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/* 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_
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