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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) 2015 INRIA Sophia Antipolis-Méditerranée (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 LANDMARK_CHOICE_BY_RANDOM_KNN_H_
#define LANDMARK_CHOICE_BY_RANDOM_KNN_H_
#include <utility> // for pair<>
#include <vector>
#include <cstddef> // for ptrdiff_t type
//#include <CGAL/Cartesian_d.h>
#include <CGAL/Search_traits.h>
#include <CGAL/Search_traits_adapter.h>
//#include <CGAL/property_map.h>
#include <CGAL/Epick_d.h>
#include <CGAL/Orthogonal_incremental_neighbor_search.h>
#include <CGAL/Orthogonal_k_neighbor_search.h>
#include <CGAL/Kd_tree.h>
#include <CGAL/Euclidean_distance.h>
//#include <CGAL/Kernel_d/Vector_d.h>
#include <CGAL/Random.h>
namespace Gudhi {
namespace witness_complex {
typedef CGAL::Epick_d<CGAL::Dynamic_dimension_tag> K;
typedef K::FT FT;
typedef K::Point_d Point_d;
typedef CGAL::Search_traits< FT,
Point_d,
typename K::Cartesian_const_iterator_d,
typename K::Construct_cartesian_const_iterator_d > Traits_base;
typedef CGAL::Euclidean_distance<Traits_base> Euclidean_distance;
typedef CGAL::Search_traits_adapter< std::ptrdiff_t,
Point_d*,
Traits_base> STraits;
typedef CGAL::Distance_adapter< std::ptrdiff_t,
Point_d*,
Euclidean_distance > Distance_adapter;
typedef CGAL::Orthogonal_incremental_neighbor_search< STraits,
Distance_adapter > Neighbor_search;
typedef CGAL::Orthogonal_k_neighbor_search< STraits > Neighbor_search2;
typedef Neighbor_search::Tree Tree;
/** \brief Landmark choice strategy by taking random vertices for landmarks.
* \details It chooses nbL distinct landmarks from a random access range `points`
* and outputs a matrix {witness}*{closest landmarks} in knn.
*/
template <typename KNearestNeighbours,
typename Point_random_access_range,
typename Distance_matrix>
void landmark_choice_by_random_knn(Point_random_access_range const & points,
int nbL,
FT alpha,
unsigned limD,
KNearestNeighbours & knn,
Distance_matrix & distances) {
int nbP = points.end() - points.begin();
assert(nbP >= nbL);
std::vector<Point_d> landmarks;
std::vector<int> landmarks_ind;
Point_d p;
int chosen_landmark;
CGAL::Random rand;
// TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety
int current_number_of_landmarks = 0; // counter for landmarks
for (; current_number_of_landmarks != nbL; current_number_of_landmarks++) {
do chosen_landmark = rand.get_int(0,nbP);
while (std::find(landmarks_ind.begin(), landmarks_ind.end(), chosen_landmark) != landmarks_ind.end());
p = points[chosen_landmark];
landmarks.push_back(p);
landmarks_ind.push_back(chosen_landmark);
}
// std::cout << "Choice finished!" << std::endl;
//int dim = points.begin()->size();
knn = KNearestNeighbours(nbP);
distances = Distance_matrix(nbP);
STraits traits(&(landmarks[0]));
CGAL::Distance_adapter<std::ptrdiff_t,Point_d*,Euclidean_distance> adapter(&(landmarks[0]));
Euclidean_distance ed;
Tree landmark_tree(boost::counting_iterator<std::ptrdiff_t>(0),
boost::counting_iterator<std::ptrdiff_t>(nbL),
typename Tree::Splitter(),
traits);
for (int points_i = 0; points_i < nbP; points_i++) {
Point_d const & w = points[points_i];
Neighbor_search search(landmark_tree,
w,
FT(0),
true,
adapter);
Neighbor_search::iterator search_it = search.begin();
// Neighbor_search2 search(landmark_tree,
// w, limD+1,
// FT(0),
// true,
// adapter);
// Neighbor_search2::iterator search_it = search.begin();
while (knn[points_i].size() < limD) {
distances[points_i].push_back(sqrt(search_it->second));
knn[points_i].push_back((search_it++)->first);
}
FT dtow = distances[points_i][limD-1];
if (alpha != 0)
while (search_it != search.end() && search_it->second < dtow + alpha) {
distances[points_i].push_back(sqrt(search_it->second));
knn[points_i].push_back((search_it++)->first);
}
//std::cout << "k = " << knn[points_i].size() << std::endl;
}
}
} // namespace witness_complex
} // namespace Gudhi
#endif // LANDMARK_CHOICE_BY_RANDOM_POINT_H_
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