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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): Siargey Kachanovich
*
* Copyright (C) 2016 INRIA
*
* 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 CHOOSE_N_FARTHEST_POINTS_H_
#define CHOOSE_N_FARTHEST_POINTS_H_
#include <boost/range.hpp>
#include <gudhi/Kd_tree_search.h>
#include <gudhi/Clock.h>
#include <CGAL/Search_traits.h>
#include <CGAL/Search_traits_adapter.h>
#include <CGAL/Fuzzy_sphere.h>
#include <iterator>
#include <algorithm> // for sort
#include <vector>
#include <random>
#include <limits> // for numeric_limits<>
namespace Gudhi {
namespace subsampling {
/**
* \ingroup subsampling
* \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
* current chosen point set to the subsampling.
* The iteration starts with the landmark `starting point`.
* \details It chooses `final_size` points from a random access range `input_pts` and
* outputs it in the output iterator `output_it`.
*
*/
template < typename Kernel,
typename Point_container,
typename OutputIterator>
void choose_n_farthest_points(Kernel const &k,
Point_container const &input_pts,
std::size_t final_size,
std::size_t starting_point,
OutputIterator output_it) {
typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
std::size_t nb_points = boost::size(input_pts);
assert(nb_points >= final_size);
std::size_t current_number_of_landmarks = 0; // counter for landmarks
const double infty = std::numeric_limits<double>::infinity(); // infinity (see next entry)
std::vector< double > dist_to_L(nb_points, infty); // vector of current distances to L from input_pts
std::size_t curr_max_w = starting_point;
for (current_number_of_landmarks = 0; current_number_of_landmarks != final_size; current_number_of_landmarks++) {
// curr_max_w at this point is the next landmark
*output_it++ = input_pts[curr_max_w];
std::size_t i = 0;
for (auto& p : input_pts) {
double curr_dist = sqdist(p, *(std::begin(input_pts) + curr_max_w));
if (curr_dist < dist_to_L[i])
dist_to_L[i] = curr_dist;
++i;
}
// choose the next curr_max_w
double curr_max_dist = 0; // used for defining the furhest point from L
for (i = 0; i < dist_to_L.size(); i++)
if (dist_to_L[i] > curr_max_dist) {
curr_max_dist = dist_to_L[i];
curr_max_w = i;
}
}
}
/**
* \ingroup subsampling
* \brief Subsample by a greedy strategy of iteratively adding the farthest point from the
* current chosen point set to the subsampling.
* The iteration starts with a random landmark.
* \details It chooses `final_size` points from a random access range `input_pts` and
* outputs it in the output iterator `output_it`.
*
*/
template < typename Kernel,
typename Point_container,
typename OutputIterator>
void choose_n_farthest_points(Kernel const& k,
Point_container const &input_pts,
unsigned final_size,
OutputIterator output_it) {
// Choose randomly the first landmark
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<> dis(1, 6);
int starting_point = dis(gen);
choose_n_farthest_points(k, input_pts, final_size, starting_point, output_it);
}
} // namespace subsampling
} // namespace Gudhi
#endif // CHOOSE_N_FARTHEST_POINTS_H_
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