diff options
Diffstat (limited to 'src/Subsampling/example')
5 files changed, 171 insertions, 0 deletions
diff --git a/src/Subsampling/example/CMakeLists.txt b/src/Subsampling/example/CMakeLists.txt new file mode 100644 index 00000000..28aab103 --- /dev/null +++ b/src/Subsampling/example/CMakeLists.txt @@ -0,0 +1,22 @@ +project(Subsampling_examples) + +if(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) + 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}) + + add_test(NAME Subsampling_example_pick_n_random_points + COMMAND $<TARGET_FILE:Subsampling_example_pick_n_random_points>) + add_test(NAME Subsampling_example_choose_n_farthest_points + COMMAND $<TARGET_FILE:Subsampling_example_choose_n_farthest_points>) + add_test(NAME Subsampling_example_sparsify_point_set + COMMAND $<TARGET_FILE:Subsampling_example_sparsify_point_set>) + + install(TARGETS Subsampling_example_pick_n_random_points DESTINATION bin) + install(TARGETS Subsampling_example_choose_n_farthest_points DESTINATION bin) + install(TARGETS Subsampling_example_custom_kernel DESTINATION bin) + install(TARGETS Subsampling_example_sparsify_point_set DESTINATION bin) + +endif(NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0) diff --git a/src/Subsampling/example/example_choose_n_farthest_points.cpp b/src/Subsampling/example/example_choose_n_farthest_points.cpp new file mode 100644 index 00000000..5cfeb4d8 --- /dev/null +++ b/src/Subsampling/example/example_choose_n_farthest_points.cpp @@ -0,0 +1,30 @@ +#include <gudhi/choose_n_farthest_points.h> + +#include <CGAL/Epick_d.h> +#include <CGAL/Random.h> + +#include <iostream> +#include <vector> +#include <iterator> + +int main(void) { + typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K; + typedef typename K::Point_d Point_d; + + CGAL::Random rd; + + std::vector<Point_d> points; + for (int i = 0; i < 500; ++i) + points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1), + rd.get_double(-1., 1), rd.get_double(-1., 1))); + + K k; + std::vector<Point_d> results; + Gudhi::subsampling::choose_n_farthest_points(k, points, 100, + 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"; + + return 0; +} 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; +} diff --git a/src/Subsampling/example/example_pick_n_random_points.cpp b/src/Subsampling/example/example_pick_n_random_points.cpp new file mode 100644 index 00000000..25266403 --- /dev/null +++ b/src/Subsampling/example/example_pick_n_random_points.cpp @@ -0,0 +1,28 @@ +#include <gudhi/pick_n_random_points.h> + +#include <CGAL/Epick_d.h> +#include <CGAL/Random.h> + +#include <iostream> +#include <vector> +#include <iterator> + +int main(void) { + typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K; + typedef typename K::Point_d Point_d; + + CGAL::Random rd; + + std::vector<Point_d> points; + for (int i = 0; i < 500; ++i) + points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1), + rd.get_double(-1., 1), rd.get_double(-1., 1))); + + K k; + std::vector<Point_d> results; + Gudhi::subsampling::pick_n_random_points(points, 100, std::back_inserter(results)); + std::cout << "Before sparsification: " << points.size() << " points.\n"; + std::cout << "After sparsification: " << results.size() << " points.\n"; + + return 0; +} diff --git a/src/Subsampling/example/example_sparsify_point_set.cpp b/src/Subsampling/example/example_sparsify_point_set.cpp new file mode 100644 index 00000000..a8caa720 --- /dev/null +++ b/src/Subsampling/example/example_sparsify_point_set.cpp @@ -0,0 +1,28 @@ +#include <gudhi/sparsify_point_set.h> + +#include <CGAL/Epick_d.h> +#include <CGAL/Random.h> + +#include <iostream> +#include <vector> +#include <iterator> + +int main(void) { + typedef CGAL::Epick_d<CGAL::Dimension_tag<4> > K; + typedef typename K::Point_d Point_d; + + CGAL::Random rd; + + std::vector<Point_d> points; + for (int i = 0; i < 500; ++i) + points.push_back(Point_d(rd.get_double(-1., 1), rd.get_double(-1., 1), + rd.get_double(-1., 1), rd.get_double(-1., 1))); + + K k; + std::vector<Point_d> results; + Gudhi::subsampling::sparsify_point_set(k, points, 0.4, std::back_inserter(results)); + std::cout << "Before sparsification: " << points.size() << " points.\n"; + std::cout << "After sparsification: " << results.size() << " points.\n"; + + return 0; +} |