CGAL 5.1 - dD Spatial Searching
CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache > Class Template Reference

#include <CGAL/Kd_tree.h>

Definition

The class Kd_tree defines a k-d tree.

Template Parameters
 Traits must be a model of the concept SearchTraits, for example Search_traits_2 >. Splitter must be a model for the concept Splitter. It defaults to Sliding_midpoint. UseExtendedNode must be Tag_true, if the tree shall be built with extended nodes, and Tag_false otherwise. EnablePointsCache can be Tag_true or Tag_false. Not storing the points coordinates inside the tree usually generates a lot of cache misses, leading to non-optimal performance. This is the case for example when indices are stored inside the tree, or to a lesser extent when the points coordinates are stored in a dynamically allocated array (e.g., Epick_d with dynamic dimension) — we says "to a lesser extent" because the points are re-created by the kd-tree in a cache-friendly order after its construction, so the coordinates are more likely to be stored in a near-optimal order on the heap. When EnablePointsCache is set to Tag_true, the points coordinates will be cached in an optimal way. This will increase memory consumption but provide better search performance. See also the GeneralDistance and FuzzyQueryItem concepts for additional requirements when using such a cache.
CGAL::Kd_tree_node<Traits>
CGAL::Search_traits_2<Kernel>
CGAL::Search_traits_3<Kernel>
CGAL::Search_traits<FT_,Point,CartesianIterator,ConstructCartesianIterator>
Examples:
Spatial_searching/circular_query.cpp, Spatial_searching/fuzzy_range_query.cpp, Spatial_searching/iso_rectangle_2_query.cpp, and Spatial_searching/searching_with_circular_query.cpp.

Types

typedef unspecified_type D
Dimension tag.

typedef Traits::Point_d Point_d
Point class.

typedef Traits::FT FT
Number type.

typedef unspecified_type Splitter
Splitter type.

typedef unspecified_type iterator
Bidirectional const iterator with value type Point_d that allows to enumerate all points in the tree.

typedef unspecified_type Node_handle
A handle with value type Kd_tree_node<Traits,Splitter>.

typedef unspecified_type Node_const_handle
A const handle with value type Kd_tree_node<Traits,Splitter>.

typedef unspecified_type Point_d_iterator
Random access const iterator with value type const Point_d*.

typedef unspecified_type size_type
A type that counts the number of elements in a k-d tree.

Creation

Kd_tree (Splitter s=Splitter(), Traits t=Traits())
Constructs an empty k-d tree.

template<class InputIterator >
Kd_tree (InputIterator first, InputIterator beyond, Splitter s=Splitter(), Traits t=Traits())
Constructs a k-d tree on the elements from the sequence [first, beyond) using the splitting rule implemented by s. More...

template<typename ConcurrencyTag >
void build ()
The constructor does not build the internal data structure, and it is also not updated after calls to insert(). More...

void invalidate_build ()
This clears the internal data structure, which then gets rebuilt either by an explicit call to build() or implicitly by the next query or removal. More...

Operations

void insert (Point_d p)
Inserts the point p in the k-d tree. More...

template<class InputIterator >
void insert (InputIterator first, InputIterator beyond)
Inserts the elements from the sequence [first, beyond) in the k-d tree. More...

template<class IdentifyPoint >
void remove (Point_d p, IdentifyPoint identify_point)
Removes the point p from the k-d tree. More...

void remove (Point_d p)
Removes point p, calling the 2-argument function remove() with a functor that simply compares coordinates.

void reserve (size_t size)

size_t capacity ()

template<class FuzzyQueryItem >
boost::optional< Point_dsearch_any_point (FuzzyQueryItem q) const
Reports any point that is approximately contained by q. More...

template<class OutputIterator , class FuzzyQueryItem >
OutputIterator search (OutputIterator it, FuzzyQueryItem q) const
Reports the points that are approximately contained by q. More...

iterator begin () const
Returns a const iterator to the first point in the tree. More...

iterator end () const
Returns the appropriate past-the-end const iterator.

void clear ()
Removes all points from the k-d tree.

size_type size () const
Returns the number of points that are stored in the tree.

