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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
*
* 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/>.
*/
#include <gudhi/Hausdorff_distances.h>
#include <gudhi/bootstrap.h>
#include <gudhi/read_persitence_from_file.h>
#include <gudhi/persistence_representations/Vector_distances_in_diagram.h>
using namespace Gudhi;
using namespace Gudhi::Gudhi_stat;
int main( int argc , char** argv )
{
std::cout << "The parameters of this program are : " << std::endl;
std::cout << "(a) a name of a file with points," << std:: endl;
std::cout << "(b) a number of repetitions of bootstrap (integer)," << std::endl;
std::cout << "(c) a size of subsample (integer, smaller than the number of points," << std::endl;
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 != 5 )
{
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_subsampling = (size_t)atoi( argv[2] );
size_t size_of_subsample = (size_t)atoi( argv[3] );
double quantile = atof( argv[4] );
std::cout << "Now we will read points from the file : " << filename << " and then perform " << number_of_repetitions_of_subsampling << " times the subsampling on it by choosing subsample of a size " << size_of_subsample << std::endl;
std::vector< std::vector< double > > points = read_numbers_from_file_line_by_line( filename );
/*
std::vector< std::vector< double > > points;
std::vector< double > point1(2);
point1[0] = -1;
point1[1] = 0;
std::vector< double > point2(2);
point2[0] = 1;
point2[1] = 0;
std::vector< double > point3(2);
point3[0] = -1;
point3[1] = 3;
std::vector< double > point4(2);
point4[0] = 1;
point4[1] = 3;
points.push_back( point1 );
points.push_back( point2 );
points.push_back( point3 );
points.push_back( point4 );
std::vector< std::vector<double> > all_to_all_distance_matrix_between_points = compute_all_to_all_distance_matrix_between_points< std::vector<double> , Euclidean_distance<double> >( points );
Hausdorff_distance_between_subspace_and_the_whole_metric_space distance( all_to_all_distance_matrix_between_points );
std::vector<size_t> characteristic_of_all_points = {0,1,2,3};
std::vector<size_t> characteristic_of_subsampled_points = {2,3};
std::cerr << "DISTANCE BETWEEN SAMPLE AND SUBSAMPLE: " << distance( characteristic_of_subsampled_points , characteristic_of_all_points ) << std::endl;
*/
std::cout << "Read : " << points.size() << " points.\n";
//comute all-to-all distance matrix:
std::vector< std::vector<double> > all_to_all_distance_matrix_between_points = compute_all_to_all_distance_matrix_between_points< std::vector<double> , Euclidean_distance<double> >( points );
Hausdorff_distance_between_subspace_and_the_whole_metric_space distance( all_to_all_distance_matrix_between_points );
identity< std::vector<size_t> > identity_char;
//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 have 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 = bootstrap<
std::vector< size_t > , //PointCloudCharacteristics
identity< std::vector<size_t> > , //CharacteristicFunction
Hausdorff_distance_between_subspace_and_the_whole_metric_space //DistanceBetweenPointsCharacteristics. This function have the information about point's coordinates.
>
( points.size() , identity_char , distance , number_of_repetitions_of_subsampling , size_of_subsample , quantile );
std::cout << "result of the subsampling : " << result << std::endl;
return 0;
}
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