_) { } + // Restart the timer void begin() const { end_called = false; startTime = boost::posix_time::microsec_clock::local_time(); } + // Stop the timer void end() const { end_called = true; endTime = boost::posix_time::microsec_clock::local_time(); } + std::string message() const { + return msg; + } + + // Print current value to std::cout void print() const { std::cout << *this << std::endl; } friend std::ostream& operator<<(std::ostream& stream, const Clock& clock) { - if (!clock.end_called) - clock.end(); + if (!clock.msg.empty()) + stream << clock.msg << ": "; - if (!clock.end_called) { - stream << "end not called"; - } else { - stream << clock.msg << ":" << clock.num_seconds() << "s"; - } + stream << clock.num_seconds() << "s"; return stream; } + // Get the number of seconds between the timer start and: + // - the last call of end() if it was called + // - or now otherwise. In this case, the timer is not stopped. double num_seconds() const { - if (!end_called) return -1; - return (endTime - startTime).total_milliseconds() / 1000.; + if (!end_called) { + auto end = boost::posix_time::microsec_clock::local_time(); + return (end - startTime).total_milliseconds() / 1000.; + } else { + return (endTime - startTime).total_milliseconds() / 1000.; + } } private: @@ -76,4 +84,6 @@ class Clock { std::string msg; }; -#endif // CLOCK_H_ +} // namespace Gudhi + +#endif // CLOCK_H_ diff --git a/include/gudhi/Debug_utils.h b/include/gudhi/Debug_utils.h index 7573a9db..8ed3b7b3 100644 --- a/include/gudhi/Debug_utils.h +++ b/include/gudhi/Debug_utils.h @@ -33,8 +33,10 @@ // Could assert in release mode, but cmake sets NDEBUG (for "NO DEBUG") in this mode, means assert does nothing. #ifdef GUDHI_DEBUG #define GUDHI_CHECK(expression, excpt) if ((expression) == 0) throw excpt + #define GUDHI_CHECK_code(CODE) CODE #else #define GUDHI_CHECK(expression, excpt) (void) 0 + #define GUDHI_CHECK_code(CODE) #endif #define PRINT(a) std::cerr << #a << ": " << (a) << " (DISP)" << std::endl diff --git a/include/gudhi/Edge_contraction.h b/include/gudhi/Edge_contraction.h index 5af13c3e..61f2d945 100644 --- a/include/gudhi/Edge_contraction.h +++ b/include/gudhi/Edge_contraction.h @@ -41,7 +41,7 @@ namespace contraction { \author David Salinas -\section Introduction +\section edgecontractionintroduction Introduction The purpose of this package is to offer a user-friendly interface for edge contraction simplification of huge simplicial complexes. It uses the \ref skbl data-structure whose size remains small during simplification diff --git a/include/gudhi/Euclidean_strong_witness_complex.h b/include/gudhi/Euclidean_strong_witness_complex.h new file mode 100644 index 00000000..fb669ef8 --- /dev/null +++ b/include/gudhi/Euclidean_strong_witness_complex.h @@ -0,0 +1,104 @@ +/* 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): Siargey Kachanovich + * + * Copyright (C) 2015 INRIA (France) + * + * 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 EUCLIDEAN_STRONG_WITNESS_COMPLEX_H_ +#define EUCLIDEAN_STRONG_WITNESS_COMPLEX_H_ + +#include <gudhi/Strong_witness_complex.h> +#include <gudhi/Active_witness/Active_witness.h> +#include <gudhi/Kd_tree_search.h> + +#include <utility> +#include <vector> + +namespace Gudhi { + +namespace witness_complex { + +/** + * \private + * \class Euclidean_strong_witness_complex + * \brief Constructs strong witness complex for given sets of witnesses and landmarks in Euclidean space. + * \ingroup witness_complex + * + * \tparam Kernel_ requires a <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_d/classCGAL_1_1Epick__d.html">CGAL::Epick_d</a> class. + */ +template< class Kernel_ > +class Euclidean_strong_witness_complex + : public Strong_witness_complex<std::vector<typename Gudhi::spatial_searching::Kd_tree_search<Kernel_, + std::vector<typename Kernel_::Point_d>>::INS_range>> { + private: + typedef Kernel_ K; + typedef typename K::Point_d Point_d; + typedef std::vector<Point_d> Point_range; + typedef Gudhi::spatial_searching::Kd_tree_search<Kernel_, Point_range> Kd_tree; + typedef typename Kd_tree::INS_range Nearest_landmark_range; + typedef typename std::vector<Nearest_landmark_range> Nearest_landmark_table; + + typedef typename Nearest_landmark_range::Point_with_transformed_distance Id_distance_pair; + typedef typename Id_distance_pair::first_type Landmark_id; + typedef Landmark_id Vertex_handle; + + private: + Point_range landmarks_; + Kd_tree landmark_tree_; + using Strong_witness_complex<Nearest_landmark_table>::nearest_landmark_table_; + + public: + ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + /* @name Constructor + */ + + //@{ + + /** + * \brief Initializes member variables before constructing simplicial complex. + * \details Records landmarks from the range 'landmarks' into a + * table internally, as well as witnesses from the range 'witnesses'. + * Both ranges should have value_type Kernel_::Point_d. + */ + template< typename LandmarkRange, + typename WitnessRange > + Euclidean_strong_witness_complex(const LandmarkRange & landmarks, + const WitnessRange & witnesses) + : landmarks_(std::begin(landmarks), std::end(landmarks)), landmark_tree_(landmarks_) { + nearest_landmark_table_.reserve(boost::size(witnesses)); + for (auto w : witnesses) + nearest_landmark_table_.push_back(landmark_tree_.query_incremental_nearest_neighbors(w)); + } + + /** \brief Returns the point corresponding to the given vertex. + */ + template <typename Vertex_handle> + Point_d get_point(Vertex_handle vertex) const { + return landmarks_[vertex]; + } + + //@} +}; + +} // namespace witness_complex + +} // namespace Gudhi + +#endif // EUCLIDEAN_STRONG_WITNESS_COMPLEX_H_ diff --git a/include/gudhi/Euclidean_witness_complex.h b/include/gudhi/Euclidean_witness_complex.h new file mode 100644 index 00000000..6afe9a5d --- /dev/null +++ b/include/gudhi/Euclidean_witness_complex.h @@ -0,0 +1,106 @@ +/* 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): Siargey Kachanovich + * + * Copyright (C) 2015 INRIA (France) + * + * 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 EUCLIDEAN_WITNESS_COMPLEX_H_ +#define EUCLIDEAN_WITNESS_COMPLEX_H_ + +#include <gudhi/Witness_complex.h> +#include <gudhi/Active_witness/Active_witness.h> +#include <gudhi/Kd_tree_search.h> + +#include <utility> +#include <vector> +#include <list> +#include <limits> + +namespace Gudhi { + +namespace witness_complex { + +/** + * \private + * \class Euclidean_witness_complex + * \brief Constructs (weak) witness complex for given sets of witnesses and landmarks in Euclidean space. + * \ingroup witness_complex + * + * \tparam Kernel_ requires a <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_d/classCGAL_1_1Epick__d.html">CGAL::Epick_d</a> class. + */ +template< class Kernel_ > +class Euclidean_witness_complex + : public Witness_complex<std::vector<typename Gudhi::spatial_searching::Kd_tree_search<Kernel_, + std::vector<typename Kernel_::Point_d>>::INS_range>> { + private: + typedef Kernel_ K; + typedef typename K::Point_d Point_d; + typedef std::vector<Point_d> Point_range; + typedef Gudhi::spatial_searching::Kd_tree_search<Kernel_, Point_range> Kd_tree; + typedef typename Kd_tree::INS_range Nearest_landmark_range; + typedef typename std::vector<Nearest_landmark_range> Nearest_landmark_table; + + typedef typename Nearest_landmark_range::Point_with_transformed_distance Id_distance_pair; + typedef typename Id_distance_pair::first_type Landmark_id; + typedef Landmark_id Vertex_handle; + + private: + Point_range landmarks_; + Kd_tree landmark_tree_; + using Witness_complex<Nearest_landmark_table>::nearest_landmark_table_; + + public: + ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + /* @name Constructor + */ + + //@{ + + /** + * \brief Initializes member variables before constructing simplicial complex. + * \details Records landmarks from the range 'landmarks' into a + * table internally, as well as witnesses from the range 'witnesses'. + * Both ranges should have value_type Kernel_::Point_d. + */ + template< typename LandmarkRange, + typename WitnessRange > + Euclidean_witness_complex(const LandmarkRange & landmarks, + const WitnessRange & witnesses) + : landmarks_(std::begin(landmarks), std::end(landmarks)), landmark_tree_(landmarks) { + nearest_landmark_table_.reserve(boost::size(witnesses)); + for (auto w : witnesses) + nearest_landmark_table_.push_back(landmark_tree_.query_incremental_nearest_neighbors(w)); + } + + /** \brief Returns the point corresponding to the given vertex. + * @param[in] vertex Vertex handle of the point to retrieve. + */ + Point_d get_point(Vertex_handle vertex) const { + return landmarks_[vertex]; + } + + //@} +}; + +} // namespace witness_complex + +} // namespace Gudhi + +#endif // EUCLIDEAN_WITNESS_COMPLEX_H_ diff --git a/include/gudhi/Graph_matching.h b/include/gudhi/Graph_matching.h new file mode 100644 index 00000000..f51e22e9 --- /dev/null +++ b/include/gudhi/Graph_matching.h @@ -0,0 +1,174 @@ +/* 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: Francois Godi + * + * Copyright (C) 2015 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 GRAPH_MATCHING_H_ +#define GRAPH_MATCHING_H_ + +#include <gudhi/Neighbors_finder.h> + +#include <vector> +#include <unordered_set> +#include <algorithm> + +namespace Gudhi { + +namespace persistence_diagram { + +/** \internal \brief Structure representing a graph matching. The graph is a Persistence_diagrams_graph. + * + * \ingroup bottleneck_distance + */ +class Graph_matching { + public: + /** \internal \brief Constructor constructing an empty matching. */ + explicit Graph_matching(Persistence_graph &g); + /** \internal \brief Is the matching perfect ? */ + bool perfect() const; + /** \internal \brief Augments the matching with a maximal set of edge-disjoint shortest augmenting paths. */ + bool multi_augment(); + /** \internal \brief Sets the maximum length of the edges allowed to be added in the matching, 0 initially. */ + void set_r(double r); + + private: + Persistence_graph* gp; + double r; + /** \internal \brief Given a point from V, provides its matched point in U, null_point_index() if there isn't. */ + std::vector<int> v_to_u; + /** \internal \brief All the unmatched points in U. */ + std::unordered_set<int> unmatched_in_u; + + /** \internal \brief Provides a Layered_neighbors_finder dividing the graph in layers. Basically a BFS. */ + Layered_neighbors_finder layering() const; + /** \internal \brief Augments the matching with a simple path no longer than max_depth. Basically a DFS. */ + bool augment(Layered_neighbors_finder & layered_nf, int u_start_index, int max_depth); + /** \internal \brief Update the matching with the simple augmenting path given as parameter. */ + void update(std::vector<int> & path); +}; + +inline Graph_matching::Graph_matching(Persistence_graph& g) + : gp(&g), r(0.), v_to_u(g.size(), null_point_index()), unmatched_in_u(g.size()) { + for (int u_point_index = 0; u_point_index < g.size(); ++u_point_index) + unmatched_in_u.insert(u_point_index); +} + +inline bool Graph_matching::perfect() const { + return unmatched_in_u.empty(); +} + +inline bool Graph_matching::multi_augment() { + if (perfect()) + return false; + Layered_neighbors_finder layered_nf(layering()); + int max_depth = layered_nf.vlayers_number()*2 - 1; + double rn = sqrt(gp->size()); + // verification of a necessary criterion in order to shortcut if possible + if (max_depth < 0 || (unmatched_in_u.size() > rn && max_depth >= rn)) + return false; + bool successful = false; + std::vector<int> tries(unmatched_in_u.cbegin(), unmatched_in_u.cend()); + for (auto it = tries.cbegin(); it != tries.cend(); it++) + // 'augment' has side-effects which have to be always executed, don't change order + successful = augment(layered_nf, *it, max_depth) || successful; + return successful; +} + +inline void Graph_matching::set_r(double r) { + this->r = r; +} + +inline bool Graph_matching::augment(Layered_neighbors_finder & layered_nf, int u_start_index, int max_depth) { + // V vertices have at most one successor, thus when we backtrack from U we can directly pop_back 2 vertices. + std::vector<int> path; + path.emplace_back(u_start_index); + do { + if (static_cast<int> (path.size()) > max_depth) { + path.pop_back(); + path.pop_back(); + } + if (path.empty()) + return false; + path.emplace_back(layered_nf.pull_near(path.back(), static_cast<int> (path.size()) / 2)); + while (path.back() == null_point_index()) { + path.pop_back(); + path.pop_back(); + if (path.empty()) + return false; + path.pop_back(); + path.emplace_back(layered_nf.pull_near(path.back(), path.size() / 2)); + } + path.emplace_back(v_to_u.at(path.back())); + } while (path.back() != null_point_index()); + // if v_to_u.at(path.back()) has no successor, path.back() is an exposed vertex + path.pop_back(); + update(path); + return true; +} + +inline Layered_neighbors_finder Graph_matching::layering() const { + std::vector<int> u_vertices(unmatched_in_u.cbegin(), unmatched_in_u.cend()); + std::vector<int> v_vertices; + Neighbors_finder nf(*gp, r); + for (int v_point_index = 0; v_point_index < gp->size(); ++v_point_index) + nf.add(v_point_index); + Layered_neighbors_finder layered_nf(*gp, r); + for (int layer = 0; !u_vertices.empty(); layer++) { + // one layer is one step in the BFS + for (auto it1 = u_vertices.cbegin(); it1 != u_vertices.cend(); ++it1) { + std::vector<int> u_succ(nf.pull_all_near(*it1)); + for (auto it2 = u_succ.begin(); it2 != u_succ.end(); ++it2) { + layered_nf.add(*it2, layer); + v_vertices.emplace_back(*it2); + } + } + // When the above for finishes, we have progress of one half-step (from U to V) in the BFS + u_vertices.clear(); + bool end = false; + for (auto it = v_vertices.cbegin(); it != v_vertices.cend(); it++) + if (v_to_u.at(*it) == null_point_index()) + // we stop when a nearest exposed V vertex (from U exposed vertices) has been found + end = true; + else + u_vertices.emplace_back(v_to_u.at(*it)); + // When the above for finishes, we have progress of one half-step (from V to U) in the BFS + if (end) + return layered_nf; + v_vertices.clear(); + } + return layered_nf; +} + +inline void Graph_matching::update(std::vector<int>& path) { + // Must return 1. + unmatched_in_u.erase(path.front()); + for (auto it = path.cbegin(); it != path.cend(); ++it) { + // Be careful, the iterator is incremented twice each time + int tmp = *it; + v_to_u[*(++it)] = tmp; + } +} + + +} // namespace persistence_diagram + +} // namespace Gudhi + +#endif // GRAPH_MATCHING_H_ diff --git a/include/gudhi/Internal_point.h b/include/gudhi/Internal_point.h new file mode 100644 index 00000000..0b2d26fe --- /dev/null +++ b/include/gudhi/Internal_point.h @@ -0,0 +1,91 @@ +/* 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: Francois Godi + * + * Copyright (C) 2015 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 INTERNAL_POINT_H_ +#define INTERNAL_POINT_H_ + +namespace Gudhi { + +namespace persistence_diagram { + +/** \internal \brief Returns the used index for encoding none of the points */ +int null_point_index(); + +/** \internal \typedef \brief Internal_point is the internal points representation, indexes used outside. */ +struct Internal_point { + double vec[2]; + int point_index; + + Internal_point() { } + + Internal_point(double x, double y, int p_i) { + vec[0] = x; + vec[1] = y; + point_index = p_i; + } + + double x() const { + return vec[ 0 ]; + } + + double y() const { + return vec[ 1 ]; + } + + double& x() { + return vec[ 0 ]; + } + + double& y() { + return vec[ 1 ]; + } + + bool operator==(const Internal_point& p) const { + return point_index == p.point_index; + } + + bool operator!=(const Internal_point& p) const { + return !(*this == p); + } +}; + +inline int null_point_index() { + return -1; +} + +struct Construct_coord_iterator { + typedef const double* result_type; + + const double* operator()(const Internal_point& p) const { + return p.vec; + } + + const double* operator()(const Internal_point& p, int) const { + return p.vec + 2; + } +}; + +} // namespace persistence_diagram + +} // namespace Gudhi + +#endif // INTERNAL_POINT_H_ diff --git a/include/gudhi/Kd_tree_search.h b/include/gudhi/Kd_tree_search.h new file mode 100644 index 00000000..6728d56e --- /dev/null +++ b/include/gudhi/Kd_tree_search.h @@ -0,0 +1,264 @@ +/* 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): Clement Jamin + * + * 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/>. + */ + +#ifndef KD_TREE_SEARCH_H_ +#define KD_TREE_SEARCH_H_ + +#include <CGAL/Orthogonal_k_neighbor_search.h> +#include <CGAL/Orthogonal_incremental_neighbor_search.h> +#include <CGAL/Search_traits.h> +#include <CGAL/Search_traits_adapter.h> +#include <CGAL/property_map.h> + +#include <boost/property_map/property_map.hpp> +#include <boost/iterator/counting_iterator.hpp> + +#include <cstddef> +#include <vector> + +namespace Gudhi { +namespace spatial_searching { + + + /** + * \class Kd_tree_search Kd_tree_search.h gudhi/Kd_tree_search.h + * \brief Spatial tree data structure to perform (approximate) nearest and farthest neighbor search. + * + * \ingroup spatial_searching + * + * \details + * The class Kd_tree_search is a tree-based data structure, based on + * <a target="_blank" href="http://doc.cgal.org/latest/Spatial_searching/index.html">CGAL dD spatial searching data structures</a>. + * It provides a simplified API to perform (approximate) nearest and farthest neighbor searches. Contrary to CGAL default behavior, the tree + * does not store the points themselves, but stores indices. + * + * There are two types of queries: the <i>k-nearest or k-farthest neighbor query</i>, where <i>k</i> is fixed and the <i>k</i> nearest + * or farthest points are computed right away, + * and the <i>incremental nearest or farthest neighbor query</i>, where no number of neighbors is provided during the call, as the + * neighbors will be computed incrementally when the iterator on the range is incremented. + * + * \tparam Search_traits must be a model of the <a target="_blank" + * href="http://doc.cgal.org/latest/Spatial_searching/classSearchTraits.html">SearchTraits</a> + * concept, such as the <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_d/classCGAL_1_1Epick__d.html">CGAL::Epick_d</a> class, which + * can be static if you know the ambiant dimension at compile-time, or dynamic if you don't. + * \tparam Point_range is the type of the range that provides the points. + * It must be a range whose iterator type is a `RandomAccessIterator`. + */ +template <typename Search_traits, typename Point_range> +class Kd_tree_search { + typedef boost::iterator_property_map< + typename Point_range::const_iterator, + CGAL::Identity_property_map<std::ptrdiff_t> > Point_property_map; + + public: + /// The Traits. + typedef Search_traits Traits; + /// Number type used for distances. + typedef typename Traits::FT FT; + /// The point type. + typedef typename Point_range::value_type Point; + + typedef CGAL::Search_traits< + FT, Point, + typename Traits::Cartesian_const_iterator_d, + typename Traits::Construct_cartesian_const_iterator_d> Traits_base; + + typedef CGAL::Search_traits_adapter< + std::ptrdiff_t, + Point_property_map, + Traits_base> STraits; + + typedef CGAL::Orthogonal_k_neighbor_search<STraits> K_neighbor_search; + typedef typename K_neighbor_search::Tree Tree; + typedef typename K_neighbor_search::Distance Distance; + /// \brief The range returned by a k-nearest or k-farthest neighbor search. + /// Its value type is `std::pair<std::size_t, FT>` where `first` is the index + /// of a point P and `second` is the squared distance between P and the query point. + typedef K_neighbor_search KNS_range; + + typedef CGAL::Orthogonal_incremental_neighbor_search< + STraits, Distance, CGAL::Sliding_midpoint<STraits>, Tree> + Incremental_neighbor_search; + /// \brief The range returned by an incremental nearest or farthest neighbor search. + /// Its value type is `std::pair<std::size_t, FT>` where `first` is the index + /// of a point P and `second` is the squared distance between P and the query point. + typedef Incremental_neighbor_search INS_range; + + /// \brief Constructor + /// @param[in] points Const reference to the point range. This range + /// is not copied, so it should not be destroyed or modified afterwards. + Kd_tree_search(Point_range const& points) + : m_points(points), + m_tree(boost::counting_iterator<std::ptrdiff_t>(0), + boost::counting_iterator<std::ptrdiff_t>(points.size()), + typename Tree::Splitter(), + STraits(std::begin(points))) { + // Build the tree now (we don't want to wait for the first query) + m_tree.build(); + } + + /// \brief Constructor + /// @param[in] points Const reference to the point range. This range + /// is not copied, so it should not be destroyed or modified afterwards. + /// @param[in] only_these_points Specifies the indices of the points that + /// should be actually inserted into the tree. The other points are ignored. + template <typename Point_indices_range> + Kd_tree_search( + Point_range const& points, + Point_indices_range const& only_these_points) + : m_points(points), + m_tree( + only_these_points.begin(), only_these_points.end(), + typename Tree::Splitter(), + STraits(std::begin(points))) { + // Build the tree now (we don't want to wait for the first query) + m_tree.build(); + } + + /// \brief Constructor + /// @param[in] points Const reference to the point range. This range + /// is not copied, so it should not be destroyed or modified afterwards. + /// @param[in] begin_idx, past_the_end_idx Define the subset of the points that + /// should be actually inserted into the tree. The other points are ignored. + Kd_tree_search( + Point_range const& points, + std::size_t begin_idx, std::size_t past_the_end_idx) + : m_points(points), + m_tree( + boost::counting_iterator<std::ptrdiff_t>(begin_idx), + boost::counting_iterator<std::ptrdiff_t>(past_the_end_idx), + typename Tree::Splitter(), + STraits(std::begin(points))) { + // Build the tree now (we don't want to wait for the first query) + m_tree.build(); + } + + // Be careful, this function invalidates the tree, + // which will be recomputed at the next query + void insert(std::ptrdiff_t point_idx) { + m_tree.insert(point_idx); + } + + /// \brief Search for the k-nearest neighbors from a query point. + /// @param[in] p The query point. + /// @param[in] k Number of nearest points to search. + /// @param[in] sorted Indicates if the computed sequence of k-nearest neighbors needs to be sorted. + /// @param[in] eps Approximation factor. + /// @return A range containing the k-nearest neighbors. + KNS_range query_k_nearest_neighbors(const + Point &p, + unsigned int k, + bool sorted = true, + FT eps = FT(0)) const { + // Initialize the search structure, and search all N points + // Note that we need to pass the Distance explicitly since it needs to + // know the property map + K_neighbor_search search( + m_tree, + p, + k, + eps, + true, + CGAL::Distance_adapter<std::ptrdiff_t, Point_property_map, CGAL::Euclidean_distance<Traits_base> >( + std::begin(m_points)), sorted); + + return search; + } + + /// \brief Search incrementally for the nearest neighbors from a query point. + /// @param[in] p The query point. + /// @param[in] eps Approximation factor. + /// @return A range containing the neighbors sorted by their distance to p. + /// All the neighbors are not computed by this function, but they will be + /// computed incrementally when the iterator on the range is incremented. + INS_range query_incremental_nearest_neighbors(const Point &p, FT eps = FT(0)) const { + // Initialize the search structure, and search all N points + // Note that we need to pass the Distance explicitly since it needs to + // know the property map + Incremental_neighbor_search search( + m_tree, + p, + eps, + true, + CGAL::Distance_adapter<std::ptrdiff_t, Point_property_map, CGAL::Euclidean_distance<Traits_base> >( + std::begin(m_points)) ); + + return search; + } + + /// \brief Search for the k-farthest points from a query point. + /// @param[in] p The query point. + /// @param[in] k Number of farthest points to search. + /// @param[in] sorted Indicates if the computed sequence of k-farthest neighbors needs to be sorted. + /// @param[in] eps Approximation factor. + /// @return A range containing the k-farthest neighbors. + KNS_range query_k_farthest_neighbors(const + Point &p, + unsigned int k, + bool sorted = true, + FT eps = FT(0)) const { + // Initialize the search structure, and search all N points + // Note that we need to pass the Distance explicitly since it needs to + // know the property map + K_neighbor_search search( + m_tree, + p, + k, + eps, + false, + CGAL::Distance_adapter<std::ptrdiff_t, Point_property_map, CGAL::Euclidean_distance<Traits_base> >( + std::begin(m_points)), sorted); + + return search; + } + + /// \brief Search incrementally for the farthest neighbors from a query point. + /// @param[in] p The query point. + /// @param[in] eps Approximation factor. + /// @return A range containing the neighbors sorted by their distance to p. + /// All the neighbors are not computed by this function, but they will be + /// computed incrementally when the iterator on the range is incremented. + INS_range query_incremental_farthest_neighbors(const Point &p, FT eps = FT(0)) const { + // Initialize the search structure, and search all N points + // Note that we need to pass the Distance explicitly since it needs to + // know the property map + Incremental_neighbor_search search( + m_tree, + p, + eps, + false, + CGAL::Distance_adapter<std::ptrdiff_t, Point_property_map, CGAL::Euclidean_distance<Traits_base> >( + std::begin(m_points)) ); + + return search; + } + + private: + Point_range const& m_points; + Tree m_tree; +}; + +} // namespace spatial_searching +} // namespace Gudhi + +#endif // KD_TREE_SEARCH_H_ diff --git a/include/gudhi/Landmark_choice_by_furthest_point.h b/include/gudhi/Landmark_choice_by_furthest_point.h deleted file mode 100644 index df93155b..00000000 --- a/include/gudhi/Landmark_choice_by_furthest_point.h +++ /dev/null @@ -1,105 +0,0 @@ -/* 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): Siargey Kachanovich - * - * Copyright (C) 2015 INRIA Sophia Antipolis-Méditerranée (France) - * - * 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 LANDMARK_CHOICE_BY_FURTHEST_POINT_H_ -#define LANDMARK_CHOICE_BY_FURTHEST_POINT_H_ - -#include <boost/range/size.hpp> - -#include <limits> // for numeric_limits<> -#include <iterator> -#include <algorithm> // for sort -#include <vector> - -namespace Gudhi { - -namespace witness_complex { - - typedef std::vector<int> typeVectorVertex; - - /** - * \ingroup witness_complex - * \brief Landmark choice strategy by iteratively adding the furthest witness from the - * current landmark set as the new landmark. - * \details It chooses nbL landmarks from a random access range `points` and - * writes {witness}*{closest landmarks} matrix in `knn`. - * - * The type KNearestNeighbors can be seen as - * Witness_range<Closest_landmark_range<Vertex_handle>>, where - * Witness_range and Closest_landmark_range are random access ranges - * - */ - - template <typename KNearestNeighbours, - typename Point_random_access_range> - void landmark_choice_by_furthest_point(Point_random_access_range const &points, - int nbL, - KNearestNeighbours &knn) { - int nb_points = boost::size(points); - assert(nb_points >= nbL); - // distance matrix witness x landmarks - std::vector<std::vector<double>> wit_land_dist(nb_points, std::vector<double>()); - // landmark list - typeVectorVertex chosen_landmarks; - - knn = KNearestNeighbours(nb_points, std::vector<int>()); - int current_number_of_landmarks = 0; // counter for landmarks - double curr_max_dist = 0; // used for defining the furhest point from L - const double infty = std::numeric_limits<double>::infinity(); // infinity (see next entry) - std::vector< double > dist_to_L(nb_points, infty); // vector of current distances to L from points - - // TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety - // or better yet std::uniform_int_distribution - int rand_int = rand() % nb_points; - int curr_max_w = rand_int; // For testing purposes a pseudo-random number is used here - - for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) { - // curr_max_w at this point is the next landmark - chosen_landmarks.push_back(curr_max_w); - unsigned i = 0; - for (auto& p : points) { - double curr_dist = euclidean_distance(p, *(std::begin(points) + chosen_landmarks[current_number_of_landmarks])); - wit_land_dist[i].push_back(curr_dist); - knn[i].push_back(current_number_of_landmarks); - if (curr_dist < dist_to_L[i]) - dist_to_L[i] = curr_dist; - ++i; - } - curr_max_dist = 0; - for (i = 0; i < dist_to_L.size(); i++) - if (dist_to_L[i] > curr_max_dist) { - curr_max_dist = dist_to_L[i]; - curr_max_w = i; - } - } - for (int i = 0; i < nb_points; ++i) - std::sort(std::begin(knn[i]), - std::end(knn[i]), - [&wit_land_dist, i](int a, int b) { - return wit_land_dist[i][a] < wit_land_dist[i][b]; }); - } - -} // namespace witness_complex - -} // namespace Gudhi - -#endif // LANDMARK_CHOICE_BY_FURTHEST_POINT_H_ diff --git a/include/gudhi/Landmark_choice_by_random_point.h b/include/gudhi/Landmark_choice_by_random_point.h deleted file mode 100644 index ebf6aad1..00000000 --- a/include/gudhi/Landmark_choice_by_random_point.h +++ /dev/null @@ -1,96 +0,0 @@ -/* 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): Siargey Kachanovich - * - * Copyright (C) 2015 INRIA Sophia Antipolis-Méditerranée (France) - * - * 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 LANDMARK_CHOICE_BY_RANDOM_POINT_H_ -#define LANDMARK_CHOICE_BY_RANDOM_POINT_H_ - -#include <boost/range/size.hpp> - -#include <queue> // for priority_queue<> -#include <utility> // for pair<> -#include <iterator> -#include <vector> -#include <set> - -namespace Gudhi { - -namespace witness_complex { - - /** - * \ingroup witness_complex - * \brief Landmark choice strategy by taking random vertices for landmarks. - * \details It chooses nbL distinct landmarks from a random access range `points` - * and outputs a matrix {witness}*{closest landmarks} in knn. - * - * The type KNearestNeighbors can be seen as - * Witness_range<Closest_landmark_range<Vertex_handle>>, where - * Witness_range and Closest_landmark_range are random access ranges and - * Vertex_handle is the label type of a vertex in a simplicial complex. - * Closest_landmark_range needs to have push_back operation. - */ - - template <typename KNearestNeighbours, - typename Point_random_access_range> - void landmark_choice_by_random_point(Point_random_access_range const &points, - int nbL, - KNearestNeighbours &knn) { - int nbP = boost::size(points); - assert(nbP >= nbL); - std::set<int> landmarks; - int current_number_of_landmarks = 0; // counter for landmarks - - // TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety - int chosen_landmark = rand() % nbP; - for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) { - while (landmarks.find(chosen_landmark) != landmarks.end()) - chosen_landmark = rand() % nbP; - landmarks.insert(chosen_landmark); - } - - int dim = boost::size(*std::begin(points)); - typedef std::pair<double, int> dist_i; - typedef bool (*comp)(dist_i, dist_i); - knn = KNearestNeighbours(nbP); - for (int points_i = 0; points_i < nbP; points_i++) { - std::priority_queue<dist_i, std::vector<dist_i>, comp> l_heap([](dist_i j1, dist_i j2) { - return j1.first > j2.first; - }); - std::set<int>::iterator landmarks_it; - int landmarks_i = 0; - for (landmarks_it = landmarks.begin(), landmarks_i = 0; landmarks_it != landmarks.end(); - ++landmarks_it, landmarks_i++) { - dist_i dist = std::make_pair(euclidean_distance(points[points_i], points[*landmarks_it]), landmarks_i); - l_heap.push(dist); - } - for (int i = 0; i < dim + 1; i++) { - dist_i dist = l_heap.top(); - knn[points_i].push_back(dist.second); - l_heap.pop(); - } - } - } - -} // namespace witness_complex - -} // namespace Gudhi - -#endif // LANDMARK_CHOICE_BY_RANDOM_POINT_H_ diff --git a/include/gudhi/Neighbors_finder.h b/include/gudhi/Neighbors_finder.h new file mode 100644 index 00000000..bdc47578 --- /dev/null +++ b/include/gudhi/Neighbors_finder.h @@ -0,0 +1,192 @@ +/* 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: Francois Godi + * + * Copyright (C) 2015 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 NEIGHBORS_FINDER_H_ +#define NEIGHBORS_FINDER_H_ + +// Inclusion order is important for CGAL patch +#include <CGAL/Kd_tree.h> +#include <CGAL/Search_traits.h> + +#include <gudhi/Persistence_graph.h> +#include <gudhi/Internal_point.h> + +#include <unordered_set> +#include <vector> + +namespace Gudhi { + +namespace persistence_diagram { + +/** \internal \brief Variant of CGAL::Fuzzy_iso_box to ensure that the box ic closed. It isn't clear how necessary that is. + */ +struct Square_query { + typedef CGAL::Dimension_tag<2> D; + typedef Internal_point Point_d; + typedef double FT; + bool contains(Point_d p) const { + return std::abs(p.x()-c.x())<=size && std::abs(p.y()-c.y())<=size; + } + bool inner_range_intersects(CGAL::Kd_tree_rectangle<FT,D> const&r) const { + return + r.max_coord(0) >= c.x() - size && + r.min_coord(0) <= c.x() + size && + r.max_coord(1) >= c.y() - size && + r.min_coord(1) <= c.y() + size; + } + bool outer_range_contains(CGAL::Kd_tree_rectangle<FT,D> const&r) const { + return + r.min_coord(0) >= c.x() - size && + r.max_coord(0) <= c.x() + size && + r.min_coord(1) >= c.y() - size && + r.max_coord(1) <= c.y() + size; + } + Point_d c; + FT size; +}; + +/** \internal \brief data structure used to find any point (including projections) in V near to a query point from U + * (which can be a projection). + * + * V points have to be added manually using their index and before the first pull. A neighbor pulled is automatically + * removed. + * + * \ingroup bottleneck_distance + */ +class Neighbors_finder { + typedef CGAL::Dimension_tag<2> D; + typedef CGAL::Search_traits<double, Internal_point, const double*, Construct_coord_iterator, D> Traits; + typedef CGAL::Kd_tree<Traits> Kd_tree; + + public: + /** \internal \brief Constructor taking the near distance definition as parameter. */ + Neighbors_finder(const Persistence_graph& g, double r); + /** \internal \brief A point added will be possibly pulled. */ + void add(int v_point_index); + /** \internal \brief Returns and remove a V point near to the U point given as parameter, null_point_index() if + * there isn't such a point. */ + int pull_near(int u_point_index); + /** \internal \brief Returns and remove all the V points near to the U point given as parameter. */ + std::vector<int> pull_all_near(int u_point_index); + + private: + const Persistence_graph& g; + const double r; + Kd_tree kd_t; + std::unordered_set<int> projections_f; +}; + +/** \internal \brief data structure used to find any point (including projections) in V near to a query point from U + * (which can be a projection) in a layered graph layer given as parmeter. + * + * V points have to be added manually using their index and before the first pull. A neighbor pulled is automatically + * removed. + * + * \ingroup bottleneck_distance + */ +class Layered_neighbors_finder { + public: + /** \internal \brief Constructor taking the near distance definition as parameter. */ + Layered_neighbors_finder(const Persistence_graph& g, double r); + /** \internal \brief A point added will be possibly pulled. */ + void add(int v_point_index, int vlayer); + /** \internal \brief Returns and remove a V point near to the U point given as parameter, null_point_index() if + * there isn't such a point. */ + int pull_near(int u_point_index, int vlayer); + /** \internal \brief Returns the number of layers. */ + int vlayers_number() const; + + private: + const Persistence_graph& g; + const double r; + std::vector<std::unique_ptr<Neighbors_finder>> neighbors_finder; +}; + +inline Neighbors_finder::Neighbors_finder(const Persistence_graph& g, double r) : + g(g), r(r), kd_t(), projections_f() { } + +inline void Neighbors_finder::add(int v_point_index) { + if (g.on_the_v_diagonal(v_point_index)) + projections_f.emplace(v_point_index); + else + kd_t.insert(g.get_v_point(v_point_index)); +} + +inline int Neighbors_finder::pull_near(int u_point_index) { + int tmp; + int c = g.corresponding_point_in_v(u_point_index); + if (g.on_the_u_diagonal(u_point_index) && !projections_f.empty()) { + // Any pair of projection is at distance 0 + tmp = *projections_f.cbegin(); + projections_f.erase(tmp); + } else if (projections_f.count(c) && (g.distance(u_point_index, c) <= r)) { + // Is the query point near to its projection ? + tmp = c; + projections_f.erase(tmp); + } else { + // Is the query point near to a V point in the plane ? + Internal_point u_point = g.get_u_point(u_point_index); + auto neighbor = kd_t.search_any_point(Square_query{u_point, r}); + if(!neighbor) + return null_point_index(); + tmp = neighbor->point_index; + auto point = g.get_v_point(tmp); + int idx = point.point_index; + kd_t.remove(point, [idx](Internal_point const&p){return p.point_index == idx;}); + } + return tmp; +} + +inline std::vector<int> Neighbors_finder::pull_all_near(int u_point_index) { + std::vector<int> all_pull; + int last_pull = pull_near(u_point_index); + while (last_pull != null_point_index()) { + all_pull.emplace_back(last_pull); + last_pull = pull_near(u_point_index); + } + return all_pull; +} + +inline Layered_neighbors_finder::Layered_neighbors_finder(const Persistence_graph& g, double r) : + g(g), r(r), neighbors_finder() { } + +inline void Layered_neighbors_finder::add(int v_point_index, int vlayer) { + for (int l = neighbors_finder.size(); l <= vlayer; l++) + neighbors_finder.emplace_back(std::unique_ptr<Neighbors_finder>(new Neighbors_finder(g, r))); + neighbors_finder.at(vlayer)->add(v_point_index); +} + +inline int Layered_neighbors_finder::pull_near(int u_point_index, int vlayer) { + if (static_cast<int> (neighbors_finder.size()) <= vlayer) + return null_point_index(); + return neighbors_finder.at(vlayer)->pull_near(u_point_index); +} + +inline int Layered_neighbors_finder::vlayers_number() const { + return static_cast<int> (neighbors_finder.size()); +} + +} // namespace persistence_diagram + +} // namespace Gudhi + +#endif // NEIGHBORS_FINDER_H_ diff --git a/include/gudhi/Null_output_iterator.h b/include/gudhi/Null_output_iterator.h new file mode 100644 index 00000000..42e6e449 --- /dev/null +++ b/include/gudhi/Null_output_iterator.h @@ -0,0 +1,48 @@ +/* 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) 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/>. + */ + +#ifndef NULL_OUTPUT_ITERATOR_H_ +#define NULL_OUTPUT_ITERATOR_H_ + +#include <iterator> + +namespace Gudhi { + +/** An output iterator that ignores whatever it is given. */ +struct Null_output_iterator { + typedef std::output_iterator_tag iterator_category; + typedef void value_type; + typedef void difference_type; + typedef void pointer; + typedef void reference; + + Null_output_iterator& operator++() {return *this;} + Null_output_iterator operator++(int) {return *this;} + struct proxy { + template<class T> + proxy& operator=(T&&){return *this;} + }; + proxy operator*()const{return {};} +}; +} // namespace Gudhi + +#endif // NULL_OUTPUT_ITERATOR_H_ diff --git a/include/gudhi/Persistence_graph.h b/include/gudhi/Persistence_graph.h new file mode 100644 index 00000000..44f4b827 --- /dev/null +++ b/include/gudhi/Persistence_graph.h @@ -0,0 +1,188 @@ +/* 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: Francois Godi + * + * Copyright (C) 2015 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 PERSISTENCE_GRAPH_H_ +#define PERSISTENCE_GRAPH_H_ + +#include <gudhi/Internal_point.h> + +#ifdef GUDHI_USE_TBB +#include <tbb/parallel_sort.h> +#endif + +#include <vector> +#include <algorithm> +#include <limits> // for numeric_limits + +namespace Gudhi { + +namespace persistence_diagram { + +/** \internal \brief Structure representing an euclidean bipartite graph containing + * the points from the two persistence diagrams (including the projections). + * + * \ingroup bottleneck_distance + */ +class Persistence_graph { + public: + /** \internal \brief Constructor taking 2 PersistenceDiagrams (concept) as parameters. */ + template<typename Persistence_diagram1, typename Persistence_diagram2> + Persistence_graph(const Persistence_diagram1& diag1, const Persistence_diagram2& diag2, double e); + /** \internal \brief Is the given point from U the projection of a point in V ? */ + bool on_the_u_diagonal(int u_point_index) const; + /** \internal \brief Is the given point from V the projection of a point in U ? */ + bool on_the_v_diagonal(int v_point_index) const; + /** \internal \brief Given a point from V, returns the corresponding (projection or projector) point in U. */ + int corresponding_point_in_u(int v_point_index) const; + /** \internal \brief Given a point from U, returns the corresponding (projection or projector) point in V. */ + int corresponding_point_in_v(int u_point_index) const; + /** \internal \brief Given a point from U and a point from V, returns the distance between those points. */ + double distance(int u_point_index, int v_point_index) const; + /** \internal \brief Returns size = |U| = |V|. */ + int size() const; + /** \internal \brief Is there as many infinite points (alive components) in both diagrams ? */ + double bottleneck_alive() const; + /** \internal \brief Returns the O(n^2) sorted distances between the points. */ + std::vector<double> sorted_distances() const; + /** \internal \brief Returns an upper bound for the diameter of the convex hull of all non infinite points */ + double diameter_bound() const; + /** \internal \brief Returns the corresponding internal point */ + Internal_point get_u_point(int u_point_index) const; + /** \internal \brief Returns the corresponding internal point */ + Internal_point get_v_point(int v_point_index) const; + + private: + std::vector<Internal_point> u; + std::vector<Internal_point> v; + double b_alive; +}; + +template<typename Persistence_diagram1, typename Persistence_diagram2> +Persistence_graph::Persistence_graph(const Persistence_diagram1 &diag1, + const Persistence_diagram2 &diag2, double e) + : u(), v(), b_alive(0.) { + std::vector<double> u_alive; + std::vector<double> v_alive; + for (auto it = std::begin(diag1); it != std::end(diag1); ++it) { + if (std::get<1>(*it) == std::numeric_limits<double>::infinity()) + u_alive.push_back(std::get<0>(*it)); + else if (std::get<1>(*it) - std::get<0>(*it) > e) + u.push_back(Internal_point(std::get<0>(*it), std::get<1>(*it), u.size())); + } + for (auto it = std::begin(diag2); it != std::end(diag2); ++it) { + if (std::get<1>(*it) == std::numeric_limits<double>::infinity()) + v_alive.push_back(std::get<0>(*it)); + else if (std::get<1>(*it) - std::get<0>(*it) > e) + v.push_back(Internal_point(std::get<0>(*it), std::get<1>(*it), v.size())); + } + if (u.size() < v.size()) + swap(u, v); + std::sort(u_alive.begin(), u_alive.end()); + std::sort(v_alive.begin(), v_alive.end()); + if (u_alive.size() != v_alive.size()) { + b_alive = std::numeric_limits<double>::infinity(); + } else { + for (auto it_u = u_alive.cbegin(), it_v = v_alive.cbegin(); it_u != u_alive.cend(); ++it_u, ++it_v) + b_alive = std::max(b_alive, std::fabs(*it_u - *it_v)); + } +} + +inline bool Persistence_graph::on_the_u_diagonal(int u_point_index) const { + return u_point_index >= static_cast<int> (u.size()); +} + +inline bool Persistence_graph::on_the_v_diagonal(int v_point_index) const { + return v_point_index >= static_cast<int> (v.size()); +} + +inline int Persistence_graph::corresponding_point_in_u(int v_point_index) const { + return on_the_v_diagonal(v_point_index) ? + v_point_index - static_cast<int> (v.size()) : v_point_index + static_cast<int> (u.size()); +} + +inline int Persistence_graph::corresponding_point_in_v(int u_point_index) const { + return on_the_u_diagonal(u_point_index) ? + u_point_index - static_cast<int> (u.size()) : u_point_index + static_cast<int> (v.size()); +} + +inline double Persistence_graph::distance(int u_point_index, int v_point_index) const { + if (on_the_u_diagonal(u_point_index) && on_the_v_diagonal(v_point_index)) + return 0.; + Internal_point p_u = get_u_point(u_point_index); + Internal_point p_v = get_v_point(v_point_index); + return std::max(std::fabs(p_u.x() - p_v.x()), std::fabs(p_u.y() - p_v.y())); +} + +inline int Persistence_graph::size() const { + return static_cast<int> (u.size() + v.size()); +} + +inline double Persistence_graph::bottleneck_alive() const { + return b_alive; +} + +inline std::vector<double> Persistence_graph::sorted_distances() const { + std::vector<double> distances; + distances.push_back(0.); // for empty diagrams + for (int u_point_index = 0; u_point_index < size(); ++u_point_index) { + distances.push_back(distance(u_point_index, corresponding_point_in_v(u_point_index))); + for (int v_point_index = 0; v_point_index < size(); ++v_point_index) + distances.push_back(distance(u_point_index, v_point_index)); + } +#ifdef GUDHI_USE_TBB + tbb::parallel_sort(distances.begin(), distances.end()); +#else + std::sort(distances.begin(), distances.end()); +#endif + return distances; +} + +inline Internal_point Persistence_graph::get_u_point(int u_point_index) const { + if (!on_the_u_diagonal(u_point_index)) + return u.at(u_point_index); + Internal_point projector = v.at(corresponding_point_in_v(u_point_index)); + double m = (projector.x() + projector.y()) / 2.; + return Internal_point(m, m, u_point_index); +} + +inline Internal_point Persistence_graph::get_v_point(int v_point_index) const { + if (!on_the_v_diagonal(v_point_index)) + return v.at(v_point_index); + Internal_point projector = u.at(corresponding_point_in_u(v_point_index)); + double m = (projector.x() + projector.y()) / 2.; + return Internal_point(m, m, v_point_index); +} + +inline double Persistence_graph::diameter_bound() const { + double max = 0.; + for (auto it = u.cbegin(); it != u.cend(); it++) + max = std::max(max, it->y()); + for (auto it = v.cbegin(); it != v.cend(); it++) + max = std::max(max, it->y()); + return max; +} + +} // namespace persistence_diagram + +} // namespace Gudhi + +#endif // PERSISTENCE_GRAPH_H_ diff --git a/include/gudhi/Persistent_cohomology.h b/include/gudhi/Persistent_cohomology.h index b31df6a4..672fda48 100644 --- a/include/gudhi/Persistent_cohomology.h +++ b/include/gudhi/Persistent_cohomology.h @@ -110,7 +110,7 @@ class Persistent_cohomology { cell_pool_() { if (cpx_->num_simplices() > std::numeric_limits<Simplex_key>::max()) { // num_simplices must be strictly lower than the limit, because a value is reserved for null_key. - throw std::out_of_range ("The number of simplices is more than Simplex_key type numeric limit."); + throw std::out_of_range("The number of simplices is more than Simplex_key type numeric limit."); } Simplex_key idx_fil = 0; for (auto sh : cpx_->filtration_simplex_range()) { @@ -300,8 +300,7 @@ class Persistent_cohomology { // with multiplicity. We used to sum the coefficients directly in // annotations_in_boundary by using a map, we now do it later. typedef std::pair<Column *, int> annotation_t; - // Danger: not thread-safe! - static std::vector<annotation_t> annotations_in_boundary; + thread_local std::vector<annotation_t> annotations_in_boundary; annotations_in_boundary.clear(); int sign = 1 - 2 * (dim_sigma % 2); // \in {-1,1} provides the sign in the // alternate sum in the boundary. @@ -604,7 +603,7 @@ class Persistent_cohomology { */ std::vector<int> betti_numbers() const { // Init Betti numbers vector with zeros until Simplicial complex dimension - std::vector<int> betti_numbers(cpx_->dimension(), 0); + std::vector<int> betti_numbers(dim_max_, 0); for (auto pair : persistent_pairs_) { // Count never ended persistence intervals @@ -643,8 +642,7 @@ class Persistent_cohomology { */ std::vector<int> persistent_betti_numbers(Filtration_value from, Filtration_value to) const { // Init Betti numbers vector with zeros until Simplicial complex dimension - std::vector<int> betti_numbers(cpx_->dimension(), 0); - + std::vector<int> betti_numbers(dim_max_, 0); for (auto pair : persistent_pairs_) { // Count persistence intervals that covers the given interval // null_simplex test : if the function is called with to=+infinity, we still get something useful. And it will @@ -690,6 +688,22 @@ class Persistent_cohomology { return persistent_pairs_; } + /** @brief Returns persistence intervals for a given dimension. + * @param[in] dimension Dimension to get the birth and death pairs from. + * @return A vector of persistence intervals (birth and death) on a fixed dimension. + */ + std::vector< std::pair< Filtration_value , Filtration_value > > + intervals_in_dimension(int dimension) { + std::vector< std::pair< Filtration_value , Filtration_value > > result; + // auto && pair, to avoid unnecessary copying + for (auto && pair : persistent_pairs_) { + if (cpx_->dimension(get<0>(pair)) == dimension) { + result.emplace_back(cpx_->filtration(get<0>(pair)), cpx_->filtration(get<1>(pair))); + } + } + return result; + } + private: /* * Structure representing a cocycle. diff --git a/include/gudhi/Points_3D_off_io.h b/include/gudhi/Points_3D_off_io.h index 2647f11e..b0d24998 100644 --- a/include/gudhi/Points_3D_off_io.h +++ b/include/gudhi/Points_3D_off_io.h @@ -132,12 +132,12 @@ class Points_3D_off_visitor_reader { * * @code template<class InputIterator > Point_3::Point_3(double x, double y, double z) @endcode * - * @section Example + * @section point3doffioexample Example * * This example loads points from an OFF file and builds a vector of CGAL points in dimension 3. * Then, it is asked to display the points. * - * @include common/CGAL_3D_points_off_reader.cpp + * @include common/example_CGAL_3D_points_off_reader.cpp * * When launching: * diff --git a/include/gudhi/Points_off_io.h b/include/gudhi/Points_off_io.h index 74b49386..29af8a8a 100644 --- a/include/gudhi/Points_off_io.h +++ b/include/gudhi/Points_off_io.h @@ -73,9 +73,8 @@ class Points_off_visitor_reader { * @details * Point_d must have a constructor with the following form: * - * @code template<class InputIterator > Point_d::Point_d(int d, InputIterator first, InputIterator last) @endcode + * @code template<class InputIterator > Point_d::Point_d(InputIterator first, InputIterator last) @endcode * - * where d is the point dimension. */ void point(const std::vector<double>& point) { #ifdef DEBUG_TRACES @@ -86,7 +85,7 @@ class Points_off_visitor_reader { std::cout << std::endl; #endif // DEBUG_TRACES // Fill the point cloud - point_cloud.push_back(Point_d(point.size(), point.begin(), point.end())); + point_cloud.push_back(Point_d(point.begin(), point.end())); } // Off_reader visitor maximal_face implementation - Only points are read @@ -115,16 +114,16 @@ class Points_off_visitor_reader { * * where d is the point dimension. * - * \section Example + * \section pointoffioexample Example * - * This example loads points from an OFF file and builds a vector of CGAL points in dimension d. + * This example loads points from an OFF file and builds a vector of points (vector of double). * Then, it is asked to display the points. * - * \include common/CGAL_points_off_reader.cpp + * \include common/example_vector_double_points_off_reader.cpp * * When launching: * - * \code $> ./cgaloffreader ../../data/points/alphacomplexdoc.off + * \code $> ./vector_double_off_reader ../../data/points/alphacomplexdoc.off * \endcode * * the program output is: diff --git a/include/gudhi/Rips_complex.h b/include/gudhi/Rips_complex.h new file mode 100644 index 00000000..1e4b76a7 --- /dev/null +++ b/include/gudhi/Rips_complex.h @@ -0,0 +1,185 @@ +/* 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): Clément Maria, 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/>. + */ + +#ifndef RIPS_COMPLEX_H_ +#define RIPS_COMPLEX_H_ + +#include <gudhi/Debug_utils.h> +#include <gudhi/graph_simplicial_complex.h> + +#include <boost/graph/adjacency_list.hpp> + +#include <iostream> +#include <vector> +#include <map> +#include <string> +#include <limits> // for numeric_limits +#include <utility> // for pair<> + + +namespace Gudhi { + +namespace rips_complex { + +/** + * \class Rips_complex + * \brief Rips complex data structure. + * + * \ingroup rips_complex + * + * \details + * The data structure is a one skeleton graph, or Rips graph, containing edges when the edge length is less or equal + * to a given threshold. Edge length is computed from a user given point cloud with a given distance function, or a + * distance matrix. + * + * \tparam Filtration_value is the type used to store the filtration values of the simplicial complex. + */ +template<typename Filtration_value> +class Rips_complex { + public: + /** + * \brief Type of the one skeleton graph stored inside the Rips complex structure. + */ + 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 >> OneSkeletonGraph; + + private: + typedef int Vertex_handle; + + public: + /** \brief Rips_complex constructor from a list of points. + * + * @param[in] points Range of points. + * @param[in] threshold Rips value. + * @param[in] distance distance function that returns a `Filtration_value` from 2 given points. + * + * \tparam ForwardPointRange must be a range for which `std::begin` and `std::end` return input iterators on a + * point. + * + * \tparam Distance furnishes `operator()(const Point& p1, const Point& p2)`, where + * `Point` is a point from the `ForwardPointRange`, and that returns a `Filtration_value`. + */ + template<typename ForwardPointRange, typename Distance > + Rips_complex(const ForwardPointRange& points, Filtration_value threshold, Distance distance) { + compute_proximity_graph(points, threshold, distance); + } + + /** \brief Rips_complex constructor from a distance matrix. + * + * @param[in] distance_matrix Range of distances. + * @param[in] threshold Rips value. + * + * \tparam DistanceMatrix must have a `size()` method and on which `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$ + */ + template<typename DistanceMatrix> + Rips_complex(const DistanceMatrix& distance_matrix, Filtration_value threshold) { + compute_proximity_graph(boost::irange((size_t)0, distance_matrix.size()), threshold, + [&](size_t i, size_t j){return distance_matrix[j][i];}); + } + + /** \brief Initializes the simplicial complex from the Rips graph and expands it until a given maximal + * dimension. + * + * \tparam SimplicialComplexForRips must meet `SimplicialComplexForRips` concept. + * + * @param[in] complex SimplicialComplexForRips to be created. + * @param[in] dim_max graph expansion for Rips until this given maximal dimension. + * @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("Rips_complex::create_complex - simplicial complex is not empty")); + + // insert the proximity graph in the simplicial complex + complex.insert_graph(rips_skeleton_graph_); + // expand the graph until dimension dim_max + complex.expansion(dim_max); + } + + private: + /** \brief Computes the proximity graph of the points. + * + * If points contains n elements, the proximity graph is the graph with n vertices, and an edge [u,v] iff the + * distance function between points u and v is smaller than threshold. + * + * \tparam ForwardPointRange furnishes `.begin()` and `.end()` + * methods. + * + * \tparam Distance furnishes `operator()(const Point& p1, const Point& p2)`, where + * `Point` is a point from the `ForwardPointRange`, and that returns a `Filtration_value`. + */ + template< typename ForwardPointRange, typename Distance > + void compute_proximity_graph(const ForwardPointRange& points, Filtration_value threshold, + Distance distance) { + std::vector< std::pair< Vertex_handle, Vertex_handle > > edges; + std::vector< Filtration_value > edges_fil; + + // Compute the proximity graph of the points. + // If points contains n elements, the proximity graph is the graph with n vertices, and an edge [u,v] iff the + // distance function between points u and v is smaller than threshold. + // -------------------------------------------------------------------------------------------- + // Creates the vector of edges and its filtration values (returned by distance function) + Vertex_handle idx_u = 0; + for (auto it_u = std::begin(points); it_u != std::end(points); ++it_u, ++idx_u) { + Vertex_handle idx_v = idx_u + 1; + for (auto it_v = it_u + 1; it_v != std::end(points); ++it_v, ++idx_v) { + Filtration_value fil = distance(*it_u, *it_v); + if (fil <= threshold) { + edges.emplace_back(idx_u, idx_v); + edges_fil.push_back(fil); + } + } + } + + // -------------------------------------------------------------------------------------------- + // Creates the proximity graph from edges and sets the property with the filtration value. + // Number of points is labeled from 0 to idx_u-1 + // -------------------------------------------------------------------------------------------- + // Do not use : rips_skeleton_graph_ = OneSkeletonGraph(...) -> deep copy of the graph (boost graph is not + // move-enabled) + rips_skeleton_graph_.~OneSkeletonGraph(); + new(&rips_skeleton_graph_)OneSkeletonGraph(edges.begin(), edges.end(), edges_fil.begin(), idx_u); + + auto vertex_prop = boost::get(vertex_filtration_t(), rips_skeleton_graph_); + + using vertex_iterator = typename boost::graph_traits<OneSkeletonGraph>::vertex_iterator; + vertex_iterator vi, vi_end; + for (std::tie(vi, vi_end) = boost::vertices(rips_skeleton_graph_); + vi != vi_end; ++vi) { + boost::put(vertex_prop, *vi, 0.); + } + } + + private: + OneSkeletonGraph rips_skeleton_graph_; +}; + +} // namespace rips_complex + +} // namespace Gudhi + +#endif // RIPS_COMPLEX_H_ diff --git a/include/gudhi/Simplex_tree.h b/include/gudhi/Simplex_tree.h index 63e3f0e5..317bce23 100644 --- a/include/gudhi/Simplex_tree.h +++ b/include/gudhi/Simplex_tree.h @@ -1029,7 +1029,7 @@ class Simplex_tree { Dictionary_it next = siblings->members().begin(); ++next; - static std::vector<std::pair<Vertex_handle, Node> > inter; // static, not thread-safe. + thread_local std::vector<std::pair<Vertex_handle, Node> > inter; for (Dictionary_it s_h = siblings->members().begin(); s_h != siblings->members().end(); ++s_h, ++next) { Simplex_handle root_sh = find_vertex(s_h->first); diff --git a/include/gudhi/Skeleton_blocker.h b/include/gudhi/Skeleton_blocker.h index 822282fd..32fe411c 100644 --- a/include/gudhi/Skeleton_blocker.h +++ b/include/gudhi/Skeleton_blocker.h @@ -1,24 +1,24 @@ - /* 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): David Salinas - * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) - * - * 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/>. - */ +/* 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): David Salinas + * + * Copyright (C) 2014 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 SKELETON_BLOCKER_H_ #define SKELETON_BLOCKER_H_ @@ -37,11 +37,12 @@ namespace Gudhi { namespace skeleton_blocker { -/** \defgroup skbl Skeleton-Blocker +/** \defgroup skbl Skeleton-Blocker +@{ \author David Salinas -\section Introduction +\section skblintroduction Introduction The Skeleton-Blocker data-structure proposes a light encoding for simplicial complexes by storing only an *implicit* representation of its simplices \cite socg_blockers_2011,\cite blockers2012. @@ -52,7 +53,7 @@ This data-structure handles all simplicial complexes operations such as are operations that do not require simplex enumeration such as edge iteration, link computation or simplex contraction. -\section Definitions +\section skbldefinitions Definitions We recall briefly classical definitions of simplicial complexes \cite Munkres-elementsalgtop1984. @@ -63,7 +64,7 @@ when \f$ \tau \neq \sigma\f$ we say that \f$ \tau\f$ is a proper-face of \f$ \si An abstract simplicial complex is a set of simplices that contains all the faces of its simplices. The 1-skeleton of a simplicial complex (or its graph) consists of its elements of dimension lower than 2. -*\image html "ds_representation.png" "Skeleton-blocker representation" width=20cm + *\image html "ds_representation.png" "Skeleton-blocker representation" width=20cm To encode, a simplicial complex, one can encodes all its simplices. @@ -85,7 +86,7 @@ in next figure. Storing the graph and blockers of such simplicial complexes is m their simplices. -*\image html "blockers_curve.png" "Number of blockers of random triangulations of 3-spheres" width=10cm + *\image html "blockers_curve.png" "Number of blockers of random triangulations of 3-spheres" width=10cm @@ -107,7 +108,7 @@ and point access in addition. -\subsection Visitor +\subsection skblvisitor Visitor The class Skeleton_blocker_complex has a visitor that is called when usual operations such as adding an edge or remove a vertex are called. You may want to use this visitor to compute statistics or to update another data-structure (for instance this visitor is heavily used in the \ref contr package). @@ -115,7 +116,7 @@ You may want to use this visitor to compute statistics or to update another data -\section Example +\section skblexample Example \subsection Iterating Iterating through vertices, edges, blockers and simplices @@ -127,46 +128,46 @@ such as the Simplex Tree. The following example computes the Euler Characteristi of a simplicial complex. \code{.cpp} - typedef Skeleton_blocker_complex<Skeleton_blocker_simple_traits> Complex; - typedef Complex::Vertex_handle Vertex_handle; - typedef Complex::Simplex Simplex; - - const int n = 15; - - // build a full complex with 10 vertices and 2^n-1 simplices - Complex complex; - for(int i=0;i<n;i++) - complex.add_vertex(); - for(int i=0;i<n;i++) - for(int j=0;j<i;j++) - complex.add_edge_without_blockers(Vertex_handle(i),Vertex_handle(j)); - - // this is just to illustrate iterators, to count number of vertices - // or edges, complex.num_vertices() and complex.num_edges() are - // more appropriated! - unsigned num_vertices = 0; - for(auto v : complex.vertex_range()){ - ++num_vertices; - } - - unsigned num_edges = 0; - for(auto e : complex.edge_range()) - ++num_edges; - - unsigned euler = 0; - unsigned num_simplices = 0; - // we use a reference to a simplex instead of a copy - // value here because a simplex is a set of integers - // and copying it cost time - for(const Simplex & s : complex.star_simplex_range()){ - ++num_simplices; - if(s.dimension()%2 == 0) - euler += 1; - else - euler -= 1; - } - std::cout << "Saw "<<num_vertices<<" vertices, "<<num_edges<<" edges and "<<num_simplices<<" simplices"<<std::endl; - std::cout << "The Euler Characteristic is "<<euler<<std::endl; + typedef Skeleton_blocker_complex<Skeleton_blocker_simple_traits> Complex; + typedef Complex::Vertex_handle Vertex_handle; + typedef Complex::Simplex Simplex; + + const int n = 15; + + // build a full complex with 10 vertices and 2^n-1 simplices + Complex complex; + for(int i=0;i<n;i++) + complex.add_vertex(); + for(int i=0;i<n;i++) + for(int j=0;j<i;j++) + complex.add_edge_without_blockers(Vertex_handle(i),Vertex_handle(j)); + + // this is just to illustrate iterators, to count number of vertices + // or edges, complex.num_vertices() and complex.num_edges() are + // more appropriated! + unsigned num_vertices = 0; + for(auto v : complex.vertex_range()){ + ++num_vertices; + } + + unsigned num_edges = 0; + for(auto e : complex.edge_range()) + ++num_edges; + + unsigned euler = 0; + unsigned num_simplices = 0; + // we use a reference to a simplex instead of a copy + // value here because a simplex is a set of integers + // and copying it cost time + for(const Simplex & s : complex.star_simplex_range()){ + ++num_simplices; + if(s.dimension()%2 == 0) + euler += 1; + else + euler -= 1; + } + std::cout << "Saw "<<num_vertices<<" vertices, "<<num_edges<<" edges and "<<num_simplices<<" simplices"<<std::endl; + std::cout << "The Euler Characteristic is "<<euler<<std::endl; \endcode @@ -181,43 +182,43 @@ The Euler Characteristic is 1 \subsection s Constructing a skeleton-blockers from a list of maximal faces or from a list of faces \code{.cpp} - std::vector<Simplex> simplices; - - //add 4 triangles of a tetrahedron 0123 - simplices.push_back(Simplex(Vertex_handle(0),Vertex_handle(1),Vertex_handle(2))); - simplices.push_back(Simplex(Vertex_handle(1),Vertex_handle(2),Vertex_handle(3))); - simplices.push_back(Simplex(Vertex_handle(3),Vertex_handle(0),Vertex_handle(2))); - simplices.push_back(Simplex(Vertex_handle(3),Vertex_handle(0),Vertex_handle(1))); - - Complex complex; - //get complex from top faces - make_complex_from_top_faces(complex,simplices.begin(),simplices.end()); - - std::cout << "Simplices:"<<std::endl; - for(const Simplex & s : complex.star_simplex_range()) - std::cout << s << " "; - std::cout << std::endl; - - //One blocker as simplex 0123 is not in the complex but all its proper faces are. - std::cout << "Blockers: "<<complex.blockers_to_string()<<std::endl; - - //now build a complex from its full list of simplices - simplices.clear(); - simplices.push_back(Simplex(Vertex_handle(0))); - simplices.push_back(Simplex(Vertex_handle(1))); - simplices.push_back(Simplex(Vertex_handle(2))); - simplices.push_back(Simplex(Vertex_handle(0),Vertex_handle(1))); - simplices.push_back(Simplex(Vertex_handle(1),Vertex_handle(2))); - simplices.push_back(Simplex(Vertex_handle(2),Vertex_handle(0))); - complex = Complex(simplices.begin(),simplices.end()); - - std::cout << "Simplices:"<<std::endl; - for(const Simplex & s : complex.star_simplex_range()) - std::cout << s << " "; - std::cout << std::endl; - - //One blocker as simplex 012 is not in the complex but all its proper faces are. - std::cout << "Blockers: "<<complex.blockers_to_string()<<std::endl; + std::vector<Simplex> simplices; + + //add 4 triangles of a tetrahedron 0123 + simplices.push_back(Simplex(Vertex_handle(0),Vertex_handle(1),Vertex_handle(2))); + simplices.push_back(Simplex(Vertex_handle(1),Vertex_handle(2),Vertex_handle(3))); + simplices.push_back(Simplex(Vertex_handle(3),Vertex_handle(0),Vertex_handle(2))); + simplices.push_back(Simplex(Vertex_handle(3),Vertex_handle(0),Vertex_handle(1))); + + Complex complex; + //get complex from top faces + make_complex_from_top_faces(complex,simplices.begin(),simplices.end()); + + std::cout << "Simplices:"<<std::endl; + for(const Simplex & s : complex.star_simplex_range()) + std::cout << s << " "; + std::cout << std::endl; + + //One blocker as simplex 0123 is not in the complex but all its proper faces are. + std::cout << "Blockers: "<<complex.blockers_to_string()<<std::endl; + + //now build a complex from its full list of simplices + simplices.clear(); + simplices.push_back(Simplex(Vertex_handle(0))); + simplices.push_back(Simplex(Vertex_handle(1))); + simplices.push_back(Simplex(Vertex_handle(2))); + simplices.push_back(Simplex(Vertex_handle(0),Vertex_handle(1))); + simplices.push_back(Simplex(Vertex_handle(1),Vertex_handle(2))); + simplices.push_back(Simplex(Vertex_handle(2),Vertex_handle(0))); + complex = Complex(simplices.begin(),simplices.end()); + + std::cout << "Simplices:"<<std::endl; + for(const Simplex & s : complex.star_simplex_range()) + std::cout << s << " "; + std::cout << std::endl; + + //One blocker as simplex 012 is not in the complex but all its proper faces are. + std::cout << "Blockers: "<<complex.blockers_to_string()<<std::endl; \endcode \verbatim ./SkeletonBlockerFromSimplices @@ -236,12 +237,12 @@ The author wishes to thank Dominique Attali and André Lieutier for their collaboration to write the two initial papers \cite socg_blockers_2011,\cite blockers2012 about this data-structure - and also Dominique for leaving him use a prototype. + and also Dominique for leaving him use a prototype. -\copyright GNU General Public License v3. -*/ -/** @} */ // end defgroup +\copyright GNU General Public License v3. + +@} */ } // namespace skeleton_blocker diff --git a/include/gudhi/Skeleton_blocker/Skeleton_blocker_complex_visitor.h b/include/gudhi/Skeleton_blocker/Skeleton_blocker_complex_visitor.h index 32f40a4b..ba3636bc 100644 --- a/include/gudhi/Skeleton_blocker/Skeleton_blocker_complex_visitor.h +++ b/include/gudhi/Skeleton_blocker/Skeleton_blocker_complex_visitor.