diff options
Diffstat (limited to 'src')
-rw-r--r-- | src/Spatial_searching/include/gudhi/Spatial_tree_data_structure.h | 24 |
1 files changed, 12 insertions, 12 deletions
diff --git a/src/Spatial_searching/include/gudhi/Spatial_tree_data_structure.h b/src/Spatial_searching/include/gudhi/Spatial_tree_data_structure.h index 740c7861..18fb864a 100644 --- a/src/Spatial_searching/include/gudhi/Spatial_tree_data_structure.h +++ b/src/Spatial_searching/include/gudhi/Spatial_tree_data_structure.h @@ -49,9 +49,9 @@ namespace spatial_searching { * It provides a simplified API to perform (approximate) nearest 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 neighbor query</i>, where `k` is fixed and the k nearest points are + * There are two types of queries: the <i>k-nearest neighbor query</i>, where <i>k</i> is fixed and the <i>k</i> nearest points are * computed right away, - * and the <i>incremental nearest neighbor query</i>, where no number of neighbors is provided during the call, and the + * and the <i>incremental nearest 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 K requires a <a target="_blank" @@ -94,7 +94,7 @@ public: /// of a point P and `second` is the squared distance between P and the query point. typedef Incremental_neighbor_search INS_range; - /// Constructor + /// \brief Constructor /// @param[in] points Const reference to the point container. This container /// is not copied, so it should not be destroyed or modified afterwards. Spatial_tree_data_structure(Point_container_ const& points) @@ -108,10 +108,10 @@ public: m_tree.build(); } - /// Constructor + /// \brief Constructor /// @param[in] points Const reference to the point container. This container /// is not copied, so it should not be destroyed or modified afterwards. - /// @param[in] Only_these_points specifies the indices of the points that + /// @param[in] Only_these_points Specifies the indices of the points that /// should be actually inserted into the tree. The other points are ignored. template <typename Point_indices_range> Spatial_tree_data_structure( @@ -127,7 +127,7 @@ public: m_tree.build(); } - /// Constructor + /// \brief Constructor /// @param[in] points Const reference to the point container. This container /// is not copied, so it should not be destroyed or modified afterwards. /// @param[in] begin_idx, past_the_end_idx Define the subset of the points that @@ -163,14 +163,14 @@ public: m_tree.insert(point_idx); } - /// Search for the k-nearest neighbors from a query point. + /// \brief Search for the k-nearest neighbors from a query point. /// @param[in] p The query point. /// @param[in] k Number of nearest points to 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 containing the k-nearest neighbors. KNS_range query_ANN(const - Point &sp, + Point &p, unsigned int k, bool sorted = true, FT eps = FT(0)) const @@ -180,7 +180,7 @@ public: // know the property map K_neighbor_search search( m_tree, - sp, + p, k, eps, true, @@ -191,20 +191,20 @@ public: return search; } - /// Search incrementally for the nearest neighbors from a query point. + /// \brief Search incrementally for the nearest neighbors from a query point. /// @param[in] p The query point. /// @param[in] eps Approximation factor. /// @return A range 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_ANN(const Point &sp, FT eps = FT(0)) const + INS_range query_incremental_ANN(const Point &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 Incremental_neighbor_search search( m_tree, - sp, + p, eps, true, CGAL::Distance_adapter<std::ptrdiff_t, Point*, CGAL::Euclidean_distance<Traits_base> >( |