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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 new file mode 100644 index 00000000..3797b4c5 --- /dev/null +++ b/src/Witness_complex/example/Landmark_choice_random_knn.h @@ -0,0 +1,142 @@ +/* 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_ |