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_SKELETON_BLOCKER_COMPLEX_VISITOR_H_ #define SKELETON_BLOCKER_SKELETON_BLOCKER_COMPLEX_VISITOR_H_ @@ -27,7 +28,7 @@ namespace Gudhi { namespace skeleton_blocker { -// todo rajouter les const +// TODO(DS): to be constified /** *@class Skeleton_blocker_complex_visitor @@ -36,7 +37,7 @@ namespace skeleton_blocker { template<typename Vertex_handle> class Skeleton_blocker_complex_visitor { public: - virtual ~Skeleton_blocker_complex_visitor() {} + virtual ~Skeleton_blocker_complex_visitor() { } virtual void on_add_vertex(Vertex_handle) = 0; virtual void on_remove_vertex(Vertex_handle) = 0; @@ -61,9 +62,9 @@ class Skeleton_blocker_complex_visitor { virtual void on_swaped_edge(Vertex_handle a, Vertex_handle b, Vertex_handle x) = 0; virtual void on_add_blocker( - const Skeleton_blocker_simplex<Vertex_handle>&) = 0; + const Skeleton_blocker_simplex<Vertex_handle>&) = 0; virtual void on_delete_blocker( - const Skeleton_blocker_simplex<Vertex_handle>*) = 0; + const Skeleton_blocker_simplex<Vertex_handle>*) = 0; }; /** @@ -73,24 +74,23 @@ class Skeleton_blocker_complex_visitor { */ template<typename Vertex_handle> class Dummy_complex_visitor : public Skeleton_blocker_complex_visitor< - Vertex_handle> { +Vertex_handle> { public: - void on_add_vertex(Vertex_handle) { - } - void on_remove_vertex(Vertex_handle) { - } - void on_add_edge_without_blockers(Vertex_handle a, Vertex_handle b) { - } - void on_remove_edge(Vertex_handle a, Vertex_handle b) { - } - void on_changed_edge(Vertex_handle a, Vertex_handle b) { - } - void on_swaped_edge(Vertex_handle a, Vertex_handle b, Vertex_handle x) { - } - void on_add_blocker(const Skeleton_blocker_simplex<Vertex_handle>&) { - } - void on_delete_blocker(const Skeleton_blocker_simplex<Vertex_handle>*) { - } + void on_add_vertex(Vertex_handle) { } + + void on_remove_vertex(Vertex_handle) { } + + void on_add_edge_without_blockers(Vertex_handle a, Vertex_handle b) { } + + void on_remove_edge(Vertex_handle a, Vertex_handle b) { } + + void on_changed_edge(Vertex_handle a, Vertex_handle b) { } + + void on_swaped_edge(Vertex_handle a, Vertex_handle b, Vertex_handle x) { } + + void on_add_blocker(const Skeleton_blocker_simplex<Vertex_handle>&) { } + + void on_delete_blocker(const Skeleton_blocker_simplex<Vertex_handle>*) { } }; /** @@ -100,29 +100,36 @@ class Dummy_complex_visitor : public Skeleton_blocker_complex_visitor< */ template<typename Vertex_handle> class Print_complex_visitor : public Skeleton_blocker_complex_visitor< - Vertex_handle> { +Vertex_handle> { public: void on_add_vertex(Vertex_handle v) { std::cerr << "on_add_vertex:" << v << std::endl; } + void on_remove_vertex(Vertex_handle v) { std::cerr << "on_remove_vertex:" << v << std::endl; } + void on_add_edge_without_blockers(Vertex_handle a, Vertex_handle b) { std::cerr << "on_add_edge_without_blockers:" << a << "," << b << std::endl; } + void on_remove_edge(Vertex_handle a, Vertex_handle b) { std::cerr << "on_remove_edge:" << a << "," << b << std::endl; } + void on_changed_edge(Vertex_handle a, Vertex_handle b) { std::cerr << "on_changed_edge:" << a << "," << b << std::endl; } + void on_swaped_edge(Vertex_handle a, Vertex_handle b, Vertex_handle x) { std::cerr << "on_swaped_edge:" << a << "," << b << "," << x << std::endl; } + void on_add_blocker(const Skeleton_blocker_simplex<Vertex_handle>& b) { std::cerr << "on_add_blocker:" << b << std::endl; } + void on_delete_blocker(const Skeleton_blocker_simplex<Vertex_handle>* b) { std::cerr << "on_delete_blocker:" << b << std::endl; } diff --git a/include/gudhi/Skeleton_blocker/Skeleton_blocker_link_superior.h b/include/gudhi/Skeleton_blocker/Skeleton_blocker_link_superior.h index 3bfb5d11..d4b60613 100644 --- a/include/gudhi/Skeleton_blocker/Skeleton_blocker_link_superior.h +++ b/include/gudhi/Skeleton_blocker/Skeleton_blocker_link_superior.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_SKELETON_BLOCKER_LINK_SUPERIOR_H_ #define SKELETON_BLOCKER_SKELETON_BLOCKER_LINK_SUPERIOR_H_ @@ -36,7 +37,7 @@ template<class ComplexType> class Skeleton_blocker_sub_complex; */ template<typename ComplexType> class Skeleton_blocker_link_superior : public Skeleton_blocker_link_complex< - ComplexType> { +ComplexType> { typedef typename ComplexType::Edge_handle Edge_handle; typedef typename ComplexType::boost_vertex_handle boost_vertex_handle; @@ -54,20 +55,17 @@ class Skeleton_blocker_link_superior : public Skeleton_blocker_link_complex< typedef typename ComplexType::Root_simplex_handle::Simplex_vertex_const_iterator IdSimplexConstIterator; Skeleton_blocker_link_superior() - : Skeleton_blocker_link_complex<ComplexType>(true) { - } + : Skeleton_blocker_link_complex<ComplexType>(true) { } Skeleton_blocker_link_superior(const ComplexType & parent_complex, Simplex& alpha_parent_adress) : Skeleton_blocker_link_complex<ComplexType>(parent_complex, - alpha_parent_adress, true) { - } + alpha_parent_adress, true) { } Skeleton_blocker_link_superior(const ComplexType & parent_complex, Vertex_handle a_parent_adress) : Skeleton_blocker_link_complex<ComplexType>(parent_complex, - a_parent_adress, true) { - } + a_parent_adress, true) { } }; } // namespace skeleton_blocker diff --git a/include/gudhi/Skeleton_blocker/Skeleton_blocker_off_io.h b/include/gudhi/Skeleton_blocker/Skeleton_blocker_off_io.h index ba46c49e..747e60f1 100644 --- a/include/gudhi/Skeleton_blocker/Skeleton_blocker_off_io.h +++ b/include/gudhi/Skeleton_blocker/Skeleton_blocker_off_io.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_SKELETON_BLOCKER_OFF_IO_H_ #define SKELETON_BLOCKER_SKELETON_BLOCKER_OFF_IO_H_ @@ -49,8 +50,8 @@ class Skeleton_blocker_off_flag_visitor_reader { load_only_points_(load_only_points) { } void init(int dim, int num_vertices, int num_faces, int num_edges) { - // todo do an assert to check that this number are correctly read - // todo reserve size for vector points + // TODO(DS): do an assert to check that this number are correctly read + // TODO(DS): reserve size for vector points } void point(const std::vector<double>& point) { @@ -108,7 +109,7 @@ class Skeleton_blocker_off_visitor_reader { void done() { complex_ = make_complex_from_top_faces<Complex>(maximal_faces_.begin(), maximal_faces_.end(), - points_.begin(), points_.end() ); + points_.begin(), points_.end()); } }; @@ -140,7 +141,7 @@ class Skeleton_blocker_off_reader { } /** - * return true iff reading did not meet problems. + * return true if reading did not meet problems. */ bool is_valid() const { return valid_; diff --git a/include/gudhi/Skeleton_blocker/Skeleton_blocker_simple_geometric_traits.h b/include/gudhi/Skeleton_blocker/Skeleton_blocker_simple_geometric_traits.h index fb4a1106..275376e6 100644 --- a/include/gudhi/Skeleton_blocker/Skeleton_blocker_simple_geometric_traits.h +++ b/include/gudhi/Skeleton_blocker/Skeleton_blocker_simple_geometric_traits.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_SKELETON_BLOCKER_SIMPLE_GEOMETRIC_TRAITS_H_ #define SKELETON_BLOCKER_SKELETON_BLOCKER_SIMPLE_GEOMETRIC_TRAITS_H_ @@ -39,7 +40,7 @@ namespace skeleton_blocker { */ template<typename GeometryTrait> struct Skeleton_blocker_simple_geometric_traits : - public Skeleton_blocker_simple_traits { +public Skeleton_blocker_simple_traits { public: typedef GeometryTrait GT; typedef typename GT::Point Point; @@ -57,19 +58,20 @@ struct Skeleton_blocker_simple_geometric_traits : Point& point() { return point_; } + const Point& point() const { return point_; } }; class Simple_geometric_edge : - public Skeleton_blocker_simple_traits::Graph_edge { + public Skeleton_blocker_simple_traits::Graph_edge { int index_; public: Simple_geometric_edge() : Skeleton_blocker_simple_traits::Graph_edge(), - index_(-1) { - } + index_(-1) { } + /** * @brief Allows to modify the index of the edge. * The indices of the edge are used to store heap information @@ -78,6 +80,7 @@ struct Skeleton_blocker_simple_geometric_traits : int& index() { return index_; } + int index() const { return index_; } diff --git a/include/gudhi/Skeleton_blocker/Skeleton_blocker_simple_traits.h b/include/gudhi/Skeleton_blocker/Skeleton_blocker_simple_traits.h index 31bec3b6..3835cf77 100644 --- a/include/gudhi/Skeleton_blocker/Skeleton_blocker_simple_traits.h +++ b/include/gudhi/Skeleton_blocker/Skeleton_blocker_simple_traits.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_SKELETON_BLOCKER_SIMPLE_TRAITS_H_ #define SKELETON_BLOCKER_SKELETON_BLOCKER_SIMPLE_TRAITS_H_ @@ -48,9 +49,9 @@ struct Skeleton_blocker_simple_traits { */ struct Root_vertex_handle { typedef int boost_vertex_handle; + explicit Root_vertex_handle(boost_vertex_handle val = -1) - : vertex(val) { - } + : vertex(val) { } boost_vertex_handle vertex; bool operator!=(const Root_vertex_handle& other) const { @@ -65,8 +66,8 @@ struct Skeleton_blocker_simple_traits { return this->vertex < other.vertex; } - friend std::ostream& operator <<(std::ostream& o, - const Root_vertex_handle & v) { + friend std::ostream& operator<<(std::ostream& o, + const Root_vertex_handle & v) { o << v.vertex; return o; } @@ -74,11 +75,13 @@ struct Skeleton_blocker_simple_traits { struct Vertex_handle { typedef int boost_vertex_handle; + explicit Vertex_handle(boost_vertex_handle val = -1) - : vertex(val) { - } + : vertex(val) { } - operator int() const { return static_cast<int>(vertex); } + operator int() const { + return static_cast<int> (vertex); + } boost_vertex_handle vertex; @@ -94,7 +97,7 @@ struct Skeleton_blocker_simple_traits { return this->vertex < other.vertex; } - friend std::ostream& operator <<(std::ostream& o, const Vertex_handle & v) { + friend std::ostream& operator<<(std::ostream& o, const Vertex_handle & v) { o << v.vertex; return o; } @@ -105,21 +108,24 @@ struct Skeleton_blocker_simple_traits { Root_vertex_handle id_; public: - virtual ~Graph_vertex() { - } + virtual ~Graph_vertex() { } void activate() { is_active_ = true; } + void deactivate() { is_active_ = false; } + bool is_active() const { return is_active_; } + void set_id(Root_vertex_handle i) { id_ = i; } + Root_vertex_handle get_id() const { return id_; } @@ -130,7 +136,7 @@ struct Skeleton_blocker_simple_traits { return res.str(); } - friend std::ostream& operator <<(std::ostream& o, const Graph_vertex & v) { + friend std::ostream& operator<<(std::ostream& o, const Graph_vertex & v) { o << v.to_string(); return o; } @@ -144,13 +150,13 @@ struct Skeleton_blocker_simple_traits { public: Graph_edge() : a_(-1), - b_(-1), - index_(-1) { - } + b_(-1), + index_(-1) { } int& index() { return index_; } + int index() const { return index_; } @@ -168,7 +174,7 @@ struct Skeleton_blocker_simple_traits { return b_; } - friend std::ostream& operator <<(std::ostream& o, const Graph_edge & v) { + friend std::ostream& operator<<(std::ostream& o, const Graph_edge & v) { o << "(" << v.a_ << "," << v.b_ << " - id = " << v.index(); return o; } diff --git a/include/gudhi/Skeleton_blocker/Skeleton_blocker_simplex.h b/include/gudhi/Skeleton_blocker/Skeleton_blocker_simplex.h index 3c7f1dd5..aa6f2215 100644 --- a/include/gudhi/Skeleton_blocker/Skeleton_blocker_simplex.h +++ b/include/gudhi/Skeleton_blocker/Skeleton_blocker_simplex.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -63,7 +63,6 @@ class Skeleton_blocker_simplex { */ //@{ - // Skeleton_blocker_simplex():simplex_set() {} void clear() { simplex_set.clear(); } @@ -89,8 +88,7 @@ class Skeleton_blocker_simplex { add_vertex(v); } - void add_vertices() { - } + void add_vertices() { } /** * Initialize a simplex with a string such as {0,1,2} @@ -192,7 +190,6 @@ class Skeleton_blocker_simplex { return simplex_set.crend(); } - typename std::set<T>::iterator begin() { return simplex_set.begin(); } @@ -236,6 +233,7 @@ class Skeleton_blocker_simplex { assert(!empty()); return *(simplex_set.rbegin()); } + /** * @return true iff the simplex contains the simplex a. */ @@ -351,8 +349,8 @@ class Skeleton_blocker_simplex { //@} - friend std::ostream& operator <<(std::ostream& o, - const Skeleton_blocker_simplex & sigma) { + friend std::ostream& operator<<(std::ostream& o, + const Skeleton_blocker_simplex & sigma) { bool first = true; o << "{"; for (auto i : sigma) { diff --git a/include/gudhi/Skeleton_blocker/Skeleton_blocker_sub_complex.h b/include/gudhi/Skeleton_blocker/Skeleton_blocker_sub_complex.h index 196fe8c0..fadf6619 100644 --- a/include/gudhi/Skeleton_blocker/Skeleton_blocker_sub_complex.h +++ b/include/gudhi/Skeleton_blocker/Skeleton_blocker_sub_complex.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -159,7 +159,7 @@ class Skeleton_blocker_sub_complex : public ComplexType { if (simplex.contains(*blocker)) { Root_simplex_handle blocker_root(parent_complex.get_id(*(blocker))); Simplex blocker_restr( - *(this->get_simplex_address(blocker_root))); + *(this->get_simplex_address(blocker_root))); this->add_blocker(new Simplex(blocker_restr)); } } @@ -190,14 +190,15 @@ class Skeleton_blocker_sub_complex : public ComplexType { // */ // boost::optional<Simplex> get_address(const Root_simplex_handle & s) const; -// private: + // private: /** * same as get_address except that it will return a simplex in any case. * The vertices that were not found are not added. */ // @remark should be private but problem with VS + std::vector<boost::optional<Vertex_handle> > get_addresses( - const Root_simplex_handle & s) const { + const Root_simplex_handle & s) const { std::vector < boost::optional<Vertex_handle> > res; for (auto i : s) { res.push_back(get_address(i)); @@ -214,14 +215,14 @@ class Skeleton_blocker_sub_complex : public ComplexType { */ template<typename ComplexType> bool proper_face_in_union( - Skeleton_blocker_sub_complex<ComplexType> & link, - std::vector<boost::optional<typename ComplexType::Vertex_handle> > & addresses_sigma_in_link, - int vertex_to_be_ignored) { + Skeleton_blocker_sub_complex<ComplexType> & link, + std::vector<boost::optional<typename ComplexType::Vertex_handle> > & addresses_sigma_in_link, + std::size_t vertex_to_be_ignored) { // we test that all vertices of 'addresses_sigma_in_link' but 'vertex_to_be_ignored' // are in link1 if it is the case we construct the corresponding simplex bool vertices_sigma_are_in_link = true; typename ComplexType::Simplex sigma_in_link; - for (int i = 0; i < addresses_sigma_in_link.size(); ++i) { + for (std::size_t i = 0; i < addresses_sigma_in_link.size(); ++i) { if (i != vertex_to_be_ignored) { if (!addresses_sigma_in_link[i]) { vertices_sigma_are_in_link = false; @@ -236,43 +237,24 @@ bool proper_face_in_union( return vertices_sigma_are_in_link && link.contains(sigma_in_link); } -/* - template<typename ComplexType> - bool - proper_faces_in_union(Skeleton_blocker_simplex<typename ComplexType::Root_vertex_handle> & sigma, Skeleton_blocker_sub_complex<ComplexType> & link1, Skeleton_blocker_sub_complex<ComplexType> & link2) - { - typedef typename ComplexType::Vertex_handle Vertex_handle; - std::vector<boost::optional<Vertex_handle> > addresses_sigma_in_link1 = link1.get_addresses(sigma); - std::vector<boost::optional<Vertex_handle> > addresses_sigma_in_link2 = link2.get_addresses(sigma); - - for (int current_index = 0; current_index < addresses_sigma_in_link1.size(); ++current_index) - { - - if (!proper_face_in_union(link1, addresses_sigma_in_link1, current_index) - && !proper_face_in_union(link2, addresses_sigma_in_link2, current_index)){ - return false; - } - } - return true; - }*/ - // Remark: this function should be friend in order to leave get_adresses private // however doing so seemes currently not possible due to a visual studio bug c2668 // "the compiler does not support partial ordering of template functions as specified in the C++ Standard" // http://www.serkey.com/error-c2668-ambiguous-call-to-overloaded-function-bb45ft.html + template<typename ComplexType> bool proper_faces_in_union( - Skeleton_blocker_simplex<typename ComplexType::Root_vertex_handle> & sigma, - Skeleton_blocker_sub_complex<ComplexType> & link1, - Skeleton_blocker_sub_complex<ComplexType> & link2) { + Skeleton_blocker_simplex<typename ComplexType::Root_vertex_handle> & sigma, + Skeleton_blocker_sub_complex<ComplexType> & link1, + Skeleton_blocker_sub_complex<ComplexType> & link2) { typedef typename ComplexType::Vertex_handle Vertex_handle; std::vector < boost::optional<Vertex_handle> > addresses_sigma_in_link1 = link1.get_addresses(sigma); std::vector < boost::optional<Vertex_handle> > addresses_sigma_in_link2 = link2.get_addresses(sigma); - for (int current_index = 0; current_index < addresses_sigma_in_link1.size(); - ++current_index) { + for (std::size_t current_index = 0; current_index < addresses_sigma_in_link1.size(); + ++current_index) { if (!proper_face_in_union(link1, addresses_sigma_in_link1, current_index) && !proper_face_in_union(link2, addresses_sigma_in_link2, current_index)) { @@ -289,4 +271,3 @@ namespace skbl = skeleton_blocker; } // namespace Gudhi #endif // SKELETON_BLOCKER_SKELETON_BLOCKER_SUB_COMPLEX_H_ - diff --git a/include/gudhi/Skeleton_blocker/internal/Top_faces.h b/include/gudhi/Skeleton_blocker/internal/Top_faces.h index 39d95661..2b681752 100644 --- a/include/gudhi/Skeleton_blocker/internal/Top_faces.h +++ b/include/gudhi/Skeleton_blocker/internal/Top_faces.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_INTERNAL_TOP_FACES_H_ #define SKELETON_BLOCKER_INTERNAL_TOP_FACES_H_ @@ -69,4 +70,4 @@ namespace skbl = skeleton_blocker; } // namespace Gudhi -#endif // SKELETON_BLOCKER_INTERNAL_TOP_FACES_H_ +#endif // SKELETON_BLOCKER_INTERNAL_TOP_FACES_H_ diff --git a/include/gudhi/Skeleton_blocker/internal/Trie.h b/include/gudhi/Skeleton_blocker/internal/Trie.h index cdc47b8a..2c9602fa 100644 --- a/include/gudhi/Skeleton_blocker/internal/Trie.h +++ b/include/gudhi/Skeleton_blocker/internal/Trie.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Méditerranée (France) + * Copyright (C) 2014 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 @@ -21,7 +21,6 @@ * */ - #ifndef SKELETON_BLOCKER_INTERNAL_TRIE_H_ #define SKELETON_BLOCKER_INTERNAL_TRIE_H_ @@ -148,7 +147,7 @@ struct Trie { } void remove_leaf() { - assert(is_leaf); + assert(is_leaf()); if (!is_root()) parent_->childs.erase(this); } @@ -240,7 +239,7 @@ struct Tries { std::vector<Simplex> next_dimension_simplices() const { std::vector<Simplex> res; - while (!to_see_.empty() && to_see_.front()->simplex().dimension() == current_dimension_) { + while (!(to_see_.empty()) && (to_see_.front()->simplex().dimension() == current_dimension_)) { res.emplace_back(to_see_.front()->simplex()); for (auto child : to_see_.front()->childs) to_see_.push_back(child.get()); @@ -257,7 +256,7 @@ struct Tries { private: mutable std::deque<STrie*> to_see_; - mutable unsigned current_dimension_ = 0; + mutable int current_dimension_ = 0; std::vector<STrie*> cofaces_; }; diff --git a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_blockers_iterators.h b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_blockers_iterators.h index 4dbc9ed3..d2fff960 100644 --- a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_blockers_iterators.h +++ b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_blockers_iterators.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_BLOCKERS_ITERATORS_H_ #define SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_BLOCKERS_ITERATORS_H_ diff --git a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_edges_iterators.h b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_edges_iterators.h index 15618932..b90dcf34 100644 --- a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_edges_iterators.h +++ b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_edges_iterators.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_EDGES_ITERATORS_H_ #define SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_EDGES_ITERATORS_H_ diff --git a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_iterators.h b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_iterators.h index cc3ed276..1351614f 100644 --- a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_iterators.h +++ b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_iterators.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 diff --git a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_simplices_iterators.h b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_simplices_iterators.h index 3b941be5..2acdb555 100644 --- a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_simplices_iterators.h +++ b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_simplices_iterators.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_SIMPLICES_ITERATORS_H_ #define SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_SIMPLICES_ITERATORS_H_ @@ -68,10 +69,11 @@ public boost::iterator_facade < Simplex_around_vertex_iterator<SkeletonBlockerCo Vertex_handle v; std::shared_ptr<Link> link_v; std::shared_ptr<Trie> trie; - std::list<Trie*> nodes_to_be_seen; // todo deque + // TODO(DS): use a deque instead + std::list<Trie*> nodes_to_be_seen; public: - Simplex_around_vertex_iterator() : complex(0) {} + Simplex_around_vertex_iterator() : complex(0) { } Simplex_around_vertex_iterator(const Complex* complex_, Vertex_handle v_) : complex(complex_), @@ -81,15 +83,16 @@ public boost::iterator_facade < Simplex_around_vertex_iterator<SkeletonBlockerCo compute_trie_and_nodes_to_be_seen(); } - // todo avoid useless copy - // todo currently just work if copy begin iterator + // TODO(DS): avoid useless copy + // TODO(DS): currently just work if copy begin iterator Simplex_around_vertex_iterator(const Simplex_around_vertex_iterator& other) : complex(other.complex), v(other.v), link_v(other.link_v), trie(other.trie), nodes_to_be_seen(other.nodes_to_be_seen) { - if (!other.is_end()) {} + if (!other.is_end()) { + } } /** @@ -159,7 +162,8 @@ public boost::iterator_facade < Simplex_around_vertex_iterator<SkeletonBlockerCo bool both_non_empty = !nodes_to_be_seen.empty() && !other.nodes_to_be_seen.empty(); - if (!both_non_empty) return false; // one is empty the other is not + // one is empty the other is not + if (!both_non_empty) return false; bool same_node = (**(nodes_to_be_seen.begin()) == **(other.nodes_to_be_seen.begin())); return same_node; @@ -238,7 +242,6 @@ public boost::iterator_facade < Simplex_iterator<SkeletonBlockerComplex> } private: - // todo return to private Simplex_iterator(const Complex* complex, bool end) : complex_(complex) { set_end(); @@ -306,7 +309,7 @@ public boost::iterator_facade < Simplex_iterator<SkeletonBlockerComplex> /** * Iterator through the maximal faces of the coboundary of a simplex. - */ + */ template<typename SkeletonBlockerComplex, typename Link> class Simplex_coboundary_iterator : public boost::iterator_facade < Simplex_coboundary_iterator<SkeletonBlockerComplex, Link> @@ -329,12 +332,12 @@ public boost::iterator_facade < Simplex_coboundary_iterator<SkeletonBlockerCompl Complex_vertex_iterator link_vertex_end; public: - Simplex_coboundary_iterator() : complex(0) {} + Simplex_coboundary_iterator() : complex(0) { } Simplex_coboundary_iterator(const Complex* complex_, const Simplex& sigma_) : complex(complex_), sigma(sigma_), - //need only vertices of the link hence the true flag + // need only vertices of the link hence the true flag link(new Link(*complex_, sigma_, false, true)) { auto link_vertex_range = link->vertex_range(); current_vertex = link_vertex_range.begin(); @@ -347,9 +350,9 @@ public boost::iterator_facade < Simplex_coboundary_iterator<SkeletonBlockerCompl link(other.link), current_vertex(other.current_vertex), link_vertex_end(other.link_vertex_end) { } - + // returns an iterator to the end - Simplex_coboundary_iterator(const Complex* complex_,const Simplex& sigma_, bool end) : + Simplex_coboundary_iterator(const Complex* complex_, const Simplex& sigma_, bool end) : complex(complex_), sigma(sigma_) { // to represent an end iterator without having to build a useless link, we use @@ -361,7 +364,7 @@ public boost::iterator_facade < Simplex_coboundary_iterator<SkeletonBlockerCompl return complex->convert_handle_from_another_complex(*link, link_vh); } -public: + public: friend std::ostream& operator<<(std::ostream& stream, const Simplex_coboundary_iterator& sci) { return stream; } @@ -369,8 +372,8 @@ public: // assume that iterator points to the same complex and comes from the same simplex bool equal(const Simplex_coboundary_iterator& other) const { assert(complex == other.complex && sigma == other.sigma); - if(is_end()) return other.is_end(); - if(other.is_end()) return is_end(); + if (is_end()) return other.is_end(); + if (other.is_end()) return is_end(); return *current_vertex == *(other.current_vertex); } @@ -384,13 +387,12 @@ public: return res; } -private: + private: bool is_end() const { return !link || current_vertex == link_vertex_end; } }; - } // namespace skeleton_blocker namespace skbl = skeleton_blocker; diff --git a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_triangles_iterators.h b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_triangles_iterators.h index b2dd9a21..736941dd 100644 --- a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_triangles_iterators.h +++ b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_triangles_iterators.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_TRIANGLES_ITERATORS_H_ #define SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_TRIANGLES_ITERATORS_H_ @@ -135,7 +136,7 @@ typename SkeletonBlockerComplex::Simplex const Triangle_iterator(const SkeletonBlockerComplex* complex) : complex_(complex), current_vertex_(complex->vertex_range().begin()), - current_triangle_(complex, *current_vertex_), // xxx this line is problematic is the complex is empty + current_triangle_(complex, *current_vertex_), // this line is problematic is the complex is empty is_end_(false) { assert(!complex->empty()); gotoFirstTriangle(); @@ -172,7 +173,8 @@ typename SkeletonBlockerComplex::Simplex const bool both_arent_finished = !is_finished() && !other.is_finished(); // if the two iterators are not finished, they must have the same state return (complex_ == other.complex_) && (both_are_finished || ((both_arent_finished) && - current_vertex_ == other.current_vertex_ && current_triangle_ == other.current_triangle_)); + current_vertex_ == other.current_vertex_ && + current_triangle_ == other.current_triangle_)); } Simplex dereference() const { @@ -183,8 +185,10 @@ typename SkeletonBlockerComplex::Simplex const // goto the next vertex that has a triangle pending or the // end vertex iterator if none exists void goto_next_vertex() { - assert(current_triangle_.finished()); // we mush have consume all triangles passing through the vertex - assert(!is_finished()); // we must not be done + // we must have consume all triangles passing through the vertex + assert(current_triangle_.finished()); + // we must not be done + assert(!is_finished()); ++current_vertex_; diff --git a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_vertices_iterators.h b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_vertices_iterators.h index f06cab71..9e9ae961 100644 --- a/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_vertices_iterators.h +++ b/include/gudhi/Skeleton_blocker/iterators/Skeleton_blockers_vertices_iterators.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_VERTICES_ITERATORS_H_ #define SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_VERTICES_ITERATORS_H_ @@ -103,7 +104,7 @@ class Vertex_iterator : public boost::iterator_facade< Vertex_iterator <Skeleton }; template<typename SkeletonBlockerComplex> -class Neighbors_vertices_iterator: public boost::iterator_facade < Neighbors_vertices_iterator<SkeletonBlockerComplex> +class Neighbors_vertices_iterator : public boost::iterator_facade < Neighbors_vertices_iterator<SkeletonBlockerComplex> , typename SkeletonBlockerComplex::Vertex_handle const , boost::forward_traversal_tag , typename SkeletonBlockerComplex::Vertex_handle const> { @@ -122,9 +123,6 @@ class Neighbors_vertices_iterator: public boost::iterator_facade < Neighbors_ver boost_adjacency_iterator end_; public: - // boost_adjacency_iterator ai, ai_end; - // for (tie(ai, ai_end) = adjacent_vertices(v.vertex, skeleton); ai != ai_end; ++ai) { - Neighbors_vertices_iterator() : complex(NULL) { } Neighbors_vertices_iterator(const Complex* complex_, Vertex_handle v_) : @@ -157,7 +155,7 @@ class Neighbors_vertices_iterator: public boost::iterator_facade < Neighbors_ver } private: - // todo remove this ugly hack + // TODO(DS): remove this ugly hack void set_end() { current_ = end_; } @@ -170,5 +168,3 @@ namespace skbl = skeleton_blocker; } // namespace Gudhi #endif // SKELETON_BLOCKER_ITERATORS_SKELETON_BLOCKERS_VERTICES_ITERATORS_H_ - - diff --git a/include/gudhi/Skeleton_blocker_complex.h b/include/gudhi/Skeleton_blocker_complex.h index 7a6d1d50..4f052ba5 100644 --- a/include/gudhi/Skeleton_blocker_complex.h +++ b/include/gudhi/Skeleton_blocker_complex.