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author | skachano <skachano@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2016-12-14 18:08:09 +0000 |
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committer | skachano <skachano@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2016-12-14 18:08:09 +0000 |
commit | 04f4501b35eaa2bd33393ef2445d038251ba1355 (patch) | |
tree | b85b6cca1b28873c9289c1c904be5c1d5d5c0aa7 /src/Subsampling/example | |
parent | 9e8db290ff0b3f69f88fa5ed54482bfb6730ad9b (diff) |
Added an example with a distance matrix for the farthest point algorithm
git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1874 636b058d-ea47-450e-bf9e-a15bfbe3eedb
Former-commit-id: 340e465189dc7ec8f8706e60e2d8097b53bfd5a0
Diffstat (limited to 'src/Subsampling/example')
-rw-r--r-- | src/Subsampling/example/CMakeLists.txt | 1 | ||||
-rw-r--r-- | src/Subsampling/example/example_custom_kernel.cpp | 69 |
2 files changed, 70 insertions, 0 deletions
diff --git a/src/Subsampling/example/CMakeLists.txt b/src/Subsampling/example/CMakeLists.txt index 54349f0c..0fd3335c 100644 --- a/src/Subsampling/example/CMakeLists.txt +++ b/src/Subsampling/example/CMakeLists.txt @@ -6,6 +6,7 @@ if(CGAL_FOUND) if (EIGEN3_FOUND) add_executable(Subsampling_example_pick_n_random_points example_pick_n_random_points.cpp) add_executable(Subsampling_example_choose_n_farthest_points example_choose_n_farthest_points.cpp) + add_executable(Subsampling_example_custom_kernel example_custom_kernel.cpp) add_executable(Subsampling_example_sparsify_point_set example_sparsify_point_set.cpp) target_link_libraries(Subsampling_example_sparsify_point_set ${CGAL_LIBRARY}) diff --git a/src/Subsampling/example/example_custom_kernel.cpp b/src/Subsampling/example/example_custom_kernel.cpp new file mode 100644 index 00000000..05797ebe --- /dev/null +++ b/src/Subsampling/example/example_custom_kernel.cpp @@ -0,0 +1,69 @@ +#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; +} |