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diff --git a/utilities/Rips_complex/rips_correlation_matrix_persistence.cpp b/utilities/Rips_complex/rips_correlation_matrix_persistence.cpp
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--- a/utilities/Rips_complex/rips_correlation_matrix_persistence.cpp
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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): Pawel Dlotko, 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/>.
- */
-
-#include <gudhi/Rips_complex.h>
-#include <gudhi/Simplex_tree.h>
-#include <gudhi/Persistent_cohomology.h>
-#include <gudhi/reader_utils.h>
-#include <gudhi/writing_persistence_to_file.h>
-
-#include <boost/program_options.hpp>
-
-#include <string>
-#include <vector>
-#include <limits> // infinity
-#include <algorithm> // for sort
-
-// Types definition
-using Simplex_tree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>;
-using Filtration_value = Simplex_tree::Filtration_value;
-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 Correlation_matrix = std::vector<std::vector<Filtration_value>>;
-using intervals_common = Gudhi::Persistence_interval_common<double, int>;
-
-void program_options(int argc, char* argv[], std::string& csv_matrix_file, std::string& filediag,
- Filtration_value& correlation_min, int& dim_max, int& p, Filtration_value& min_persistence);
-
-int main(int argc, char* argv[]) {
- std::string csv_matrix_file;
- std::string filediag;
- Filtration_value correlation_min;
- int dim_max;
- int p;
- Filtration_value min_persistence;
-
- program_options(argc, argv, csv_matrix_file, filediag, correlation_min, dim_max, p, min_persistence);
-
- Correlation_matrix correlations =
- Gudhi::read_lower_triangular_matrix_from_csv_file<Filtration_value>(csv_matrix_file);
-
- Filtration_value threshold = 0;
-
- // Given a correlation matrix M, we compute component-wise M'[i,j] = 1-M[i,j] to get a distance matrix:
- for (size_t i = 0; i != correlations.size(); ++i) {
- for (size_t j = 0; j != correlations[i].size(); ++j) {
- correlations[i][j] = 1 - correlations[i][j];
- // Here we make sure that the values of corelations lie between -1 and 1.
- // If not, we throw an exception.
- if ((correlations[i][j] < -1) || (correlations[i][j] > 1)) {
- std::cerr << "The input matrix is not a correlation matrix. The program will now terminate. \n";
- throw "The input matrix is not a correlation matrix. The program will now terminate. \n";
- }
- if (correlations[i][j] > threshold) threshold = correlations[i][j];
- }
- }
-
- Rips_complex rips_complex_from_file(correlations, threshold);
-
- // Construct the Rips complex in a Simplex Tree
- Simplex_tree simplex_tree;
-
- rips_complex_from_file.create_complex(simplex_tree, dim_max);
- std::cout << "The complex contains " << simplex_tree.num_simplices() << " simplices \n";
- std::cout << " and has dimension " << simplex_tree.dimension() << " \n";
-
- // Sort the simplices in the order of the filtration
- simplex_tree.initialize_filtration();
-
- // Compute the persistence diagram of the complex
- Persistent_cohomology pcoh(simplex_tree);
- // initializes the coefficient field for homology
- pcoh.init_coefficients(p);
- // compute persistence
- pcoh.compute_persistent_cohomology(min_persistence);
-
- // invert the persistence diagram. The reason for this procedure is the following:
- // The input to the program is a corelation matrix M. When processing it, it is
- // turned into 1-M and the obtained persistence intervals are in '1-M' units.
- // Below we reverse every (birth,death) pair into (1-birth, 1-death) pair
- // so that the input and the output to the program is expressed in the same
- // units.
- auto pairs = pcoh.get_persistent_pairs();
- std::vector<intervals_common> processed_persistence_intervals;
- processed_persistence_intervals.reserve(pairs.size());
- for (auto pair : pairs) {
- double birth = 1 - simplex_tree.filtration(get<0>(pair));
- double death = 1 - simplex_tree.filtration(get<1>(pair));
- unsigned dimension = (unsigned)simplex_tree.dimension(get<0>(pair));
- int field = get<2>(pair);
- processed_persistence_intervals.push_back(intervals_common(birth, death, dimension, field));
- }
-
- // sort the processed intervals:
- std::sort(processed_persistence_intervals.begin(), processed_persistence_intervals.end());
-
- // and write them to a file
- if (filediag.empty()) {
- write_persistence_intervals_to_stream(processed_persistence_intervals);
- } else {
- std::ofstream out(filediag);
- write_persistence_intervals_to_stream(processed_persistence_intervals, out);
- }
- return 0;
-}
-
-void program_options(int argc, char* argv[], std::string& csv_matrix_file, std::string& filediag,
- Filtration_value& correlation_min, int& dim_max, int& p, Filtration_value& min_persistence) {
- namespace po = boost::program_options;
- po::options_description hidden("Hidden options");
- hidden.add_options()(
- "input-file", po::value<std::string>(&csv_matrix_file),
- "Name of file containing a corelation matrix. Can be square or lower triangular matrix. Separator is ';'.");
- po::options_description visible("Allowed options", 100);
- visible.add_options()("help,h", "produce help message")(
- "output-file,o", po::value<std::string>(&filediag)->default_value(std::string()),
- "Name of file in which the persistence diagram is written. Default print in std::cout")(
- "min-edge-corelation,c", po::value<Filtration_value>(&correlation_min)->default_value(0),
- "Minimal corelation of an edge for the Rips complex construction.")(
- "cpx-dimension,d", po::value<int>(&dim_max)->default_value(1),
- "Maximal dimension of the Rips complex we want to compute.")(
- "field-charac,p", po::value<int>(&p)->default_value(11),
- "Characteristic p of the coefficient field Z/pZ for computing homology.")(
- "min-persistence,m", po::value<Filtration_value>(&min_persistence),
- "Minimal lifetime of homology feature to be recorded. Default is 0. Enter a negative value to see zero length "
- "intervals");
-
- po::positional_options_description pos;
- pos.add("input-file", 1);
-
- po::options_description all;
- all.add(visible).add(hidden);
-
- po::variables_map vm;
- po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm);
- po::notify(vm);
-
- if (vm.count("help") || !vm.count("input-file")) {
- std::cout << std::endl;
- std::cout << "Compute the persistent homology with coefficient field Z/pZ \n";
- std::cout << "of a Rips complex defined on a corelation matrix.\n \n";
- std::cout << "The output diagram contains one bar per line, written with the convention: \n";
- std::cout << " p dim b d \n";
- std::cout << "where dim is the dimension of the homological feature,\n";
- std::cout << "b and d are respectively the birth and death of the feature and \n";
- std::cout << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl;
-
- std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl;
- std::cout << visible << std::endl;
- exit(-1);
- }
-}