rips_correlation_matrix_persistence.cpp
/* 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): Pawel Dlotko, Vincent Rouvreau
*
* Copyright (C) 2016 Inria
*
* Modification(s):
* - YYYY/MM Author: Description of the modification
*/
#include <gudhi/Rips_complex.h>
#include <gudhi/Simplex_tree.h>
#include <gudhi/Persistent_cohomology.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 Correlation_matrix = std::vector<std::vector<Filtration_value>>;
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::clog << "The complex contains " << simplex_tree.num_simplices() << " simplices \n";
std::clog << " and has dimension " << simplex_tree.dimension() << " \n";
// 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::clog")(
"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::clog << std::endl;
std::clog << "Compute the persistent homology with coefficient field Z/pZ \n";
std::clog << "of a Rips complex defined on a corelation matrix.\n \n";
std::clog << "The output diagram contains one bar per line, written with the convention: \n";
std::clog << " p dim b d \n";
std::clog << "where dim is the dimension of the homological feature,\n";
std::clog << "b and d are respectively the birth and death of the feature and \n";
std::clog << "p is the characteristic of the field Z/pZ used for homology coefficients." << std::endl << std::endl;
std::clog << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl;
std::clog << visible << std::endl;
exit(-1);
}
}
Definition: writing_persistence_to_file.h:26
Options::Filtration_value Filtration_value
Type for the value of the filtration function.
Definition: Simplex_tree.h:86
static Filtration_value filtration(Simplex_handle sh)
Returns the filtration value of a simplex.
Definition: Simplex_tree.h:535
int dimension(Simplex_handle sh)
Returns the dimension of a simplex.
Definition: Simplex_tree.h:600
size_t num_simplices()
returns the number of simplices in the simplex_tree.
Definition: Simplex_tree.h:578
Structure representing the coefficient field .
Definition: Field_Zp.h:27
Computes the persistent cohomology of a filtered complex.
Definition: Persistent_cohomology.h:52
Rips complex data structure.
Definition: Rips_complex.h:45
This file includes common file reader for GUDHI.
Value type for a filtration function on a cell complex.
Definition: FiltrationValue.h:20