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -211,7 +211,6 @@ class Skeleton_blocker_complex { add_edge_without_blockers(e.first, e.second); } - template<typename SimpleHandleOutputIterator> void add_blockers(SimpleHandleOutputIterator simplices_begin, SimpleHandleOutputIterator simplices_end) { Tries<Simplex> tries(num_vertices(), simplices_begin, simplices_end); @@ -414,7 +413,8 @@ class Skeleton_blocker_complex { /** */ bool contains_vertex(Vertex_handle u) const { - if (u.vertex < 0 || u.vertex >= boost::num_vertices(skeleton)) + Vertex_handle num_vertices(boost::num_vertices(skeleton)); + if (u.vertex < 0 || u.vertex >= num_vertices) return false; return (*this)[u].is_active(); } @@ -441,11 +441,11 @@ class Skeleton_blocker_complex { * @brief Given an Id return the address of the vertex having this Id in the complex. * @remark For a simplicial complex, the address is the id but it may not be the case for a SubComplex. */ - virtual boost::optional<Vertex_handle> get_address( - Root_vertex_handle id) const { + virtual boost::optional<Vertex_handle> get_address(Root_vertex_handle id) const { boost::optional<Vertex_handle> res; - if (id.vertex < boost::num_vertices(skeleton)) - res = Vertex_handle(id.vertex); // xxx + int num_vertices = boost::num_vertices(skeleton); + if (id.vertex < num_vertices) + res = Vertex_handle(id.vertex); return res; } @@ -560,7 +560,7 @@ class Skeleton_blocker_complex { return res; } - /** + /** * @brief Adds all edges of s in the complex. */ void add_edge(const Simplex& s) { @@ -591,7 +591,6 @@ class Skeleton_blocker_complex { return *edge_handle; } - /** * @brief Adds all edges of s in the complex without adding blockers. */ @@ -844,12 +843,13 @@ class Skeleton_blocker_complex { boost_adjacency_iterator ai, ai_end; for (tie(ai, ai_end) = adjacent_vertices(v.vertex, skeleton); ai != ai_end; ++ai) { + Vertex_handle value(*ai); if (keep_only_superior) { - if (*ai > v.vertex) { - n.add_vertex(Vertex_handle(*ai)); + if (value > v.vertex) { + n.add_vertex(value); } } else { - n.add_vertex(Vertex_handle(*ai)); + n.add_vertex(value); } } } @@ -929,7 +929,7 @@ class Skeleton_blocker_complex { // xxx rename get_address et place un using dans sub_complex boost::optional<Simplex> get_simplex_address( - const Root_simplex_handle& s) const { + const Root_simplex_handle& s) const { boost::optional<Simplex> res; Simplex s_address; @@ -1001,10 +1001,9 @@ class Skeleton_blocker_complex { return std::distance(triangles.begin(), triangles.end()); } - /* * @brief returns the number of simplices of a given dimension in the complex. - */ + */ size_t num_simplices() const { auto simplices = complex_simplex_range(); return std::distance(simplices.begin(), simplices.end()); @@ -1012,8 +1011,8 @@ class Skeleton_blocker_complex { /* * @brief returns the number of simplices of a given dimension in the complex. - */ - size_t num_simplices(unsigned dimension) const { + */ + size_t num_simplices(int dimension) const { // TODO(DS): iterator on k-simplices size_t res = 0; for (const auto& s : complex_simplex_range()) diff --git a/include/gudhi/Skeleton_blocker_geometric_complex.h b/include/gudhi/Skeleton_blocker_geometric_complex.h index 1130ca9f..95331b7a 100644 --- a/include/gudhi/Skeleton_blocker_geometric_complex.h +++ b/include/gudhi/Skeleton_blocker_geometric_complex.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_GEOMETRIC_COMPLEX_H_ #define SKELETON_BLOCKER_GEOMETRIC_COMPLEX_H_ diff --git a/include/gudhi/Skeleton_blocker_link_complex.h b/include/gudhi/Skeleton_blocker_link_complex.h index 1bd66289..4db075b0 100644 --- a/include/gudhi/Skeleton_blocker_link_complex.h +++ b/include/gudhi/Skeleton_blocker_link_complex.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_LINK_COMPLEX_H_ #define SKELETON_BLOCKER_LINK_COMPLEX_H_ @@ -39,7 +40,7 @@ template<class ComplexType> class Skeleton_blocker_sub_complex; */ template<typename ComplexType> class Skeleton_blocker_link_complex : public Skeleton_blocker_sub_complex< - ComplexType> { +ComplexType> { template<typename T> friend class Skeleton_blocker_link_superior; typedef typename ComplexType::Edge_handle Edge_handle; @@ -60,8 +61,7 @@ class Skeleton_blocker_link_complex : public Skeleton_blocker_sub_complex< typedef typename ComplexType::Root_simplex_handle::Simplex_vertex_const_iterator Root_simplex_handle_iterator; explicit Skeleton_blocker_link_complex(bool only_superior_vertices = false) - : only_superior_vertices_(only_superior_vertices) { - } + : only_superior_vertices_(only_superior_vertices) { } /** * If the parameter only_superior_vertices is true, @@ -95,10 +95,10 @@ class Skeleton_blocker_link_complex : public Skeleton_blocker_sub_complex< */ Skeleton_blocker_link_complex(const ComplexType & parent_complex, Edge_handle edge, bool only_superior_vertices = - false) + false) : only_superior_vertices_(only_superior_vertices) { Simplex alpha_simplex(parent_complex.first_vertex(edge), - parent_complex.second_vertex(edge)); + parent_complex.second_vertex(edge)); build_link(parent_complex, alpha_simplex); } @@ -151,7 +151,7 @@ class Skeleton_blocker_link_complex : public Skeleton_blocker_sub_complex< bool only_superior_vertices) { // for a vertex we know exactly the number of vertices of the link (and the size of the corresponding vector this->skeleton.m_vertices.reserve( - parent_complex.degree(alpha_parent_adress)); + parent_complex.degree(alpha_parent_adress)); // For all vertex 'v' in this intersection, we go through all its adjacent blockers. // If one blocker minus 'v' is included in alpha then the vertex is not in the link complex. @@ -169,21 +169,21 @@ class Skeleton_blocker_link_complex : public Skeleton_blocker_sub_complex< return; for (auto x_link = this->vertex_range().begin(); - x_link != this->vertex_range().end(); ++x_link) { + x_link != this->vertex_range().end(); ++x_link) { for (auto y_link = x_link; ++y_link != this->vertex_range().end();) { Vertex_handle x_parent = *parent_complex.get_address( - this->get_id(*x_link)); + this->get_id(*x_link)); Vertex_handle y_parent = *parent_complex.get_address( - this->get_id(*y_link)); + this->get_id(*y_link)); if (parent_complex.contains_edge(x_parent, y_parent)) { // we check that there is no blocker subset of alpha passing trough x and y bool new_edge = true; for (auto blocker_parent : parent_complex.const_blocker_range( - x_parent)) { + x_parent)) { if (!is_alpha_blocker || *blocker_parent != alpha_parent_adress) { if (blocker_parent->contains(y_parent)) { new_edge = !(alpha_parent_adress.contains_difference( - *blocker_parent, x_parent, y_parent)); + *blocker_parent, x_parent, y_parent)); if (!new_edge) break; } @@ -201,8 +201,8 @@ class Skeleton_blocker_link_complex : public Skeleton_blocker_sub_complex< * corresponding address in 'other_complex'. * It assumes that other_complex have a vertex 'this.get_id(address)' */ - boost::optional<Vertex_handle> give_equivalent_vertex( - const ComplexType & other_complex, Vertex_handle address) const { + boost::optional<Vertex_handle> give_equivalent_vertex(const ComplexType & other_complex, + Vertex_handle address) const { Root_vertex_handle id((*this)[address].get_id()); return other_complex.get_address(id); } @@ -269,7 +269,7 @@ class Skeleton_blocker_link_complex : public Skeleton_blocker_sub_complex< bool only_vertices = false) { assert(is_alpha_blocker || parent_complex.contains(alpha_parent_adress)); compute_link_vertices(parent_complex, alpha_parent_adress, only_superior_vertices_); - if(!only_vertices) { + if (!only_vertices) { compute_link_edges(parent_complex, alpha_parent_adress, is_alpha_blocker); compute_link_blockers(parent_complex, alpha_parent_adress, is_alpha_blocker); } diff --git a/include/gudhi/Skeleton_blocker_simplifiable_complex.h b/include/gudhi/Skeleton_blocker_simplifiable_complex.h index 171efd4b..544e02e8 100644 --- a/include/gudhi/Skeleton_blocker_simplifiable_complex.h +++ b/include/gudhi/Skeleton_blocker_simplifiable_complex.h @@ -4,7 +4,7 @@ * * Author(s): David Salinas * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) + * Copyright (C) 2014 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 @@ -19,6 +19,7 @@ * 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 SKELETON_BLOCKER_SIMPLIFIABLE_COMPLEX_H_ #define SKELETON_BLOCKER_SIMPLIFIABLE_COMPLEX_H_ @@ -33,7 +34,7 @@ namespace Gudhi { namespace skeleton_blocker { /** - * Returns true iff the blocker 'sigma' is popable. + * Returns true if the blocker 'sigma' is popable. * To define popable, let us call 'L' the complex that * consists in the current complex without the blocker 'sigma'. * A blocker 'sigma' is then "popable" if the link of 'sigma' @@ -145,8 +146,7 @@ void Skeleton_blocker_complex<SkeletonBlockerDS>::remove_star(Vertex_handle v) { * whenever the dimension of tau is at least 2. */ template<typename SkeletonBlockerDS> -void Skeleton_blocker_complex<SkeletonBlockerDS>::update_blockers_after_remove_star_of_vertex_or_edge( - const Simplex& simplex_to_be_removed) { +void Skeleton_blocker_complex<SkeletonBlockerDS>::update_blockers_after_remove_star_of_vertex_or_edge(const Simplex& simplex_to_be_removed) { std::list <Blocker_handle> blockers_to_update; if (simplex_to_be_removed.empty()) return; @@ -224,8 +224,6 @@ void Skeleton_blocker_complex<SkeletonBlockerDS>::add_simplex(const Simplex& sig add_blockers_after_simplex_insertion(sigma); } - - template<typename SkeletonBlockerDS> void Skeleton_blocker_complex<SkeletonBlockerDS>::add_blockers_after_simplex_insertion(Simplex sigma) { if (sigma.dimension() < 1) return; @@ -385,7 +383,8 @@ Skeleton_blocker_complex<SkeletonBlockerDS>::contract_edge(Vertex_handle a, Vert template<typename SkeletonBlockerDS> void Skeleton_blocker_complex<SkeletonBlockerDS>::get_blockers_to_be_added_after_contraction(Vertex_handle a, - Vertex_handle b, std::set<Simplex>& blockers_to_add) { + Vertex_handle b, + std::set<Simplex>& blockers_to_add) { blockers_to_add.clear(); typedef Skeleton_blocker_link_complex<Skeleton_blocker_complex<SkeletonBlockerDS> > LinkComplexType; diff --git a/include/gudhi/Strong_witness_complex.h b/include/gudhi/Strong_witness_complex.h new file mode 100644 index 00000000..a973ddb7 --- /dev/null +++ b/include/gudhi/Strong_witness_complex.h @@ -0,0 +1,185 @@ +/* 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): Siargey Kachanovich + * + * Copyright (C) 2015 INRIA (France) + * + * 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 STRONG_WITNESS_COMPLEX_H_ +#define STRONG_WITNESS_COMPLEX_H_ + +#include <gudhi/Active_witness/Active_witness.h> + +#include <utility> +#include <vector> +#include <list> +#include <limits> + +namespace Gudhi { + +namespace witness_complex { + +/* \private + * \class Strong_witness_complex + * \brief Constructs strong witness complex for a given table of nearest landmarks with respect to witnesses. + * \ingroup witness_complex + * + * \tparam Nearest_landmark_table_ needs to be a range of a range of pairs of nearest landmarks and distances. + * The class Nearest_landmark_table_::value_type must be a copiable range. + * The range of pairs must admit a member type 'iterator'. The dereference type + * of the pair range iterator needs to be 'std::pair<std::size_t, double>'. + */ +template< class Nearest_landmark_table_ > +class Strong_witness_complex { + private: + typedef typename Nearest_landmark_table_::value_type Nearest_landmark_range; + typedef std::size_t Witness_id; + typedef std::size_t Landmark_id; + typedef std::pair<Landmark_id, double> Id_distance_pair; + typedef Active_witness<Id_distance_pair, Nearest_landmark_range> ActiveWitness; + typedef std::list< ActiveWitness > ActiveWitnessList; + typedef std::vector< Landmark_id > typeVectorVertex; + typedef std::vector<Nearest_landmark_range> Nearest_landmark_table_internal; + typedef Landmark_id Vertex_handle; + + protected: + Nearest_landmark_table_internal nearest_landmark_table_; + + public: + ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + /* @name Constructor + */ + + //@{ + + Strong_witness_complex() { + } + + /** + * \brief Initializes member variables before constructing simplicial complex. + * \details Records nearest landmark table. + * @param[in] nearest_landmark_table needs to be a range of a range of pairs of nearest landmarks and distances. + * The class Nearest_landmark_table_::value_type must be a copiable range. + * The range of pairs must admit a member type 'iterator'. The dereference type + * of the pair range iterator needs to be 'std::pair<std::size_t, double>'. + */ + Strong_witness_complex(Nearest_landmark_table_ const & nearest_landmark_table) + : nearest_landmark_table_(std::begin(nearest_landmark_table), std::end(nearest_landmark_table)) { + } + + /** \brief Outputs the strong witness complex of relaxation 'max_alpha_square' + * in a simplicial complex data structure. + * \details The function returns true if the construction is successful and false otherwise. + * @param[out] complex Simplicial complex data structure, which is a model of + * SimplicialComplexForWitness concept. + * @param[in] max_alpha_square Maximal squared relaxation parameter. + * @param[in] limit_dimension Represents the maximal dimension of the simplicial complex + * (default value = no limit). + */ + template < typename SimplicialComplexForWitness > + bool create_complex(SimplicialComplexForWitness& complex, + double max_alpha_square, + Landmark_id limit_dimension = std::numeric_limits<Landmark_id>::max()) const { + Landmark_id complex_dim = 0; + if (complex.num_vertices() > 0) { + std::cerr << "Strong witness complex cannot create complex - complex is not empty.\n"; + return false; + } + if (max_alpha_square < 0) { + std::cerr << "Strong witness complex cannot create complex - squared relaxation parameter must be " + << "non-negative.\n"; + return false; + } + if (limit_dimension < 0) { + std::cerr << "Strong witness complex cannot create complex - limit dimension must be non-negative.\n"; + return false; + } + for (auto w : nearest_landmark_table_) { + ActiveWitness aw(w); + typeVectorVertex simplex; + typename ActiveWitness::iterator aw_it = aw.begin(); + float lim_dist2 = aw.begin()->second + max_alpha_square; + while ((Landmark_id)simplex.size() <= limit_dimension && aw_it != aw.end() && aw_it->second < lim_dist2) { + simplex.push_back(aw_it->first); + complex.insert_simplex_and_subfaces(simplex, aw_it->second - aw.begin()->second); + aw_it++; + } + // continue inserting limD-faces of the following simplices + typeVectorVertex& vertices = simplex; // 'simplex' now will be called vertices + while (aw_it != aw.end() && aw_it->second < lim_dist2) { + typeVectorVertex facet = {}; + add_all_faces_of_dimension(limit_dimension, vertices, vertices.begin(), aw_it, + aw_it->second - aw.begin()->second, facet, complex); + vertices.push_back(aw_it->first); + aw_it++; + } + if ((Landmark_id)simplex.size() - 1 > complex_dim) + complex_dim = simplex.size() - 1; + } + complex.set_dimension(complex_dim); + return true; + } + + private: + /* \brief Adds recursively all the faces of a certain dimension dim-1 witnessed by the same witness. + * Iterator is needed to know until how far we can take landmarks to form simplexes. + * simplex is the prefix of the simplexes to insert. + * The landmark pointed by aw_it is added to all formed simplices. + */ + template < typename SimplicialComplexForWitness > + void add_all_faces_of_dimension(Landmark_id dim, + typeVectorVertex& vertices, + typename typeVectorVertex::iterator curr_it, + typename ActiveWitness::iterator aw_it, + double filtration_value, + typeVectorVertex& simplex, + SimplicialComplexForWitness& sc) const { + if (dim > 0) { + while (curr_it != vertices.end()) { + simplex.push_back(*curr_it); + ++curr_it; + add_all_faces_of_dimension(dim-1, + vertices, + curr_it, + aw_it, + filtration_value, + simplex, + sc); + simplex.pop_back(); + add_all_faces_of_dimension(dim, + vertices, + curr_it, + aw_it, + filtration_value, + simplex, + sc); + } + } else if (dim == 0) { + simplex.push_back(aw_it->first); + sc.insert_simplex_and_subfaces(simplex, filtration_value); + simplex.pop_back(); + } + } + //@} +}; + +} // namespace witness_complex + +} // namespace Gudhi + +#endif // STRONG_WITNESS_COMPLEX_H_ diff --git a/include/gudhi/Tangential_complex.h b/include/gudhi/Tangential_complex.h new file mode 100644 index 00000000..cfc82eb1 --- /dev/null +++ b/include/gudhi/Tangential_complex.h @@ -0,0 +1,2276 @@ +/* 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): Clement Jamin + * + * 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/>. + */ + +#ifndef TANGENTIAL_COMPLEX_H_ +#define TANGENTIAL_COMPLEX_H_ + +#include <gudhi/Tangential_complex/config.h> +#include <gudhi/Tangential_complex/Simplicial_complex.h> +#include <gudhi/Tangential_complex/utilities.h> +#include <gudhi/Kd_tree_search.h> +#include <gudhi/console_color.h> +#include <gudhi/Clock.h> +#include <gudhi/Simplex_tree.h> + +#include <CGAL/Default.h> +#include <CGAL/Dimension.h> +#include <CGAL/function_objects.h> // for CGAL::Identity +#include <CGAL/Epick_d.h> +#include <CGAL/Regular_triangulation_traits_adapter.h> +#include <CGAL/Regular_triangulation.h> +#include <CGAL/Delaunay_triangulation.h> +#include <CGAL/Combination_enumerator.h> +#include <CGAL/point_generators_d.h> + +#include <Eigen/Core> +#include <Eigen/Eigen> + +#include <boost/optional.hpp> +#include <boost/iterator/transform_iterator.hpp> +#include <boost/range/adaptor/transformed.hpp> +#include <boost/range/counting_range.hpp> +#include <boost/math/special_functions/factorials.hpp> +#include <boost/container/flat_set.hpp> + +#include <tuple> +#include <vector> +#include <set> +#include <utility> +#include <sstream> +#include <iostream> +#include <limits> +#include <algorithm> +#include <functional> +#include <iterator> +#include <cmath> // for std::sqrt +#include <string> +#include <cstddef> // for std::size_t + +#ifdef GUDHI_USE_TBB +#include <tbb/parallel_for.h> +#include <tbb/combinable.h> +#include <tbb/mutex.h> +#endif + +// #define GUDHI_TC_EXPORT_NORMALS // Only for 3D surfaces (k=2, d=3) + +namespace sps = Gudhi::spatial_searching; + +namespace Gudhi { + +namespace tangential_complex { + +using namespace internal; + +class Vertex_data { + public: + Vertex_data(std::size_t data = (std::numeric_limits<std::size_t>::max)()) + : m_data(data) { } + + operator std::size_t() { + return m_data; + } + + operator std::size_t() const { + return m_data; + } + + private: + std::size_t m_data; +}; + +/** + * \class Tangential_complex Tangential_complex.h gudhi/Tangential_complex.h + * \brief Tangential complex data structure. + * + * \ingroup tangential_complex + * + * \details + * The class Tangential_complex represents a tangential complex. + * After the computation of the complex, an optional post-processing called perturbation can + * be run to attempt to remove inconsistencies. + * + * \tparam Kernel_ requires a <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_d/classCGAL_1_1Epick__d.html">CGAL::Epick_d</a> class, which + * can be static if you know the ambiant dimension at compile-time, or dynamic if you don't. + * \tparam DimensionTag can be either <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_23/classCGAL_1_1Dimension__tag.html">Dimension_tag<d></a> + * if you know the intrinsic dimension at compile-time, + * or <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_23/classCGAL_1_1Dynamic__dimension__tag.html">CGAL::Dynamic_dimension_tag</a> + * if you don't. + * \tparam Concurrency_tag enables sequential versus parallel computation. Possible values are `CGAL::Parallel_tag` (the default) and `CGAL::Sequential_tag`. + * \tparam Triangulation_ is the type used for storing the local regular triangulations. We highly recommend to use the default value (`CGAL::Regular_triangulation`). + * + */ +template +< + typename Kernel_, // ambiant kernel + typename DimensionTag, // intrinsic dimension + typename Concurrency_tag = CGAL::Parallel_tag, + typename Triangulation_ = CGAL::Default +> +class Tangential_complex { + typedef Kernel_ K; + typedef typename K::FT FT; + typedef typename K::Point_d Point; + typedef typename K::Weighted_point_d Weighted_point; + typedef typename K::Vector_d Vector; + + typedef typename CGAL::Default::Get + < + Triangulation_, + CGAL::Regular_triangulation + < + CGAL::Epick_d<DimensionTag>, + CGAL::Triangulation_data_structure + < + typename CGAL::Epick_d<DimensionTag>::Dimension, + CGAL::Triangulation_vertex + < + CGAL::Regular_triangulation_traits_adapter< CGAL::Epick_d<DimensionTag> >, Vertex_data + >, + CGAL::Triangulation_full_cell<CGAL::Regular_triangulation_traits_adapter< CGAL::Epick_d<DimensionTag> > > + > + > + >::type Triangulation; + typedef typename Triangulation::Geom_traits Tr_traits; + typedef typename Triangulation::Weighted_point Tr_point; + typedef typename Triangulation::Bare_point Tr_bare_point; + typedef typename Triangulation::Vertex_handle Tr_vertex_handle; + typedef typename Triangulation::Full_cell_handle Tr_full_cell_handle; + typedef typename Tr_traits::Vector_d Tr_vector; + +#if defined(GUDHI_USE_TBB) + typedef tbb::mutex Mutex_for_perturb; + typedef Vector Translation_for_perturb; + typedef std::vector<Atomic_wrapper<FT> > Weights; +#else + typedef Vector Translation_for_perturb; + typedef std::vector<FT> Weights; +#endif + typedef std::vector<Translation_for_perturb> Translations_for_perturb; + + // Store a local triangulation and a handle to its center vertex + + struct Tr_and_VH { + public: + Tr_and_VH() + : m_tr(NULL) { } + + Tr_and_VH(int dim) + : m_tr(new Triangulation(dim)) { } + + ~Tr_and_VH() { + destroy_triangulation(); + } + + Triangulation & construct_triangulation(int dim) { + delete m_tr; + m_tr = new Triangulation(dim); + return tr(); + } + + void destroy_triangulation() { + delete m_tr; + m_tr = NULL; + } + + Triangulation & tr() { + return *m_tr; + } + + Triangulation const& tr() const { + return *m_tr; + } + + Tr_vertex_handle const& center_vertex() const { + return m_center_vertex; + } + + Tr_vertex_handle & center_vertex() { + return m_center_vertex; + } + + private: + Triangulation* m_tr; + Tr_vertex_handle m_center_vertex; + }; + + public: + typedef Basis<K> Tangent_space_basis; + typedef Basis<K> Orthogonal_space_basis; + typedef std::vector<Tangent_space_basis> TS_container; + typedef std::vector<Orthogonal_space_basis> OS_container; + + typedef std::vector<Point> Points; + + typedef boost::container::flat_set<std::size_t> Simplex; + typedef std::set<Simplex> Simplex_set; + + private: + typedef sps::Kd_tree_search<K, Points> Points_ds; + typedef typename Points_ds::KNS_range KNS_range; + typedef typename Points_ds::INS_range INS_range; + + typedef std::vector<Tr_and_VH> Tr_container; + typedef std::vector<Vector> Vectors; + + // An Incident_simplex is the list of the vertex indices + // except the center vertex + typedef boost::container::flat_set<std::size_t> Incident_simplex; + typedef std::vector<Incident_simplex> Star; + typedef std::vector<Star> Stars_container; + + // For transform_iterator + + static const Tr_point &vertex_handle_to_point(Tr_vertex_handle vh) { + return vh->point(); + } + + template <typename P, typename VH> + static const P &vertex_handle_to_point(VH vh) { + return vh->point(); + } + + public: + typedef internal::Simplicial_complex Simplicial_complex; + + /** \brief Constructor from a range of points. + * Points are copied into the instance, and a search data structure is initialized. + * Note the complex is not computed: `compute_tangential_complex` must be called after the creation + * of the object. + * + * @param[in] points Range of points (`Point_range::value_type` must be the same as `Kernel_::Point_d`). + * @param[in] intrinsic_dimension Intrinsic dimension of the manifold. + * @param[in] k Kernel instance. + */ + template <typename Point_range> + Tangential_complex(Point_range points, + int intrinsic_dimension, +#ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM + InputIterator first_for_tse, InputIterator last_for_tse, +#endif + const K &k = K() + ) + : m_k(k), + m_intrinsic_dim(intrinsic_dimension), + m_ambient_dim(points.empty() ? 0 : k.point_dimension_d_object()(*points.begin())), + m_points(points.begin(), points.end()), + m_weights(m_points.size(), FT(0)) +#if defined(GUDHI_USE_TBB) && defined(GUDHI_TC_PERTURB_POSITION) + , m_p_perturb_mutexes(NULL) +#endif + , m_points_ds(m_points) + , m_last_max_perturb(0.) + , m_are_tangent_spaces_computed(m_points.size(), false) + , m_tangent_spaces(m_points.size(), Tangent_space_basis()) +#ifdef GUDHI_TC_EXPORT_NORMALS + , m_orth_spaces(m_points.size(), Orthogonal_space_basis()) +#endif +#ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM + , m_points_for_tse(first_for_tse, last_for_tse) + , m_points_ds_for_tse(m_points_for_tse) +#endif + { } + + /// Destructor + ~Tangential_complex() { +#if defined(GUDHI_USE_TBB) && defined(GUDHI_TC_PERTURB_POSITION) + delete [] m_p_perturb_mutexes; +#endif + } + + /// Returns the intrinsic dimension of the manifold. + int intrinsic_dimension() const { + return m_intrinsic_dim; + } + + /// Returns the ambient dimension. + int ambient_dimension() const { + return m_ambient_dim; + } + + Points const& points() const { + return m_points; + } + + /** \brief Returns the point corresponding to the vertex given as parameter. + * + * @param[in] vertex Vertex handle of the point to retrieve. + * @return The point found. + */ + Point get_point(std::size_t vertex) const { + return m_points[vertex]; + } + + /** \brief Returns the perturbed position of the point corresponding to the vertex given as parameter. + * + * @param[in] vertex Vertex handle of the point to retrieve. + * @return The perturbed position of the point found. + */ + Point get_perturbed_point(std::size_t vertex) const { + return compute_perturbed_point(vertex); + } + + /// Returns the number of vertices. + + std::size_t number_of_vertices() const { + return m_points.size(); + } + + void set_weights(const Weights& weights) { + m_weights = weights; + } + + void set_tangent_planes(const TS_container& tangent_spaces +#ifdef GUDHI_TC_EXPORT_NORMALS + , const OS_container& orthogonal_spaces +#endif + ) { +#ifdef GUDHI_TC_EXPORT_NORMALS + GUDHI_CHECK( + m_points.size() == tangent_spaces.size() + && m_points.size() == orthogonal_spaces.size(), + std::logic_error("Wrong sizes")); +#else + GUDHI_CHECK( + m_points.size() == tangent_spaces.size(), + std::logic_error("Wrong sizes")); +#endif + m_tangent_spaces = tangent_spaces; +#ifdef GUDHI_TC_EXPORT_NORMALS + m_orth_spaces = orthogonal_spaces; +#endif + for (std::size_t i = 0; i < m_points.size(); ++i) + m_are_tangent_spaces_computed[i] = true; + } + + /// Computes the tangential complex. + void compute_tangential_complex() { +#ifdef GUDHI_TC_PERFORM_EXTRA_CHECKS + std::cerr << red << "WARNING: GUDHI_TC_PERFORM_EXTRA_CHECKS is defined. " + << "Computation might be slower than usual.\n" << white; +#endif + +#if defined(GUDHI_TC_PROFILING) && defined(GUDHI_USE_TBB) + Gudhi::Clock t; +#endif + + // We need to do that because we don't want the container to copy the + // already-computed triangulations (while resizing) since it would + // invalidate the vertex handles stored beside the triangulations + m_triangulations.resize(m_points.size()); + m_stars.resize(m_points.size()); + m_squared_star_spheres_radii_incl_margin.resize(m_points.size(), FT(-1)); +#ifdef GUDHI_TC_PERTURB_POSITION + if (m_points.empty()) + m_translations.clear(); + else + m_translations.resize(m_points.size(), + m_k.construct_vector_d_object()(m_ambient_dim)); +#if defined(GUDHI_USE_TBB) + delete [] m_p_perturb_mutexes; + m_p_perturb_mutexes = new Mutex_for_perturb[m_points.size()]; +#endif +#endif + +#ifdef GUDHI_USE_TBB + // Parallel + if (boost::is_convertible<Concurrency_tag, CGAL::Parallel_tag>::value) { + tbb::parallel_for(tbb::blocked_range<size_t>(0, m_points.size()), + Compute_tangent_triangulation(*this)); + } else { +#endif // GUDHI_USE_TBB + // Sequential + for (std::size_t i = 0; i < m_points.size(); ++i) + compute_tangent_triangulation(i); +#ifdef GUDHI_USE_TBB + } +#endif // GUDHI_USE_TBB + +#if defined(GUDHI_TC_PROFILING) && defined(GUDHI_USE_TBB) + t.end(); + std::cerr << "Tangential complex computed in " << t.num_seconds() + << " seconds.\n"; +#endif + } + + /// \brief Type returned by `Tangential_complex::fix_inconsistencies_using_perturbation`. + struct Fix_inconsistencies_info { + /// `true` if all inconsistencies could be removed, `false` if the time limit has been reached before + bool success = false; + /// number of steps performed + unsigned int num_steps = 0; + /// initial number of inconsistent stars + std::size_t initial_num_inconsistent_stars = 0; + /// best number of inconsistent stars during the process + std::size_t best_num_inconsistent_stars = 0; + /// final number of inconsistent stars + std::size_t final_num_inconsistent_stars = 0; + }; + + /** \brief Attempts to fix inconsistencies by perturbing the point positions. + * + * @param[in] max_perturb Maximum length of the translations used by the perturbation. + * @param[in] time_limit Time limit in seconds. If -1, no time limit is set. + */ + Fix_inconsistencies_info fix_inconsistencies_using_perturbation(double max_perturb, double time_limit = -1.) { + Fix_inconsistencies_info info; + + if (time_limit == 0.) + return info; + + Gudhi::Clock t; + +#ifdef GUDHI_TC_SHOW_DETAILED_STATS_FOR_INCONSISTENCIES + std::tuple<std::size_t, std::size_t, std::size_t> stats_before = + number_of_inconsistent_simplices(false); + + if (std::get<1>(stats_before) == 0) { +#ifdef DEBUG_TRACES + std::cerr << "Nothing to fix.\n"; +#endif + info.success = false; + return info; + } +#endif // GUDHI_TC_SHOW_DETAILED_STATS_FOR_INCONSISTENCIES + + m_last_max_perturb = max_perturb; + + bool done = false; + info.best_num_inconsistent_stars = m_triangulations.size(); + info.num_steps = 0; + while (!done) { +#ifdef GUDHI_TC_SHOW_DETAILED_STATS_FOR_INCONSISTENCIES + std::cerr + << "\nBefore fix step:\n" + << " * Total number of simplices in stars (incl. duplicates): " + << std::get<0>(stats_before) << "\n" + << " * Num inconsistent simplices in stars (incl. duplicates): " + << red << std::get<1>(stats_before) << white << " (" + << 100. * std::get<1>(stats_before) / std::get<0>(stats_before) << "%)\n" + << " * Number of stars containing inconsistent simplices: " + << red << std::get<2>(stats_before) << white << " (" + << 100. * std::get<2>(stats_before) / m_points.size() << "%)\n"; +#endif + +#if defined(DEBUG_TRACES) || defined(GUDHI_TC_PROFILING) + std::cerr << yellow + << "\nAttempt to fix inconsistencies using perturbations - step #" + << info.num_steps + 1 << "... " << white; +#endif + + std::size_t num_inconsistent_stars = 0; + std::vector<std::size_t> updated_points; + +#ifdef GUDHI_TC_PROFILING + Gudhi::Clock t_fix_step; +#endif + + // Parallel +#if defined(GUDHI_USE_TBB) + if (boost::is_convertible<Concurrency_tag, CGAL::Parallel_tag>::value) { + tbb::combinable<std::size_t> num_inconsistencies; + tbb::combinable<std::vector<std::size_t> > tls_updated_points; + tbb::parallel_for( + tbb::blocked_range<size_t>(0, m_triangulations.size()), + Try_to_solve_inconsistencies_in_a_local_triangulation(*this, max_perturb, + num_inconsistencies, + tls_updated_points)); + num_inconsistent_stars = + num_inconsistencies.combine(std::plus<std::size_t>()); + updated_points = tls_updated_points.combine( + [](std::vector<std::size_t> const& x, + std::vector<std::size_t> const& y) { + std::vector<std::size_t> res; + res.reserve(x.size() + y.size()); + res.insert(res.end(), x.begin(), x.end()); + res.insert(res.end(), y.begin(), y.end()); + return res; + }); + } else { +#endif // GUDHI_USE_TBB + // Sequential + for (std::size_t i = 0; i < m_triangulations.size(); ++i) { + num_inconsistent_stars += + try_to_solve_inconsistencies_in_a_local_triangulation(i, max_perturb, + std::back_inserter(updated_points)); + } +#if defined(GUDHI_USE_TBB) + } +#endif // GUDHI_USE_TBB + +#ifdef GUDHI_TC_PROFILING + t_fix_step.end(); +#endif + +#if defined(GUDHI_TC_SHOW_DETAILED_STATS_FOR_INCONSISTENCIES) || defined(DEBUG_TRACES) + std::cerr + << "\nEncountered during fix:\n" + << " * Num stars containing inconsistent simplices: " + << red << num_inconsistent_stars << white + << " (" << 100. * num_inconsistent_stars / m_points.size() << "%)\n"; +#endif + +#ifdef GUDHI_TC_PROFILING + std::cerr << yellow << "done in " << t_fix_step.num_seconds() + << " seconds.\n" << white; +#elif defined(DEBUG_TRACES) + std::cerr << yellow << "done.\n" << white; +#endif + + if (num_inconsistent_stars > 0) + refresh_tangential_complex(updated_points); + +#ifdef GUDHI_TC_PERFORM_EXTRA_CHECKS + // Confirm that all stars were actually refreshed + std::size_t num_inc_1 = + std::get<1>(number_of_inconsistent_simplices(false)); + refresh_tangential_complex(); + std::size_t num_inc_2 = + std::get<1>(number_of_inconsistent_simplices(false)); + if (num_inc_1 != num_inc_2) + std::cerr << red << "REFRESHMENT CHECK: FAILED. (" + << num_inc_1 << " vs " << num_inc_2 << ")\n" << white; + else + std::cerr << green << "REFRESHMENT CHECK: PASSED.\n" << white; +#endif + +#ifdef GUDHI_TC_SHOW_DETAILED_STATS_FOR_INCONSISTENCIES + std::tuple<std::size_t, std::size_t, std::size_t> stats_after = + number_of_inconsistent_simplices(false); + + std::cerr + << "\nAfter fix:\n" + << " * Total number of simplices in stars (incl. duplicates): " + << std::get<0>(stats_after) << "\n" + << " * Num inconsistent simplices in stars (incl. duplicates): " + << red << std::get<1>(stats_after) << white << " (" + << 100. * std::get<1>(stats_after) / std::get<0>(stats_after) << "%)\n" + << " * Number of stars containing inconsistent simplices: " + << red << std::get<2>(stats_after) << white << " (" + << 100. * std::get<2>(stats_after) / m_points.size() << "%)\n"; + + stats_before = stats_after; +#endif + + if (info.num_steps == 0) + info.initial_num_inconsistent_stars = num_inconsistent_stars; + + if (num_inconsistent_stars < info.best_num_inconsistent_stars) + info.best_num_inconsistent_stars = num_inconsistent_stars; + + info.final_num_inconsistent_stars = num_inconsistent_stars; + + done = (num_inconsistent_stars == 0); + if (!done) { + ++info.num_steps; + if (time_limit > 0. && t.num_seconds() > time_limit) { +#ifdef DEBUG_TRACES + std::cerr << red << "Time limit reached.