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author | Gard Spreemann <gspr@nonempty.org> | 2019-09-25 14:29:41 +0200 |
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committer | Gard Spreemann <gspr@nonempty.org> | 2019-09-25 14:29:41 +0200 |
commit | 599d68cd916f533bdb66dd9e684dd5703233b6bb (patch) | |
tree | 4b825dc642cb6eb9a060e54bf8d69288fbee4904 /example/Subsampling/example_custom_kernel.cpp | |
parent | a2e642954ae39025e041471d486ecbac25dff440 (diff) |
Delete all files in order to incorporate upstream's move to git.
Diffstat (limited to 'example/Subsampling/example_custom_kernel.cpp')
-rw-r--r-- | example/Subsampling/example_custom_kernel.cpp | 65 |
1 files changed, 0 insertions, 65 deletions
diff --git a/example/Subsampling/example_custom_kernel.cpp b/example/Subsampling/example_custom_kernel.cpp deleted file mode 100644 index 2d42bdde..00000000 --- a/example/Subsampling/example_custom_kernel.cpp +++ /dev/null @@ -1,65 +0,0 @@ -#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; -} |