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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/>.
*/
#ifndef PSSK_H_
#define PSSK_H_
// gudhi include
#include <gudhi/Persistence_heat_maps.h>
#include <limits>
#include <utility>
#include <vector>
namespace Gudhi {
namespace Persistence_representations {
/**
* This is a version of a representation presented in https://arxiv.org/abs/1412.6821
* In that paper the authors are using the representation just to compute kernel. Over here, we extend the usability by
*far.
* Note that the version presented here is not exact, since we are discretizing the kernel.
* The only difference with respect to the original class is the method of creation. We have full (square) image, and for
*every point (p,q), we add a kernel at (p,q) and the negative kernel
* at (q,p)
**/
class PSSK : public Persistence_heat_maps<constant_scaling_function> {
public:
PSSK() : Persistence_heat_maps() {}
PSSK(const std::vector<std::pair<double, double> >& interval,
std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1), size_t number_of_pixels = 1000,
double min_ = -1, double max_ = -1)
: Persistence_heat_maps() {
this->construct(interval, filter, number_of_pixels, min_, max_);
}
PSSK(const char* filename, std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1),
size_t number_of_pixels = 1000, double min_ = -1, double max_ = -1,
unsigned dimension = std::numeric_limits<unsigned>::max())
: Persistence_heat_maps() {
std::vector<std::pair<double, double> > intervals_;
if (dimension == std::numeric_limits<unsigned>::max()) {
intervals_ = read_persistence_intervals_in_one_dimension_from_file(filename);
} else {
intervals_ = read_persistence_intervals_in_one_dimension_from_file(filename, dimension);
}
this->construct(intervals_, filter, number_of_pixels, min_, max_);
}
protected:
void construct(const std::vector<std::pair<double, double> >& intervals_,
std::vector<std::vector<double> > filter = create_Gaussian_filter(5, 1),
size_t number_of_pixels = 1000, double min_ = -1, double max_ = -1);
};
// if min_ == max_, then the program is requested to set up the values itself based on persistence intervals
void PSSK::construct(const std::vector<std::pair<double, double> >& intervals_,
std::vector<std::vector<double> > filter, size_t number_of_pixels, double min_, double max_) {
bool dbg = false;
if (dbg) {
std::cerr << "Entering construct procedure \n";
getchar();
}
if (min_ == max_) {
// in this case, we want the program to set up the min_ and max_ values by itself.
min_ = std::numeric_limits<int>::max();
max_ = -std::numeric_limits<int>::max();
for (size_t i = 0; i != intervals_.size(); ++i) {
if (intervals_[i].first < min_) min_ = intervals_[i].first;
if (intervals_[i].second > max_) max_ = intervals_[i].second;
}
// now we have the structure filled in, and moreover we know min_ and max_ values of the interval, so we know the
// range.
// add some more space:
min_ -= fabs(max_ - min_) / 100;
max_ += fabs(max_ - min_) / 100;
}
if (dbg) {
std::cerr << "min_ : " << min_ << std::endl;
std::cerr << "max_ : " << max_ << std::endl;
std::cerr << "number_of_pixels : " << number_of_pixels << std::endl;
getchar();
}
this->min_ = min_;
this->max_ = max_;
// initialization of the structure heat_map
std::vector<std::vector<double> > heat_map_;
for (size_t i = 0; i != number_of_pixels; ++i) {
std::vector<double> v(number_of_pixels, 0);
heat_map_.push_back(v);
}
this->heat_map = heat_map_;
if (dbg) std::cerr << "Done creating of the heat map, now we will fill in the structure \n";
for (size_t pt_nr = 0; pt_nr != intervals_.size(); ++pt_nr) {
// compute the value of intervals_[pt_nr] in the grid:
int x_grid =
static_cast<int>((intervals_[pt_nr].first - this->min_) / (this->max_ - this->min_) * number_of_pixels);
int y_grid =
static_cast<int>((intervals_[pt_nr].second - this->min_) / (this->max_ - this->min_) * number_of_pixels);
if (dbg) {
std::cerr << "point : " << intervals_[pt_nr].first << " , " << intervals_[pt_nr].second << std::endl;
std::cerr << "x_grid : " << x_grid << std::endl;
std::cerr << "y_grid : " << y_grid << std::endl;
}
// x_grid and y_grid gives a center of the kernel. We want to have its lower left corner. To get this, we need to
// shift x_grid and y_grid by a grid diameter.
x_grid -= filter.size() / 2;
y_grid -= filter.size() / 2;
// note that the numbers x_grid and y_grid may be negative.
if (dbg) {
std::cerr << "After shift : \n";
std::cerr << "x_grid : " << x_grid << std::endl;
std::cerr << "y_grid : " << y_grid << std::endl;
std::cerr << "filter.size() : " << filter.size() << std::endl;
getchar();
}
for (size_t i = 0; i != filter.size(); ++i) {
for (size_t j = 0; j != filter.size(); ++j) {
// if the point (x_grid+i,y_grid+j) is the correct point in the grid.
if (((x_grid + i) >= 0) && (x_grid + i < this->heat_map.size()) && ((y_grid + j) >= 0) &&
(y_grid + j < this->heat_map.size())) {
if (dbg) {
std::cerr << y_grid + j << " " << x_grid + i << std::endl;
}
this->heat_map[y_grid + j][x_grid + i] += filter[i][j];
this->heat_map[x_grid + i][y_grid + j] += -filter[i][j];
}
}
}
}
} // construct
} // namespace Persistence_representations
} // namespace Gudhi
#endif // PSSK_H_
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