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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_POINT_H_
#define LANDMARK_CHOICE_BY_RANDOM_POINT_H_
#include <boost/range/size.hpp>
#include <queue> // for priority_queue<>
#include <utility> // for pair<>
#include <iterator>
#include <vector>
#include <set>
namespace Gudhi {
namespace witness_complex {
/**
* \ingroup witness_complex
* \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.
*
* The type KNearestNeighbors can be seen as
* Witness_range<Closest_landmark_range<Vertex_handle>>, where
* Witness_range and Closest_landmark_range are random access ranges and
* Vertex_handle is the label type of a vertex in a simplicial complex.
* Closest_landmark_range needs to have push_back operation.
*/
template <typename KNearestNeighbours,
typename Point_random_access_range>
void landmark_choice_by_random_point(Point_random_access_range const &points,
int nbL,
KNearestNeighbours &knn) {
int nbP = boost::size(points);
assert(nbP >= nbL);
std::set<int> landmarks;
int current_number_of_landmarks = 0; // counter for landmarks
// TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety
int chosen_landmark = rand() % nbP;
for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) {
while (landmarks.find(chosen_landmark) != landmarks.end())
chosen_landmark = rand() % nbP;
landmarks.insert(chosen_landmark);
}
int dim = boost::size(*std::begin(points));
typedef std::pair<double, int> dist_i;
typedef bool (*comp)(dist_i, dist_i);
knn = KNearestNeighbours(nbP);
for (int points_i = 0; points_i < nbP; points_i++) {
std::priority_queue<dist_i, std::vector<dist_i>, comp> l_heap([](dist_i j1, dist_i j2) {
return j1.first > j2.first;
});
std::set<int>::iterator landmarks_it;
int landmarks_i = 0;
for (landmarks_it = landmarks.begin(), landmarks_i = 0; landmarks_it != landmarks.end();
++landmarks_it, landmarks_i++) {
dist_i dist = std::make_pair(euclidean_distance(points[points_i], points[*landmarks_it]), landmarks_i);
l_heap.push(dist);
}
for (int i = 0; i < dim + 1; i++) {
dist_i dist = l_heap.top();
knn[points_i].push_back(dist.second);
l_heap.pop();
}
}
}
} // namespace witness_complex
} // namespace Gudhi
#endif // LANDMARK_CHOICE_BY_RANDOM_POINT_H_
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