\n" << white; +#endif + info.success = false; + return info; + } + } + } + +#ifdef DEBUG_TRACES + std::cerr << green << "Fixed!\n" << white; +#endif + info.success = true; + return info; + } + + /// \brief Type returned by `Tangential_complex::number_of_inconsistent_simplices`. + struct Num_inconsistencies { + /// Total number of simplices in stars (including duplicates that appear in several stars) + std::size_t num_simplices = 0; + /// Number of inconsistent simplices + std::size_t num_inconsistent_simplices = 0; + /// Number of stars containing at least one inconsistent simplex + std::size_t num_inconsistent_stars = 0; + }; + + /// Returns the number of inconsistencies + /// @param[in] verbose If true, outputs a message into `std::cerr`. + + Num_inconsistencies + number_of_inconsistent_simplices( +#ifdef DEBUG_TRACES + bool verbose = true +#else + bool verbose = false +#endif + ) const { + Num_inconsistencies stats; + + // For each triangulation + for (std::size_t idx = 0; idx < m_points.size(); ++idx) { + bool is_star_inconsistent = false; + + // For each cell + Star::const_iterator it_inc_simplex = m_stars[idx].begin(); + Star::const_iterator it_inc_simplex_end = m_stars[idx].end(); + for (; it_inc_simplex != it_inc_simplex_end; ++it_inc_simplex) { + // Don't check infinite cells + if (is_infinite(*it_inc_simplex)) + continue; + + Simplex c = *it_inc_simplex; + c.insert(idx); // Add the missing index + + if (!is_simplex_consistent(c)) { + ++stats.num_inconsistent_simplices; + is_star_inconsistent = true; + } + + ++stats.num_simplices; + } + stats.num_inconsistent_stars += is_star_inconsistent; + } + + if (verbose) { + std::cerr + << "\n==========================================================\n" + << "Inconsistencies:\n" + << " * Total number of simplices in stars (incl. duplicates): " + << stats.num_simplices << "\n" + << " * Number of inconsistent simplices in stars (incl. duplicates): " + << stats.num_inconsistent_simplices << " (" + << 100. * stats.num_inconsistent_simplices / stats.num_simplices << "%)\n" + << " * Number of stars containing inconsistent simplices: " + << stats.num_inconsistent_stars << " (" + << 100. * stats.num_inconsistent_stars / m_points.size() << "%)\n" + << "==========================================================\n"; + } + + return stats; + } + + /** \brief Exports the complex into a Simplex_tree. + * + * \tparam Simplex_tree_ must be a `Simplex_tree`. + * + * @param[out] tree The result, where each `Vertex_handle` is the index of the + * corresponding point in the range provided to the constructor (it can also be + * retrieved through the `Tangential_complex::get_point` function. + * @param[in] export_inconsistent_simplices Also export inconsistent simplices or not? + * @return The maximal dimension of the simplices. + */ + template <typename Simplex_tree_> + int create_complex(Simplex_tree_ &tree + , bool export_inconsistent_simplices = true + /// \cond ADVANCED_PARAMETERS + , bool export_infinite_simplices = false + , Simplex_set *p_inconsistent_simplices = NULL + /// \endcond + ) const { +#if defined(DEBUG_TRACES) || defined(GUDHI_TC_PROFILING) + std::cerr << yellow + << "\nExporting the TC as a Simplex_tree... " << white; +#endif +#ifdef GUDHI_TC_PROFILING + Gudhi::Clock t; +#endif + + int max_dim = -1; + + // For each triangulation + for (std::size_t idx = 0; idx < m_points.size(); ++idx) { + // For each cell of the star + Star::const_iterator it_inc_simplex = m_stars[idx].begin(); + Star::const_iterator it_inc_simplex_end = m_stars[idx].end(); + for (; it_inc_simplex != it_inc_simplex_end; ++it_inc_simplex) { + Simplex c = *it_inc_simplex; + + // Don't export infinite cells + if (!export_infinite_simplices && is_infinite(c)) + continue; + + if (!export_inconsistent_simplices && !is_simplex_consistent(c)) + continue; + + if (static_cast<int> (c.size()) > max_dim) + max_dim = static_cast<int> (c.size()); + // Add the missing center vertex + c.insert(idx); + + // Try to insert the simplex + bool inserted = tree.insert_simplex_and_subfaces(c).second; + + // Inconsistent? + if (p_inconsistent_simplices && inserted && !is_simplex_consistent(c)) { + p_inconsistent_simplices->insert(c); + } + } + } + +#ifdef GUDHI_TC_PROFILING + t.end(); + std::cerr << yellow << "done in " << t.num_seconds() + << " seconds.\n" << white; +#elif defined(DEBUG_TRACES) + std::cerr << yellow << "done.\n" << white; +#endif + + return max_dim; + } + + // First clears the complex then exports the TC into it + // Returns the max dimension of the simplices + // check_lower_and_higher_dim_simplices : 0 (false), 1 (true), 2 (auto) + // If the check is enabled, the function: + // - won't insert the simplex if it is already in a higher dim simplex + // - will erase any lower-dim simplices that are faces of the new simplex + // "auto" (= 2) will enable the check as a soon as it encounters a + // simplex whose dimension is different from the previous ones. + // N.B.: The check is quite expensive. + + int create_complex(Simplicial_complex &complex, + bool export_inconsistent_simplices = true, + bool export_infinite_simplices = false, + int check_lower_and_higher_dim_simplices = 2, + Simplex_set *p_inconsistent_simplices = NULL) const { +#if defined(DEBUG_TRACES) || defined(GUDHI_TC_PROFILING) + std::cerr << yellow + << "\nExporting the TC as a Simplicial_complex... " << white; +#endif +#ifdef GUDHI_TC_PROFILING + Gudhi::Clock t; +#endif + + int max_dim = -1; + complex.clear(); + + // For each triangulation + for (std::size_t idx = 0; idx < m_points.size(); ++idx) { + // For each cell of the star + Star::const_iterator it_inc_simplex = m_stars[idx].begin(); + Star::const_iterator it_inc_simplex_end = m_stars[idx].end(); + for (; it_inc_simplex != it_inc_simplex_end; ++it_inc_simplex) { + Simplex c = *it_inc_simplex; + + // Don't export infinite cells + if (!export_infinite_simplices && is_infinite(c)) + continue; + + if (!export_inconsistent_simplices && !is_simplex_consistent(c)) + continue; + + // Unusual simplex dim? + if (check_lower_and_higher_dim_simplices == 2 + && max_dim != -1 + && static_cast<int> (c.size()) != max_dim) { + // Let's activate the check + std::cerr << red << + "Info: check_lower_and_higher_dim_simplices ACTIVATED. " + "Export might be take some time...\n" << white; + check_lower_and_higher_dim_simplices = 1; + } + + if (static_cast<int> (c.size()) > max_dim) + max_dim = static_cast<int> (c.size()); + // Add the missing center vertex + c.insert(idx); + + // Try to insert the simplex + bool added = + complex.add_simplex(c, check_lower_and_higher_dim_simplices == 1); + + // Inconsistent? + if (p_inconsistent_simplices && added && !is_simplex_consistent(c)) { + p_inconsistent_simplices->insert(c); + } + } + } + +#ifdef GUDHI_TC_PROFILING + t.end(); + std::cerr << yellow << "done in " << t.num_seconds() + << " seconds.\n" << white; +#elif defined(DEBUG_TRACES) + std::cerr << yellow << "done.\n" << white; +#endif + + return max_dim; + } + + template<typename ProjectionFunctor = CGAL::Identity<Point> > + std::ostream &export_to_off( + const Simplicial_complex &complex, std::ostream & os, + Simplex_set const *p_simpl_to_color_in_red = NULL, + Simplex_set const *p_simpl_to_color_in_green = NULL, + Simplex_set const *p_simpl_to_color_in_blue = NULL, + ProjectionFunctor const& point_projection = ProjectionFunctor()) + const { + return export_to_off( + os, false, p_simpl_to_color_in_red, p_simpl_to_color_in_green, + p_simpl_to_color_in_blue, &complex, point_projection); + } + + template<typename ProjectionFunctor = CGAL::Identity<Point> > + std::ostream &export_to_off( + std::ostream & os, bool color_inconsistencies = false, + Simplex_set const *p_simpl_to_color_in_red = NULL, + Simplex_set const *p_simpl_to_color_in_green = NULL, + Simplex_set const *p_simpl_to_color_in_blue = NULL, + const Simplicial_complex *p_complex = NULL, + ProjectionFunctor const& point_projection = ProjectionFunctor()) const { + if (m_points.empty()) + return os; + + if (m_ambient_dim < 2) { + std::cerr << "Error: export_to_off => ambient dimension should be >= 2.\n"; + os << "Error: export_to_off => ambient dimension should be >= 2.\n"; + return os; + } + if (m_ambient_dim > 3) { + std::cerr << "Warning: export_to_off => ambient dimension should be " + "<= 3. Only the first 3 coordinates will be exported.\n"; + } + + if (m_intrinsic_dim < 1 || m_intrinsic_dim > 3) { + std::cerr << "Error: export_to_off => intrinsic dimension should be " + "between 1 and 3.\n"; + os << "Error: export_to_off => intrinsic dimension should be " + "between 1 and 3.\n"; + return os; + } + + std::stringstream output; + std::size_t num_simplices, num_vertices; + export_vertices_to_off(output, num_vertices, false, point_projection); + if (p_complex) { + export_simplices_to_off( + *p_complex, output, num_simplices, p_simpl_to_color_in_red, + p_simpl_to_color_in_green, p_simpl_to_color_in_blue); + } else { + export_simplices_to_off( + output, num_simplices, color_inconsistencies, p_simpl_to_color_in_red, + p_simpl_to_color_in_green, p_simpl_to_color_in_blue); + } + +#ifdef GUDHI_TC_EXPORT_NORMALS + os << "N"; +#endif + + os << "OFF \n" + << num_vertices << " " + << num_simplices << " " + << "0 \n" + << output.str(); + + return os; + } + + private: + void refresh_tangential_complex() { +#if defined(DEBUG_TRACES) || defined(GUDHI_TC_PROFILING) + std::cerr << yellow << "\nRefreshing TC... " << white; +#endif + +#ifdef GUDHI_TC_PROFILING + Gudhi::Clock t; +#endif +#ifdef GUDHI_USE_TBB + // Parallel + if (boost::is_convertible<Concurrency_tag, CGAL::Parallel_tag>::value) { + tbb::parallel_for(tbb::blocked_range<size_t>(0, m_points.size()), + Compute_tangent_triangulation(*this)); + } else { +#endif // GUDHI_USE_TBB + // Sequential + for (std::size_t i = 0; i < m_points.size(); ++i) + compute_tangent_triangulation(i); +#ifdef GUDHI_USE_TBB + } +#endif // GUDHI_USE_TBB + +#ifdef GUDHI_TC_PROFILING + t.end(); + std::cerr << yellow << "done in " << t.num_seconds() + << " seconds.\n" << white; +#elif defined(DEBUG_TRACES) + std::cerr << yellow << "done.\n" << white; +#endif + } + + // If the list of perturbed points is provided, it is much faster + template <typename Point_indices_range> + void refresh_tangential_complex( + Point_indices_range const& perturbed_points_indices) { +#if defined(DEBUG_TRACES) || defined(GUDHI_TC_PROFILING) + std::cerr << yellow << "\nRefreshing TC... " << white; +#endif + +#ifdef GUDHI_TC_PROFILING + Gudhi::Clock t; +#endif + + // ANN tree containing only the perturbed points + Points_ds updated_pts_ds(m_points, perturbed_points_indices); + +#ifdef GUDHI_USE_TBB + // Parallel + if (boost::is_convertible<Concurrency_tag, CGAL::Parallel_tag>::value) { + tbb::parallel_for(tbb::blocked_range<size_t>(0, m_points.size()), + Refresh_tangent_triangulation(*this, updated_pts_ds)); + } else { +#endif // GUDHI_USE_TBB + // Sequential + for (std::size_t i = 0; i < m_points.size(); ++i) + refresh_tangent_triangulation(i, updated_pts_ds); +#ifdef GUDHI_USE_TBB + } +#endif // GUDHI_USE_TBB + +#ifdef GUDHI_TC_PROFILING + t.end(); + std::cerr << yellow << "done in " << t.num_seconds() + << " seconds.\n" << white; +#elif defined(DEBUG_TRACES) + std::cerr << yellow << "done.\n" << white; +#endif + } + + void export_inconsistent_stars_to_OFF_files(std::string const& filename_base) const { + // For each triangulation + for (std::size_t idx = 0; idx < m_points.size(); ++idx) { + // We build a SC along the way in case it's inconsistent + Simplicial_complex sc; + // For each cell + bool is_inconsistent = false; + Star::const_iterator it_inc_simplex = m_stars[idx].begin(); + Star::const_iterator it_inc_simplex_end = m_stars[idx].end(); + for (; it_inc_simplex != it_inc_simplex_end; + ++it_inc_simplex) { + // Skip infinite cells + if (is_infinite(*it_inc_simplex)) + continue; + + Simplex c = *it_inc_simplex; + c.insert(idx); // Add the missing index + + sc.add_simplex(c); + + // If we do not already know this star is inconsistent, test it + if (!is_inconsistent && !is_simplex_consistent(c)) + is_inconsistent = true; + } + + if (is_inconsistent) { + // Export star to OFF file + std::stringstream output_filename; + output_filename << filename_base << "_" << idx << ".off"; + std::ofstream off_stream(output_filename.str().c_str()); + export_to_off(sc, off_stream); + } + } + } + + class Compare_distance_to_ref_point { + public: + Compare_distance_to_ref_point(Point const& ref, K const& k) + : m_ref(ref), m_k(k) { } + + bool operator()(Point const& p1, Point const& p2) { + typename K::Squared_distance_d sqdist = + m_k.squared_distance_d_object(); + return sqdist(p1, m_ref) < sqdist(p2, m_ref); + } + + private: + Point const& m_ref; + K const& m_k; + }; + +#ifdef GUDHI_USE_TBB + // Functor for compute_tangential_complex function + class Compute_tangent_triangulation { + Tangential_complex & m_tc; + + public: + // Constructor + Compute_tangent_triangulation(Tangential_complex &tc) + : m_tc(tc) { } + + // Constructor + Compute_tangent_triangulation(const Compute_tangent_triangulation &ctt) + : m_tc(ctt.m_tc) { } + + // operator() + void operator()(const tbb::blocked_range<size_t>& r) const { + for (size_t i = r.begin(); i != r.end(); ++i) + m_tc.compute_tangent_triangulation(i); + } + }; + + // Functor for refresh_tangential_complex function + class Refresh_tangent_triangulation { + Tangential_complex & m_tc; + Points_ds const& m_updated_pts_ds; + + public: + // Constructor + Refresh_tangent_triangulation(Tangential_complex &tc, Points_ds const& updated_pts_ds) + : m_tc(tc), m_updated_pts_ds(updated_pts_ds) { } + + // Constructor + Refresh_tangent_triangulation(const Refresh_tangent_triangulation &ctt) + : m_tc(ctt.m_tc), m_updated_pts_ds(ctt.m_updated_pts_ds) { } + + // operator() + void operator()(const tbb::blocked_range<size_t>& r) const { + for (size_t i = r.begin(); i != r.end(); ++i) + m_tc.refresh_tangent_triangulation(i, m_updated_pts_ds); + } + }; +#endif // GUDHI_USE_TBB + + bool is_infinite(Simplex const& s) const { + return *s.rbegin() == (std::numeric_limits<std::size_t>::max)(); + } + + // Output: "triangulation" is a Regular Triangulation containing at least the + // star of "center_pt" + // Returns the handle of the center vertex + Tr_vertex_handle compute_star(std::size_t i, const Point ¢er_pt, const Tangent_space_basis &tsb, + Triangulation &triangulation, bool verbose = false) { + int tangent_space_dim = tsb.dimension(); + const Tr_traits &local_tr_traits = triangulation.geom_traits(); + Tr_vertex_handle center_vertex; + + // Kernel functor & objects + typename K::Squared_distance_d k_sqdist = m_k.squared_distance_d_object(); + + // Triangulation's traits functor & objects + typename Tr_traits::Compute_weight_d point_weight = local_tr_traits.compute_weight_d_object(); + typename Tr_traits::Power_center_d power_center = local_tr_traits.power_center_d_object(); + + //*************************************************** + // Build a minimal triangulation in the tangent space + // (we only need the star of p) + //*************************************************** + + // Insert p + Tr_point proj_wp; + if (i == tsb.origin()) { + // Insert {(0, 0, 0...), m_weights[i]} + proj_wp = local_tr_traits.construct_weighted_point_d_object()(local_tr_traits.construct_point_d_object()(tangent_space_dim, CGAL::ORIGIN), + m_weights[i]); + } else { + const Weighted_point& wp = compute_perturbed_weighted_point(i); + proj_wp = project_point_and_compute_weight(wp, tsb, local_tr_traits); + } + + center_vertex = triangulation.insert(proj_wp); + center_vertex->data() = i; + if (verbose) + std::cerr << "* Inserted point #" << i << "\n"; + +#ifdef GUDHI_TC_VERY_VERBOSE + std::size_t num_attempts_to_insert_points = 1; + std::size_t num_inserted_points = 1; +#endif + // const int NUM_NEIGHBORS = 150; + // KNS_range ins_range = m_points_ds.query_k_nearest_neighbors(center_pt, NUM_NEIGHBORS); + INS_range ins_range = m_points_ds.query_incremental_nearest_neighbors(center_pt); + + // While building the local triangulation, we keep the radius + // of the sphere "star sphere" centered at "center_vertex" + // and which contains all the + // circumspheres of the star of "center_vertex" + boost::optional<FT> squared_star_sphere_radius_plus_margin; + + // Insert points until we find a point which is outside "star sphere" + for (auto nn_it = ins_range.begin(); + nn_it != ins_range.end(); + ++nn_it) { + std::size_t neighbor_point_idx = nn_it->first; + + // ith point = p, which is already inserted + if (neighbor_point_idx != i) { + // No need to lock the Mutex_for_perturb here since this will not be + // called while other threads are perturbing the positions + Point neighbor_pt; + FT neighbor_weight; + compute_perturbed_weighted_point(neighbor_point_idx, neighbor_pt, neighbor_weight); + + if (squared_star_sphere_radius_plus_margin && + k_sqdist(center_pt, neighbor_pt) > *squared_star_sphere_radius_plus_margin) + break; + + Tr_point proj_pt = project_point_and_compute_weight(neighbor_pt, neighbor_weight, tsb, + local_tr_traits); + +#ifdef GUDHI_TC_VERY_VERBOSE + ++num_attempts_to_insert_points; +#endif + + + Tr_vertex_handle vh = triangulation.insert_if_in_star(proj_pt, center_vertex); + // Tr_vertex_handle vh = triangulation.insert(proj_pt); + if (vh != Tr_vertex_handle() && vh->data() == (std::numeric_limits<std::size_t>::max)()) { +#ifdef GUDHI_TC_VERY_VERBOSE + ++num_inserted_points; +#endif + if (verbose) + std::cerr << "* Inserted point #" << neighbor_point_idx << "\n"; + + vh->data() = neighbor_point_idx; + + // Let's recompute squared_star_sphere_radius_plus_margin + if (triangulation.current_dimension() >= tangent_space_dim) { + squared_star_sphere_radius_plus_margin = boost::none; + // Get the incident cells and look for the biggest circumsphere + std::vector<Tr_full_cell_handle> incident_cells; + triangulation.incident_full_cells( + center_vertex, + std::back_inserter(incident_cells)); + for (typename std::vector<Tr_full_cell_handle>::iterator cit = + incident_cells.begin(); cit != incident_cells.end(); ++cit) { + Tr_full_cell_handle cell = *cit; + if (triangulation.is_infinite(cell)) { + squared_star_sphere_radius_plus_margin = boost::none; + break; + } else { + // Note that this uses the perturbed point since it uses + // the points of the local triangulation + Tr_point c = power_center(boost::make_transform_iterator(cell->vertices_begin(), + vertex_handle_to_point<Tr_point, + Tr_vertex_handle>), + boost::make_transform_iterator(cell->vertices_end(), + vertex_handle_to_point<Tr_point, + Tr_vertex_handle>)); + + FT sq_power_sphere_diam = 4 * point_weight(c); + + if (!squared_star_sphere_radius_plus_margin || + sq_power_sphere_diam > *squared_star_sphere_radius_plus_margin) { + squared_star_sphere_radius_plus_margin = sq_power_sphere_diam; + } + } + } + + // Let's add the margin, now + // The value depends on whether we perturb weight or position + if (squared_star_sphere_radius_plus_margin) { + // "2*m_last_max_perturb" because both points can be perturbed + squared_star_sphere_radius_plus_margin = CGAL::square(std::sqrt(*squared_star_sphere_radius_plus_margin) + + 2 * m_last_max_perturb); + + // Save it in `m_squared_star_spheres_radii_incl_margin` + m_squared_star_spheres_radii_incl_margin[i] = + *squared_star_sphere_radius_plus_margin; + } else { + m_squared_star_spheres_radii_incl_margin[i] = FT(-1); + } + } + } + } + } + + return center_vertex; + } + + void refresh_tangent_triangulation(std::size_t i, Points_ds const& updated_pts_ds, bool verbose = false) { + if (verbose) + std::cerr << "** Refreshing tangent tri #" << i << " **\n"; + + if (m_squared_star_spheres_radii_incl_margin[i] == FT(-1)) + return compute_tangent_triangulation(i, verbose); + + Point center_point = compute_perturbed_point(i); + // Among updated point, what is the closer from our center point? + std::size_t closest_pt_index = + updated_pts_ds.query_k_nearest_neighbors(center_point, 1, false).begin()->first; + + typename K::Construct_weighted_point_d k_constr_wp = + m_k.construct_weighted_point_d_object(); + typename K::Power_distance_d k_power_dist = m_k.power_distance_d_object(); + + // Construct a weighted point equivalent to the star sphere + Weighted_point star_sphere = k_constr_wp(compute_perturbed_point(i), + m_squared_star_spheres_radii_incl_margin[i]); + Weighted_point closest_updated_point = + compute_perturbed_weighted_point(closest_pt_index); + + // Is the "closest point" inside our star sphere? + if (k_power_dist(star_sphere, closest_updated_point) <= FT(0)) + compute_tangent_triangulation(i, verbose); + } + + void compute_tangent_triangulation(std::size_t i, bool verbose = false) { + if (verbose) + std::cerr << "** Computing tangent tri #" << i << " **\n"; + // std::cerr << "***********************************************\n"; + + // No need to lock the mutex here since this will not be called while + // other threads are perturbing the positions + const Point center_pt = compute_perturbed_point(i); + Tangent_space_basis &tsb = m_tangent_spaces[i]; + + // Estimate the tangent space + if (!m_are_tangent_spaces_computed[i]) { +#ifdef GUDHI_TC_EXPORT_NORMALS + tsb = compute_tangent_space(center_pt, i, true /*normalize*/, &m_orth_spaces[i]); +#else + tsb = compute_tangent_space(center_pt, i); +#endif + } + +#if defined(GUDHI_TC_PROFILING) && defined(GUDHI_TC_VERY_VERBOSE) + Gudhi::Clock t; +#endif + int tangent_space_dim = tangent_basis_dim(i); + Triangulation &local_tr = + m_triangulations[i].construct_triangulation(tangent_space_dim); + + m_triangulations[i].center_vertex() = + compute_star(i, center_pt, tsb, local_tr, verbose); + +#if defined(GUDHI_TC_PROFILING) && defined(GUDHI_TC_VERY_VERBOSE) + t.end(); + std::cerr << " - triangulation construction: " << t.num_seconds() << " s.\n"; + t.reset(); +#endif + +#ifdef GUDHI_TC_VERY_VERBOSE + std::cerr << "Inserted " << num_inserted_points << " points / " + << num_attempts_to_insert_points << " attemps to compute the star\n"; +#endif + + update_star(i); + +#if defined(GUDHI_TC_PROFILING) && defined(GUDHI_TC_VERY_VERBOSE) + t.end(); + std::cerr << " - update_star: " << t.num_seconds() << " s.\n"; +#endif + } + + // Updates m_stars[i] directly from m_triangulations[i] + + void update_star(std::size_t i) { + Star &star = m_stars[i]; + star.clear(); + Triangulation &local_tr = m_triangulations[i].tr(); + Tr_vertex_handle center_vertex = m_triangulations[i].center_vertex(); + int cur_dim_plus_1 = local_tr.current_dimension() + 1; + + std::vector<Tr_full_cell_handle> incident_cells; + local_tr.incident_full_cells( + center_vertex, std::back_inserter(incident_cells)); + + typename std::vector<Tr_full_cell_handle>::const_iterator it_c = incident_cells.begin(); + typename std::vector<Tr_full_cell_handle>::const_iterator it_c_end = incident_cells.end(); + // For each cell + for (; it_c != it_c_end; ++it_c) { + // Will contain all indices except center_vertex + Incident_simplex incident_simplex; + for (int j = 0; j < cur_dim_plus_1; ++j) { + std::size_t index = (*it_c)->vertex(j)->data(); + if (index != i) + incident_simplex.insert(index); + } + GUDHI_CHECK(incident_simplex.size() == cur_dim_plus_1 - 1, + std::logic_error("update_star: wrong size of incident simplex")); + star.push_back(incident_simplex); + } + } + + // Estimates tangent subspaces using PCA + + Tangent_space_basis compute_tangent_space(const Point &p + , const std::size_t i + , bool normalize_basis = true + , Orthogonal_space_basis *p_orth_space_basis = NULL + ) { + unsigned int num_pts_for_pca = (std::min)(static_cast<unsigned int> (std::pow(GUDHI_TC_BASE_VALUE_FOR_PCA, m_intrinsic_dim)), + static_cast<unsigned int> (m_points.size())); + + // Kernel functors + typename K::Construct_vector_d constr_vec = + m_k.construct_vector_d_object(); + typename K::Compute_coordinate_d coord = + m_k.compute_coordinate_d_object(); + +#ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM + KNS_range kns_range = m_points_ds_for_tse.query_k_nearest_neighbors( + p, num_pts_for_pca, false); + const Points &points_for_pca = m_points_for_tse; +#else + KNS_range kns_range = m_points_ds.query_k_nearest_neighbors(p, num_pts_for_pca, false); + const Points &points_for_pca = m_points; +#endif + + // One row = one point + Eigen::MatrixXd mat_points(num_pts_for_pca, m_ambient_dim); + auto nn_it = kns_range.begin(); + for (unsigned int j = 0; + j < num_pts_for_pca && nn_it != kns_range.end(); + ++j, ++nn_it) { + for (int i = 0; i < m_ambient_dim; ++i) { + mat_points(j, i) = CGAL::to_double(coord(points_for_pca[nn_it->first], i)); + } + } + Eigen::MatrixXd centered = mat_points.rowwise() - mat_points.colwise().mean(); + Eigen::MatrixXd cov = centered.adjoint() * centered; + Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> eig(cov); + + Tangent_space_basis tsb(i); // p = compute_perturbed_point(i) here + + // The eigenvectors are sorted in increasing order of their corresponding + // eigenvalues + for (int j = m_ambient_dim - 1; + j >= m_ambient_dim - m_intrinsic_dim; + --j) { + if (normalize_basis) { + Vector v = constr_vec(m_ambient_dim, + eig.eigenvectors().col(j).data(), + eig.eigenvectors().col(j).data() + m_ambient_dim); + tsb.push_back(normalize_vector(v, m_k)); + } else { + tsb.push_back(constr_vec( + m_ambient_dim, + eig.eigenvectors().col(j).data(), + eig.eigenvectors().col(j).data() + m_ambient_dim)); + } + } + + if (p_orth_space_basis) { + p_orth_space_basis->set_origin(i); + for (int j = m_ambient_dim - m_intrinsic_dim - 1; + j >= 0; + --j) { + if (normalize_basis) { + Vector v = constr_vec(m_ambient_dim, + eig.eigenvectors().col(j).data(), + eig.eigenvectors().col(j).data() + m_ambient_dim); + p_orth_space_basis->push_back(normalize_vector(v, m_k)); + } else { + p_orth_space_basis->push_back(constr_vec( + m_ambient_dim, + eig.eigenvectors().col(j).data(), + eig.eigenvectors().col(j).data() + m_ambient_dim)); + } + } + } + + m_are_tangent_spaces_computed[i] = true; + + return tsb; + } + + // Compute the space tangent to a simplex (p1, p2, ... pn) + // TODO(CJ): Improve this? + // Basically, it takes all the neighbor points to p1, p2... pn and runs PCA + // on it. Note that most points are duplicated. + + Tangent_space_basis compute_tangent_space(const Simplex &s, bool normalize_basis = true) { + unsigned int num_pts_for_pca = (std::min)(static_cast<unsigned int> (std::pow(GUDHI_TC_BASE_VALUE_FOR_PCA, m_intrinsic_dim)), + static_cast<unsigned int> (m_points.size())); + + // Kernel functors + typename K::Construct_vector_d constr_vec = + m_k.construct_vector_d_object(); + typename K::Compute_coordinate_d coord = + m_k.compute_coordinate_d_object(); + typename K::Squared_length_d sqlen = + m_k.squared_length_d_object(); + typename K::Scaled_vector_d scaled_vec = + m_k.scaled_vector_d_object(); + typename K::Scalar_product_d scalar_pdct = + m_k.scalar_product_d_object(); + typename K::Difference_of_vectors_d diff_vec = + m_k.difference_of_vectors_d_object(); + + // One row = one point + Eigen::MatrixXd mat_points(s.size() * num_pts_for_pca, m_ambient_dim); + unsigned int current_row = 0; + + for (Simplex::const_iterator it_index = s.begin(); + it_index != s.end(); ++it_index) { + const Point &p = m_points[*it_index]; + +#ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM + KNS_range kns_range = m_points_ds_for_tse.query_k_nearest_neighbors( + p, num_pts_for_pca, false); + const Points &points_for_pca = m_points_for_tse; +#else + KNS_range kns_range = m_points_ds.query_k_nearest_neighbors(p, num_pts_for_pca, false); + const Points &points_for_pca = m_points; +#endif + + auto nn_it = kns_range.begin(); + for (; + current_row < num_pts_for_pca && nn_it != kns_range.end(); + ++current_row, ++nn_it) { + for (int i = 0; i < m_ambient_dim; ++i) { + mat_points(current_row, i) = + CGAL::to_double(coord(points_for_pca[nn_it->first], i)); + } + } + } + Eigen::MatrixXd centered = mat_points.rowwise() - mat_points.colwise().mean(); + Eigen::MatrixXd cov = centered.adjoint() * centered; + Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> eig(cov); + + Tangent_space_basis tsb; + + // The eigenvectors are sorted in increasing order of their corresponding + // eigenvalues + for (int j = m_ambient_dim - 1; + j >= m_ambient_dim - m_intrinsic_dim; + --j) { + if (normalize_basis) { + Vector v = constr_vec(m_ambient_dim, + eig.eigenvectors().col(j).data(), + eig.eigenvectors().col(j).data() + m_ambient_dim); + tsb.push_back(normalize_vector(v, m_k)); + } else { + tsb.push_back(constr_vec( + m_ambient_dim, + eig.eigenvectors().col(j).data(), + eig.eigenvectors().col(j).data() + m_ambient_dim)); + } + } + + return tsb; + } + + // Returns the dimension of the ith local triangulation + + int tangent_basis_dim(std::size_t i) const { + return m_tangent_spaces[i].dimension(); + } + + Point compute_perturbed_point(std::size_t pt_idx) const { +#ifdef GUDHI_TC_PERTURB_POSITION + return m_k.translated_point_d_object()( + m_points[pt_idx], m_translations[pt_idx]); +#else + return m_points[pt_idx]; +#endif + } + + void compute_perturbed_weighted_point(std::size_t pt_idx, Point &p, FT &w) const { +#ifdef GUDHI_TC_PERTURB_POSITION + p = m_k.translated_point_d_object()( + m_points[pt_idx], m_translations[pt_idx]); +#else + p = m_points[pt_idx]; +#endif + w = m_weights[pt_idx]; + } + + Weighted_point compute_perturbed_weighted_point(std::size_t pt_idx) const { + typename K::Construct_weighted_point_d k_constr_wp = + m_k.construct_weighted_point_d_object(); + + Weighted_point wp = k_constr_wp( +#ifdef GUDHI_TC_PERTURB_POSITION + m_k.translated_point_d_object()(m_points[pt_idx], m_translations[pt_idx]), +#else + m_points[pt_idx], +#endif + m_weights[pt_idx]); + + return wp; + } + + Point unproject_point(const Tr_point &p, + const Tangent_space_basis &tsb, + const Tr_traits &tr_traits) const { + typename K::Translated_point_d k_transl = + m_k.translated_point_d_object(); + typename K::Scaled_vector_d k_scaled_vec = + m_k.scaled_vector_d_object(); + typename Tr_traits::Compute_coordinate_d coord = + tr_traits.compute_coordinate_d_object(); + + Point global_point = compute_perturbed_point(tsb.origin()); + for (int i = 0; i < m_intrinsic_dim; ++i) + global_point = k_transl(global_point, + k_scaled_vec(tsb[i], coord(p, i))); + + return global_point; + } + + // Project the point in the tangent space + // Resulting point coords are expressed in tsb's space + Tr_bare_point project_point(const Point &p, + const Tangent_space_basis &tsb, + const Tr_traits &tr_traits) const { + typename K::Scalar_product_d scalar_pdct = + m_k.scalar_product_d_object(); + typename K::Difference_of_points_d diff_points = + m_k.difference_of_points_d_object(); + + Vector v = diff_points(p, compute_perturbed_point(tsb.origin())); + + std::vector<FT> coords; + // Ambiant-space coords of the projected point + coords.reserve(tsb.dimension()); + for (std::size_t i = 0; i < m_intrinsic_dim; ++i) { + // Local coords are given by the scalar product with the vectors of tsb + FT coord = scalar_pdct(v, tsb[i]); + coords.push_back(coord); + } + + return tr_traits.construct_point_d_object()( + static_cast<int> (coords.size()), coords.begin(), coords.end()); + } + + // Project the point in the tangent space + // The weight will be the squared distance between p and the projection of p + // Resulting point coords are expressed in tsb's space + + Tr_point project_point_and_compute_weight(const Weighted_point &wp, + const Tangent_space_basis &tsb, + const Tr_traits &tr_traits) const { + typename K::Point_drop_weight_d k_drop_w = + m_k.point_drop_weight_d_object(); + typename K::Compute_weight_d k_point_weight = + m_k.compute_weight_d_object(); + return project_point_and_compute_weight( + k_drop_w(wp), k_point_weight(wp), tsb, tr_traits); + } + + // Same as above, with slightly different parameters + Tr_point project_point_and_compute_weight(const Point &p, const FT w, + const Tangent_space_basis &tsb, + const Tr_traits &tr_traits) const { + const int point_dim = m_k.point_dimension_d_object()(p); + + typename K::Construct_point_d constr_pt = + m_k.construct_point_d_object(); + typename K::Scalar_product_d scalar_pdct = + m_k.scalar_product_d_object(); + typename K::Difference_of_points_d diff_points = + m_k.difference_of_points_d_object(); + typename K::Compute_coordinate_d coord = + m_k.compute_coordinate_d_object(); + typename K::Construct_cartesian_const_iterator_d ccci = + m_k.construct_cartesian_const_iterator_d_object(); + + Point origin = compute_perturbed_point(tsb.origin()); + Vector v = diff_points(p, origin); + + // Same dimension? Then the weight is 0 + bool same_dim = (point_dim == tsb.dimension()); + + std::vector<FT> coords; + // Ambiant-space coords of the projected point + std::vector<FT> p_proj(ccci(origin), ccci(origin, 0)); + coords.reserve(tsb.dimension()); + for (int i = 0; i < tsb.dimension(); ++i) { + // Local coords are given by the scalar product with the vectors of tsb + FT c = scalar_pdct(v, tsb[i]); + coords.push_back(c); + + // p_proj += c * tsb[i] + if (!same_dim) { + for (int j = 0; j < point_dim; ++j) + p_proj[j] += c * coord(tsb[i], j); + } + } + + // Same dimension? Then the weight is 0 + FT sq_dist_to_proj_pt = 0; + if (!same_dim) { + Point projected_pt = constr_pt(point_dim, p_proj.begin(), p_proj.end()); + sq_dist_to_proj_pt = m_k.squared_distance_d_object()(p, projected_pt); + } + + return tr_traits.construct_weighted_point_d_object() + (tr_traits.construct_point_d_object()(static_cast<int> (coords.size()), coords.begin(), coords.end()), + w - sq_dist_to_proj_pt); + } + + // Project all the points in the tangent space + + template <typename Indexed_point_range> + std::vector<Tr_point> project_points_and_compute_weights( + const Indexed_point_range &point_indices, + const Tangent_space_basis &tsb, + const Tr_traits &tr_traits) const { + std::vector<Tr_point> ret; + for (typename Indexed_point_range::const_iterator + it = point_indices.begin(), it_end = point_indices.end(); + it != it_end; ++it) { + ret.push_back(project_point_and_compute_weight( + compute_perturbed_weighted_point(*it), tsb, tr_traits)); + } + return ret; + } + + // A simplex here is a local tri's full cell handle + + bool is_simplex_consistent(Tr_full_cell_handle fch, int cur_dim) const { + Simplex c; + for (int i = 0; i < cur_dim + 1; ++i) { + std::size_t data = fch->vertex(i)->data(); + c.insert(data); + } + return is_simplex_consistent(c); + } + + // A simplex here is a list of point indices + // TODO(CJ): improve it like the other "is_simplex_consistent" below + + bool is_simplex_consistent(Simplex const& simplex) const { + // Check if the simplex is in the stars of all its vertices + Simplex::const_iterator it_point_idx = simplex.begin(); + // For each point p of the simplex, we parse the incidents cells of p + // and we check if "simplex" is among them + for (; it_point_idx != simplex.end(); ++it_point_idx) { + std::size_t point_idx = *it_point_idx; + // Don't check infinite simplices + if (point_idx == (std::numeric_limits<std::size_t>::max)()) + continue; + + Star const& star = m_stars[point_idx]; + + // What we're looking for is "simplex" \ point_idx + Incident_simplex is_to_find = simplex; + is_to_find.erase(point_idx); + + // For each cell + if (std::find(star.begin(), star.end(), is_to_find) == star.end()) + return false; + } + + return true; + } + + // A simplex here is a list of point indices + // "s" contains all the points of the simplex except "center_point" + // This function returns the points whose star doesn't contain the simplex + // N.B.: the function assumes that the simplex is contained in + // star(center_point) + + template <typename OutputIterator> // value_type = std::size_t + bool is_simplex_consistent( + std::size_t center_point, + Incident_simplex const& s, // without "center_point" + OutputIterator points_whose_star_does_not_contain_s, + bool check_also_in_non_maximal_faces = false) const { + Simplex full_simplex = s; + full_simplex.insert(center_point); + + // Check if the simplex is in the stars of all its vertices + Incident_simplex::const_iterator it_point_idx = s.begin(); + // For each point p of the simplex, we parse the incidents cells of p + // and we check if "simplex" is among them + for (; it_point_idx != s.end(); ++it_point_idx) { + std::size_t point_idx = *it_point_idx; + // Don't check infinite simplices + if (point_idx == (std::numeric_limits<std::size_t>::max)()) + continue; + + Star const& star = m_stars[point_idx]; + + // What we're looking for is full_simplex \ point_idx + Incident_simplex is_to_find = full_simplex; + is_to_find.erase(point_idx); + + if (check_also_in_non_maximal_faces) { + // For each simplex "is" of the star, check if ic_to_simplex is + // included in "is" + bool found = false; + for (Star::const_iterator is = star.begin(), is_end = star.end(); + !found && is != is_end; ++is) { + if (std::includes(is->begin(), is->end(), + is_to_find.begin(), is_to_find.end())) + found = true; + } + + if (!found) + *points_whose_star_does_not_contain_s++ = point_idx; + } else { + // Does the star contain is_to_find? + if (std::find(star.begin(), star.end(), is_to_find) == star.end()) + *points_whose_star_does_not_contain_s++ = point_idx; + } + } + + return true; + } + + // A simplex here is a list of point indices + // It looks for s in star(p). + // "s" contains all the points of the simplex except p. + bool is_simplex_in_star(std::size_t p, + Incident_simplex const& s, + bool check_also_in_non_maximal_faces = true) const { + Star const& star = m_stars[p]; + + if (check_also_in_non_maximal_faces) { + // For each simplex "is" of the star, check if ic_to_simplex is + // included in "is" + bool found = false; + for (Star::const_iterator is = star.begin(), is_end = star.end(); + !found && is != is_end; ++is) { + if (std::includes(is->begin(), is->end(), s.begin(), s.end())) + found = true; + } + + return found; + } else { + return !(std::find(star.begin(), star.end(), s) == star.end()); + } + } + +#ifdef GUDHI_USE_TBB + // Functor for try_to_solve_inconsistencies_in_a_local_triangulation function + class Try_to_solve_inconsistencies_in_a_local_triangulation { + Tangential_complex & m_tc; + double m_max_perturb; + tbb::combinable<std::size_t> &m_num_inconsistencies; + tbb::combinable<std::vector<std::size_t> > &m_updated_points; + + public: + // Constructor + Try_to_solve_inconsistencies_in_a_local_triangulation(Tangential_complex &tc, + double max_perturb, + tbb::combinable<std::size_t> &num_inconsistencies, + tbb::combinable<std::vector<std::size_t> > &updated_points) + : m_tc(tc), + m_max_perturb(max_perturb), + m_num_inconsistencies(num_inconsistencies), + m_updated_points(updated_points) { } + + // Constructor + Try_to_solve_inconsistencies_in_a_local_triangulation(const Try_to_solve_inconsistencies_in_a_local_triangulation& + tsilt) + : m_tc(tsilt.m_tc), + m_max_perturb(tsilt.m_max_perturb), + m_num_inconsistencies(tsilt.m_num_inconsistencies), + m_updated_points(tsilt.m_updated_points) { } + + // operator() + void operator()(const tbb::blocked_range<size_t>& r) const { + for (size_t i = r.begin(); i != r.end(); ++i) { + m_num_inconsistencies.local() += + m_tc.try_to_solve_inconsistencies_in_a_local_triangulation(i, m_max_perturb, + std::back_inserter(m_updated_points.local())); + } + } + }; +#endif // GUDHI_USE_TBB + + void perturb(std::size_t point_idx, double max_perturb) { + const Tr_traits &local_tr_traits = + m_triangulations[point_idx].tr().geom_traits(); + typename Tr_traits::Compute_coordinate_d coord = + local_tr_traits.compute_coordinate_d_object(); + typename K::Translated_point_d k_transl = + m_k.translated_point_d_object(); + typename K::Construct_vector_d k_constr_vec = + m_k.construct_vector_d_object(); + typename K::Scaled_vector_d k_scaled_vec = + m_k.scaled_vector_d_object(); + + CGAL::Random_points_in_ball_d<Tr_bare_point> + tr_point_in_ball_generator(m_intrinsic_dim, + m_random_generator.get_double(0., max_perturb)); + + Tr_point local_random_transl = + local_tr_traits.construct_weighted_point_d_object()(*tr_point_in_ball_generator++, 0); + Translation_for_perturb global_transl = k_constr_vec(m_ambient_dim); + const Tangent_space_basis &tsb = m_tangent_spaces[point_idx]; + for (int i = 0; i < m_intrinsic_dim; ++i) { + global_transl = k_transl(global_transl, + k_scaled_vec(tsb[i], coord(local_random_transl, i))); + } + // Parallel +#if defined(GUDHI_USE_TBB) + m_p_perturb_mutexes[point_idx].lock(); + m_translations[point_idx] = global_transl; + m_p_perturb_mutexes[point_idx].unlock(); + // Sequential +#else + m_translations[point_idx] = global_transl; +#endif + } + + // Return true if inconsistencies were found + template <typename OutputIt> + bool try_to_solve_inconsistencies_in_a_local_triangulation(std::size_t tr_index, + double max_perturb, + OutputIt perturbed_pts_indices = CGAL::Emptyset_iterator()) { + bool is_inconsistent = false; + + Star const& star = m_stars[tr_index]; + Tr_vertex_handle center_vh = m_triangulations[tr_index].center_vertex(); + + // For each incident simplex + Star::const_iterator it_inc_simplex = star.begin(); + Star::const_iterator it_inc_simplex_end = star.end(); + for (; it_inc_simplex != it_inc_simplex_end; ++it_inc_simplex) { + const Incident_simplex &incident_simplex = *it_inc_simplex; + + // Don't check infinite cells + if (is_infinite(incident_simplex)) + continue; + + Simplex c = incident_simplex; + c.insert(tr_index); // Add the missing index + + // Perturb the center point + if (!is_simplex_consistent(c)) { + is_inconsistent = true; + + std::size_t idx = tr_index; + + perturb(tr_index, max_perturb); + *perturbed_pts_indices++ = idx; + + // We will try the other cells next time + break; + } + } + + return is_inconsistent; + } + + + // 1st line: number of points + // Then one point per line + std::ostream &export_point_set(std::ostream & os, + bool use_perturbed_points = false, + const char *coord_separator = " ") const { + if (use_perturbed_points) { + std::vector<Point> perturbed_points; + perturbed_points.reserve(m_points.size()); + for (std::size_t i = 0; i < m_points.size(); ++i) + perturbed_points.push_back(compute_perturbed_point(i)); + + return export_point_set( + m_k, perturbed_points, os, coord_separator); + } else { + return export_point_set( + m_k, m_points, os, coord_separator); + } + } + + template<typename ProjectionFunctor = CGAL::Identity<Point> > + std::ostream &export_vertices_to_off( + std::ostream & os, std::size_t &num_vertices, + bool use_perturbed_points = false, + ProjectionFunctor const& point_projection = ProjectionFunctor()) const { + if (m_points.empty()) { + num_vertices = 0; + return os; + } + + // If m_intrinsic_dim = 1, we output each point two times + // to be able to export each segment as a flat triangle with 3 different + // indices (otherwise, Meshlab detects degenerated simplices) + const int N = (m_intrinsic_dim == 1 ? 2 : 1); + + // Kernel functors + typename K::Compute_coordinate_d coord = + m_k.compute_coordinate_d_object(); + +#ifdef GUDHI_TC_EXPORT_ALL_COORDS_IN_OFF + int num_coords = m_ambient_dim; +#else + int num_coords = (std::min)(m_ambient_dim, 3); +#endif + +#ifdef GUDHI_TC_EXPORT_NORMALS + OS_container::const_iterator it_os = m_orth_spaces.begin(); +#endif + typename Points::const_iterator it_p = m_points.begin(); + typename Points::const_iterator it_p_end = m_points.end(); + // For each point p + for (std::size_t i = 0; it_p != it_p_end; ++it_p, ++i) { + Point p = point_projection( + use_perturbed_points ? compute_perturbed_point(i) : *it_p); + for (int ii = 0; ii < N; ++ii) { + int j = 0; + for (; j < num_coords; ++j) + os << CGAL::to_double(coord(p, j)) << " "; + if (j == 2) + os << "0"; + +#ifdef GUDHI_TC_EXPORT_NORMALS + for (j = 0; j < num_coords; ++j) + os << " " << CGAL::to_double(coord(*it_os->begin(), j)); +#endif + os << "\n"; + } +#ifdef GUDHI_TC_EXPORT_NORMALS + ++it_os; +#endif + } + + num_vertices = N * m_points.size(); + return os; + } + + std::ostream &export_simplices_to_off(std::ostream & os, std::size_t &num_OFF_simplices, + bool color_inconsistencies = false, + Simplex_set const *p_simpl_to_color_in_red = NULL, + Simplex_set const *p_simpl_to_color_in_green = NULL, + Simplex_set const *p_simpl_to_color_in_blue = NULL) + const { + // If m_intrinsic_dim = 1, each point is output two times + // (see export_vertices_to_off) + num_OFF_simplices = 0; + std::size_t num_maximal_simplices = 0; + std::size_t num_inconsistent_maximal_simplices = 0; + std::size_t num_inconsistent_stars = 0; + typename Tr_container::const_iterator it_tr = m_triangulations.begin(); + typename Tr_container::const_iterator it_tr_end = m_triangulations.end(); + // For each triangulation + for (std::size_t idx = 0; it_tr != it_tr_end; ++it_tr, ++idx) { + bool is_star_inconsistent = false; + + Triangulation const& tr = it_tr->tr(); + Tr_vertex_handle center_vh = it_tr->center_vertex(); + + if (tr.current_dimension() < m_intrinsic_dim) + continue; + + // Color for this star + std::stringstream color; + // color << rand()%256 << " " << 100+rand()%156 << " " << 100+rand()%156; + color << 128 << " " << 128 << " " << 128; + + // Gather the triangles here, with an int telling its color + typedef std::vector<std::pair<Simplex, int> > Star_using_triangles; + Star_using_triangles star_using_triangles; + + // For each cell of the star + Star::const_iterator it_inc_simplex = m_stars[idx].begin(); + Star::const_iterator it_inc_simplex_end = m_stars[idx].end(); + for (; it_inc_simplex != it_inc_simplex_end; ++it_inc_simplex) { + Simplex c = *it_inc_simplex; + c.insert(idx); + std::size_t num_vertices = c.size(); + ++num_maximal_simplices; + + int color_simplex = -1; // -1=no color, 0=yellow, 1=red, 2=green, 3=blue + if (color_inconsistencies && !is_simplex_consistent(c)) { + ++num_inconsistent_maximal_simplices; + color_simplex = 0; + is_star_inconsistent = true; + } else { + if (p_simpl_to_color_in_red && + std::find( + p_simpl_to_color_in_red->begin(), + p_simpl_to_color_in_red->end(), + c) != p_simpl_to_color_in_red->end()) { + color_simplex = 1; + } else if (p_simpl_to_color_in_green && + std::find( + p_simpl_to_color_in_green->begin(), + p_simpl_to_color_in_green->end(), + c) != p_simpl_to_color_in_green->end()) { + color_simplex = 2; + } else if (p_simpl_to_color_in_blue && + std::find( + p_simpl_to_color_in_blue->begin(), + p_simpl_to_color_in_blue->end(), + c) != p_simpl_to_color_in_blue->end()) { + color_simplex = 3; + } + } + + // If m_intrinsic_dim = 1, each point is output two times, + // so we need to multiply each index by 2 + // And if only 2 vertices, add a third one (each vertex is duplicated in + // the file when m_intrinsic dim = 2) + if (m_intrinsic_dim == 1) { + Simplex tmp_c; + Simplex::iterator it = c.begin(); + for (; it != c.end(); ++it) + tmp_c.insert(*it * 2); + if (num_vertices == 2) + tmp_c.insert(*tmp_c.rbegin() + 1); + + c = tmp_c; + } + + if (num_vertices <= 3) { + star_using_triangles.push_back(std::make_pair(c, color_simplex)); + } else { + // num_vertices >= 4: decompose the simplex in triangles + std::vector<bool> booleans(num_vertices, false); + std::fill(booleans.begin() + num_vertices - 3, booleans.end(), true); + do { + Simplex triangle; + Simplex::iterator it = c.begin(); + for (int i = 0; it != c.end(); ++i, ++it) { + if (booleans[i]) + triangle.insert(*it); + } + star_using_triangles.push_back( + std::make_pair(triangle, color_simplex)); + } while (std::next_permutation(booleans.begin(), booleans.end())); + } + } + + // For each cell + Star_using_triangles::const_iterator it_simplex = + star_using_triangles.begin(); + Star_using_triangles::const_iterator it_simplex_end = + star_using_triangles.end(); + for (; it_simplex != it_simplex_end; ++it_simplex) { + const Simplex &c = it_simplex->first; + + // Don't export infinite cells + if (is_infinite(c)) + continue; + + int color_simplex = it_simplex->second; + + std::stringstream sstr_c; + + Simplex::const_iterator it_point_idx = c.begin(); + for (; it_point_idx != c.end(); ++it_point_idx) { + sstr_c << *it_point_idx << " "; + } + + os << 3 << " " << sstr_c.str(); + if (color_inconsistencies || p_simpl_to_color_in_red + || p_simpl_to_color_in_green || p_simpl_to_color_in_blue) { + switch (color_simplex) { + case 0: os << " 255 255 0"; + break; + case 1: os << " 255 0 0"; + break; + case 2: os << " 0 255 0"; + break; + case 3: os << " 0 0 255"; + break; + default: os << " " << color.str(); + break; + } + } + ++num_OFF_simplices; + os << "\n"; + } + if (is_star_inconsistent) + ++num_inconsistent_stars; + } + +#ifdef DEBUG_TRACES + std::cerr + << "\n==========================================================\n" + << "Export from list of stars to OFF:\n" + << " * Number of vertices: " << m_points.size() << "\n" + << " * Total number of maximal simplices: " << num_maximal_simplices + << "\n"; + if (color_inconsistencies) { + std::cerr + << " * Number of inconsistent stars: " + << num_inconsistent_stars << " (" + << (m_points.size() > 0 ? + 100. * num_inconsistent_stars / m_points.size() : 0.) << "%)\n" + << " * Number of inconsistent maximal simplices: " + << num_inconsistent_maximal_simplices << " (" + << (num_maximal_simplices > 0 ? + 100. * num_inconsistent_maximal_simplices / num_maximal_simplices + : 0.) << "%)\n"; + } + std::cerr << "==========================================================\n"; +#endif + + return os; + } + + public: + std::ostream &export_simplices_to_off( + const Simplicial_complex &complex, + std::ostream & os, std::size_t &num_OFF_simplices, + Simplex_set const *p_simpl_to_color_in_red = NULL, + Simplex_set const *p_simpl_to_color_in_green = NULL, + Simplex_set const *p_simpl_to_color_in_blue = NULL) + const { + typedef Simplicial_complex::Simplex Simplex; + typedef Simplicial_complex::Simplex_set Simplex_set; + + // If m_intrinsic_dim = 1, each point is output two times + // (see export_vertices_to_off) + num_OFF_simplices = 0; + std::size_t num_maximal_simplices = 0; + + typename Simplex_set::const_iterator it_s = + complex.simplex_range().begin(); + typename Simplex_set::const_iterator it_s_end = + complex.simplex_range().end(); + // For each simplex + for (; it_s != it_s_end; ++it_s) { + Simplex c = *it_s; + ++num_maximal_simplices; + + int color_simplex = -1; // -1=no color, 0=yellow, 1=red, 2=green, 3=blue + if (p_simpl_to_color_in_red && + std::find( + p_simpl_to_color_in_red->begin(), + p_simpl_to_color_in_red->end(), + c) != p_simpl_to_color_in_red->end()) { + color_simplex = 1; + } else if (p_simpl_to_color_in_green && + std::find(p_simpl_to_color_in_green->begin(), + p_simpl_to_color_in_green->end(), + c) != p_simpl_to_color_in_green->end()) { + color_simplex = 2; + } else if (p_simpl_to_color_in_blue && + std::find(p_simpl_to_color_in_blue->begin(), + p_simpl_to_color_in_blue->end(), + c) != p_simpl_to_color_in_blue->end()) { + color_simplex = 3; + } + + // Gather the triangles here + typedef std::vector<Simplex> Triangles; + Triangles triangles; + + int num_vertices = static_cast<int>(c.size()); + // Do not export smaller dimension simplices + if (num_vertices < m_intrinsic_dim + 1) + continue; + + // If m_intrinsic_dim = 1, each point is output two times, + // so we need to multiply each index by 2 + // And if only 2 vertices, add a third one (each vertex is duplicated in + // the file when m_intrinsic dim = 2) + if (m_intrinsic_dim == 1) { + Simplex tmp_c; + Simplex::iterator it = c.begin(); + for (; it != c.end(); ++it) + tmp_c.insert(*it * 2); + if (num_vertices == 2) + tmp_c.insert(*tmp_c.rbegin() + 1); + + c = tmp_c; + } + + if (num_vertices <= 3) { + triangles.push_back(c); + } else { + // num_vertices >= 4: decompose the simplex in triangles + std::vector<bool> booleans(num_vertices, false); + std::fill(booleans.begin() + num_vertices - 3, booleans.end(), true); + do { + Simplex triangle; + Simplex::iterator it = c.begin(); + for (int i = 0; it != c.end(); ++i, ++it) { + if (booleans[i]) + triangle.insert(*it); + } + triangles.push_back(triangle); + } while (std::next_permutation(booleans.begin(), booleans.end())); + } + + // For each cell + Triangles::const_iterator it_tri = triangles.begin(); + Triangles::const_iterator it_tri_end = triangles.end(); + for (; it_tri != it_tri_end; ++it_tri) { + // Don't export infinite cells + if (is_infinite(*it_tri)) + continue; + + os << 3 << " "; + Simplex::const_iterator it_point_idx = it_tri->begin(); + for (; it_point_idx != it_tri->end(); ++it_point_idx) { + os << *it_point_idx << " "; + } + + if (p_simpl_to_color_in_red || p_simpl_to_color_in_green + || p_simpl_to_color_in_blue) { + switch (color_simplex) { + case 0: os << " 255 255 0"; + break; + case 1: os << " 255 0 0"; + break; + case 2: os << " 0 255 0"; + break; + case 3: os << " 0 0 255"; + break; + default: os << " 128 128 128"; + break; + } + } + + ++num_OFF_simplices; + os << "\n"; + } + } + +#ifdef DEBUG_TRACES + std::cerr + << "\n==========================================================\n" + << "Export from complex to OFF:\n" + << " * Number of vertices: " << m_points.size() << "\n" + << " * Total number of maximal simplices: " << num_maximal_simplices + << "\n" + << "==========================================================\n"; +#endif + + return os; + } + + private: + const K m_k; + const int m_intrinsic_dim; + const int m_ambient_dim; + + Points m_points; + Weights m_weights; +#ifdef GUDHI_TC_PERTURB_POSITION + Translations_for_perturb m_translations; +#if defined(GUDHI_USE_TBB) + Mutex_for_perturb *m_p_perturb_mutexes; +#endif +#endif + + Points_ds m_points_ds; + double m_last_max_perturb; + std::vector<bool> m_are_tangent_spaces_computed; + TS_container m_tangent_spaces; +#ifdef GUDHI_TC_EXPORT_NORMALS + OS_container m_orth_spaces; +#endif + Tr_container m_triangulations; // Contains the triangulations + // and their center vertex + Stars_container m_stars; + std::vector<FT> m_squared_star_spheres_radii_incl_margin; + +#ifdef GUDHI_TC_USE_ANOTHER_POINT_SET_FOR_TANGENT_SPACE_ESTIM + Points m_points_for_tse; + Points_ds m_points_ds_for_tse; +#endif + + mutable CGAL::Random m_random_generator; +}; // /class Tangential_complex + +} // end namespace tangential_complex +} // end namespace Gudhi + +#endif // TANGENTIAL_COMPLEX_H_ diff --git a/include/gudhi/Tangential_complex/Simplicial_complex.h b/include/gudhi/Tangential_complex/Simplicial_complex.h new file mode 100644 index 00000000..65c74ca5 --- /dev/null +++ b/include/gudhi/Tangential_complex/Simplicial_complex.h @@ -0,0 +1,539 @@ +/* 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): Clement Jamin + * + * 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/>. + */ + +#ifndef TANGENTIAL_COMPLEX_SIMPLICIAL_COMPLEX_H_ +#define TANGENTIAL_COMPLEX_SIMPLICIAL_COMPLEX_H_ + +#include <gudhi/Tangential_complex/config.h> +#include <gudhi/Tangential_complex/utilities.h> +#include <gudhi/Debug_utils.h> +#include <gudhi/console_color.h> + +#include <CGAL/iterator.h> + +// For is_pure_pseudomanifold +#include <boost/graph/graph_traits.hpp> +#include <boost/graph/adjacency_list.hpp> +#include <boost/graph/connected_components.hpp> +#include <boost/container/flat_set.hpp> + +#include <algorithm> +#include <string> +#include <fstream> +#include <map> // for map<> +#include <vector> // for vector<> +#include <set> // for set<> + +namespace Gudhi { +namespace tangential_complex { +namespace internal { + +class Simplicial_complex { + public: + typedef boost::container::flat_set<std::size_t> Simplex; + typedef std::set<Simplex> Simplex_set; + + // If perform_checks = true, the function: + // - won't insert the simplex if it is already in a higher dim simplex + // - will erase any lower-dim simplices that are faces of the new simplex + // Returns true if the simplex was added + bool add_simplex( + const Simplex &s, bool perform_checks = true) { + if (perform_checks) { + unsigned int num_pts = static_cast<int> (s.size()); + std::vector<Complex::iterator> to_erase; + bool check_higher_dim_simpl = true; + for (Complex::iterator it_simplex = m_complex.begin(), + it_simplex_end = m_complex.end(); + it_simplex != it_simplex_end; + ++it_simplex) { + // Check if the simplex is not already in a higher dim simplex + if (check_higher_dim_simpl + && it_simplex->size() > num_pts + && std::includes(it_simplex->begin(), it_simplex->end(), + s.begin(), s.end())) { + // No need to insert it, then + return false; + } + // Check if the simplex includes some lower-dim simplices + if (it_simplex->size() < num_pts + && std::includes(s.begin(), s.end(), + it_simplex->begin(), it_simplex->end())) { + to_erase.push_back(it_simplex); + // We don't need to check higher-sim simplices any more + check_higher_dim_simpl = false; + } + } + for (std::vector<Complex::iterator>::const_iterator it = to_erase.begin(); + it != to_erase.end(); ++it) { + m_complex.erase(*it); + } + } + return m_complex.insert(s).second; + } + + const Simplex_set &simplex_range() const { + return m_complex; + } + + bool empty() { + return m_complex.empty(); + } + + void clear() { + m_complex.clear(); + } + + template <typename Test, typename Output_it> + void get_simplices_matching_test(Test test, Output_it out) { + for (Complex::const_iterator it_simplex = m_complex.begin(), + it_simplex_end = m_complex.end(); + it_simplex != it_simplex_end; + ++it_simplex) { + if (test(*it_simplex)) + *out++ = *it_simplex; + } + } + + // When a simplex S has only one co-face C, we can remove S and C + // without changing the topology + + void collapse(int max_simplex_dim, bool quiet = false) { +#ifdef DEBUG_TRACES + if (!quiet) + std::cerr << "Collapsing... "; +#endif + // We note k = max_simplex_dim - 1 + int k = max_simplex_dim - 1; + + typedef Complex::iterator Simplex_iterator; + typedef std::vector<Simplex_iterator> Simplex_iterator_list; + typedef std::map<Simplex, Simplex_iterator_list> Cofaces_map; + + std::size_t num_collapsed_maximal_simplices = 0; + do { + num_collapsed_maximal_simplices = 0; + // Create a map associating each non-maximal k-faces to the list of its + // maximal cofaces + Cofaces_map cofaces_map; + for (Complex::const_iterator it_simplex = m_complex.begin(), + it_simplex_end = m_complex.end(); + it_simplex != it_simplex_end; + ++it_simplex) { + if (static_cast<int> (it_simplex->size()) > k + 1) { + std::vector<Simplex> k_faces; + // Get the k-faces composing the simplex + combinations(*it_simplex, k + 1, std::back_inserter(k_faces)); + for (const auto &comb : k_faces) + cofaces_map[comb].push_back(it_simplex); + } + } + + // For each non-maximal k-face F, if F has only one maximal coface Cf: + // - Look for the other k-faces F2, F3... of Cf in the map and: + // * if the list contains only Cf, clear the list (we don't remove the + // list since it creates troubles with the iterators) and add the F2, + // F3... to the complex + // * otherwise, remove Cf from the associated list + // - Remove Cf from the complex + for (Cofaces_map::const_iterator it_map_elt = cofaces_map.begin(), + it_map_end = cofaces_map.end(); + it_map_elt != it_map_end; + ++it_map_elt) { + if (it_map_elt->second.size() == 1) { + std::vector<Simplex> k_faces; + const Simplex_iterator_list::value_type &it_Cf = + *it_map_elt->second.begin(); + GUDHI_CHECK(it_Cf->size() == max_simplex_dim + 1, + std::logic_error("Wrong dimension")); + // Get the k-faces composing the simplex + combinations(*it_Cf, k + 1, std::back_inserter(k_faces)); + for (const auto &f2 : k_faces) { + // Skip F + if (f2 != it_map_elt->first) { + Cofaces_map::iterator it_comb_in_map = cofaces_map.find(f2); + if (it_comb_in_map->second.size() == 1) { + it_comb_in_map->second.clear(); + m_complex.insert(f2); + } else { // it_comb_in_map->second.size() > 1 + Simplex_iterator_list::iterator it = std::find(it_comb_in_map->second.begin(), + it_comb_in_map->second.end(), + it_Cf); + GUDHI_CHECK(it != it_comb_in_map->second.end(), + std::logic_error("Error: it == it_comb_in_map->second.end()")); + it_comb_in_map->second.erase(it); + } + } + } + m_complex.erase(it_Cf); + ++num_collapsed_maximal_simplices; + } + } + // Repeat until no maximal simplex got removed + } while (num_collapsed_maximal_simplices > 0); + + // Collapse the lower dimension simplices + if (k > 0) + collapse(max_simplex_dim - 1, true); + +#ifdef DEBUG_TRACES + if (!quiet) + std::cerr << "done.\n"; +#endif + } + + void display_stats() const { + std::cerr << yellow << "Complex stats:\n" << white; + + if (m_complex.empty()) { + std::cerr << " * No simplices.\n"; + } else { + // Number of simplex for each dimension + std::map<int, std::size_t> simplex_stats; + + for (Complex::const_iterator it_simplex = m_complex.begin(), + it_simplex_end = m_complex.end(); + it_simplex != it_simplex_end; + ++it_simplex) { + ++simplex_stats[static_cast<int> (it_simplex->size()) - 1]; + } + + for (std::map<int, std::size_t>::const_iterator it_map = simplex_stats.begin(); + it_map != simplex_stats.end(); ++it_map) { + std::cerr << " * " << it_map->first << "-simplices: " + << it_map->second << "\n"; + } + } + } + + // verbose_level = 0, 1 or 2 + bool is_pure_pseudomanifold__do_not_check_if_stars_are_connected(int simplex_dim, + bool allow_borders = false, + bool exit_at_the_first_problem = false, + int verbose_level = 0, + std::size_t *p_num_wrong_dim_simplices = NULL, + std::size_t *p_num_wrong_number_of_cofaces = NULL) const { + typedef Simplex K_1_face; + typedef std::map<K_1_face, std::size_t> Cofaces_map; + + std::size_t num_wrong_dim_simplices = 0; + std::size_t num_wrong_number_of_cofaces = 0; + + // Counts the number of cofaces of each K_1_face + + // Create a map associating each non-maximal k-faces to the list of its + // maximal cofaces + Cofaces_map cofaces_map; + for (Complex::const_iterator it_simplex = m_complex.begin(), + it_simplex_end = m_complex.end(); + it_simplex != it_simplex_end; + ++it_simplex) { + if (static_cast<int> (it_simplex->size()) != simplex_dim + 1) { + if (verbose_level >= 2) + std::cerr << "Found a simplex with dim = " + << it_simplex->size() - 1 << "\n"; + ++num_wrong_dim_simplices; + } else { + std::vector<K_1_face> k_1_faces; + // Get the facets composing the simplex + combinations( + *it_simplex, simplex_dim, std::back_inserter(k_1_faces)); + for (const auto &k_1_face : k_1_faces) { + ++cofaces_map[k_1_face]; + } + } + } + + for (Cofaces_map::const_iterator it_map_elt = cofaces_map.begin(), + it_map_end = cofaces_map.end(); + it_map_elt != it_map_end; + ++it_map_elt) { + if (it_map_elt->second != 2 + && (!allow_borders || it_map_elt->second != 1)) { + if (verbose_level >= 2) + std::cerr << "Found a k-1-face with " + << it_map_elt->second << " cofaces\n"; + + if (exit_at_the_first_problem) + return false; + else + ++num_wrong_number_of_cofaces; + } + } + + bool ret = num_wrong_dim_simplices == 0 && num_wrong_number_of_cofaces == 0; + + if (verbose_level >= 1) { + std::cerr << "Pure pseudo-manifold: "; + if (ret) { + std::cerr << green << "YES" << white << "\n"; + } else { + std::cerr << red << "NO" << white << "\n" + << " * Number of wrong dimension simplices: " + << num_wrong_dim_simplices << "\n" + << " * Number of wrong number of cofaces: " + << num_wrong_number_of_cofaces << "\n"; + } + } + + if (p_num_wrong_dim_simplices) + *p_num_wrong_dim_simplices = num_wrong_dim_simplices; + if (p_num_wrong_number_of_cofaces) + *p_num_wrong_number_of_cofaces = num_wrong_number_of_cofaces; + + return ret; + } + + template <int K> + std::size_t num_K_simplices() const { + Simplex_set k_simplices; + + for (Complex::const_iterator it_simplex = m_complex.begin(), + it_simplex_end = m_complex.end(); + it_simplex != it_simplex_end; + ++it_simplex) { + if (it_simplex->size() == K + 1) { + k_simplices.insert(*it_simplex); + } else if (it_simplex->size() > K + 1) { + // Get the k-faces composing the simplex + combinations( + *it_simplex, K + 1, std::inserter(k_simplices, k_simplices.begin())); + } + } + + return k_simplices.size(); + } + + std::ptrdiff_t euler_characteristic(bool verbose = false) const { + if (verbose) + std::cerr << "\nComputing Euler characteristic of the complex...