From b0859ffb8c5d030f3d37ba758a325250f1d1c982 Mon Sep 17 00:00:00 2001 From: glisse Date: Fri, 24 Feb 2017 14:23:48 +0000 Subject: 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 --- .../include/gudhi/choose_n_farthest_points.h | 57 +++++++++++----------- 1 file changed, 28 insertions(+), 29 deletions(-) (limited to 'src/Subsampling/include/gudhi') 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 -#include - +#include #include -#include -#include -#include - #include -#include // for sort #include #include #include // 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 Kernel_d::Squared_distance_d @@ -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 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 -- cgit v1.2.3