diff options
| -rw-r--r-- | src/Spatial_searching/example/CMakeLists.txt | 2 | ||||
| -rw-r--r-- | src/Spatial_searching/test/CMakeLists.txt | 2 | ||||
| -rw-r--r-- | src/Subsampling/example/CMakeLists.txt | 1 | ||||
| -rw-r--r-- | src/Subsampling/example/example_custom_kernel.cpp | 69 | ||||
| -rw-r--r-- | src/Subsampling/include/gudhi/choose_n_farthest_points.h | 36 | ||||
| -rw-r--r-- | src/Subsampling/test/test_choose_n_farthest_points.cpp | 55 |
6 files changed, 106 insertions, 59 deletions
diff --git a/src/Spatial_searching/example/CMakeLists.txt b/src/Spatial_searching/example/CMakeLists.txt index e73b201c..6238a0ec 100644 --- a/src/Spatial_searching/example/CMakeLists.txt +++ b/src/Spatial_searching/example/CMakeLists.txt @@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 2.6) project(Spatial_searching_examples) if(CGAL_FOUND) - if (NOT CGAL_VERSION VERSION_LESS 4.9.0) + if (NOT CGAL_VERSION VERSION_LESS 4.8.1) if (EIGEN3_FOUND) add_executable( Spatial_searching_example_spatial_searching example_spatial_searching.cpp ) target_link_libraries(Spatial_searching_example_spatial_searching ${CGAL_LIBRARY}) diff --git a/src/Spatial_searching/test/CMakeLists.txt b/src/Spatial_searching/test/CMakeLists.txt index 7f443b79..2c685c72 100644 --- a/src/Spatial_searching/test/CMakeLists.txt +++ b/src/Spatial_searching/test/CMakeLists.txt @@ -11,7 +11,7 @@ if (GPROF_PATH) endif() if(CGAL_FOUND) - if (NOT CGAL_VERSION VERSION_LESS 4.9.0) + if (NOT CGAL_VERSION VERSION_LESS 4.8.1) if (EIGEN3_FOUND) add_executable( Spatial_searching_test_Kd_tree_search test_Kd_tree_search.cpp ) target_link_libraries(Spatial_searching_test_Kd_tree_search 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; +} diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h index 9b45c640..ea387bf9 100644 --- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h +++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h @@ -48,15 +48,28 @@ namespace subsampling { * \brief Subsample by a greedy strategy of iteratively adding the farthest point from the * current chosen point set to the subsampling. * The iteration starts with the landmark `starting point`. + * \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the + * concept <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a> + * concept. + * It must also contain a public member 'squared_distance_d_object' of this type. + * \tparam Point_range Range whose value type is Kernel::Point_d. It must provide random-access + * via `operator[]` and the points should be stored contiguously in memory. + * \tparam OutputIterator Output iterator whose value type is Kernel::Point_d. * \details It chooses `final_size` points from a random access range `input_pts` and * outputs it in the output iterator `output_it`. + * @param[in] k A kernel object. + * @param[in] input_pts Const reference to the input points. + * @param[in] final_size The size of the subsample to compute. + * @param[in] starting_point The seed in the farthest point algorithm. + * @param[out] output_it The output iterator. * */ template < typename Kernel, -typename Point_container, +typename Point_range, typename OutputIterator> void choose_n_farthest_points(Kernel const &k, - Point_container const &input_pts, + Point_range const &input_pts, std::size_t final_size, std::size_t starting_point, OutputIterator output_it) { @@ -101,15 +114,27 @@ void choose_n_farthest_points(Kernel const &k, * \brief Subsample by a greedy strategy of iteratively adding the farthest point from the * current chosen point set to the subsampling. * The iteration starts with a random landmark. + * \tparam Kernel must provide a type Kernel::Squared_distance_d which is a model of the + * concept <a target="_blank" + * href="http://doc.cgal.org/latest/Kernel_d/classKernel__d_1_1Squared__distance__d.html">Kernel_d::Squared_distance_d</a> + * concept. + * It must also contain a public member 'squared_distance_d_object' of this type. + * \tparam Point_range Range whose value type is Kernel::Point_d. It must provide random-access + * via `operator[]` and the points should be stored contiguously