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Diffstat (limited to 'src/Witness_complex/example/Landmark_choice_random_knn.h')
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diff --git a/src/Witness_complex/example/Landmark_choice_random_knn.h b/src/Witness_complex/example/Landmark_choice_random_knn.h deleted file mode 100644 index fb91e116..00000000 --- a/src/Witness_complex/example/Landmark_choice_random_knn.h +++ /dev/null @@ -1,142 +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_KNN_H_ -#define LANDMARK_CHOICE_BY_RANDOM_KNN_H_ - -#include <utility> // for pair<> -#include <vector> -#include <cstddef> // for ptrdiff_t type - -//#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> - - -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; - - - /** \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 landmark_choice_by_random_knn(Point_random_access_range const & points, - int nbL, - FT alpha, - unsigned limD, - KNearestNeighbours & knn, - Distance_matrix & distances) { - int nbP = points.end() - points.begin(); - assert(nbP >= nbL); - std::vector<Point_d> landmarks; - std::vector<int> landmarks_ind; - Point_d p; - int chosen_landmark; - CGAL::Random rand; - // TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety - int current_number_of_landmarks = 0; // counter for landmarks - for (; current_number_of_landmarks != nbL; current_number_of_landmarks++) { - do chosen_landmark = rand.get_int(0,nbP); - while (std::find(landmarks_ind.begin(), landmarks_ind.end(), chosen_landmark) != landmarks_ind.end()); - p = points[chosen_landmark]; - landmarks.push_back(p); - landmarks_ind.push_back(chosen_landmark); - } - // std::cout << "Choice finished!" << std::endl; - - //int dim = points.begin()->size(); - 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>(nbL), - 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(sqrt(search_it->second)); - knn[points_i].push_back((search_it++)->first); - } - FT dtow = distances[points_i][limD-1]; - - if (alpha != 0) - while (search_it != search.end() && search_it->second < dtow + alpha) { - distances[points_i].push_back(sqrt(search_it->second)); - knn[points_i].push_back((search_it++)->first); - } - //std::cout << "k = " << knn[points_i].size() << std::endl; - } - } - -} // namespace witness_complex - -} // namespace Gudhi - -#endif // LANDMARK_CHOICE_BY_RANDOM_POINT_H_ |