#include <CGAL/Kd_tree.h>

Definition

template<typename Traits, typename Splitter, typename UseExtendedNode, typename EnablePointsCache>
class CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >

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<Simple_cartesian<double> >`.
Splitter	must be a model for the concept `Splitter`. It defaults to `Sliding_midpoint<Traits>`.
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.

See also: 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, Spatial_searching/iso_rectangle_2_query_projection.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`.

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

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.

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

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

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

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_d >	search_any_point (FuzzyQueryItem q) const
	Reports any point that is approximately contained by `q`.

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

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

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
	Returns 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`.

std::ostream &	write_graphviz (std::ostream &s) const
	Inserts the tree in the `Graphviz` format into the output stream `s`.

Constructor & Destructor Documentation

◆ 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.

Member Function Documentation

◆ 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 >

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.

◆ insert() [2/2]

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.

◆ 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< Point_d > 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.

Definition

Types

Creation

Operations

Constructor & Destructor Documentation

◆ Kd_tree()

Member Function Documentation

◆ begin()

◆ build()

◆ insert() [1/2]

◆ insert() [2/2]

◆ invalidate_build()

◆ remove()

◆ search()

◆ search_any_point()