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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): Mathieu Carriere
*
* Copyright (C) 2018 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/>.
*/
#ifndef LANDSCAPE_H_
#define LANDSCAPE_H_
// gudhi include
#include <gudhi/read_persistence_from_file.h>
#include <gudhi/common_persistence_representations.h>
#include <gudhi/Debug_utils.h>
// standard include
#include <cmath>
#include <iostream>
#include <vector>
#include <limits>
#include <fstream>
#include <sstream>
#include <algorithm>
#include <string>
#include <utility>
#include <functional>
namespace Gudhi {
namespace Persistence_representations {
/**
* \class Persistence_landscape_on_grid_exact gudhi/Persistence_landscape_on_grid_exact.h
* \brief A class implementing exact persistence landscapes by approximating them on a collection of grid points
*
* \ingroup Persistence_representations
*
* \details
* In this class, we propose a way to approximate landscapes by sampling the x-axis of the persistence diagram and evaluating the (exact) landscape functions on the sample projections onto the diagonal. Note that this is a different approximation scheme
* from the one proposed in Persistence_landscape_on_grid, which puts a grid on the diagonal, maps the persistence intervals on this grid and computes the (approximate) landscape functions on the samples.
* Hence, the difference is that we do not modify the diagram in this implementation, but the code can be slower to run.
**/
class Persistence_landscape_on_grid_exact {
protected:
Persistence_diagram diagram;
int res_x, nb_ls;
double min_x, max_x;
public:
/** \brief Persistence_landscape_on_grid_exact constructor.
* \ingroup Persistence_landscape_on_grid_exact
*
* @param[in] _diagram persistence diagram.
* @param[in] _nb_ls number of landscape functions.
* @param[in] _min_x minimum value of samples.
* @param[in] _max_x maximum value of samples.
* @param[in] _res_x number of samples.
*
*/
Persistence_landscape_on_grid_exact(const Persistence_diagram & _diagram, int _nb_ls = 5, double _min_x = 0.0, double _max_x = 1.0, int _res_x = 10){diagram = _diagram; nb_ls = _nb_ls; min_x = _min_x; max_x = _max_x; res_x = _res_x;}
/** \brief Computes the landscape approximation of a diagram.
* \ingroup Persistence_landscape_on_grid_exact
*
*/
std::vector<std::vector<double> > vectorize() const {
std::vector<std::vector<double> > ls; for(int i = 0; i < nb_ls; i++) ls.emplace_back();
int num_pts = diagram.size(); double step = (max_x - min_x)/(res_x - 1);
std::vector<std::vector<double> > ls_t; for(int i = 0; i < res_x; i++) ls_t.emplace_back();
for(int j = 0; j < num_pts; j++){
double px = diagram[j].first; double py = diagram[j].second; double mid = (px+py)/2;
int first = std::ceil((px-min_x)/step); int middle = std::ceil((mid-min_x)/step); int last = std::ceil((py-min_x)/step); double x = min_x + first*step;
for(int i = first; i < middle; i++){ double value = std::sqrt(2)*(x-px); ls_t[i].push_back(value); x += step; }
for(int i = middle; i < last; i++){ double value = std::sqrt(2)*(py-x); ls_t[i].push_back(value); x += step; }
}
for(int i = 0; i < res_x; i++){
std::sort(ls_t[i].begin(), ls_t[i].end(), [](const double & a, const double & b){return a > b;});
int nb_events_i = ls_t[i].size();
for (int j = 0; j < nb_ls; j++){ if(j < nb_events_i) ls[j].push_back(ls_t[i][j]); else ls[j].push_back(0); }
}
return ls;
}
}; // class Persistence_landscape_on_grid_exact
} // namespace Persistence_representations
} // namespace Gudhi
#endif // LANDSCAPE_H_
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