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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) 2016 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/Persistence_heat_maps.h>
#include <iostream>
#include <vector>
#include <utility>
using constant_scaling_function = Gudhi::Persistence_representations::constant_scaling_function;
using Persistence_heat_maps = Gudhi::Persistence_representations::Persistence_heat_maps<constant_scaling_function>;
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 = Gudhi::Persistence_representations::create_Gaussian_filter(5, 1);
// creating two heat maps.
Persistence_heat_maps hm1(persistence1, filter, false, 20, 0, 11);
Persistence_heat_maps hm2(persistence2, filter, false, 20, 0, 11);
std::vector<Persistence_heat_maps*> 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 mean;
mean.compute_mean(vector_of_maps);
Persistence_heat_maps 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*> to_average;
to_average.push_back(&hm1);
to_average.push_back(&hm2);
Persistence_heat_maps 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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