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Diffstat (limited to 'src/Gudhi_stat/example/persistence_heat_maps.cpp')
| -rw-r--r-- | src/Gudhi_stat/example/persistence_heat_maps.cpp | 93 |
1 files changed, 93 insertions, 0 deletions
diff --git a/src/Gudhi_stat/example/persistence_heat_maps.cpp b/src/Gudhi_stat/example/persistence_heat_maps.cpp new file mode 100644 index 00000000..da87486d --- /dev/null +++ b/src/Gudhi_stat/example/persistence_heat_maps.cpp @@ -0,0 +1,93 @@ +/* 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 standarg 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 disance 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; +} + + + + |