\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL - dD Spatial Searching
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CGAL::Orthogonal_incremental_neighbor_search< Traits, OrthogonalDistance, Splitter, SpatialTree > Class Template Reference

#include <CGAL/Orthogonal_incremental_neighbor_search.h>


The class Orthogonal_incremental_neighbor_search implements incremental nearest and furthest neighbor searching on a tree.

Template Parameters
Traitsmust be a model of the concept SearchTraits, for example Search_traits_2<Simple_cartesian<double> >.
OrthogonalDistancemust be a model of the concept OrthogonalDistance. If Traits is Search_traits_adapter<Key,PointPropertyMap,BaseTraits> the default type is Distance_adapter<Key,PointPropertyMap,Euclidean_distance<BaseTraits> >, and Euclidean_distance<Traits> otherwise.
Splittermust be a model of the concept Splitter. The default type is Sliding_midpoint<Traits>.
SpatialTreemust be a model of the concept SpatialTree. The default type is Kd_tree<Traits, Splitter, Tag_true>. The template argument must be Tag_true because orthogonal search needs extended kd tree nodes.
See Also
CGAL::Incremental_neighbor_search<Traits, GeneralDistance, SpatialTree>


typedef Traits::Point_d Point_d
 Point type.
typedef Traits::FT FT
 Number type.
typedef OrthogonalDistance Distance
 Distance type.
 Query item.
typedef std::pair< Point_d, FTPoint_with_transformed_distance
 Pair of point and transformed distance.
typedef unspecified_type iterator
 const input iterator with value type Point_with_transformed_distance for enumerating approximate neighbors.
typedef SpatialTree Tree
 The tree type.


 Orthogonal_incremental_neighbor_search (SpatialTree &tree, Query_item query, FT eps=FT(0.0), bool search_nearest=true, OrthogonalDistance d=OrthogonalDistance())
 Constructor for incremental neighbor searching of the query item query in the points stored tree using a distance d and approximation factor eps.


iterator begin () const
 Returns a const iterator to the approximate nearest or furthest neighbor.
iterator end () const
 Returns the appropriate past-the-end const iterator.
std::ostream & statistics (std::ostream &s) const
 Inserts statistics of the search process into the output stream s.