/* 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 .
*/
#ifndef LANDMARK_CHOICE_BY_FURTHEST_POINT_H_
#define LANDMARK_CHOICE_BY_FURTHEST_POINT_H_
#include // for numeric_limits<>
#include // for sort
#include
namespace Gudhi {
namespace witness_complex {
/**
* \class Landmark_choice_by_furthest_point
* \brief The class `Landmark_choice_by_furthest_point` allows to construct the matrix
* of closest landmarks per witness by iteratively choosing the furthest witness
* from the set of already chosen landmarks as the new landmark.
* \ingroup witness_complex
*/
class Landmark_choice_by_furthest_point {
private:
typedef std::vector typeVectorVertex;
public:
/**
* \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`.
*/
template
Landmark_choice_by_furthest_point(Point_random_access_range const &points,
int nbL,
KNearestNeighbours &knn) {
int nb_points = points.end() - points.begin();
assert(nb_points >= nbL);
// distance matrix witness x landmarks
std::vector> wit_land_dist(nb_points, std::vector());
// landmark list
typeVectorVertex chosen_landmarks;
knn = KNearestNeighbours(nb_points, std::vector());
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::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
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, *(points.begin() + 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 (unsigned i = 0; i < points.size(); ++i)
std::sort(knn[i].begin(),
knn[i].end(),
[&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_