#include #include #include #include #include #include #include #include #include #include #include using K = CGAL::Epick_d; using Point_d = typename K::Point_d; using Witness_complex = Gudhi::witness_complex::Euclidean_strong_witness_complex; using Point_vector = std::vector; 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 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(end - start) / CLOCKS_PER_SEC << " s. \n"; std::clog << "Number of simplices is: " << simplex_tree.num_simplices() << "\n"; }