Traits traits () const
return the instance of the traits used to construct the tree.

Node_handle root ()
Returns a handle to the root node of the tree.

Node_const_handle root () const
Returns a const handle to the root node of the tree.

const Kd_tree_rectangle< FT, D > & bounding_box () const
Returns a const reference to the bounding box of the root node of the tree.

std::ostream & statistics (std::ostream &s) const
Inserts statistics of the tree into the output stream s.

◆ Kd_tree()

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
template<class InputIterator >
 CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::Kd_tree ( InputIterator first, InputIterator beyond, Splitter s = Splitter(), Traits t = Traits() )

Constructs a k-d tree on the elements from the sequence [first, beyond) using the splitting rule implemented by s.

The value type of the InputIterator must be Point_d.

◆ begin()

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
 iterator CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::begin ( ) const

Returns a const iterator to the first point in the tree.

Note
Starting with CGAL 4.6, the order of the points in the iterator range [begin() , end()) is not the order of insertion of the points into the tree. This was not guaranteed before but might have beeen observed and exploited.

◆ build()

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
template<typename ConcurrencyTag >
 void CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::build ( )

The constructor does not build the internal data structure, and it is also not updated after calls to insert().

The method build() is called implicitly at the first call to a query or removal member function. You can call build() explicitly to ensure that the next call to query functions will not trigger the reconstruction of the data structure.

Template Parameters
 ConcurrencyTag enables sequential versus parallel algorithm. Possible values are Sequential_tag, Parallel_tag, and Parallel_if_available_tag. This template parameter is optional: calling build() without specifying the concurrency tag will result in Sequential_tag being used. If build() is not called by the user but called implicitly at the first call to a query or removal member function, Sequential_tag is also used.

◆ insert() [1/2]

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
 void CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::insert ( Point_d p )

Inserts the point p in the k-d tree.

Note
Insertions do not dynamically update the internal data structure. The next query, or a call to build(), automatically triggers a rebuild of the whole structure.
Examples:
Spatial_searching/circular_query.cpp.

◆ insert() [2/2]

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
template<class InputIterator >
 void CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::insert ( InputIterator first, InputIterator beyond )

Inserts the elements from the sequence [first, beyond) in the k-d tree.

The value type of the InputIterator must be Point_d.

◆ invalidate_build()

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
 void CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::invalidate_build ( )

This clears the internal data structure, which then gets rebuilt either by an explicit call to build() or implicitly by the next query or removal.

The only reason to call this function explicitly is to rebalance the tree after some number of removals.

◆ remove()

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
template<class IdentifyPoint >
 void CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::remove ( Point_d p, IdentifyPoint identify_point )

Removes the point p from the k-d tree.

It uses equal_to_p to identify the point after locating it, which can matter in particular when 2 points are in the same place. IdentifyPoint is a unary functor that takes a Point_d and returns a bool. This is a limited and naive implementation that does not rebalance the tree. On the other hand, the tree remains valid and ready for queries. If the internal data structure is not already built, for instance because the last operation was an insertion, it first calls build().

◆ search()

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
template<class OutputIterator , class FuzzyQueryItem >
 OutputIterator CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::search ( OutputIterator it, FuzzyQueryItem q ) const

Reports the points that are approximately contained by q.

The types FuzzyQueryItem::Point_d and Point_d must be equivalent. To use this function Traits must be a model of the concept RangeSearchTraits.

◆ search_any_point()

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
template<class FuzzyQueryItem >
 boost::optional CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::search_any_point ( FuzzyQueryItem q ) const

Reports any point that is approximately contained by q.

The types FuzzyQueryItem::Point_d and Point_d must be equivalent. To use this function Traits must be a model of the concept RangeSearchTraits.