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diff --git a/include/gudhi/Landmark_choice_by_furthest_point.h b/include/gudhi/Landmark_choice_by_furthest_point.h
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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_FURTHEST_POINT_H_
-#define LANDMARK_CHOICE_BY_FURTHEST_POINT_H_
-
-#include <boost/range/size.hpp>
-
-#include <limits> // for numeric_limits<>
-#include <iterator>
-#include <algorithm> // for sort
-#include <vector>
-
-namespace Gudhi {
-
-namespace witness_complex {
-
- typedef std::vector<int> typeVectorVertex;
-
- /**
- * \ingroup witness_complex
- * \brief Landmark choice strategy by iteratively adding the furthest witness from the
- * current landmark set as the new landmark.
- * \details It chooses nbL landmarks from a random access range `points` and
- * writes {witness}*{closest landmarks} matrix 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
- *
- */
-
- template <typename KNearestNeighbours,
- typename Point_random_access_range>
- void landmark_choice_by_furthest_point(Point_random_access_range const &points,
- int nbL,
- KNearestNeighbours &knn) {
- int nb_points = boost::size(points);
- assert(nb_points >= nbL);
- // distance matrix witness x landmarks
- std::vector<std::vector<double>> wit_land_dist(nb_points, std::vector<double>());
- // landmark list
- typeVectorVertex chosen_landmarks;
-
- knn = KNearestNeighbours(nb_points, std::vector<int>());
- 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
-
- // TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety
- // or better yet std::uniform_int_distribution
- int rand_int = rand() % nb_points;
- int curr_max_w = rand_int; // For testing purposes a pseudo-random number is used here
-
- for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) {
- // curr_max_w at this point is the next landmark
- chosen_landmarks.push_back(curr_max_w);
- unsigned i = 0;
- for (auto& p : points) {
- double curr_dist = euclidean_distance(p, *(std::begin(points) + chosen_landmarks[current_number_of_landmarks]));
- wit_land_dist[i].push_back(curr_dist);
- knn[i].push_back(current_number_of_landmarks);
- if (curr_dist < dist_to_L[i])
- dist_to_L[i] = curr_dist;
- ++i;
- }
- 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;
- }
- }
- for (int i = 0; i < nb_points; ++i)
- std::sort(std::begin(knn[i]),
- std::end(knn[i]),
- [&wit_land_dist, i](int a, int b) {
- return wit_land_dist[i][a] < wit_land_dist[i][b]; });
- }
-
-} // namespace witness_complex
-
-} // namespace Gudhi
-
-#endif // LANDMARK_CHOICE_BY_FURTHEST_POINT_H_