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Diffstat (limited to 'cython/include/Subsampling_interface.h')
-rw-r--r-- | cython/include/Subsampling_interface.h | 119 |
1 files changed, 0 insertions, 119 deletions
diff --git a/cython/include/Subsampling_interface.h b/cython/include/Subsampling_interface.h deleted file mode 100644 index f990da0c..00000000 --- a/cython/include/Subsampling_interface.h +++ /dev/null @@ -1,119 +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): Vincent Rouvreau - * - * 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 INCLUDE_SUBSAMPLING_INTERFACE_H_ -#define INCLUDE_SUBSAMPLING_INTERFACE_H_ - -#include <gudhi/choose_n_farthest_points.h> -#include <gudhi/pick_n_random_points.h> -#include <gudhi/sparsify_point_set.h> -#include <gudhi/Points_off_io.h> -#include <CGAL/Epick_d.h> - -#include <iostream> -#include <vector> -#include <string> - -namespace Gudhi { - -namespace subsampling { - -using Subsampling_dynamic_kernel = CGAL::Epick_d< CGAL::Dynamic_dimension_tag >; -using Subsampling_point_d = Subsampling_dynamic_kernel::Point_d; -using Subsampling_ft = Subsampling_dynamic_kernel::FT; - -// ------ choose_n_farthest_points ------ -std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points, - unsigned nb_points) { - std::vector<std::vector<double>> landmarks; - Subsampling_dynamic_kernel k; - choose_n_farthest_points(k, points, nb_points, random_starting_point, std::back_inserter(landmarks)); - - return landmarks; -} - -std::vector<std::vector<double>> subsampling_n_farthest_points(const std::vector<std::vector<double>>& points, - unsigned nb_points, unsigned starting_point) { - std::vector<std::vector<double>> landmarks; - Subsampling_dynamic_kernel k; - choose_n_farthest_points(k, points, nb_points, starting_point, std::back_inserter(landmarks)); - - return landmarks; -} - -std::vector<std::vector<double>> subsampling_n_farthest_points_from_file(const std::string& off_file, - unsigned nb_points) { - Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file); - std::vector<std::vector<double>> points = off_reader.get_point_cloud(); - return subsampling_n_farthest_points(points, nb_points); -} - -std::vector<std::vector<double>> subsampling_n_farthest_points_from_file(const std::string& off_file, - unsigned nb_points, unsigned starting_point) { - Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file); - std::vector<std::vector<double>> points = off_reader.get_point_cloud(); - return subsampling_n_farthest_points(points, nb_points, starting_point); -} - -// ------ pick_n_random_points ------ -std::vector<std::vector<double>> subsampling_n_random_points(const std::vector<std::vector<double>>& points, - unsigned nb_points) { - std::vector<std::vector<double>> landmarks; - pick_n_random_points(points, nb_points, std::back_inserter(landmarks)); - - return landmarks; -} - -std::vector<std::vector<double>> subsampling_n_random_points_from_file(const std::string& off_file, - unsigned nb_points) { - Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file); - std::vector<std::vector<double>> points = off_reader.get_point_cloud(); - return subsampling_n_random_points(points, nb_points); -} - -// ------ sparsify_point_set ------ -std::vector<std::vector<double>> subsampling_sparsify_points(const std::vector<std::vector<double>>& points, - double min_squared_dist) { - std::vector<Subsampling_point_d> input, output; - for (auto point : points) - input.push_back(Subsampling_point_d(point.size(), point.begin(), point.end())); - Subsampling_dynamic_kernel k; - sparsify_point_set(k, input, min_squared_dist, std::back_inserter(output)); - - std::vector<std::vector<double>> landmarks; - for (auto point : output) - landmarks.push_back(std::vector<double>(point.cartesian_begin(), point.cartesian_end())); - return landmarks; -} - -std::vector<std::vector<double>> subsampling_sparsify_points_from_file(const std::string& off_file, - double min_squared_dist) { - Gudhi::Points_off_reader<std::vector<double>> off_reader(off_file); - std::vector<std::vector<double>> points = off_reader.get_point_cloud(); - return subsampling_sparsify_points(points, min_squared_dist); -} - -} // namespace subsampling - -} // namespace Gudhi - -#endif // INCLUDE_SUBSAMPLING_INTERFACE_H_ |