diff options
Diffstat (limited to 'src/Subsampling/include/gudhi/choose_by_farthest_point.h')
| -rw-r--r-- | src/Subsampling/include/gudhi/choose_by_farthest_point.h | 31 |
1 files changed, 15 insertions, 16 deletions
diff --git a/src/Subsampling/include/gudhi/choose_by_farthest_point.h b/src/Subsampling/include/gudhi/choose_by_farthest_point.h index 2918983f..52647c16 100644 --- a/src/Subsampling/include/gudhi/choose_by_farthest_point.h +++ b/src/Subsampling/include/gudhi/choose_by_farthest_point.h @@ -31,32 +31,29 @@ #include <random> namespace Gudhi { - + +namespace subsampling { /** - * \ingroup witness_complex - * \brief Landmark choice strategy by iteratively adding the farthest witness from the - * current landmark set as the new landmark. - * \details It chooses nbL landmarks from a random access range `points` and - * writes {witness}*{closest landmarks} matrix in `knn`. - * - * The type KNearestNeighbors can be seen as - * Witness_range<Closest_landmark_range<Vertex_handle>>, where - * Witness_range and Closest_landmark_range are random access ranges + * \ingroup subsampling + * \brief Subsample by a greedy strategy of iteratively adding the farthest point from the + * current chosen point set to the subsampling. + * \details It chooses `final_size` points from a random access range `points` and + * outputs it in the output iterator `output_it`. * */ template < typename Kernel, typename Point_container, typename OutputIterator> - void choose_by_farthest_point(Kernel& k, - Point_container const &points, - int nbL, - OutputIterator output_it) + void choose_by_farthest_point( Kernel& k, + Point_container const &points, + int final_size, + OutputIterator output_it) { typename Kernel::Squared_distance_d sqdist = k.squared_distance_d_object(); int nb_points = boost::size(points); - assert(nb_points >= nbL); + assert(nb_points >= final_size); int current_number_of_landmarks = 0; // counter for landmarks double curr_max_dist = 0; // used for defining the furhest point from L @@ -69,7 +66,7 @@ namespace Gudhi { std::uniform_int_distribution<> dis(1, 6); int curr_max_w = dis(gen); - for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) { + 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++ = points[curr_max_w]; // std::cout << curr_max_w << "\n"; @@ -89,6 +86,8 @@ namespace Gudhi { } } } + +} // namespace subsampling } // namespace Gudhi |