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/* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
* See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
* Author(s): Pawel Dlotko and Mathieu Carriere
*
* Copyright (C) 2019 Inria
*
* Modifications:
* - 2018/04 MC: Add persistence heat maps computation
*
* Modification(s):
* - YYYY/MM Author: Description of the modification
*/
#include <gudhi/Persistence_heat_maps.h>
#include <gudhi/common_persistence_representations.h>
#include <iostream>
#include <vector>
#include <utility>
#include <functional>
#include <cmath>
std::function<double(std::pair<double, double>, std::pair<double, double>)> Gaussian_function(double sigma) {
return [=](std::pair<double, double> p, std::pair<double, double> q) {
return std::exp(-((p.first - q.first) * (p.first - q.first) + (p.second - q.second) * (p.second - q.second)) /
(sigma));
};
}
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::clog << "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::clog << "Scalar product is : " << hm1.compute_scalar_product(hm2) << std::endl;
Persistence_heat_maps hm1k(persistence1, Gaussian_function(1.0));
Persistence_heat_maps hm2k(persistence2, Gaussian_function(1.0));
Persistence_heat_maps hm1i(persistence1, Gaussian_function(1.0), 20, 20, 0, 11, 0, 11);
Persistence_heat_maps hm2i(persistence2, Gaussian_function(1.0), 20, 20, 0, 11, 0, 11);
std::clog << "Scalar product computed with exact 2D kernel on grid is : " << hm1i.compute_scalar_product(hm2i)
<< std::endl;
std::clog << "Scalar product computed with exact 2D kernel is : " << hm1k.compute_scalar_product(hm2k) << std::endl;
return 0;
}
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