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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_
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