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author | glisse <glisse@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2017-03-26 11:32:25 +0000 |
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committer | glisse <glisse@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2017-03-26 11:32:25 +0000 |
commit | 1208d423e700764a3453767886e6a3b4c0a09125 (patch) | |
tree | 3e938a2e1e984e80e1137fd30968e385068ec8e4 /src/cython/include/Subsampling_interface.h | |
parent | 6f9560fe6d6cca6c5a0de35fcf7938912e103930 (diff) | |
parent | 6ed42daddfede2288bc02ab2e98fc12b47cac74e (diff) |
merge from trunk
git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/farthest_distance@2244 636b058d-ea47-450e-bf9e-a15bfbe3eedb
Former-commit-id: 7d261d59dae839dd5de8f6a03c5a167d015f7d85
Diffstat (limited to 'src/cython/include/Subsampling_interface.h')
-rw-r--r-- | src/cython/include/Subsampling_interface.h | 119 |
1 files changed, 119 insertions, 0 deletions
diff --git a/src/cython/include/Subsampling_interface.h b/src/cython/include/Subsampling_interface.h new file mode 100644 index 00000000..1c6032c0 --- /dev/null +++ b/src/cython/include/Subsampling_interface.h @@ -0,0 +1,119 @@ +/* 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, 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_ |