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example_custom_distance.cpp
#include <gudhi/choose_n_farthest_points.h>
#include <iostream>
#include <vector>
#include <iterator>
typedef unsigned Point;
/* The class Distance contains a distance function defined on the set of points {0, 1, 2, 3}
* and computes a distance according to the matrix:
* 0 1 2 4
* 1 0 4 2
* 2 4 0 1
* 4 2 1 0
*/
class Distance {
private:
std::vector<std::vector<double>> matrix_;
public:
Distance() {
matrix_.push_back({0, 1, 2, 4});
matrix_.push_back({1, 0, 4, 2});
matrix_.push_back({2, 4, 0, 1});
matrix_.push_back({4, 2, 1, 0});
}
double operator()(Point p1, Point p2) const {
return matrix_[p1][p2];
}
};
int main(void) {
std::vector<Point> points = {0, 1, 2, 3};
std::vector<Point> results;
Gudhi::subsampling::choose_n_farthest_points(Distance(), points, 2,
Gudhi::subsampling::random_starting_point,
std::back_inserter(results));
std::clog << "Before sparsification: " << points.size() << " points.\n";
std::clog << "After sparsification: " << results.size() << " points.\n";
std::clog << "Result table: {" << results[0] << "," << results[1] << "}\n";
}
Gudhi::subsampling::choose_n_farthest_points
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 an iterative, greedy strategy.
Definition choose_n_farthest_points.h:75
Gudhi::subsampling::random_starting_point
@ random_starting_point
Definition choose_n_farthest_points.h:43