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author | Gard Spreemann <gspreemann@gmail.com> | 2017-04-20 11:15:58 +0200 |
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committer | Gard Spreemann <gspreemann@gmail.com> | 2017-04-20 11:15:58 +0200 |
commit | eadd3e18b55fc3b7a7d0420015902df2d58dcea5 (patch) | |
tree | ce025060ea9045415b1f738886c8c70ed32218e8 /example/Subsampling/example_custom_kernel.cpp | |
parent | 5638527781e1d8cd916cd28f9d375eef7b5d820b (diff) | |
parent | 8d7329f3e5ad843e553c3c5503cecc28ef2eead6 (diff) |
Merge tag 'upstream/2.0.0' into dfsg/latest
Upstream's 2.0.0 release.
Diffstat (limited to 'example/Subsampling/example_custom_kernel.cpp')
-rw-r--r-- | example/Subsampling/example_custom_kernel.cpp | 65 |
1 files changed, 65 insertions, 0 deletions
diff --git a/example/Subsampling/example_custom_kernel.cpp b/example/Subsampling/example_custom_kernel.cpp new file mode 100644 index 00000000..2d42bdde --- /dev/null +++ b/example/Subsampling/example_custom_kernel.cpp @@ -0,0 +1,65 @@ +#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, + Gudhi::subsampling::random_starting_point, + 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; +} |