|
The class K_neighbor_search<Traits, GeneralDistance, Splitter, SpatialTree> implements approximate -nearest and -furthest neighbor searching using standard search on a tree using a general distance class. The tree may be built with extended or unextended nodes.
#include <CGAL/K_neighbor_search.h>
Expects for the first template argument an implementation of the concept SearchTraits, for example CGAL::Cartesian_d<double>.
Expects for the second template argument a model of the concept GeneralDistance. The default type is CGAL::Euclidean_distance<Traits>.
Expects for fourth template argument an implementation of the concept SpatialTree. The default type is CGAL::Kd_tree<Traits, Splitter, CGAL::Tag_false>. The template argument CGAL::Tag_false makes that the tree is built with unextended nodes.
|
|
| Point type. |
|
|
| Number type. |
|
|
| Pair of point and transformed distance. |
|
| |
|
Bidirectional iterator with value type Point_with_distance
for enumerating approximate neighbors.
| |
|
|
| Query item type. |
|
|
| The tree type. |
| |||||
|
Constructor for searching approximately neighbors of the query item q
in the points stored in tree using
distance class and approximation factor eps.
| |||||
|
|
| Returns an iterator to the approximate neighbors. |
|
|
| Past-the-end iterator. |
| advanced |
|
|
|
| Inserts statistics of the search process into the output stream s. |
| advanced |
|
CGAL::Orthogonal_k_neighbor_search<Traits, OrthogonalDistance, Splitter, SpatialTree>.