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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/reader_utils.h>
#include <gudhi/Persistence_heat_maps.h>
#include <iostream>
#include <vector>
using namespace Gudhi;
using namespace Gudhi::Persistence_representations;
double epsilon = 0.0000005;
int main( int argc , char** argv )
{
//create two simple vectors with birth--death pairs:
std::vector< std::pair< double , double > > persistence1;
std::vector< std::pair< double , double > > persistence2;
persistence1.push_back( std::make_pair(1,2) );
persistence1.push_back( std::make_pair(6,8) );
persistence1.push_back( std::make_pair(0,4) );
persistence1.push_back( std::make_pair(3,8) );
persistence2.push_back( std::make_pair(2,9) );
persistence2.push_back( std::make_pair(1,6) );
persistence2.push_back( std::make_pair(3,5) );
persistence2.push_back( std::make_pair(6,10) );
//over here we define a function we sill put on a top on every birth--death pair in the persistence interval. It can be anything. Over here we will use standard Gaussian
std::vector< std::vector<double> > filter = create_Gaussian_filter(5,1);
//creating two heat maps.
Persistence_heat_maps<constant_scaling_function> hm1( persistence1 , filter , false , 20 , 0 , 11 );
Persistence_heat_maps<constant_scaling_function> hm2( persistence2 , filter , false , 20 , 0 , 11 );
std::vector<Persistence_heat_maps<constant_scaling_function>*> vector_of_maps;
vector_of_maps.push_back( &hm1 );
vector_of_maps.push_back( &hm2 );
//compute median/mean of a vector of heat maps:
Persistence_heat_maps<constant_scaling_function> mean;
mean.compute_mean( vector_of_maps );
Persistence_heat_maps<constant_scaling_function> median;
median.compute_median( vector_of_maps );
//to compute L^1 distance between hm1 and hm2:
std::cout << "The L^1 distance is : " << hm1.distance( hm2 , 1 ) << std::endl;
//to average of hm1 and hm2:
std::vector< Persistence_heat_maps<constant_scaling_function>* > to_average;
to_average.push_back( &hm1 );
to_average.push_back( &hm2 );
Persistence_heat_maps<constant_scaling_function> av;
av.compute_average( to_average );
//to compute scalar product of hm1 and hm2:
std::cout << "Scalar product is : " << hm1.compute_scalar_product( hm2 ) << std::endl;
return 0;
}
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