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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): Siargey Kachanovich
*
* Copyright (C) 2016 INRIA (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/>.
*/
#include <gudhi/Simplex_tree.h>
#include <gudhi/Euclidean_strong_witness_complex.h>
#include <gudhi/pick_n_random_points.h>
#include <gudhi/choose_n_farthest_points.h>
#include <gudhi/Points_off_io.h>
#include <CGAL/Epick_d.h>
#include <iostream>
#include <fstream>
#include <ctime>
#include <string>
#include <vector>
using K = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>;
using Point_d = typename K::Point_d;
using Witness_complex = Gudhi::witness_complex::Euclidean_strong_witness_complex<K>;
using Point_vector = std::vector<Point_d>;
int main(int argc, char* const argv[]) {
if (argc != 5) {
std::cerr << "Usage: " << argv[0] << " path_to_point_file number_of_landmarks max_squared_alpha limit_dimension\n";
return 0;
}
std::string file_name = argv[1];
int nbL = atoi(argv[2]), lim_dim = atoi(argv[4]);
double alpha2 = atof(argv[3]);
clock_t start, end;
Gudhi::Simplex_tree<> simplex_tree;
// Read the point file
Point_vector point_vector, landmarks;
Gudhi::Points_off_reader<Point_d> off_reader(file_name);
if (!off_reader.is_valid()) {
std::cerr << "Strong witness complex - Unable to read file " << file_name << "\n";
exit(-1); // ----- >>
}
point_vector = Point_vector(off_reader.get_point_cloud());
std::cout << "Successfully read " << point_vector.size() << " points.\n";
std::cout << "Ambient dimension is " << point_vector[0].dimension() << ".\n";
// Choose landmarks (decomment one of the following two lines)
// Gudhi::subsampling::pick_n_random_points(point_vector, nbL, std::back_inserter(landmarks));
Gudhi::subsampling::choose_n_farthest_points(K(), point_vector, nbL, Gudhi::subsampling::random_starting_point,
std::back_inserter(landmarks));
// Compute witness complex
start = clock();
Witness_complex witness_complex(landmarks, point_vector);
witness_complex.create_complex(simplex_tree, alpha2, lim_dim);
end = clock();
std::cout << "Strong witness complex took " << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n";
std::cout << "Number of simplices is: " << simplex_tree.num_simplices() << "\n";
}
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