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Diffstat (limited to 'src/Witness_complex/example/Landmark_choice_sparsification.h')
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diff --git a/src/Witness_complex/example/Landmark_choice_sparsification.h b/src/Witness_complex/example/Landmark_choice_sparsification.h deleted file mode 100644 index d5d5fba1..00000000 --- a/src/Witness_complex/example/Landmark_choice_sparsification.h +++ /dev/null @@ -1,230 +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_SPARSIFICATION_H_ -#define LANDMARK_CHOICE_BY_SPARSIFICATION_H_ - -#include <utility> // for pair<> -#include <vector> -#include <cstddef> // for ptrdiff_t type -#include <algorithm> - -//#include <CGAL/Cartesian_d.h> -#include <CGAL/Search_traits.h> -#include <CGAL/Search_traits_adapter.h> -//#include <CGAL/property_map.h> -#include <CGAL/Epick_d.h> -#include <CGAL/Orthogonal_incremental_neighbor_search.h> -#include <CGAL/Orthogonal_k_neighbor_search.h> -#include <CGAL/Kd_tree.h> -#include <CGAL/Euclidean_distance.h> -//#include <CGAL/Kernel_d/Vector_d.h> -#include <CGAL/Random.h> -#include <CGAL/Fuzzy_sphere.h> - -namespace Gudhi { - -namespace witness_complex { - - typedef CGAL::Epick_d<CGAL::Dynamic_dimension_tag> K; - typedef K::FT FT; - typedef K::Point_d Point_d; - typedef CGAL::Search_traits< FT, - Point_d, - typename K::Cartesian_const_iterator_d, - typename K::Construct_cartesian_const_iterator_d > Traits_base; - typedef CGAL::Euclidean_distance<Traits_base> Euclidean_distance; - typedef CGAL::Search_traits_adapter< std::ptrdiff_t, - Point_d*, - Traits_base> STraits; - typedef CGAL::Distance_adapter< std::ptrdiff_t, - Point_d*, - Euclidean_distance > Distance_adapter; - typedef CGAL::Orthogonal_incremental_neighbor_search< STraits, - Distance_adapter > Neighbor_search; - typedef CGAL::Orthogonal_k_neighbor_search< STraits > Neighbor_search2; - typedef Neighbor_search::Tree Tree; - typedef CGAL::Fuzzy_sphere<STraits> Fuzzy_sphere; - - /** \brief Landmark selection function by as a sub-epsilon-net of the - * given set of points. - */ - template <typename Point_random_access_range> - void landmark_choice_by_sparsification(Point_random_access_range & points, - unsigned nbL, - FT mu_epsilon, - Point_random_access_range & landmarks) - { - unsigned nbP = points.end() - points.begin(); - assert(nbP >= nbL); - CGAL::Random rand; - // TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety - STraits points_traits(&(points[0])); - CGAL::Distance_adapter<std::ptrdiff_t,Point_d*,Euclidean_distance> points_adapter(&(points[0])); - std::vector<bool> dropped_points(nbP, false); - - Tree witness_tree(boost::counting_iterator<std::ptrdiff_t>(0), - boost::counting_iterator<std::ptrdiff_t>(nbP), - typename Tree::Splitter(), - points_traits); - - for (unsigned points_i = 0; points_i < nbP; points_i++) { - if (dropped_points[points_i]) - continue; - Point_d & w = points[points_i]; - Fuzzy_sphere fs(w, mu_epsilon, 0, points_traits); - std::vector<int> close_neighbors; - witness_tree.search(std::insert_iterator<std::vector<int>>(close_neighbors,close_neighbors.begin()),fs); - for (int i: close_neighbors) - dropped_points[i] = true; - } - - for (unsigned points_i = 0; points_i < nbP; points_i++) { - if (dropped_points[points_i]) - landmarks.push_back(points[points_i]); - } - - if (nbL < landmarks.size()) { - std::random_shuffle(landmarks.begin(), landmarks.end()); - landmarks.resize(nbL); - } - } - - - - - /** \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. - */ - template <typename KNearestNeighbours, - typename Point_random_access_range, - typename Distance_matrix> - void build_distance_matrix(Point_random_access_range const & points, - Point_random_access_range & landmarks, - FT alpha, - unsigned limD, - KNearestNeighbours & knn, - Distance_matrix & distances) - { - int nbP = points.end() - points.begin(); - knn = KNearestNeighbours(nbP); - distances = Distance_matrix(nbP); - STraits traits(&(landmarks[0])); - CGAL::Distance_adapter<std::ptrdiff_t,Point_d*,Euclidean_distance> adapter(&(landmarks[0])); - Euclidean_distance ed; - Tree landmark_tree(boost::counting_iterator<std::ptrdiff_t>(0), - boost::counting_iterator<std::ptrdiff_t>(landmarks.size()), - typename Tree::Splitter(), - traits); - for (int points_i = 0; points_i < nbP; points_i++) { - Point_d const & w = points[points_i]; - Neighbor_search search(landmark_tree, - w, - FT(0), - true, - adapter); - Neighbor_search::iterator search_it = search.begin(); - // Neighbor_search2 search(landmark_tree, - // w, limD+1, - // FT(0), - // true, - // adapter); - // Neighbor_search2::iterator search_it = search.begin(); - - while (knn[points_i].size() <= limD) { - distances[points_i].push_back(search_it->second); //!sq_dist - knn[points_i].push_back((search_it++)->first); - } - FT dtow = distances[points_i][limD]; - - while (search_it != search.end() && search_it->second < dtow + alpha) { - distances[points_i].push_back(search_it->second); - knn[points_i].push_back((search_it++)->first); - } - //std::cout << "k = " << knn[points_i].size() << std::endl; - } - } - - /* - template <typename Kernel, typename Point_container> - std::vector<typename Point_container::value_type> - sparsify_point_set(const Kernel &k, - Point_container const& input_pts, - typename Kernel::FT min_squared_dist) - { - typedef typename CGAL::Tangential_complex_::Point_cloud_data_structure<Kernel, Point_container> Points_ds; - typedef typename Points_ds::INS_iterator INS_iterator; - typedef typename Points_ds::INS_range INS_range; - - typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object(); - - // Create the output container - std::vector<typename Point_container::value_type> output; - - 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_container::const_iterator it_pt = input_pts.begin() ; - it_pt != input_pts.end(); - ++it_pt, ++pt_idx) - { - if (dropped_points[pt_idx]) - continue; - - output.push_back(*it_pt); - - INS_range ins_range = points_ds.query_incremental_ANN(*it_pt); - - // If another point Q is closer that min_squared_dist, mark Q to be dropped - for (INS_iterator nn_it = ins_range.begin() ; - nn_it != ins_range.end() ; - ++nn_it) - { - std::size_t neighbor_point_idx = nn_it->first; - // If the neighbor is too close, we drop the neighbor - if (nn_it->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; - } - } - - return output; -} - */ - - -} // namespace witness_complex - -} // namespace Gudhi - -#endif // LANDMARK_CHOICE_BY_RANDOM_POINT_H_ |