\n"; + + std::size_t num_vertices = num_K_simplices<0>(); + std::size_t num_edges = num_K_simplices<1>(); + std::size_t num_triangles = num_K_simplices<2>(); + + std::ptrdiff_t ec = + (std::ptrdiff_t) num_vertices + - (std::ptrdiff_t) num_edges + + (std::ptrdiff_t) num_triangles; + + if (verbose) + std::cerr << "Euler characteristic: V - E + F = " + << num_vertices << " - " << num_edges << " + " << num_triangles << " = " + << blue + << ec + << white << "\n"; + + return ec; + } + + // TODO(CJ): ADD COMMENTS + + bool is_pure_pseudomanifold( + int simplex_dim, + std::size_t num_vertices, + bool allow_borders = false, + bool exit_at_the_first_problem = false, + int verbose_level = 0, + std::size_t *p_num_wrong_dim_simplices = NULL, + std::size_t *p_num_wrong_number_of_cofaces = NULL, + std::size_t *p_num_unconnected_stars = NULL, + Simplex_set *p_wrong_dim_simplices = NULL, + Simplex_set *p_wrong_number_of_cofaces_simplices = NULL, + Simplex_set *p_unconnected_stars_simplices = NULL) const { + // If simplex_dim == 1, we do not need to check if stars are connected + if (simplex_dim == 1) { + if (p_num_unconnected_stars) + *p_num_unconnected_stars = 0; + return is_pure_pseudomanifold__do_not_check_if_stars_are_connected(simplex_dim, + allow_borders, + exit_at_the_first_problem, + verbose_level, + p_num_wrong_dim_simplices, + p_num_wrong_number_of_cofaces); + } + // Associates each vertex (= the index in the vector) + // to its star (list of simplices) + typedef std::vector<std::vector<Complex::const_iterator> > Stars; + std::size_t num_wrong_dim_simplices = 0; + std::size_t num_wrong_number_of_cofaces = 0; + std::size_t num_unconnected_stars = 0; + + // Fills a Stars data structure + Stars stars; + stars.resize(num_vertices); + for (Complex::const_iterator it_simplex = m_complex.begin(), + it_simplex_end = m_complex.end(); + it_simplex != it_simplex_end; + ++it_simplex) { + if (static_cast<int> (it_simplex->size()) != simplex_dim + 1) { + if (verbose_level >= 2) + std::cerr << "Found a simplex with dim = " + << it_simplex->size() - 1 << "\n"; + ++num_wrong_dim_simplices; + if (p_wrong_dim_simplices) + p_wrong_dim_simplices->insert(*it_simplex); + } else { + for (Simplex::const_iterator it_point_idx = it_simplex->begin(); + it_point_idx != it_simplex->end(); + ++it_point_idx) { + stars[*it_point_idx].push_back(it_simplex); + } + } + } + + // Now, for each star, we have a vector of its d-simplices + // i.e. one index for each d-simplex + // Boost Graph only deals with indexes, so we also need indexes for the + // (d-1)-simplices + std::size_t center_vertex_index = 0; + for (Stars::const_iterator it_star = stars.begin(); + it_star != stars.end(); + ++it_star, ++center_vertex_index) { + typedef std::map<Simplex, std::vector<std::size_t> > + Dm1_faces_to_adj_D_faces; + Dm1_faces_to_adj_D_faces dm1_faces_to_adj_d_faces; + + for (std::size_t i_dsimpl = 0; i_dsimpl < it_star->size(); ++i_dsimpl) { + Simplex dm1_simpl_of_link = *((*it_star)[i_dsimpl]); + dm1_simpl_of_link.erase(center_vertex_index); + // Copy it to a vector so that we can use operator[] on it + std::vector<std::size_t> dm1_simpl_of_link_vec( + dm1_simpl_of_link.begin(), dm1_simpl_of_link.end()); + + CGAL::Combination_enumerator<int> dm2_simplices( + simplex_dim - 1, 0, simplex_dim); + for (; !dm2_simplices.finished(); ++dm2_simplices) { + Simplex dm2_simpl; + for (int j = 0; j < simplex_dim - 1; ++j) + dm2_simpl.insert(dm1_simpl_of_link_vec[dm2_simplices[j]]); + dm1_faces_to_adj_d_faces[dm2_simpl].push_back(i_dsimpl); + } + } + + Adj_graph adj_graph; + std::vector<Graph_vertex> d_faces_descriptors; + d_faces_descriptors.resize(it_star->size()); + for (std::size_t j = 0; j < it_star->size(); ++j) + d_faces_descriptors[j] = boost::add_vertex(adj_graph); + + Dm1_faces_to_adj_D_faces::const_iterator dm1_to_d_it = + dm1_faces_to_adj_d_faces.begin(); + Dm1_faces_to_adj_D_faces::const_iterator dm1_to_d_it_end = + dm1_faces_to_adj_d_faces.end(); + for (std::size_t i_km1_face = 0; + dm1_to_d_it != dm1_to_d_it_end; + ++dm1_to_d_it, ++i_km1_face) { + Graph_vertex km1_gv = boost::add_vertex(adj_graph); + + for (std::vector<std::size_t>::const_iterator kface_it = + dm1_to_d_it->second.begin(); + kface_it != dm1_to_d_it->second.end(); + ++kface_it) { + boost::add_edge(km1_gv, *kface_it, adj_graph); + } + + if (dm1_to_d_it->second.size() != 2 + && (!allow_borders || dm1_to_d_it->second.size() != 1)) { + ++num_wrong_number_of_cofaces; + if (p_wrong_number_of_cofaces_simplices) { + for (auto idx : dm1_to_d_it->second) + p_wrong_number_of_cofaces_simplices->insert(*((*it_star)[idx])); + } + } + } + + // What is left is to check the connexity + bool is_connected = true; + if (boost::num_vertices(adj_graph) > 0) { + std::vector<int> components(boost::num_vertices(adj_graph)); + is_connected = + (boost::connected_components(adj_graph, &components[0]) == 1); + } + + if (!is_connected) { + if (verbose_level >= 2) + std::cerr << "Error: star #" << center_vertex_index + << " is not connected\n"; + ++num_unconnected_stars; + if (p_unconnected_stars_simplices) { + for (std::vector<Complex::const_iterator>::const_iterator + it_simpl = it_star->begin(), + it_simpl_end = it_star->end(); + it_simpl != it_simpl_end; + ++it_simpl) { + p_unconnected_stars_simplices->insert(**it_simpl); + } + } + } + } + + // Each one has been counted several times ("simplex_dim" times) + num_wrong_number_of_cofaces /= simplex_dim; + + bool ret = + num_wrong_dim_simplices == 0 + && num_wrong_number_of_cofaces == 0 + && num_unconnected_stars == 0; + + if (verbose_level >= 1) { + std::cerr << "Pure pseudo-manifold: "; + if (ret) { + std::cerr << green << "YES" << white << "\n"; + } else { + std::cerr << red << "NO" << white << "\n" + << " * Number of wrong dimension simplices: " + << num_wrong_dim_simplices << "\n" + << " * Number of wrong number of cofaces: " + << num_wrong_number_of_cofaces << "\n" + << " * Number of not-connected stars: " + << num_unconnected_stars << "\n"; + } + } + + if (p_num_wrong_dim_simplices) + *p_num_wrong_dim_simplices = num_wrong_dim_simplices; + if (p_num_wrong_number_of_cofaces) + *p_num_wrong_number_of_cofaces = num_wrong_number_of_cofaces; + if (p_num_unconnected_stars) + *p_num_unconnected_stars = num_unconnected_stars; + + return ret; + } + + private: + typedef Simplex_set Complex; + + // graph is an adjacency list + typedef boost::adjacency_list<boost::vecS, boost::vecS, boost::undirectedS> Adj_graph; + // map that gives to a certain simplex its node in graph and its dimension + typedef boost::graph_traits<Adj_graph>::vertex_descriptor Graph_vertex; + typedef boost::graph_traits<Adj_graph>::edge_descriptor Graph_edge; + + Complex m_complex; +}; // class Simplicial_complex + +} // namespace internal +} // namespace tangential_complex +} // namespace Gudhi + +#endif // TANGENTIAL_COMPLEX_SIMPLICIAL_COMPLEX_H_ diff --git a/include/gudhi/Tangential_complex/config.h b/include/gudhi/Tangential_complex/config.h new file mode 100644 index 00000000..ffefcd6b --- /dev/null +++ b/include/gudhi/Tangential_complex/config.h @@ -0,0 +1,43 @@ +/* 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): Clement Jamin + * + * 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/>. + */ + +#ifndef TANGENTIAL_COMPLEX_CONFIG_H_ +#define TANGENTIAL_COMPLEX_CONFIG_H_ + +#include <cstddef> + +// ========================= Debugging & profiling ============================= +// #define GUDHI_TC_PROFILING +// #define GUDHI_TC_VERY_VERBOSE +// #define GUDHI_TC_PERFORM_EXTRA_CHECKS +// #define GUDHI_TC_SHOW_DETAILED_STATS_FOR_INCONSISTENCIES + +// ========================= Strategy ========================================== +#define GUDHI_TC_PERTURB_POSITION +// #define GUDHI_TC_PERTURB_WEIGHT + +// ========================= Parameters ======================================== + +// PCA will use GUDHI_TC_BASE_VALUE_FOR_PCA^intrinsic_dim points +const std::size_t GUDHI_TC_BASE_VALUE_FOR_PCA = 5; + +#endif // TANGENTIAL_COMPLEX_CONFIG_H_ diff --git a/include/gudhi/Tangential_complex/utilities.h b/include/gudhi/Tangential_complex/utilities.h new file mode 100644 index 00000000..b2d6d674 --- /dev/null +++ b/include/gudhi/Tangential_complex/utilities.h @@ -0,0 +1,195 @@ +/* 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): Clement Jamin + * + * 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/>. + */ + +#ifndef TANGENTIAL_COMPLEX_UTILITIES_H_ +#define TANGENTIAL_COMPLEX_UTILITIES_H_ + +#include <CGAL/Dimension.h> +#include <CGAL/Combination_enumerator.h> +#include <CGAL/IO/Triangulation_off_ostream.h> + +#include <boost/container/flat_set.hpp> + +#include <Eigen/Core> +#include <Eigen/Eigen> + +#include <set> +#include <vector> +#include <array> +#include <fstream> +#include <atomic> +#include <cmath> // for std::sqrt + +namespace Gudhi { +namespace tangential_complex { +namespace internal { + +// Provides copy constructors to std::atomic so that +// it can be used in a vector +template <typename T> +struct Atomic_wrapper +: public std::atomic<T> { + typedef std::atomic<T> Base; + + Atomic_wrapper() { } + + Atomic_wrapper(const T &t) : Base(t) { } + + Atomic_wrapper(const std::atomic<T> &a) : Base(a.load()) { } + + Atomic_wrapper(const Atomic_wrapper &other) : Base(other.load()) { } + + Atomic_wrapper &operator=(const T &other) { + Base::store(other); + return *this; + } + + Atomic_wrapper &operator=(const std::atomic<T> &other) { + Base::store(other.load()); + return *this; + } + + Atomic_wrapper &operator=(const Atomic_wrapper &other) { + Base::store(other.load()); + return *this; + } +}; + +// Modifies v in-place +template <typename K> +typename K::Vector_d& normalize_vector(typename K::Vector_d& v, + K const& k) { + v = k.scaled_vector_d_object()( + v, typename K::FT(1) / std::sqrt(k.squared_length_d_object()(v))); + return v; +} + +template<typename Kernel> +struct Basis { + typedef typename Kernel::FT FT; + typedef typename Kernel::Point_d Point; + typedef typename Kernel::Vector_d Vector; + typedef typename std::vector<Vector>::const_iterator const_iterator; + + std::size_t m_origin; + std::vector<Vector> m_vectors; + + std::size_t origin() const { + return m_origin; + } + + void set_origin(std::size_t o) { + m_origin = o; + } + + const_iterator begin() const { + return m_vectors.begin(); + } + + const_iterator end() const { + return m_vectors.end(); + } + + std::size_t size() const { + return m_vectors.size(); + } + + Vector& operator[](const std::size_t i) { + return m_vectors[i]; + } + + const Vector& operator[](const std::size_t i) const { + return m_vectors[i]; + } + + void push_back(const Vector& v) { + m_vectors.push_back(v); + } + + void reserve(const std::size_t s) { + m_vectors.reserve(s); + } + + Basis() { } + + Basis(std::size_t origin) : m_origin(origin) { } + + Basis(std::size_t origin, const std::vector<Vector>& vectors) + : m_origin(origin), m_vectors(vectors) { } + + int dimension() const { + return static_cast<int> (m_vectors.size()); + } +}; + +// 1st line: number of points +// Then one point per line +template <typename Kernel, typename Point_range> +std::ostream &export_point_set( + Kernel const& k, + Point_range const& points, + std::ostream & os, + const char *coord_separator = " ") { + // Kernel functors + typename Kernel::Construct_cartesian_const_iterator_d ccci = + k.construct_cartesian_const_iterator_d_object(); + + os << points.size() << "\n"; + + typename Point_range::const_iterator it_p = points.begin(); + typename Point_range::const_iterator it_p_end = points.end(); + // For each point p + for (; it_p != it_p_end; ++it_p) { + for (auto it = ccci(*it_p); it != ccci(*it_p, 0); ++it) + os << CGAL::to_double(*it) << coord_separator; + + os << "\n"; + } + + return os; +} + +// Compute all the k-combinations of elements +// Output_iterator::value_type must be +// boost::container::flat_set<std::size_t> +template <typename Elements_container, typename Output_iterator> +void combinations(const Elements_container elements, int k, + Output_iterator combinations) { + std::size_t n = elements.size(); + std::vector<bool> booleans(n, false); + std::fill(booleans.begin() + n - k, booleans.end(), true); + do { + boost::container::flat_set<std::size_t> combination; + typename Elements_container::const_iterator it_elt = elements.begin(); + for (std::size_t i = 0; i < n; ++i, ++it_elt) { + if (booleans[i]) + combination.insert(*it_elt); + } + *combinations++ = combination; + } while (std::next_permutation(booleans.begin(), booleans.end())); +} + +} // namespace internal +} // namespace tangential_complex +} // namespace Gudhi + +#endif // TANGENTIAL_COMPLEX_UTILITIES_H_ diff --git a/include/gudhi/Test.h b/include/gudhi/Test.h deleted file mode 100644 index 6024c822..00000000 --- a/include/gudhi/Test.h +++ /dev/null @@ -1,105 +0,0 @@ -/* 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): David Salinas - * - * Copyright (C) 2014 INRIA Sophia Antipolis-Mediterranee (France) - * - * 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 TEST_H_ -#define TEST_H_ - -#include <list> -#include <string> -#include <vector> -#include <sstream> -#include <iostream> - - -#define TEST(a) std::cout << "TEST: " << (a) << std::endl -#define TESTMSG(a, b) std::cout << "TEST: " << a << b << std::endl -#define TESTVALUE(a) std::cout << "TEST: " << #a << ": " << a << std::endl - -/** - * Class to perform test - */ - -class Test { - private: - std::string name; - bool (*test)(); - - std::string separation() const { - return "+++++++++++++++++++++++++++++++++++++++++++++++++\n"; - } - - std::string print_between_plus(std::string& s) const { - std::stringstream res; - res << "+++++++++++++++++" << s << "+++++++++++++++++\n"; - return res.str(); - } - - public: - Test(std::string name_, bool (*test_)()) { - name = name_; - test = test_; - } - - bool run() { - std::cout << print_between_plus(name); - return test(); - } - - std::string getName() { - return name; - } -}; - -class Tests { - private: - std::list<Test> tests; - - public: - void add(std::string name_, bool (*test_)()) { - Test test(name_, test_); - tests.push_back(test); - } - - bool run() { - bool tests_succesful(true); - std::vector<bool> res; - for (Test test : tests) { - res.push_back(test.run()); - } - std::cout << "\n\n results of tests : " << std::endl; - int i = 0; - for (Test t : tests) { - std::cout << "Test " << i << " \"" << t.getName() << "\" --> "; - if (res[i++]) { - std::cout << "OK" << std::endl; - } else { - std::cout << "Fail" << std::endl; - tests_succesful = false; - break; - } - } - return tests_succesful; - } -}; - -#endif // TEST_H_ diff --git a/include/gudhi/Witness_complex.h b/include/gudhi/Witness_complex.h index 489cdf11..63f03687 100644 --- a/include/gudhi/Witness_complex.h +++ b/include/gudhi/Witness_complex.h @@ -4,7 +4,7 @@ * * Author(s): Siargey Kachanovich * - * Copyright (C) 2015 INRIA Sophia Antipolis-Méditerranée (France) + * Copyright (C) 2015 INRIA (France) * * 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 @@ -23,65 +23,44 @@ #ifndef WITNESS_COMPLEX_H_ #define WITNESS_COMPLEX_H_ -// Needed for the adjacency graph in bad link search -#include <boost/graph/graph_traits.hpp> -#include <boost/graph/adjacency_list.hpp> -#include <boost/graph/connected_components.hpp> +#include <gudhi/Active_witness/Active_witness.h> +#include <gudhi/Witness_complex/all_faces_in.h> -#include <boost/range/size.hpp> - -#include <gudhi/distance_functions.h> - -#include <algorithm> #include <utility> #include <vector> #include <list> -#include <set> -#include <queue> #include <limits> -#include <ctime> -#include <iostream> namespace Gudhi { namespace witness_complex { -// /* -// * \private -// \class Witness_complex -// \brief Constructs the witness complex for the given set of witnesses and landmarks. -// \ingroup witness_complex -// */ -template< class SimplicialComplex> +/** + * \private + * \class Witness_complex + * \brief Constructs (weak) witness complex for a given table of nearest landmarks with respect to witnesses. + * \ingroup witness_complex + * + * \tparam Nearest_landmark_table_ needs to be a range of a range of pairs of nearest landmarks and distances. + * The class Nearest_landmark_table_::value_type must be a copiable range. + * The range of pairs must admit a member type 'iterator'. The dereference type + * of the pair range iterator needs to be 'std::pair<std::size_t, double>'. +*/ +template< class Nearest_landmark_table_ > class Witness_complex { private: - struct Active_witness { - int witness_id; - int landmark_id; - - Active_witness(int witness_id_, int landmark_id_) - : witness_id(witness_id_), - landmark_id(landmark_id_) { } - }; - - private: - typedef typename SimplicialComplex::Simplex_handle Simplex_handle; - typedef typename SimplicialComplex::Vertex_handle Vertex_handle; - - typedef std::vector< double > Point_t; - typedef std::vector< Point_t > Point_Vector; - - typedef std::vector< Vertex_handle > typeVectorVertex; - typedef std::pair< typeVectorVertex, Filtration_value> typeSimplex; - typedef std::pair< Simplex_handle, bool > typePairSimplexBool; - - typedef int Witness_id; - typedef int Landmark_id; - typedef std::list< Vertex_handle > ActiveWitnessList; - - private: - int nbL_; // Number of landmarks - SimplicialComplex& sc_; // Simplicial complex + typedef typename Nearest_landmark_table_::value_type Nearest_landmark_range; + typedef std::size_t Witness_id; + typedef std::size_t Landmark_id; + typedef std::pair<Landmark_id, double> Id_distance_pair; + typedef Active_witness<Id_distance_pair, Nearest_landmark_range> ActiveWitness; + typedef std::list< ActiveWitness > ActiveWitnessList; + typedef std::vector< Landmark_id > typeVectorVertex; + typedef std::vector<Nearest_landmark_range> Nearest_landmark_table_internal; + typedef Landmark_id Vertex_handle; + + protected: + Nearest_landmark_table_internal nearest_landmark_table_; public: ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -90,174 +69,136 @@ class Witness_complex { //@{ - // Witness_range<Closest_landmark_range<Vertex_handle>> + Witness_complex() { + } - /* - * \brief Iterative construction of the witness complex. - * \details The witness complex is written in sc_ basing on a matrix knn of - * nearest neighbours of the form {witnesses}x{landmarks}. - * - * The type KNearestNeighbors can be seen as - * Witness_range<Closest_landmark_range<Vertex_handle>>, where - * Witness_range and Closest_landmark_range are random access ranges. - * - * Constructor takes into account at most (dim+1) - * first landmarks from each landmark range to construct simplices. - * - * Landmarks are supposed to be in [0,nbL_-1] + /** + * \brief Initializes member variables before constructing simplicial complex. + * \details Records nearest landmark table. + * @param[in] nearest_landmark_table needs to be a range of a range of pairs of nearest landmarks and distances. + * The class Nearest_landmark_table_::value_type must be a copiable range. + * The range of pairs must admit a member type 'iterator'. The dereference type + * of the pair range iterator needs to be 'std::pair<std::size_t, double>'. */ - template< typename KNearestNeighbors > - Witness_complex(KNearestNeighbors const & knn, - int nbL, - int dim, - SimplicialComplex & sc) : nbL_(nbL), sc_(sc) { - // Construction of the active witness list - int nbW = boost::size(knn); - typeVectorVertex vv; - int counter = 0; - /* The list of still useful witnesses - * it will diminuish in the course of iterations - */ - ActiveWitnessList active_w; // = new ActiveWitnessList(); - for (Vertex_handle i = 0; i != nbL_; ++i) { - // initial fill of 0-dimensional simplices - // by doing it we don't assume that landmarks are necessarily witnesses themselves anymore - counter++; - vv = {i}; - sc_.insert_simplex(vv); - // TODO(SK) Error if not inserted : normally no need here though + + Witness_complex(Nearest_landmark_table_ const & nearest_landmark_table) + : nearest_landmark_table_(std::begin(nearest_landmark_table), std::end(nearest_landmark_table)) { + } + + /** \brief Outputs the (weak) witness complex of relaxation 'max_alpha_square' + * in a simplicial complex data structure. + * \details The function returns true if the construction is successful and false otherwise. + * @param[out] complex Simplicial complex data structure compatible which is a model of + * SimplicialComplexForWitness concept. + * @param[in] max_alpha_square Maximal squared relaxation parameter. + * @param[in] limit_dimension Represents the maximal dimension of the simplicial complex + * (default value = no limit). + */ + template < typename SimplicialComplexForWitness > + bool create_complex(SimplicialComplexForWitness& complex, + double max_alpha_square, + std::size_t limit_dimension = std::numeric_limits<std::size_t>::max()) const { + if (complex.num_vertices() > 0) { + std::cerr << "Witness complex cannot create complex - complex is not empty.\n"; + return false; } - int k = 1; /* current dimension in iterative construction */ - for (int i = 0; i != nbW; ++i) - active_w.push_back(i); - while (!active_w.empty() && k < dim) { - typename ActiveWitnessList::iterator it = active_w.begin(); - while (it != active_w.end()) { - typeVectorVertex simplex_vector; - /* THE INSERTION: Checking if all the subfaces are in the simplex tree*/ - bool ok = all_faces_in(knn, *it, k); - if (ok) { - for (int i = 0; i != k + 1; ++i) - simplex_vector.push_back(knn[*it][i]); - sc_.insert_simplex(simplex_vector); - // TODO(SK) Error if not inserted : normally no need here though - ++it; - } else { - active_w.erase(it++); // First increase the iterator and then erase the previous element - } + if (max_alpha_square < 0) { + std::cerr << "Witness complex cannot create complex - squared relaxation parameter must be non-negative.\n"; + return false; + } + if (limit_dimension < 0) { + std::cerr << "Witness complex cannot create complex - limit dimension must be non-negative.\n"; + return false; + } + ActiveWitnessList active_witnesses; + Landmark_id k = 0; /* current dimension in iterative construction */ + for (auto w : nearest_landmark_table_) + active_witnesses.push_back(ActiveWitness(w)); + while (!active_witnesses.empty() && k <= limit_dimension) { + typename ActiveWitnessList::iterator aw_it = active_witnesses.begin(); + std::vector<Landmark_id> simplex; + simplex.reserve(k+1); + while (aw_it != active_witnesses.end()) { + bool ok = add_all_faces_of_dimension(k, + max_alpha_square, + std::numeric_limits<double>::infinity(), + aw_it->begin(), + simplex, + complex, + aw_it->end()); + assert(simplex.empty()); + if (!ok) + active_witnesses.erase(aw_it++); // First increase the iterator and then erase the previous element + else + aw_it++; } k++; } + complex.set_dimension(k-1); + return true; } //@} private: - /* \brief Check if the facets of the k-dimensional simplex witnessed - * by witness witness_id are already in the complex. - * inserted_vertex is the handle of the (k+1)-th vertex witnessed by witness_id + /* \brief Adds recursively all the faces of a certain dimension dim witnessed by the same witness. + * Iterator is needed to know until how far we can take landmarks to form simplexes. + * simplex is the prefix of the simplexes to insert. + * The output value indicates if the witness rests active or not. */ - template <typename KNearestNeighbors> - bool all_faces_in(KNearestNeighbors const &knn, int witness_id, int k) { - std::vector< Vertex_handle > facet; - // CHECK ALL THE FACETS - for (int i = 0; i != k + 1; ++i) { - facet = {}; - for (int j = 0; j != k + 1; ++j) { - if (j != i) { - facet.push_back(knn[witness_id][j]); + template < typename SimplicialComplexForWitness > + bool add_all_faces_of_dimension(int dim, + double alpha2, + double norelax_dist2, + typename ActiveWitness::iterator curr_l, + std::vector<Landmark_id>& simplex, + SimplicialComplexForWitness& sc, + typename ActiveWitness::iterator end) const { + if (curr_l == end) + return false; + bool will_be_active = false; + typename ActiveWitness::iterator l_it = curr_l; + if (dim > 0) { + for (; l_it != end && l_it->second - alpha2 <= norelax_dist2; ++l_it) { + simplex.push_back(l_it->first); + if (sc.find(simplex) != sc.null_simplex()) { + typename ActiveWitness::iterator next_it = l_it; + will_be_active = add_all_faces_of_dimension(dim-1, + alpha2, + norelax_dist2, + ++next_it, + simplex, + sc, + end) || will_be_active; } - } // endfor - if (sc_.find(facet) == sc_.null_simplex()) - return false; - } // endfor - return true; - } - - template <typename T> - static void print_vector(const std::vector<T>& v) { - std::cout << "["; - if (!v.empty()) { - std::cout << *(v.begin()); - for (auto it = v.begin() + 1; it != v.end(); ++it) { - std::cout << ","; - std::cout << *it; + assert(!simplex.empty()); + simplex.pop_back(); + // If norelax_dist is infinity, change to first omitted distance + if (l_it->second <= norelax_dist2) + norelax_dist2 = l_it->second; } - } - std::cout << "]"; - } - - public: - // /* - // * \brief Verification if every simplex in the complex is witnessed by witnesses in knn. - // * \param print_output =true will print the witnesses for each simplex - // * \remark Added for debugging purposes. - // */ - template< class KNearestNeighbors > - bool is_witness_complex(KNearestNeighbors const & knn, bool print_output) { - for (Simplex_handle sh : sc_.complex_simplex_range()) { - bool is_witnessed = false; - typeVectorVertex simplex; - int nbV = 0; // number of verticed in the simplex - for (Vertex_handle v : sc_.simplex_vertex_range(sh)) - simplex.push_back(v); - nbV = simplex.size(); - for (typeVectorVertex w : knn) { - bool has_vertices = true; - for (Vertex_handle v : simplex) - if (std::find(w.begin(), w.begin() + nbV, v) == w.begin() + nbV) { - has_vertices = false; - } - if (has_vertices) { - is_witnessed = true; - if (print_output) { - std::cout << "The simplex "; - print_vector(simplex); - std::cout << " is witnessed by the witness "; - print_vector(w); - std::cout << std::endl; - } - break; - } - } - if (!is_witnessed) { - if (print_output) { - std::cout << "The following simplex is not witnessed "; - print_vector(simplex); - std::cout << std::endl; + } else if (dim == 0) { + for (; l_it != end && l_it->second - alpha2 <= norelax_dist2; ++l_it) { + simplex.push_back(l_it->first); + double filtration_value = 0; + // if norelax_dist is infinite, relaxation is 0. + if (l_it->second > norelax_dist2) + filtration_value = l_it->second - norelax_dist2; + if (all_faces_in(simplex, &filtration_value, sc)) { + will_be_active = true; + sc.insert_simplex(simplex, filtration_value); } - assert(is_witnessed); - return false; + assert(!simplex.empty()); + simplex.pop_back(); + // If norelax_dist is infinity, change to first omitted distance + if (l_it->second < norelax_dist2) + norelax_dist2 = l_it->second; } } - return true; + return will_be_active; } }; - /** - * \ingroup witness_complex - * \brief Iterative construction of the witness complex. - * \details The witness complex is written in simplicial complex sc_ - * basing on a matrix knn of - * nearest neighbours of the form {witnesses}x{landmarks}. - * - * The type KNearestNeighbors can be seen as - * Witness_range<Closest_landmark_range<Vertex_handle>>, where - * Witness_range and Closest_landmark_range are random access ranges. - * - * Procedure takes into account at most (dim+1) - * first landmarks from each landmark range to construct simplices. - * - * Landmarks are supposed to be in [0,nbL_-1] - */ - template <class KNearestNeighbors, class SimplicialComplexForWitness> - void witness_complex(KNearestNeighbors const & knn, - int nbL, - int dim, - SimplicialComplexForWitness & sc) { - Witness_complex<SimplicialComplexForWitness>(knn, nbL, dim, sc); - } - } // namespace witness_complex } // namespace Gudhi diff --git a/include/gudhi/Witness_complex/all_faces_in.h b/include/gudhi/Witness_complex/all_faces_in.h new file mode 100644 index 00000000..b68d75a1 --- /dev/null +++ b/include/gudhi/Witness_complex/all_faces_in.h @@ -0,0 +1,55 @@ +/* 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): Siargey Kachanovich + * + * Copyright (C) 2015 INRIA (France) + * + * 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 WITNESS_COMPLEX_ALL_FACES_IN_H_ +#define WITNESS_COMPLEX_ALL_FACES_IN_H_ + +/* \brief Check if the facets of the k-dimensional simplex witnessed + * by witness witness_id are already in the complex. + * inserted_vertex is the handle of the (k+1)-th vertex witnessed by witness_id + */ +template < typename SimplicialComplexForWitness, + typename Simplex > + bool all_faces_in(Simplex& simplex, + double* filtration_value, + SimplicialComplexForWitness& sc) { + typedef typename SimplicialComplexForWitness::Simplex_handle Simplex_handle; + + if (simplex.size() == 1) + return true; /* Add vertices unconditionally */ + + Simplex facet; + for (typename Simplex::iterator not_it = simplex.begin(); not_it != simplex.end(); ++not_it) { + facet.clear(); + for (typename Simplex::iterator it = simplex.begin(); it != simplex.end(); ++it) + if (it != not_it) + facet.push_back(*it); + Simplex_handle facet_sh = sc.find(facet); + if (facet_sh == sc.null_simplex()) + return false; + else if (sc.filtration(facet_sh) > *filtration_value) + *filtration_value = sc.filtration(facet_sh); + } + return true; + } + +#endif // WITNESS_COMPLEX_ALL_FACES_IN_H_ diff --git a/include/gudhi/choose_n_farthest_points.h b/include/gudhi/choose_n_farthest_points.h new file mode 100644 index 00000000..86500b28 --- /dev/null +++ b/include/gudhi/choose_n_farthest_points.h @@ -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): Siargey Kachanovich + * + * 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/>. + */ + +#ifndef CHOOSE_N_FARTHEST_POINTS_H_ +#define CHOOSE_N_FARTHEST_POINTS_H_ + +#include <boost/range.hpp> + +#include <gudhi/Null_output_iterator.h> + +#include <iterator> +#include <vector> +#include <random> +#include <limits> // for numeric_limits<> + +namespace Gudhi { + +namespace subsampling { + +/** + * \ingroup subsampling + */ +enum : std::size_t { +/** + * Argument for `choose_n_farthest_points` to indicate that the starting point should be picked randomly. + */ + random_starting_point = std::size_t(-1) +}; + +/** + * \ingroup subsampling + * \brief Subsample by a greedy strategy of iteratively adding the farthest point from the + * current chosen point set to the subsampling. + * The iteration starts with the landmark `starting point` or, if `starting point==random_starting_point`, with a random landmark. + * \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the + * concept <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a> (despite the name, taken from CGAL, this can be any kind of metric or proximity measure). + * It must also contain a public member `squared_distance_d_object()` that returns an object of this type. + * \tparam Point_range Range whose value type is Kernel::Point_d. It must provide random-access + * via `operator[]` and the points should be stored contiguously in memory. + * \tparam PointOutputIterator Output iterator whose value type is Kernel::Point_d. + * \tparam DistanceOutputIterator Output iterator for distances. + * \details It chooses `final_size` points from a random access range + * `input_pts` and outputs them in the output iterator `output_it`. It also + * outputs the distance from each of those points to the set of previous + * points in `dist_it`. + * @param[in] k A kernel object. + * @param[in] input_pts Const reference to the input points. + * @param[in] final_size The size of the subsample to compute. + * @param[in] starting_point The seed in the farthest point algorithm. + * @param[out] output_it The output iterator for points. + * @param[out] dist_it The optional output iterator for distances. + * + */ +template < typename Kernel, +typename Point_range, +typename PointOutputIterator, +typename DistanceOutputIterator = Null_output_iterator> +void choose_n_farthest_points(Kernel const &k, + Point_range const &input_pts, + std::size_t final_size, + std::size_t starting_point, + PointOutputIterator output_it, + DistanceOutputIterator dist_it = {}) { + std::size_t nb_points = boost::size(input_pts); + if (final_size > nb_points) + final_size = nb_points; + + // Tests to the limit + if (final_size < 1) + return; + + if (starting_point == random_starting_point) { + // Choose randomly the first landmark + std::random_device rd; + std::mt19937 gen(rd()); + std::uniform_int_distribution<std::size_t> dis(0, (input_pts.size() - 1)); + starting_point = dis(gen); + } + + typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object(); + + std::size_t current_number_of_landmarks = 0; // counter for landmarks + const double infty = std::numeric_limits<double>::infinity(); // infinity (see next entry) + std::vector< double > dist_to_L(nb_points, infty); // vector of current distances to L from input_pts + + std::size_t curr_max_w = starting_point; + + for (current_number_of_landmarks = 0; current_number_of_landmarks != final_size; current_number_of_landmarks++) { + // curr_max_w at this point is the next landmark + *output_it++ = input_pts[curr_max_w]; + *dist_it++ = dist_to_L[curr_max_w]; + std::size_t i = 0; + for (auto& p : input_pts) { + double curr_dist = sqdist(p, *(std::begin(input_pts) + curr_max_w)); + if (curr_dist < dist_to_L[i]) + dist_to_L[i] = curr_dist; + ++i; + } + // choose the next curr_max_w + double curr_max_dist = 0; // used for defining the furhest point from L + for (i = 0; i < dist_to_L.size(); i++) + if (dist_to_L[i] > curr_max_dist) { + curr_max_dist = dist_to_L[i]; + curr_max_w = i; + } + } +} + +} // namespace subsampling + +} // namespace Gudhi + +#endif // CHOOSE_N_FARTHEST_POINTS_H_ diff --git a/include/gudhi/console_color.h b/include/gudhi/console_color.h new file mode 100644 index 00000000..c4671da3 --- /dev/null +++ b/include/gudhi/console_color.h @@ -0,0 +1,97 @@ +/* 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): Clement Jamin + * + * Copyright (C) 2016 INRIA Sophia-Antipolis (France) + * + * 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 CONSOLE_COLOR_H_ +#define CONSOLE_COLOR_H_ + +#include <iostream> + +#if defined(WIN32) +#include <windows.h> +#endif + +inline std::ostream& blue(std::ostream &s) { +#if defined(WIN32) + HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE); + SetConsoleTextAttribute(hStdout, + FOREGROUND_BLUE | FOREGROUND_GREEN | FOREGROUND_INTENSITY); +#else + s << "\x1b[0;34m"; +#endif + return s; +} + +inline std::ostream& red(std::ostream &s) { +#if defined(WIN32) + HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE); + SetConsoleTextAttribute(hStdout, FOREGROUND_RED | FOREGROUND_INTENSITY); +#else + s << "\x1b[0;31m"; +#endif + return s; +} + +inline std::ostream& green(std::ostream &s) { +#if defined(WIN32) + HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE); + SetConsoleTextAttribute(hStdout, FOREGROUND_GREEN | FOREGROUND_INTENSITY); +#else + s << "\x1b[0;32m"; +#endif + return s; +} + +inline std::ostream& yellow(std::ostream &s) { +#if defined(WIN32) + HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE); + SetConsoleTextAttribute(hStdout, + FOREGROUND_GREEN | FOREGROUND_RED | FOREGROUND_INTENSITY); +#else + s << "\x1b[0;33m"; +#endif + return s; +} + +inline std::ostream& white(std::ostream &s) { +#if defined(WIN32) + HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE); + SetConsoleTextAttribute(hStdout, + FOREGROUND_RED | FOREGROUND_GREEN | FOREGROUND_BLUE); +#else + s << "\x1b[0;37m"; +#endif + return s; +} + +inline std::ostream& black_on_white(std::ostream &s) { +#if defined(WIN32) + HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE); + SetConsoleTextAttribute(hStdout, + BACKGROUND_RED | BACKGROUND_GREEN | BACKGROUND_BLUE); +#else + s << "\x1b[0;33m"; +#endif + return s; +} + + +#endif // CONSOLE_COLOR_H_ diff --git a/include/gudhi/distance_functions.h b/include/gudhi/distance_functions.h index cd518581..f6e2ab5a 100644 --- a/include/gudhi/distance_functions.h +++ b/include/gudhi/distance_functions.h @@ -4,7 +4,7 @@ * * Author(s): Clément Maria * - * Copyright (C) 2014 INRIA Sophia Antipolis-Méditerranée (France) + * Copyright (C) 2014 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 @@ -24,20 +24,32 @@ #define DISTANCE_FUNCTIONS_H_ #include <cmath> // for std::sqrt +#include <type_traits> // for std::decay +#include <iterator> // for std::begin, std::end -/* Compute the Euclidean distance between two Points given - * by a range of coordinates. The points are assumed to have - * the same dimension. */ -template< typename Point > -double euclidean_distance(Point &p1, Point &p2) { - double dist = 0.; - auto it1 = p1.begin(); - auto it2 = p2.begin(); - for (; it1 != p1.end(); ++it1, ++it2) { - double tmp = *it1 - *it2; - dist += tmp*tmp; +namespace Gudhi { + +/** @file + * @brief Global distance functions + */ + +/** @brief Compute the Euclidean distance between two Points given by a range of coordinates. The points are assumed to + * have the same dimension. */ +class Euclidean_distance { + public: + template< typename Point > + auto operator()(const Point& p1, const Point& p2) const -> typename std::decay<decltype(*std::begin(p1))>::type { + auto it1 = p1.begin(); + auto it2 = p2.begin(); + typename Point::value_type dist = 0.; + for (; it1 != p1.end(); ++it1, ++it2) { + typename Point::value_type tmp = (*it1) - (*it2); + dist += tmp*tmp; + } + return std::sqrt(dist); } - return std::sqrt(dist); -} +}; + +} // namespace Gudhi #endif // DISTANCE_FUNCTIONS_H_ diff --git a/include/gudhi/graph_simplicial_complex.h b/include/gudhi/graph_simplicial_complex.h index 042ef516..5fe7c826 100644 --- a/include/gudhi/graph_simplicial_complex.h +++ b/include/gudhi/graph_simplicial_complex.h @@ -4,7 +4,7 @@ * * Author(s): Clément Maria * - * Copyright (C) 2014 INRIA Sophia Antipolis-Méditerranée (France) + * Copyright (C) 2014 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 @@ -39,61 +39,4 @@ struct vertex_filtration_t { typedef boost::vertex_property_tag kind; }; -typedef int Vertex_handle; -typedef double Filtration_value; -typedef boost::adjacency_list < boost::vecS, boost::vecS, boost::undirectedS -, boost::property < vertex_filtration_t, Filtration_value > -, boost::property < edge_filtration_t, Filtration_value > -> Graph_t; -typedef std::pair< Vertex_handle, Vertex_handle > Edge_t; - -/** \brief Output the proximity graph of the points. - * - * If points contains n elements, the proximity graph is the graph - * with n vertices, and an edge [u,v] iff the distance function between - * points u and v is smaller than threshold. - * - * The type PointCloud furnishes .begin() and .end() methods, that return - * iterators with value_type Point. - */ -template< typename PointCloud -, typename Point > -Graph_t compute_proximity_graph(PointCloud &points - , Filtration_value threshold - , Filtration_value distance(Point p1, Point p2)) { - std::vector< Edge_t > edges; - std::vector< Filtration_value > edges_fil; - std::map< Vertex_handle, Filtration_value > vertices; - - Vertex_handle idx_u, idx_v; - Filtration_value fil; - idx_u = 0; - for (auto it_u = points.begin(); it_u != points.end(); ++it_u) { - idx_v = idx_u + 1; - for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) { - fil = distance(*it_u, *it_v); - if (fil <= threshold) { - edges.emplace_back(idx_u, idx_v); - edges_fil.push_back(fil); - } - } - ++idx_u; - } - - Graph_t skel_graph(edges.begin() - , edges.end() - , edges_fil.begin() - , idx_u); // number of points labeled from 0 to idx_u-1 - - auto vertex_prop = boost::get(vertex_filtration_t(), skel_graph); - - boost::graph_traits<Graph_t>::vertex_iterator vi, vi_end; - for (std::tie(vi, vi_end) = boost::vertices(skel_graph); - vi != vi_end; ++vi) { - boost::put(vertex_prop, *vi, 0.); - } - - return skel_graph; -} - #endif // GRAPH_SIMPLICIAL_COMPLEX_H_ diff --git a/include/gudhi/pick_n_random_points.h b/include/gudhi/pick_n_random_points.h new file mode 100644 index 00000000..f0e3f1f1 --- /dev/null +++ b/include/gudhi/pick_n_random_points.h @@ -0,0 +1,86 @@ +/* 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): Siargey Kachanovich + * + * 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/>. + */ + +#ifndef PICK_N_RANDOM_POINTS_H_ +#define PICK_N_RANDOM_POINTS_H_ + +#include <gudhi/Clock.h> + +#include <boost/range/size.hpp> + +#include <cstddef> +#include <random> // random_device, mt19937 +#include <algorithm> // shuffle +#include <numeric> // iota +#include <iterator> +#include <vector> + + +namespace Gudhi { + +namespace subsampling { + +/** + * \ingroup subsampling + * \brief Subsample a point set by picking random vertices. + * + * \details It chooses `final_size` distinct points from a random access range `points` + * and outputs them to the output iterator `output_it`. + * Point_container::iterator should be ValueSwappable and RandomAccessIterator. + */ +template <typename Point_container, +typename OutputIterator> +void pick_n_random_points(Point_container const &points, + std::size_t final_size, + OutputIterator output_it) { +#ifdef GUDHI_SUBS_PROFILING + Gudhi::Clock t; +#endif + + std::size_t nbP = boost::size(points); + if (final_size > nbP) + final_size = nbP; + + std::vector<int> landmarks(nbP); + std::iota(landmarks.begin(), landmarks.end(), 0); + + std::random_device rd; + std::mt19937 g(rd()); + + std::shuffle(landmarks.begin(), landmarks.end(), g); + landmarks.resize(final_size); + + for (int l : landmarks) + *output_it++ = points[l]; + +#ifdef GUDHI_SUBS_PROFILING + t.end(); + std::cerr << "Random landmark choice took " << t.num_seconds() + << " seconds." << std::endl; +#endif +} + +} // namespace subsampling + +} // namespace Gudhi + +#endif // PICK_N_RANDOM_POINTS_H_ diff --git a/include/gudhi/random_point_generators.h b/include/gudhi/random_point_generators.h new file mode 100644 index 00000000..2ec465ef --- /dev/null +++ b/include/gudhi/random_point_generators.h @@ -0,0 +1,474 @@ +/* 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): Clement Jamin + * + * 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/>. + */ + +#ifndef RANDOM_POINT_GENERATORS_H_ +#define RANDOM_POINT_GENERATORS_H_ + +#include <CGAL/number_utils.h> +#include <CGAL/Random.h> +#include <CGAL/point_generators_d.h> + +#include <vector> // for vector<> + +namespace Gudhi { + +/////////////////////////////////////////////////////////////////////////////// +// Note: All these functions have been tested with the CGAL::Epick_d kernel +/////////////////////////////////////////////////////////////////////////////// + +// construct_point: dim 2 + +template <typename Kernel> +typename Kernel::Point_d construct_point(const Kernel &k, + typename Kernel::FT x1, typename Kernel::FT x2) { + typename Kernel::FT tab[2]; + tab[0] = x1; + tab[1] = x2; + return k.construct_point_d_object()(2, &tab[0], &tab[2]); +} + +// construct_point: dim 3 + +template <typename Kernel> +typename Kernel::Point_d construct_point(const Kernel &k, + typename Kernel::FT x1, typename Kernel::FT x2, typename Kernel::FT x3) { + typename Kernel::FT tab[3]; + tab[0] = x1; + tab[1] = x2; + tab[2] = x3; + return k.construct_point_d_object()(3, &tab[0], &tab[3]); +} + +// construct_point: dim 4 + +template <typename Kernel> +typename Kernel::Point_d construct_point(const Kernel &k, + typename Kernel::FT x1, typename Kernel::FT x2, typename Kernel::FT x3, + typename Kernel::FT x4) { + typename Kernel::FT tab[4]; + tab[0] = x1; + tab[1] = x2; + tab[2] = x3; + tab[3] = x4; + return k.construct_point_d_object()(4, &tab[0], &tab[4]); +} + +// construct_point: dim 5 + +template <typename Kernel> +typename Kernel::Point_d construct_point(const Kernel &k, + typename Kernel::FT x1, typename Kernel::FT x2, typename Kernel::FT x3, + typename Kernel::FT x4, typename Kernel::FT x5) { + typename Kernel::FT tab[5]; + tab[0] = x1; + tab[1] = x2; + tab[2] = x3; + tab[3] = x4; + tab[4] = x5; + return k.construct_point_d_object()(5, &tab[0], &tab[5]); +} + +// construct_point: dim 6 + +template <typename Kernel> +typename Kernel::Point_d construct_point(const Kernel &k, + typename Kernel::FT x1, typename Kernel::FT x2, typename Kernel::FT x3, + typename Kernel::FT x4, typename Kernel::FT x5, typename Kernel::FT x6) { + typename Kernel::FT tab[6]; + tab[0] = x1; + tab[1] = x2; + tab[2] = x3; + tab[3] = x4; + tab[4] = x5; + tab[5] = x6; + return k.construct_point_d_object()(6, &tab[0], &tab[6]); +} + +template <typename Kernel> +std::vector<typename Kernel::Point_d> generate_points_on_plane(std::size_t num_points, int intrinsic_dim, + int ambient_dim, + double coord_min = -5., double coord_max = 5.) { + typedef typename Kernel::Point_d Point; + typedef typename Kernel::FT FT; + Kernel k; + CGAL::Random rng; + std::vector<Point> points; + points.reserve(num_points); + for (std::size_t i = 0; i < num_points;) { + std::vector<FT> pt(ambient_dim, FT(0)); + for (int j = 0; j < intrinsic_dim; ++j) + pt[j] = rng.get_double(coord_min, coord_max); + + Point p = k.construct_point_d_object()(ambient_dim, pt.begin(), pt.end()); + points.push_back(p); + ++i; + } + return points; +} + +template <typename Kernel> +std::vector<typename Kernel::Point_d> generate_points_on_moment_curve(std::size_t num_points, int dim, + typename Kernel::FT min_x, + typename Kernel::FT max_x) { + typedef typename Kernel::Point_d Point; + typedef typename Kernel::FT FT; + Kernel k; + CGAL::Random rng; + std::vector<Point> points; + points.reserve(num_points); + for (std::size_t i = 0; i < num_points;) { + FT x = rng.get_double(min_x, max_x); + std::vector<FT> coords; + coords.reserve(dim); + for (int p = 1; p <= dim; ++p) + coords.push_back(std::pow(CGAL::to_double(x), p)); + Point p = k.construct_point_d_object()( + dim, coords.begin(), coords.end()); + points.push_back(p); + ++i; + } + return points; +} + + +// R = big radius, r = small radius +template <typename Kernel/*, typename TC_basis*/> +std::vector<typename Kernel::Point_d> generate_points_on_torus_3D(std::size_t num_points, double R, double r, + bool uniform = false) { + typedef typename Kernel::Point_d Point; + typedef typename Kernel::FT FT; + Kernel k; + CGAL::Random rng; + + // if uniform + std::size_t num_lines = (std::size_t)sqrt(num_points); + + std::vector<Point> points; + points.reserve(num_points); + for (std::size_t i = 0; i < num_points;) { + FT u, v; + if (uniform) { + std::size_t k1 = i / num_lines; + std::size_t k2 = i % num_lines; + u = 6.2832 * k1 / num_lines; + v = 6.2832 * k2 / num_lines; + } else { + u = rng.get_double(0, 6.2832); + v = rng.get_double(0, 6.2832); + } + Point p = construct_point(k, + (R + r * std::cos(u)) * std::cos(v), + (R + r * std::cos(u)) * std::sin(v), + r * std::sin(u)); + points.push_back(p); + ++i; + } + return points; +} + +// "Private" function used by generate_points_on_torus_d +template <typename Kernel, typename OutputIterator> +static void generate_uniform_points_on_torus_d(const Kernel &k, int dim, std::size_t num_slices, + OutputIterator out, + double radius_noise_percentage = 0., + std::vector<typename Kernel::FT> current_point = std::vector<typename Kernel::FT>()) { + CGAL::Random rng; + int point_size = static_cast<int>(current_point.size()); + if (point_size == 2 * dim) { + *out++ = k.construct_point_d_object()(point_size, current_point.begin(), current_point.end()); + } else { + for (std::size_t slice_idx = 0; slice_idx < num_slices; ++slice_idx) { + double radius_noise_ratio = 1.; + if (radius_noise_percentage > 0.) { + radius_noise_ratio = rng.get_double( + (100. - radius_noise_percentage) / 100., + (100. + radius_noise_percentage) / 100.); + } + std::vector<typename Kernel::FT> cp2 = current_point; + double alpha = 6.2832 * slice_idx / num_slices; + cp2.push_back(radius_noise_ratio * std::cos(alpha)); + cp2.push_back(radius_noise_ratio * std::sin(alpha)); + generate_uniform_points_on_torus_d( + k, dim, num_slices, out, radius_noise_percentage, cp2); + } + } +} + +template <typename Kernel> +std::vector<typename Kernel::Point_d> generate_points_on_torus_d(std::size_t num_points, int dim, bool uniform = false, + double radius_noise_percentage = 0.) { + typedef typename Kernel::Point_d Point; + typedef typename Kernel::FT FT; + Kernel k; + CGAL::Random rng; + + std::vector<Point> points; + points.reserve(num_points); + if (uniform) { + std::size_t num_slices = (std::size_t)std::pow(num_points, 1. / dim); + generate_uniform_points_on_torus_d( + k, dim, num_slices, std::back_inserter(points), radius_noise_percentage); + } else { + for (std::size_t i = 0; i < num_points;) { + double radius_noise_ratio = 1.; + if (radius_noise_percentage > 0.) { + radius_noise_ratio = rng.get_double( + (100. - radius_noise_percentage) / 100., + (100. + radius_noise_percentage) / 100.); + } + std::vector<typename Kernel::FT> pt; + pt.reserve(dim * 2); + for (int curdim = 0; curdim < dim; ++curdim) { + FT alpha = rng.get_double(0, 6.2832); + pt.push_back(radius_noise_ratio * std::cos(alpha)); + pt.push_back(radius_noise_ratio * std::sin(alpha)); + } + + Point p = k.construct_point_d_object()(pt.begin(), pt.end()); + points.push_back(p); + ++i; + } + } + return points; +} + +template <typename Kernel> +std::vector<typename Kernel::Point_d> generate_points_on_sphere_d(std::size_t num_points, int dim, double radius, + double radius_noise_percentage = 0.) { + typedef typename Kernel::Point_d Point; + Kernel k; + CGAL::Random rng; + CGAL::Random_points_on_sphere_d<Point> generator(dim, radius); + std::vector<Point> points; + points.reserve(num_points); + for (std::size_t i = 0; i < num_points;) { + Point p = *generator++; + if (radius_noise_percentage > 0.) { + double radius_noise_ratio = rng.get_double( + (100. - radius_noise_percentage) / 100., + (100. + radius_noise_percentage) / 100.); + + typename Kernel::Point_to_vector_d k_pt_to_vec = + k.point_to_vector_d_object(); + typename Kernel::Vector_to_point_d k_vec_to_pt = + k.vector_to_point_d_object(); + typename Kernel::Scaled_vector_d k_scaled_vec = + k.scaled_vector_d_object(); + p = k_vec_to_pt(k_scaled_vec(k_pt_to_vec(p), radius_noise_ratio)); + } + points.push_back(p); + ++i; + } + return points; +} + +template <typename Kernel> +std::vector<typename Kernel::Point_d> generate_points_on_two_spheres_d(std::size_t num_points, int dim, double radius, + double distance_between_centers, + double radius_noise_percentage = 0.) { + typedef typename Kernel::FT FT; + typedef typename Kernel::Point_d Point; + typedef typename Kernel::Vector_d Vector; + Kernel k; + CGAL::Random rng; + CGAL::Random_points_on_sphere_d<Point> generator(dim, radius); + std::vector<Point> points; + points.reserve(num_points); + + std::vector<FT> t(dim, FT(0)); + t[0] = distance_between_centers; + Vector c1_to_c2(t.begin(), t.end()); + + for (std::size_t i = 0; i < num_points;) { + Point p = *generator++; + if (radius_noise_percentage > 0.) { + double radius_noise_ratio = rng.get_double( + (100. - radius_noise_percentage) / 100., + (100. + radius_noise_percentage) / 100.); + + typename Kernel::Point_to_vector_d k_pt_to_vec = + k.point_to_vector_d_object(); + typename Kernel::Vector_to_point_d k_vec_to_pt = + k.vector_to_point_d_object(); + typename Kernel::Scaled_vector_d k_scaled_vec = + k.scaled_vector_d_object(); + p = k_vec_to_pt(k_scaled_vec(k_pt_to_vec(p), radius_noise_ratio)); + } + + typename Kernel::Translated_point_d k_transl = + k.translated_point_d_object(); + Point p2 = k_transl(p, c1_to_c2); + points.push_back(p); + points.push_back(p2); + i += 2; + } + return points; +} + +// Product of a 3-sphere and a circle => d = 3 / D = 5 + +template <typename Kernel> +std::vector<typename Kernel::Point_d> generate_points_on_3sphere_and_circle(std::size_t num_points, + double sphere_radius) { + typedef typename Kernel::FT FT; + typedef typename Kernel::Point_d Point; + Kernel k; + CGAL::Random rng; + CGAL::Random_points_on_sphere_d<Point> generator(3, sphere_radius); + std::vector<Point> points; + points.reserve(num_points); + + typename Kernel::Compute_coordinate_d k_coord = + k.compute_coordinate_d_object(); + for (std::size_t i = 0; i < num_points;) { + Point p_sphere = *generator++; // First 3 coords + + FT alpha = rng.get_double(0, 6.2832); + std::vector<FT> pt(5); + pt[0] = k_coord(p_sphere, 0); + pt[1] = k_coord(p_sphere, 1); + pt[2] = k_coord(p_sphere, 2); + pt[3] = std::cos(alpha); + pt[4] = std::sin(alpha); + Point p(pt.begin(), pt.end()); + points.push_back(p); + ++i; + } + return points; +} + +// a = big radius, b = small radius +template <typename Kernel> +std::vector<typename Kernel::Point_d> generate_points_on_klein_bottle_3D(std::size_t num_points, double a, double b, + bool uniform = false) { + typedef typename Kernel::Point_d Point; + typedef typename Kernel::FT FT; + Kernel k; + CGAL::Random rng; + + // if uniform + std::size_t num_lines = (std::size_t)sqrt(num_points); + + std::vector<Point> points; + points.reserve(num_points); + for (std::size_t i = 0; i < num_points;) { + FT u, v; + if (uniform) { + std::size_t k1 = i / num_lines; + std::size_t k2 = i % num_lines; + u = 6.2832 * k1 / num_lines; + v = 6.2832 * k2 / num_lines; + } else { + u = rng.get_double(0, 6.2832); + v = rng.get_double(0, 6.2832); + } + double tmp = cos(u / 2) * sin(v) - sin(u / 2) * sin(2. * v); + Point p = construct_point(k, + (a + b * tmp) * cos(u), + (a + b * tmp) * sin(u), + b * (sin(u / 2) * sin(v) + cos(u / 2) * sin(2. * v))); + points.push_back(p); + ++i; + } + return points; +} + +// a = big radius, b = small radius +template <typename Kernel> +std::vector<typename Kernel::Point_d> generate_points_on_klein_bottle_4D(std::size_t num_points, double a, double b, + double noise = 0., bool uniform = false) { + typedef typename Kernel::Point_d Point; + typedef typename Kernel::FT FT; + Kernel k; + CGAL::Random rng; + + // if uniform + std::size_t num_lines = (std::size_t)sqrt(num_points); + + std::vector<Point> points; + points.reserve(num_points); + for (std::size_t i = 0; i < num_points;) { + FT u, v; + if (uniform) { + std::size_t k1 = i / num_lines; + std::size_t k2 = i % num_lines; + u = 6.2832 * k1 / num_lines; + v = 6.2832 * k2 / num_lines; + } else { + u = rng.get_double(0, 6.2832); + v = rng.get_double(0, 6.2832); + } + Point p = construct_point(k, + (a + b * cos(v)) * cos(u) + (noise == 0. ? 0. : rng.get_double(0, noise)), + (a + b * cos(v)) * sin(u) + (noise == 0. ? 0. : rng.get_double(0, noise)), + b * sin(v) * cos(u / 2) + (noise == 0. ? 0. : rng.get_double(0, noise)), + b * sin(v) * sin(u / 2) + (noise == 0. ? 0. : rng.get_double(0, noise))); + points.push_back(p); + ++i; + } + return points; +} + + +// a = big radius, b = small radius + +template <typename Kernel> +std::vector<typename Kernel::Point_d> +generate_points_on_klein_bottle_variant_5D( + std::size_t num_points, double a, double b, bool uniform = false) { + typedef typename Kernel::Point_d Point; + typedef typename Kernel::FT FT; + Kernel k; + CGAL::Random rng; + + // if uniform + std::size_t num_lines = (std::size_t)sqrt(num_points); + + std::vector<Point> points; + points.reserve(num_points); + for (std::size_t i = 0; i < num_points;) { + FT u, v; + if (uniform) { + std::size_t k1 = i / num_lines; + std::size_t k2 = i % num_lines; + u = 6.2832 * k1 / num_lines; + v = 6.2832 * k2 / num_lines; + } else { + u = rng.get_double(0, 6.2832); + v = rng.get_double(0, 6.2832); + } + FT x1 = (a + b * cos(v)) * cos(u); + FT x2 = (a + b * cos(v)) * sin(u); + FT x3 = b * sin(v) * cos(u / 2); + FT x4 = b * sin(v) * sin(u / 2); + FT x5 = x1 + x2 + x3 + x4; + + Point p = construct_point(k, x1, x2, x3, x4, x5); + points.push_back(p); + ++i; + } + return points; +} + +} // namespace Gudhi + +#endif // RANDOM_POINT_GENERATORS_H_ diff --git a/include/gudhi/reader_utils.h b/include/gudhi/reader_utils.h index 899f9df6..97a87edd 100644 --- a/include/gudhi/reader_utils.h +++ b/include/gudhi/reader_utils.h @@ -2,9 +2,9 @@ * (Geometric Understanding in Higher Dimensions) is a generic C++ * library for computational topology. * - * Author(s): Clément Maria + * Author(s): Clement Maria, Pawel Dlotko * - * Copyright (C) 2014 INRIA Sophia Antipolis-Méditerranée (France) + * Copyright (C) 2014 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 @@ -30,18 +30,25 @@ #include <iostream> #include <fstream> #include <map> -#include <limits> // for numeric_limits<> +#include <limits> // for numeric_limits #include <string> #include <vector> +#include <utility> // for pair + +// Keep this file tag for Doxygen to parse the code, otherwise, functions are not documented. +// It is required for global functions and variables. + +/** @file + * @brief This file includes common file reader for GUDHI + */ /** - * \brief Read a set of points to turn it - * into a vector< vector<double> > by filling points + * @brief Read a set of points to turn it into a vector< vector<double> > by filling points. * - * File format: 1 point per line - * X11 X12 ... X1d - * X21 X22 ... X2d - * etc + * File format: 1 point per line<br> + * X11 X12 ... X1d<br> + * X21 X22 ... X2d<br> + * etc<br> */ inline void read_points(std::string file_name, std::vector< std::vector< double > > & points) { std::ifstream in_file(file_name.c_str(), std::ios::in); @@ -66,23 +73,29 @@ inline void read_points(std::string file_name, std::vector< std::vector< double } /** - * \brief Read a graph from a file. + * @brief Read a graph from a file. + * + * \tparam Graph_t Type for the return graph. Must be constructible from iterators on pairs of Vertex_handle + * \tparam Filtration_value Type for the value of the read filtration + * \tparam Vertex_handle Type for the value of the read vertices * - * File format: 1 simplex per line - * Dim1 X11 X12 ... X1d Fil1 - * Dim2 X21 X22 ... X2d Fil2 - * etc + * File format: 1 simplex per line<br> + * Dim1 X11 X12 ... X1d Fil1<br> + * Dim2 X21 X22 ... X2d Fil2<br> + * etc<br> * * The vertices must be labeled from 0 to n-1. * Every simplex must appear exactly once. * Simplices of dimension more than 1 are ignored. */ -inline Graph_t read_graph(std::string file_name) { +template< typename Graph_t, typename Filtration_value, typename Vertex_handle > +Graph_t read_graph(std::string file_name) { std::ifstream in_(file_name.c_str(), std::ios::in); if (!in_.is_open()) { std::cerr << "Unable to open file " << file_name << std::endl; } + typedef std::pair< Vertex_handle, Vertex_handle > Edge_t; std::vector< Edge_t > edges; std::vector< Filtration_value > edges_fil; std::map< Vertex_handle, Filtration_value > vertices; @@ -130,7 +143,7 @@ inline Graph_t read_graph(std::string file_name) { Graph_t skel_graph(edges.begin(), edges.end(), edges_fil.begin(), vertices.size()); auto vertex_prop = boost::get(vertex_filtration_t(), skel_graph); - boost::graph_traits<Graph_t>::vertex_iterator vi, vi_end; + typename boost::graph_traits<Graph_t>::vertex_iterator vi, vi_end; auto v_it = vertices.begin(); for (std::tie(vi, vi_end) = boost::vertices(skel_graph); vi != vi_end; ++vi, ++v_it) { boost::put(vertex_prop, *vi, v_it->second); @@ -140,12 +153,12 @@ inline Graph_t read_graph(std::string file_name) { } /** - * \brief Read a face from a file. + * @brief Read a face from a file. * - * File format: 1 simplex per line - * Dim1 X11 X12 ... X1d Fil1 - * Dim2 X21 X22 ... X2d Fil2 - * etc + * File format: 1 simplex per line<br> + * Dim1 X11 X12 ... X1d Fil1<br> + * Dim2 X21 X22 ... X2d Fil2<br> + * etc<br> * * The vertices must be labeled from 0 to n-1. * Every simplex must appear exactly once. @@ -166,18 +179,16 @@ bool read_simplex(std::istream & in_, std::vector< Vertex_handle > & simplex, Fi } /** - * \brief Read a hasse simplex from a file. - * - * File format: 1 simplex per line - * Dim1 k11 k12 ... k1Dim1 Fil1 - * Dim2 k21 k22 ... k2Dim2 Fil2 - * etc - * - * The key of a simplex is its position in the filtration order - * and also the number of its row in the file. - * Dimi ki1 ki2 ... kiDimi Fili means that the ith simplex in the - * filtration has dimension Dimi, filtration value fil1 and simplices with - * key ki1 ... kiDimi in its boundary.*/ + * @brief Read a hasse simplex from a file. + * + * File format: 1 simplex per line<br> + * Dim1 k11 k12 ... k1Dim1 Fil1<br> + * Dim2 k21 k22 ... k2Dim2 Fil2<br> + * etc<br> + * + * The key of a simplex is its position in the filtration order and also the number of its row in the file. + * Dimi ki1 ki2 ... kiDimi Fili means that the ith simplex in the filtration has dimension Dimi, filtration value + * fil1 and simplices with key ki1 ... kiDimi in its boundary.*/ template< typename Simplex_key, typename Filtration_value > bool read_hasse_simplex(std::istream & in_, std::vector< Simplex_key > & boundary, Filtration_value & fil) { int dim; @@ -195,4 +206,93 @@ bool read_hasse_simplex(std::istream & in_, std::vector< Simplex_key > & boundar return true; } +/** + * @brief Read a lower triangular distance matrix from a csv file. We assume that the .csv store the whole + * (square) matrix. + * + * @author Pawel Dlotko + * + * Square matrix file format:<br> + * 0;D12;...;D1j<br> + * D21;0;...;D2j<br> + * ...<br> + * Dj1;Dj2;...;0<br> + * + * lower matrix file format:<br> + * 0<br> + * D21;<br> + * D31;D32;<br> + * ...<br> + * Dj1;Dj2;...;Dj(j-1);<br> + * + **/ +template< typename Filtration_value > +std::vector< std::vector< Filtration_value > > read_lower_triangular_matrix_from_csv_file(const std::string& filename, + const char separator = ';') { +#ifdef DEBUG_TRACES + std::cout << "Using procedure read_lower_triangular_matrix_from_csv_file \n"; +#endif // DEBUG_TRACES + std::vector< std::vector< Filtration_value > > result; + std::ifstream in; + in.open(filename.c_str()); + if (!in.is_open()) { + return result; + } + + std::string line; + + // the first line is emtpy, so we ignore it: + std::getline(in, line); + std::vector< Filtration_value > values_in_this_line; + result.push_back(values_in_this_line); + + int number_of_line = 0; + + // first, read the file line by line to a string: + while (std::getline(in, line)) { + // if line is empty, break + if (line.size() == 0) + break; + + // if the last element of a string is comma: + if (line[ line.size() - 1 ] == separator) { + // then shrink the string by one + line.pop_back(); + } + + // replace all commas with spaces + std::replace(line.begin(), line.end(), separator, ' '); + + // put the new line to a stream + std::istringstream iss(line); + // and now read the doubles. + + int number_of_entry = 0; + std::vector< Filtration_value > values_in_this_line; + while (iss.good()) { + double entry; + iss >> entry; + if (number_of_entry <= number_of_line) { + values_in_this_line.push_back(entry); + } + ++number_of_entry; + } + if (!values_in_this_line.empty())result.push_back(values_in_this_line); + ++number_of_line; + } + in.close(); + +#ifdef DEBUG_TRACES + std::cerr << "Here is the matrix we read : \n"; + for (size_t i = 0; i != result.size(); ++i) { + for (size_t j = 0; j != result[i].size(); ++j) { + std::cerr << result[i][j] << " "; + } + std::cerr << std::endl; + } +#endif // DEBUG_TRACES + + return result; +} // read_lower_triangular_matrix_from_csv_file + #endif // READER_UTILS_H_ diff --git a/include/gudhi/sparsify_point_set.h b/include/gudhi/sparsify_point_set.h new file mode 100644 index 00000000..507f8c79 --- /dev/null +++ b/include/gudhi/sparsify_point_set.h @@ -0,0 +1,113 @@ +/* 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): Clement Jamin + * + * 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/>. + */ + +#ifndef SPARSIFY_POINT_SET_H_ +#define SPARSIFY_POINT_SET_H_ + +#include <gudhi/Kd_tree_search.h> +#ifdef GUDHI_SUBSAMPLING_PROFILING +#include <gudhi/Clock.h> +#endif + +#include <cstddef> +#include <vector> + +namespace Gudhi { + +namespace subsampling { + +/** + * \ingroup subsampling + * \brief Outputs a subset of the input points so that the + * squared distance between any two points + * is greater than or equal to `min_squared_dist`. + * + * \tparam Kernel must be a model of the <a target="_blank" + * href="http://doc.cgal.org/latest/Spatial_searching/classSearchTraits.html">SearchTraits</a> + * concept, such as the <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_d/classCGAL_1_1Epick__d.html">CGAL::Epick_d</a> class, which + * can be static if you know the ambiant dimension at compile-time, or dynamic if you don't. + * \tparam Point_range Range whose value type is Kernel::Point_d. It must provide random-access + * via `operator[]` and the points should be stored contiguously in memory. + * \tparam OutputIterator Output iterator whose value type is Kernel::Point_d. + * + * @param[in] k A kernel object. + * @param[in] input_pts Const reference to the input points. + * @param[in] min_squared_dist Minimum squared distance separating the output points. + * @param[out] output_it The output iterator. + */ +template <typename Kernel, typename Point_range, typename OutputIterator> +void +sparsify_point_set( + const Kernel &k, Point_range const& input_pts, + typename Kernel::FT min_squared_dist, + OutputIterator output_it) { + typedef typename Gudhi::spatial_searching::Kd_tree_search< + Kernel, Point_range> Points_ds; + +#ifdef GUDHI_SUBSAMPLING_PROFILING + Gudhi::Clock t; +#endif + + Points_ds points_ds(input_pts); + + std::vector<bool> dropped_points(input_pts.size(), false); + + // Parse the input points, and add them if they are not too close to + // the other points + std::size_t pt_idx = 0; + for (typename Point_range::const_iterator it_pt = input_pts.begin(); + it_pt != input_pts.end(); + ++it_pt, ++pt_idx) { + if (dropped_points[pt_idx]) + continue; + + *output_it++ = *it_pt; + + auto ins_range = points_ds.query_incremental_nearest_neighbors(*it_pt); + + // If another point Q is closer that min_squared_dist, mark Q to be dropped + for (auto const& neighbor : ins_range) { + std::size_t neighbor_point_idx = neighbor.first; + // If the neighbor is too close, we drop the neighbor + if (neighbor.second < min_squared_dist) { + // N.B.: If neighbor_point_idx < pt_idx, + // dropped_points[neighbor_point_idx] is already true but adding a + // test doesn't make things faster, so why bother? + dropped_points[neighbor_point_idx] = true; + } else { + break; + } + } + } + +#ifdef GUDHI_SUBSAMPLING_PROFILING + t.end(); + std::cerr << "Point set sparsified in " << t.num_seconds() + << " seconds." << std::endl; +#endif +} + +} // namespace subsampling +} // namespace Gudhi + +#endif // SPARSIFY_POINT_SET_H_ |