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author | Gard Spreemann <gspr@nonempty.org> | 2019-09-25 14:53:36 +0200 |
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committer | Gard Spreemann <gspr@nonempty.org> | 2019-09-25 14:53:36 +0200 |
commit | 70ad5fc411b0e0a3d590ad281fc97d488994062b (patch) | |
tree | b0ddd84d7ee935936787eee2b75f78f2e21c41f0 /src/Subsampling/example/example_custom_kernel.cpp | |
parent | b50046e487ab42cdef19b02128a9f498d6a36482 (diff) | |
parent | 5ccee32ec2ba38743c6b96867db3e1b5151e45e4 (diff) |
Merge branch 'dfsg/latest' into debian/sid
Diffstat (limited to 'src/Subsampling/example/example_custom_kernel.cpp')
-rw-r--r-- | src/Subsampling/example/example_custom_kernel.cpp | 63 |
1 files changed, 63 insertions, 0 deletions
diff --git a/src/Subsampling/example/example_custom_kernel.cpp b/src/Subsampling/example/example_custom_kernel.cpp new file mode 100644 index 00000000..f1eb757b --- /dev/null +++ b/src/Subsampling/example/example_custom_kernel.cpp @@ -0,0 +1,63 @@ +#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::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; +} |