1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
|
#include <gudhi/choose_n_farthest_points.h>
#include <CGAL/Epick_d.h>
#include <CGAL/Random.h>
#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, std::back_inserter(results));
std::cout << "Before sparsification: " << points.size() << " points.\n";
std::cout << "After sparsification: " << results.size() << " points.\n";
std::cout << "Result table: {" << results[0] << "," << results[1] << "}\n";
return 0;
}
|
