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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 GUDHI_LANDMARK_CHOICE_BY_FURTHEST_POINT_H_
#define GUDHI_LANDMARK_CHOICE_BY_FURTHEST_POINT_H_
/**
* \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_random_point {
/**
* \brief Landmark choice strategy by iteratively adding the furthest witness from the
* current landmark set as the new landmark. It takes a random access range `points` and
* writes {witness}*{closest landmarks} matrix in `knn`.
*/
template <typename KNearestNeighbours,
typename Point_random_access_range>
Landmark_choice_by_furthest_points(Point_random_access_range &points,
KNearestNeighbours &knn)
{
int nb_points = points.end() - points.begin();
std::vector<std::vector<double>> wit_land_dist(nb_points, std::vector<double>()); // distance matrix witness x landmarks
typeVectorVertex chosen_landmarks; // landmark list
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
//CHOICE OF THE FIRST LANDMARK
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);
for (auto v: knn)
v.push_back(current_number_of_landmarks);
int i = 0;
for (const auto& p: points)
{
// used to stock the distance from the current point to L
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;
int j = current_number_of_landmarks;
while (j > 0 && wit_land_dist[i][j-1] > wit_land_dist[i][j])
{
std::swap(knn[i][j], knn[i][j-1]);
std::swap(wit_land_dist[i][j-1], wit_land_dist[i][j-1]);
--j;
}
++i;
}
curr_max_dist = 0;
for (auto dist: dist_to_L) {
if (dist > curr_max_dist)
{
curr_max_dist = dist;
curr_max_w = i;
}
}
}
}
};
#endif
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