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#include <gudhi/choose_n_farthest_points.h>
#include <iostream>
#include <vector>
#include <iterator>
/* The class Kernel 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 Kernel {
public:
typedef double FT;
typedef unsigned Point_d;
// Class Squared_distance_d
class Squared_distance_d {
private:
std::vector<std::vector<FT>> matrix_;
public:
Squared_distance_d() {
matrix_.push_back(std::vector<FT>({0, 1, 2, 4}));
matrix_.push_back(std::vector<FT>({1, 0, 4, 2}));
matrix_.push_back(std::vector<FT>({2, 4, 0, 1}));
matrix_.push_back(std::vector<FT>({4, 2, 1, 0}));
}
FT operator()(Point_d p1, Point_d p2) {
return matrix_[p1][p2];
}
};
// Constructor
Kernel() {}
// Object of type Squared_distance_d
Squared_distance_d squared_distance_d_object() const {
return Squared_distance_d();
}
};
int main(void) {
typedef Kernel K;
typedef typename K::Point_d Point_d;
K k;
std::vector<Point_d> points = {0, 1, 2, 3};
std::vector<Point_d> results;
Gudhi::subsampling::choose_n_farthest_points(k, 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";
return 0;
}
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