in memory. + * \tparam OutputIterator Output iterator whose value type is Kernel::Point_d. * \details It chooses `final_size` points from a random access range `input_pts` and * outputs it in the output iterator `output_it`. + * @param[in] k A kernel object. + * @param[in] input_pts Const reference to the input points. + * @param[in] final_size The size of the subsample to compute. + * @param[out] output_it The output iterator. * */ template < typename Kernel, -typename Point_container, +typename Point_range, typename OutputIterator> void choose_n_farthest_points(Kernel const& k, - Point_container const &input_pts, + Point_range const &input_pts, unsigned final_size, OutputIterator output_it) { // Tests to the limit @@ -120,8 +145,7 @@ void choose_n_farthest_points(Kernel const& k, std::random_device rd; std::mt19937 gen(rd()); std::uniform_int_distribution<> dis(0, (input_pts.size() - 1)); - std::size_t starting_point = dis(gen); - + int starting_point = dis(gen); choose_n_farthest_points(k, input_pts, final_size, starting_point, output_it); } diff --git a/src/Subsampling/test/test_choose_n_farthest_points.cpp b/src/Subsampling/test/test_choose_n_farthest_points.cpp index 0bc0dff4..d064899a 100644 --- a/src/Subsampling/test/test_choose_n_farthest_points.cpp +++ b/src/Subsampling/test/test_choose_n_farthest_points.cpp @@ -39,65 +39,18 @@ typedef CGAL::Epick_d<CGAL::Dynamic_dimension_tag> K; typedef typename K::FT FT; typedef typename K::Point_d Point_d; -typedef boost::mpl::list<CGAL::Epick_d<CGAL::Dynamic_dimension_tag>, CGAL::Epick_d<CGAL::Dimension_tag<4>>> list_of_tested_kernels; - -BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point, Kernel, list_of_tested_kernels) { - typedef typename Kernel::FT FT; - typedef typename Kernel::Point_d Point_d; +BOOST_AUTO_TEST_CASE(test_choose_farthest_point) { std::vector< Point_d > points, landmarks; // Add grid points (625 points) for (FT i = 0; i < 5; i += 1.0) for (FT j = 0; j < 5; j += 1.0) for (FT k = 0; k < 5; k += 1.0) - for (FT l = 0; l < 5; l += 1.0) { - std::vector<FT> point({i, j, k, l}); - points.push_back(Point_d(point.begin(), point.end())); - } + for (FT l = 0; l < 5; l += 1.0) + points.push_back(Point_d(std::vector<FT>({i, j, k, l}))); landmarks.clear(); - Kernel k; + K k; Gudhi::subsampling::choose_n_farthest_points(k, points, 100, std::back_inserter(landmarks)); BOOST_CHECK(landmarks.size() == 100); - for (auto landmark : landmarks) - { - // Check all landmarks are in points - BOOST_CHECK(std::find (points.begin(), points.end(), landmark) != points.end()); - } -} - -BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point_limits, Kernel, list_of_tested_kernels) { - typedef typename Kernel::FT FT; - typedef typename Kernel::Point_d Point_d; - std::vector< Point_d > points, landmarks; - landmarks.clear(); - Kernel k; - // Choose -1 farthest points in an empty point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, -1, std::back_inserter(landmarks)); - BOOST_CHECK(landmarks.size() == 0); - landmarks.clear(); - // Choose 0 farthest points in an empty point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 0, std::back_inserter(landmarks)); - BOOST_CHECK(landmarks.size() == 0); - landmarks.clear(); - // Choose 1 farthest points in an empty point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 1, std::back_inserter(landmarks)); - BOOST_CHECK(landmarks.size() == 0); - landmarks.clear(); - - std::vector<FT> point({0.0, 0.0, 0.0, 0.0}); - points.push_back(Point_d(point.begin(), point.end())); - // Choose -1 farthest points in an empty point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, -1, std::back_inserter(landmarks)); - BOOST_CHECK(landmarks.size() == 1); - landmarks.clear(); - // Choose 0 farthest points in a one point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 0, std::back_inserter(landmarks)); - BOOST_CHECK(landmarks.size() == 0); - landmarks.clear(); - // Choose 1 farthest points in a one point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 1, std::back_inserter(landmarks)); - BOOST_CHECK(landmarks.size() == 1); - landmarks.clear(); - } |