diff options
Diffstat (limited to 'src/Subsampling/include/gudhi/choose_n_farthest_points.h')
| -rw-r--r-- | src/Subsampling/include/gudhi/choose_n_farthest_points.h | 156 |
1 files changed, 156 insertions, 0 deletions
diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h new file mode 100644 index 00000000..ea387bf9 --- /dev/null +++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h @@ -0,0 +1,156 @@ +/* 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/Kd_tree_search.h> + +#include <gudhi/Clock.h> + +#include <CGAL/Search_traits.h> +#include <CGAL/Search_traits_adapter.h> +#include <CGAL/Fuzzy_sphere.h> + +#include <iterator> +#include <algorithm> // for sort +#include <vector> +#include <random> +#include <limits> // for numeric_limits<> + +namespace Gudhi { + +namespace subsampling { + +/** + * \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`. + * \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> + * concept. + * It must also contain a public member 'squared_distance_d_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 OutputIterator Output iterator whose value type is Kernel::Point_d. + * \details It chooses `final_size` points from a random access range `input_pts` and + * outputs it in the output iterator `output_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. + * + */ +template < typename Kernel, +typename Point_range, +typename OutputIterator> +void choose_n_farthest_points(Kernel const &k, + Point_range const &input_pts, + std::size_t final_size, + std::size_t starting_point, + OutputIterator output_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; + + 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]; + 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; + } + } +} + +/** + * \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 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> + * concept. + * It must also contain a public member 'squared_distance_d_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 OutputIterator Output iterator whose value type is Kernel::Point_d. + * \details It chooses `final_size` points from a random access range `input_pts` and + * outputs it in the output iterator `output_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[out] output_it The output iterator. + * + */ +template < typename Kernel, +typename Point_range, +typename OutputIterator> +void choose_n_farthest_points(Kernel const& k, + Point_range const &input_pts, + unsigned final_size, + OutputIterator output_it) { + // Tests to the limit + if ((final_size < 1) || (input_pts.size() == 0)) + return; + + // Choose randomly the first landmark + std::random_device rd; + std::mt19937 gen(rd()); + std::uniform_int_distribution<> dis(0, (input_pts.size() - 1)); + int starting_point = dis(gen); + choose_n_farthest_points(k, input_pts, final_size, starting_point, output_it); +} + +} // namespace subsampling + +} // namespace Gudhi + +#endif // CHOOSE_N_FARTHEST_POINTS_H_ |