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Diffstat (limited to 'src/Gudhi_stat/utilities/Landscape_bootstrap.cpp')
| -rw-r--r-- | src/Gudhi_stat/utilities/Landscape_bootstrap.cpp | 186 |
1 files changed, 0 insertions, 186 deletions
diff --git a/src/Gudhi_stat/utilities/Landscape_bootstrap.cpp b/src/Gudhi_stat/utilities/Landscape_bootstrap.cpp deleted file mode 100644 index a2ca93a9..00000000 --- a/src/Gudhi_stat/utilities/Landscape_bootstrap.cpp +++ /dev/null @@ -1,186 +0,0 @@ -/* 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 - * - * Copyright (C) 2015 INRIA (France) - * - * 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/>. - */ - -//stat part: -#include <gudhi/Hausdorff_distances.h> -#include <gudhi/bootstrap.h> -#include <gudhi/Persistence_landscape.h> -#include <gudhi/read_persistence_from_file.h> -#include <gudhi/persistence_vectors.h> -//persistence part: -#include <gudhi/reader_utils.h> -#include <gudhi/Rips_complex.h> -#include <gudhi/distance_functions.h> -#include <gudhi/Simplex_tree.h> -#include <gudhi/Persistent_cohomology.h> - - -using namespace Gudhi::Gudhi_stat; -using namespace Gudhi::Persistence_representations; -using Persistence_landscape = Gudhi::Persistence_representations::Persistence_landscape; - -typedef int Vertex_handle; -//typedef double Filtration_value; - - -//if this variable is -1, then the infinite interals are ignored. If not, they infinite values are replaced with what_to_replace_infinite_intervals_with: -double what_to_replace_infinite_intervals_with = -1; - - - -class compute_persistence_landscape_of_a_point_cloud_in_certain_dimension -{ -public: - compute_persistence_landscape_of_a_point_cloud_in_certain_dimension( std::vector< std::vector< double > >& points_ , int dimension , double threshold_ , int coeficient_field_ = 11 , double min_persistence_ = 0 ):dim( dimension ),points(points_),threshold(threshold_),coeficient_field(coeficient_field_),min_persistence(min_persistence_){} - //This function takes a vector of indices (numbers_to_sample). It will select the points from this->points having those indices, construct Rips complex and persistence intervals based on this. - //Then it will filter the intervals to find only those in the dimension this->dim, and construct a persistence landascape based on this. Thie will be the result of the procedure. - Persistence_landscape operator()( std::vector< size_t > numbers_to_sample ) - { - bool dbg = false; - //take the subsampled points: - std::vector< std::vector< double > > points_in_subsample; - points_in_subsample.reserve( numbers_to_sample.size() ); - for ( size_t i = 0 ; i != numbers_to_sample.size() ; ++i ) - { - points_in_subsample.push_back( this->points[ numbers_to_sample[i] ] ); - } - - using Stree = Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_fast_persistence>; - using Filtration_value = Stree::Filtration_value; - using Rips_complex = Gudhi::rips_complex::Rips_complex<Filtration_value>; - //construct a Rips complex based on it and compute its persistence: - Rips_complex rips_complex(points_in_subsample, this->threshold, Euclidean_distance()); - // Construct the Rips complex in a Simplex Tree - Stree st; - // expand the graph until dimension dim_max - rips_complex.create_complex(st, this->dim + 1); - // Compute the persistence diagram of the complex - Gudhi::persistent_cohomology::Persistent_cohomology<Stree, Gudhi::persistent_cohomology::Field_Zp > pcoh(st); - // initializes the coefficient field for homology - pcoh.init_coefficients( this->coeficient_field ); - pcoh.compute_persistent_cohomology(this->min_persistence); - auto persistence_pairs = pcoh.get_persistent_pairs(); - //From the persistence take only this in the dimension this->dim: - - if ( dbg )std::cerr << "Here are the persistence pairs :\n"; - std::vector< std::pair< double,double > > persistence_in_fixed_dimension; - for ( size_t i = 0 ; i != persistence_pairs.size() ; ++i ) - { - if ( st.dimension( std::get<0>(persistence_pairs[i]) ) == this->dim ) - { - double birth = st.filtration( std::get<0>(persistence_pairs[i]) ); - double death = st.filtration( std::get<1>(persistence_pairs[i]) ); - - if ( std::get<1>(persistence_pairs[i]) != st.null_simplex() ) - { - //finite interval - persistence_in_fixed_dimension.push_back( std::pair<double,double>( birth , death ) ); - if (dbg){std::cout << "birth : " << birth << " , death : " << death << std::endl;} - } - else - { - //infinite interval - if ( what_to_replace_infinite_intervals_with != -1 ) - { - persistence_in_fixed_dimension.push_back( std::pair<double,double>( birth , what_to_replace_infinite_intervals_with ) ); - if (dbg){std::cout << "birth : " << birth << " , death : " << what_to_replace_infinite_intervals_with << std::endl;} - } - } - } - } - if ( dbg )std::cerr << "Persistence pairs computed \n"; - //Construct and return the persistence landscape: - return Persistence_landscape( persistence_in_fixed_dimension ); - } -private: - int dim; - std::vector< std::vector< double > >& points; - double threshold; - int coeficient_field; - double min_persistence; -}; - -class distance_between_landscapes -{ -public: - distance_between_landscapes( double exponent_ ):exponent(exponent_){} - double operator()( const Persistence_landscape& first , const Persistence_landscape& second ) - { - return first.distance( second, this->exponent ); - } -private: - double exponent; -}; - - -int main( int argc , char** argv ) -{ - std::cout << "The parameters of this program are : " << std::endl; - std::cout << "(1) a name of a file with points," << std:: endl; - std::cout << "(2) a number of repetitions of bootstrap (integer)," << std::endl; - std::cout << "(3) a size of subsample (integer, smaller than the number of points. " << std::endl; - std::cout << "(4) An real value p such that L^p distance is going to be computed. \n"; - std::cout << "(5) A dimension of persistence that is to be taken into account (positive integer) \n"; - std::cout << "(6) A maximal diameter to which complex is to be grown (positive integer) \n"; - std::cout << "(d) a quantile (real number between 0 and 1. If you do not know what to set, set it to 0.95." << std::endl; - if ( argc != 8 ) - { - std::cerr << "Wrong number of parameters, the program will now terminate.\n"; - return 1; - } - - const char* filename = argv[1]; - size_t number_of_repetitions_of_bootstrap = (size_t)atoi( argv[2] ); - size_t size_of_subsample = (size_t)atoi( argv[3] ); - double p = atoi( argv[4] ); - int dimension = atoi( argv[5] ); - double threshold = atof( argv[6] ); - double quantile = atof( argv[7] ); - - std::cout << "Now we will read points from the file : " << filename << " and then perform " << number_of_repetitions_of_bootstrap << " times the bootstrap on it by choosing subsample of a size " << size_of_subsample << std::endl; - - std::vector< std::vector< double > > points = Gudhi::Persistence_representations::read_numbers_from_file_line_by_line( filename ); - - std::cout << "Read : " << points.size() << " points.\n"; - - distance_between_landscapes distance( p );//L^p distance. - compute_persistence_landscape_of_a_point_cloud_in_certain_dimension characteristic_fun( points , dimension , threshold ); - - //and now we can run the real bootstrap. - //template < typename PointCloudCharacteristics , typename CharacteristicFunction , typename DistanceBetweenPointsCharacteristics > - //In this case, the PointCloudCharacteristics is just a vector of numbers of points (in a order fixed on points vector). - //CharacteristicFunction is just identity, transforming std::vector< size_t > to itself. - //DistanceBetweenPointsCharacteristics is the place were all happens. This class hace the information about the coordinates of the points, and allows to compute a Hausdorff distance between - //the collection of all points, and the subsample. - double result = Gudhi::Gudhi_stat::bootstrap< - Persistence_landscape , //PointCloudCharacteristics, persistence landascapes constructed based on vector of - //pairs of birth--death values in a cartain dimension. - compute_persistence_landscape_of_a_point_cloud_in_certain_dimension , //CharacteristicFunction, in this case, we will need to compute persistence in a certain dimension. - distance_between_landscapes //DistanceBetweenPointsCharacteristics. In this case - > - ( points.size() , characteristic_fun , distance , number_of_repetitions_of_bootstrap , size_of_subsample , quantile ); - - std::cout << "result of bootstrap : " << result << std::endl; - - - return 0; -} |