/*    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;
}