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diff --git a/src/Witness_complex/example/Landmark_choice_sparsification.h b/src/Witness_complex/example/Landmark_choice_sparsification.h new file mode 100644 index 00000000..1052b0c4 --- /dev/null +++ b/src/Witness_complex/example/Landmark_choice_sparsification.h @@ -0,0 +1,230 @@ +/* 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) + { + int 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 (int 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 (int 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_ |