Loading...
Searching...
No Matches
example_strong_witness_complex_off.cpp
#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::clog << "Successfully read " << point_vector.size() << " points.\n";
std::clog << "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().squared_distance_d_object(), 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::clog << "Strong witness complex took " << static_cast<double>(end - start) / CLOCKS_PER_SEC << " s. \n";
std::clog << "Number of simplices is: " << simplex_tree.num_simplices() << "\n";
}
Gudhi::Points_off_reader
OFF file reader implementation in order to read points from an OFF file.
Definition Points_off_io.h:122
Gudhi::Points_off_reader::get_point_cloud
const std::vector< Point_d > & get_point_cloud() const
Point cloud getter.
Definition Points_off_io.h:158
Gudhi::Points_off_reader::is_valid
bool is_valid() const
Returns if the OFF file read operation was successful or not.
Definition Points_off_io.h:150
Gudhi::Simplex_tree
Simplex Tree data structure for representing simplicial complexes.
Definition Simplex_tree.h:98
Gudhi::Simplex_tree::num_simplices
size_t num_simplices() const
Returns the number of simplices in the simplex_tree.
Definition Simplex_tree.h:791
Gudhi::witness_complex::Euclidean_strong_witness_complex
Constructs strong witness complex for given sets of witnesses and landmarks in Euclidean space.
Definition Euclidean_strong_witness_complex.h:51
Gudhi::witness_complex::Strong_witness_complex::create_complex
bool create_complex(SimplicialComplexForWitness &complex, double max_alpha_square, Landmark_id limit_dimension=std::numeric_limits< Landmark_id >::max()) const
Outputs the strong witness complex of relaxation 'max_alpha_square' in a simplicial complex data stru...
Definition Strong_witness_complex.h:87
Gudhi::subsampling::choose_n_farthest_points
void choose_n_farthest_points(Distance dist, Point_range const &input_pts, std::size_t final_size, std::size_t starting_point, PointOutputIterator output_it, DistanceOutputIterator dist_it={})
Subsample by an iterative, greedy strategy.
Definition choose_n_farthest_points.h:75
Gudhi::subsampling::random_starting_point
@ random_starting_point
Definition choose_n_farthest_points.h:43