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/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
* See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
* Author(s): Vincent Rouvreau
*
* Copyright (C) 2020 Inria
*
* Modification(s):
* - YYYY/MM Author: Description of the modification
*/
#include <iostream>
#include <fstream>
#include <string>
#include <algorithm>
#include <utility> // std::pair, std::make_pair
#include <cmath> // float comparison
#include <limits>
#include <functional> // greater
#include <tuple> // std::tie
#define BOOST_TEST_DYN_LINK
#define BOOST_TEST_MODULE "collapse"
#include <boost/test/unit_test.hpp>
#include <boost/mpl/list.hpp>
// ^
// /!\ Nothing else from Simplex_tree shall be included to test includes are well defined.
#include "gudhi/FlagComplexSpMatrix.h"
#include "gudhi/Rips_edge_list.h"
#include "gudhi/graph_simplicial_complex.h"
#include "gudhi/distance_functions.h"
using namespace Gudhi;
// Types definition
using Vector_of_points = std::vector<std::vector<double>>;
//using Simplex_tree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>;
using Filtration_value = double;
using Rips_edge_list = Gudhi::rips_edge_list::Rips_edge_list<double>;
/*using Rips_complex = Gudhi::rips_complex::Rips_complex<Filtration_value>;
using Field_Zp = Gudhi::persistent_cohomology::Field_Zp;
using Persistent_cohomology = Gudhi::persistent_cohomology::Persistent_cohomology<Simplex_tree, Field_Zp>;
*/
using Distance_matrix = std::vector<std::vector<double>>;
BOOST_AUTO_TEST_CASE(collapse) {
typedef size_t Vertex_handle;
typedef std::vector<std::tuple<Filtration_value, Vertex_handle, Vertex_handle>> Filtered_sorted_edge_list;
std::size_t number_of_points;
std::string off_file_points;
std::string filediag;
Map map_empty;
Distance_matrix sparse_distances;
Vector_of_points point_vector {{0., 0.},{0., 1.},{1., 0.},{1., 1.}};
int dimension = point_vector[0].size();
number_of_points = point_vector.size();
std::cout << "Successfully read " << number_of_points << " point_vector.\n";
std::cout << "Ambient dimension is " << dimension << ".\n";
std::cout << "Point Set Generated." << std::endl;
for (double threshold = 1. ; threshold <= 2.; threshold +=1.) {
Filtered_sorted_edge_list edge_t;
std::cout << "Computing the one-skeleton for threshold: " << threshold << std::endl;
Rips_edge_list Rips_edge_list_from_file(point_vector, threshold, Gudhi::Euclidean_distance());
Rips_edge_list_from_file.create_edges(edge_t);
std::cout << "Sorted edge list computed" << std::endl;
std::cout << "Total number of edges before collapse are: " << edge_t.size() << std::endl;
if (edge_t.size() <= 0) {
std::cerr << "Total number of egdes are zero." << std::endl;
exit(-1);
}
// Now we will perform filtered edge collapse to sparsify the edge list edge_t.
std::cout << "Filtered edge collapse begins" << std::endl;
FlagComplexSpMatrix mat_filt_edge_coll(number_of_points, edge_t);
std::cout << "Matrix instansiated" << std::endl;
Filtered_sorted_edge_list collapse_edges;
collapse_edges = mat_filt_edge_coll.filtered_edge_collapse();
}
}
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