diff options
author | glisse <glisse@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2017-02-24 14:23:48 +0000 |
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committer | glisse <glisse@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2017-02-24 14:23:48 +0000 |
commit | b0859ffb8c5d030f3d37ba758a325250f1d1c982 (patch) | |
tree | da20bb5d80a805977915d22455261a2613769a56 /src/Subsampling | |
parent | 707120336966af3dffb8b54cd0095fc1bcc3836d (diff) |
Let choose_n_farthest_points output distances (optional).
Drop unused CGAL includes.
git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/trunk@2105 636b058d-ea47-450e-bf9e-a15bfbe3eedb
Former-commit-id: c5a34492a451c8455c42eb8db1c6c7ca5f1bc7b2
Diffstat (limited to 'src/Subsampling')
-rw-r--r-- | src/Subsampling/include/gudhi/choose_n_farthest_points.h | 57 | ||||
-rw-r--r-- | src/Subsampling/test/test_choose_n_farthest_points.cpp | 43 |
2 files changed, 55 insertions, 45 deletions
diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h index 5e908090..a77d0cd7 100644 --- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h +++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h @@ -25,16 +25,10 @@ #include <boost/range.hpp> -#include <gudhi/Kd_tree_search.h> - +#include <gudhi/Null_output_iterator.h> #include <gudhi/Clock.h> -#include <CGAL/Search_traits.h> -#include <CGAL/Search_traits_adapter.h> -#include <CGAL/Fuzzy_sphere.h> - #include <iterator> -#include <algorithm> // for sort #include <vector> #include <random> #include <limits> // for numeric_limits<> @@ -47,7 +41,7 @@ namespace subsampling { * \ingroup 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`. + * The iteration starts with the landmark `starting point` or, if `starting point==-1`, 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> @@ -55,24 +49,30 @@ namespace subsampling { * 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`. + * \tparam PointOutputIterator Output iterator whose value type is Kernel::Point_d. + * \tparam DistanceOutputIterator Output iterator for distances. + * \details It chooses `final_size` points from a random access range + * `input_pts` and outputs them in the output iterator `output_it`. It also + * outputs the distance from each of those points to the set of previous + * points in `dist_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. + * @param[out] output_it The output iterator for points. + * @param[out] dist_it The optional output iterator for distances. * */ template < typename Kernel, typename Point_range, -typename OutputIterator> +typename PointOutputIterator, +typename DistanceOutputIterator=Null_output_iterator> void choose_n_farthest_points(Kernel const &k, Point_range const &input_pts, std::size_t final_size, std::size_t starting_point, - OutputIterator output_it) { + PointOutputIterator output_it, + DistanceOutputIterator dist_it={}) { std::size_t nb_points = boost::size(input_pts); if (final_size > nb_points) final_size = nb_points; @@ -81,6 +81,14 @@ void choose_n_farthest_points(Kernel const &k, if (final_size < 1) return; + if (starting_point == std::size_t(-1)) { + // Choose randomly the first landmark + std::random_device rd; + std::mt19937 gen(rd()); + std::uniform_int_distribution<std::size_t> dis(0, (input_pts.size() - 1)); + starting_point = dis(gen); + } + typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object(); std::size_t current_number_of_landmarks = 0; // counter for landmarks @@ -92,6 +100,7 @@ void choose_n_farthest_points(Kernel const &k, for (current_number_of_landmarks = 0; current_number_of_landmarks != final_size; current_number_of_landmarks++) { // curr_max_w at this point is the next landmark *output_it++ = input_pts[curr_max_w]; + *dist_it++ = dist_to_L[curr_max_w]; std::size_t i = 0; for (auto& p : input_pts) { double curr_dist = sqdist(p, *(std::begin(input_pts) + curr_max_w)); @@ -121,7 +130,7 @@ void choose_n_farthest_points(Kernel const &k, * 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. + * \tparam PointOutputIterator 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. @@ -132,22 +141,12 @@ void choose_n_farthest_points(Kernel const &k, */ template < typename Kernel, typename Point_range, -typename OutputIterator> +typename PointOutputIterator> void choose_n_farthest_points(Kernel const& k, Point_range const &input_pts, - unsigned final_size, - OutputIterator output_it) { - // Tests to the limit - if ((final_size < 1) || (input_pts.size() == 0)) - return; - - // Choose randomly the first landmark - 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); - - choose_n_farthest_points(k, input_pts, final_size, starting_point, output_it); + std::size_t final_size, + PointOutputIterator output_it) { + choose_n_farthest_points(k, input_pts, final_size, -1, output_it); } } // namespace subsampling diff --git a/src/Subsampling/test/test_choose_n_farthest_points.cpp b/src/Subsampling/test/test_choose_n_farthest_points.cpp index 0bc0dff4..6bc5f7b0 100644 --- a/src/Subsampling/test/test_choose_n_farthest_points.cpp +++ b/src/Subsampling/test/test_choose_n_farthest_points.cpp @@ -70,34 +70,45 @@ BOOST_AUTO_TEST_CASE_TEMPLATE(test_choose_farthest_point_limits, Kernel, list_of typedef typename Kernel::FT FT; typedef typename Kernel::Point_d Point_d; std::vector< Point_d > points, landmarks; + std::vector< FT > distances; 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)); + Gudhi::subsampling::choose_n_farthest_points(k, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 0); - landmarks.clear(); + landmarks.clear(); distances.clear(); // Choose 0 farthest points in an empty point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 0, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(k, points, 0, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 0); - landmarks.clear(); + landmarks.clear(); distances.clear(); // Choose 1 farthest points in an empty point cloud - Gudhi::subsampling::choose_n_farthest_points(k, points, 1, std::back_inserter(landmarks)); + Gudhi::subsampling::choose_n_farthest_points(k, points, 1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); BOOST_CHECK(landmarks.size() == 0); - landmarks.clear(); + landmarks.clear(); distances.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 -1 farthest points in a one point cloud + Gudhi::subsampling::choose_n_farthest_points(k, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + BOOST_CHECK(landmarks.size() == 1 && distances.size() == 1); + BOOST_CHECK(distances[0] == std::numeric_limits<FT>::infinity()); + landmarks.clear(); distances.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(); + Gudhi::subsampling::choose_n_farthest_points(k, points, 0, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + BOOST_CHECK(landmarks.size() == 0 && distances.size() == 0); + landmarks.clear(); distances.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(); + Gudhi::subsampling::choose_n_farthest_points(k, points, 1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + BOOST_CHECK(landmarks.size() == 1 && distances.size() == 1); + BOOST_CHECK(distances[0] == std::numeric_limits<FT>::infinity()); + landmarks.clear(); distances.clear(); + std::vector<FT> point2({1.0, 0.0, 0.0, 0.0}); + points.push_back(Point_d(point2.begin(), point2.end())); + // Choose all farthest points in a one point cloud + Gudhi::subsampling::choose_n_farthest_points(k, points, -1, -1, std::back_inserter(landmarks), std::back_inserter(distances)); + BOOST_CHECK(landmarks.size() == 2 && distances.size() == 2); + BOOST_CHECK(distances[0] == std::numeric_limits<FT>::infinity()); + BOOST_CHECK(distances[1] == 1); + landmarks.clear(); distances.clear(); } |