diff options
author | skachano <skachano@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2016-12-14 14:03:59 +0000 |
---|---|---|
committer | skachano <skachano@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2016-12-14 14:03:59 +0000 |
commit | 9e8db290ff0b3f69f88fa5ed54482bfb6730ad9b (patch) | |
tree | 0f7200a069258651be70a5c63ac5e88b7435c3eb /src/Subsampling | |
parent | c0ae9d5915f52269ba387f037024d222d91b6bdd (diff) |
Improved the documentation for choose_farthest_points
git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/subsampling_and_spatialsearching@1869 636b058d-ea47-450e-bf9e-a15bfbe3eedb
Former-commit-id: 08223b7d1788c73b0fb3fc7255a6386896b63626
Diffstat (limited to 'src/Subsampling')
-rw-r--r-- | src/Subsampling/include/gudhi/choose_n_farthest_points.h | 31 |
1 files changed, 27 insertions, 4 deletions
diff --git a/src/Subsampling/include/gudhi/choose_n_farthest_points.h b/src/Subsampling/include/gudhi/choose_n_farthest_points.h index 40c7808d..43bf6402 100644 --- a/src/Subsampling/include/gudhi/choose_n_farthest_points.h +++ b/src/Subsampling/include/gudhi/choose_n_farthest_points.h @@ -48,15 +48,27 @@ 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. + * \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) { @@ -96,15 +108,26 @@ 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. + * \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) { // Choose randomly the first landmark |