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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_BY_FARTHEST_POINT_H_
#define CHOOSE_BY_FARTHEST_POINT_H_
#include <boost/range.hpp>
#include <gudhi/Spatial_tree_data_structure.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>
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.
* \details It chooses `final_size` points from a random access range `points` and
* outputs it in the output iterator `output_it`.
*
*/
template < typename Kernel,
typename Point_container,
typename OutputIterator>
void choose_by_farthest_point_old( Kernel& k,
Point_container const &points,
int final_size,
int starting_point,
OutputIterator output_it)
{
typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
int nb_points = boost::size(points);
assert(nb_points >= final_size);
int current_number_of_landmarks = 0; // counter for landmarks
double curr_max_dist = 0; // used for defining the furhest point from L
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 points
int 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++ = points[curr_max_w];
// std::cout << curr_max_w << "\n";
unsigned i = 0;
for (auto& p : points) {
double curr_dist = sqdist(p, *(std::begin(points) + curr_max_w));
if (curr_dist < dist_to_L[i])
dist_to_L[i] = curr_dist;
++i;
}
// choose the next curr_max_w
curr_max_dist = 0;
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;
}
}
}
template < typename Kernel,
typename Point_container,
typename OutputIterator>
void choose_by_farthest_point_old( Kernel& k,
Point_container const &points,
int 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_by_farthest_point_old(k, points, final_size, starting_point, output_it);
}
template < typename Kernel,
typename Point_container,
typename OutputIterator>
void choose_by_farthest_point( Kernel& k,
Point_container const &points,
int final_size,
int starting_point,
OutputIterator output_it)
{
// typedef typename Kernel::Point_d Point_d;
// typedef typename Kernel::FT FT;
// typedef CGAL::Search_traits<
// FT, Point_d,
// typename Kernel::Cartesian_const_iterator_d,
// typename Kernel::Construct_cartesian_const_iterator_d> Traits_base;
// typedef CGAL::Search_traits_adapter< std::ptrdiff_t, Point_d*, Traits_base > STraits;
// typedef CGAL::Fuzzy_sphere< STraits > Fuzzy_sphere;
typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object();
int nb_points = boost::size(points);
assert(nb_points >= final_size);
Clock t;
Gudhi::spatial_searching::Spatial_tree_data_structure< Kernel, Point_container> tree(points);
t.end();
//std::cout << "Constructed the Kd tree: " << t.num_seconds() << " s." << std::endl;
//CGAL::Fuzzy_sphere< CGAL::Search_trai>
int current_number_of_landmarks = 0; // counter for landmarks
const double infty = std::numeric_limits<double>::infinity(); // infinity (see next entry)
double curr_max_dist = infty; // used for defining the furhest point from L
std::vector< double > dist_to_L(nb_points, infty); // vector of current distances to L from points
// Choose randomly the first landmark
int 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++ = points[curr_max_w];
// std::cout << curr_max_w << "\n";
//for (auto& p : points) {
auto search = tree.query_incremental_ANN(points[curr_max_w]);
auto search_it = search.begin();
while (search_it != search.end() && search_it->second <= curr_max_dist ) {
//std::cout << search_it->second << " " << curr_max_dist << "\n";
if (dist_to_L[search_it->first] > search_it->second)
dist_to_L[search_it->first] = search_it->second;
search_it++;
}
// choose the next curr_max_w
curr_max_dist = 0;
for (unsigned 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;
}
}
}
template < typename Kernel,
typename Point_container,
typename OutputIterator>
void choose_by_farthest_point( Kernel& k,
Point_container const &points,
int 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_by_farthest_point_old(k, points, final_size, starting_point, output_it);
}
} // namespace subsampling
} // namespace Gudhi
#endif // CHOOSE_BY_FARTHEST_POINT_H_
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