example_choose_n_farthest_points.cpp
#include <gudhi/choose_n_farthest_points.h>
#include <CGAL/Epick_d.h>
#include <CGAL/Random.h>
#include <iostream>
#include <vector>
#include <iterator>
int main(void) {
typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K;
typedef typename K::Point_d Point_d;
CGAL::Random rd;
std::vector<Point_d> points;
for (int i = 0; i < 500; ++i)
points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1),
rd.get_double(-1., 1), rd.get_double(-1., 1)));
K k;
std::vector<Point_d> results;
Gudhi::subsampling::choose_n_farthest_points(k.squared_distance_d_object(), points, 100,
std::back_inserter(results));
std::clog << "Before sparsification: " << points.size() << " points.\n";
std::clog << "After sparsification: " << results.size() << " points.\n";
return 0;
}
void choose_n_farthest_points(Distance dist, Point_range const &input_pts, std::size_t final_size, std::size_t starting_point, PointOutputIterator output_it, DistanceOutputIterator dist_it={})
Subsample by a greedy strategy of iteratively adding the farthest point from the current chosen point...
Definition: choose_n_farthest_points.h:69
@ random_starting_point
Definition: choose_n_farthest_points.h:34
GUDHI  Version 3.5.0  - C++ library for Topological Data Analysis (TDA) and Higher Dimensional Geometry Understanding.  - Copyright : MIT Generated on Thu Jan 13 2022 08:34:27 for GUDHI by Doxygen 1.9.2