diff options
Diffstat (limited to 'src/Spatial_searching/include/gudhi/Kd_tree_search.h')
| -rw-r--r-- | src/Spatial_searching/include/gudhi/Kd_tree_search.h | 34 |
1 files changed, 17 insertions, 17 deletions
diff --git a/src/Spatial_searching/include/gudhi/Kd_tree_search.h b/src/Spatial_searching/include/gudhi/Kd_tree_search.h index af04736b..ef428002 100644 --- a/src/Spatial_searching/include/gudhi/Kd_tree_search.h +++ b/src/Spatial_searching/include/gudhi/Kd_tree_search.h @@ -42,19 +42,19 @@ namespace spatial_searching { /** * \class Kd_tree_search Kd_tree_search.h gudhi/Kd_tree_search.h - * \brief Spatial tree data structure to perform (approximate) nearest and farthest neighbor search. + * \brief Spatial tree data structure to perform (approximate) nearest and furthest neighbor search. * * \ingroup spatial_searching * * \details * The class Kd_tree_search is a tree-based data structure, based on * <a target="_blank" href="http://doc.cgal.org/latest/Spatial_searching/index.html">CGAL dD spatial searching data structures</a>. - * It provides a simplified API to perform (approximate) nearest and farthest neighbor searches. Contrary to CGAL default behavior, the tree + * It provides a simplified API to perform (approximate) nearest and furthest neighbor searches. Contrary to CGAL default behavior, the tree * does not store the points themselves, but stores indices. * - * There are two types of queries: the <i>k-nearest or k-farthest neighbor query</i>, where <i>k</i> is fixed and the <i>k</i> nearest - * or farthest points are computed right away, - * and the <i>incremental nearest or farthest neighbor query</i>, where no number of neighbors is provided during the call, as the + * There are two types of queries: the <i>k-nearest or k-furthest neighbor query</i>, where <i>k</i> is fixed and the <i>k</i> nearest + * or furthest points are computed right away, + * and the <i>incremental nearest or furthest neighbor query</i>, where no number of neighbors is provided during the call, as the * neighbors will be computed incrementally when the iterator on the range is incremented. * * \tparam Search_traits must be a model of the <a target="_blank" @@ -96,7 +96,7 @@ class Kd_tree_search { typedef CGAL::Orthogonal_k_neighbor_search<STraits> K_neighbor_search; typedef typename K_neighbor_search::Tree Tree; typedef typename K_neighbor_search::Distance Distance; - /// \brief The range returned by a k-nearest or k-farthest neighbor search. + /// \brief The range returned by a k-nearest or k-furthest neighbor search. /// Its value type is `std::pair<std::size_t, FT>` where `first` is the index /// of a point P and `second` is the squared distance between P and the query point. typedef K_neighbor_search KNS_range; @@ -104,7 +104,7 @@ class Kd_tree_search { typedef CGAL::Orthogonal_incremental_neighbor_search< STraits, Distance, CGAL::Sliding_midpoint<STraits>, Tree> Incremental_neighbor_search; - /// \brief The range returned by an incremental nearest or farthest neighbor search. + /// \brief The range returned by an incremental nearest or furthest neighbor search. /// Its value type is `std::pair<std::size_t, FT>` where `first` is the index /// of a point P and `second` is the squared distance between P and the query point. typedef Incremental_neighbor_search INS_range; @@ -171,7 +171,7 @@ class Kd_tree_search { /// @param[in] sorted Indicates if the computed sequence of k-nearest neighbors needs to be sorted. /// @param[in] eps Approximation factor. /// @return A range (whose `value_type` is `std::size_t`) containing the k-nearest neighbors. - KNS_range query_k_nearest_neighbors( + KNS_range k_nearest_neighbors( Point const& p, unsigned int k, bool sorted = true, @@ -197,7 +197,7 @@ class Kd_tree_search { /// neighbors sorted by their distance to p. /// All the neighbors are not computed by this function, but they will be /// computed incrementally when the iterator on the range is incremented. - INS_range query_incremental_nearest_neighbors(Point const& p, FT eps = FT(0)) const { + INS_range incremental_nearest_neighbors(Point const& p, FT eps = FT(0)) const { // Initialize the search structure, and search all N points // Note that we need to pass the Distance explicitly since it needs to // know the property map @@ -211,13 +211,13 @@ class Kd_tree_search { return search; } - /// \brief Search for the k-farthest points from a query point. + /// \brief Search for the k-furthest points from a query point. /// @param[in] p The query point. - /// @param[in] k Number of farthest points to search. - /// @param[in] sorted Indicates if the computed sequence of k-farthest neighbors needs to be sorted. + /// @param[in] k Number of furthest points to search. + /// @param[in] sorted Indicates if the computed sequence of k-furthest neighbors needs to be sorted. /// @param[in] eps Approximation factor. - /// @return A range (whose `value_type` is `std::size_t`) containing the k-farthest neighbors. - KNS_range query_k_farthest_neighbors( + /// @return A range (whose `value_type` is `std::size_t`) containing the k-furthest neighbors. + KNS_range k_furthest_neighbors( Point const& p, unsigned int k, bool sorted = true, @@ -236,14 +236,14 @@ class Kd_tree_search { return search; } - /// \brief Search incrementally for the farthest neighbors from a query point. + /// \brief Search incrementally for the furthest neighbors from a query point. /// @param[in] p The query point. /// @param[in] eps Approximation factor. /// @return A range (whose `value_type` is `std::size_t`) /// containing the neighbors sorted by their distance to p. /// All the neighbors are not computed by this function, but they will be /// computed incrementally when the iterator on the range is incremented. - INS_range query_incremental_farthest_neighbors(Point const& p, FT eps = FT(0)) const { + INS_range incremental_furthest_neighbors(Point const& p, FT eps = FT(0)) const { // Initialize the search structure, and search all N points // Note that we need to pass the Distance explicitly since it needs to // know the property map @@ -264,7 +264,7 @@ class Kd_tree_search { /// Note: `it` is used this way: `*it++ = each_point`. /// @param[in] eps Approximation factor. template <typename OutputIterator> - void near_search(Point const& p, + void all_near_neighbors(Point const& p, FT radius, OutputIterator it, FT eps = FT(0)) const { |