CGAL 6.1.2 - dD Spatial Searching: CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache > Class Template Reference

CGAL 6.1.2 - dD Spatial Searching

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CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache > Class Template Reference
Reference Manual » Search Classes

`#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 say "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<class PointRange >
| Kd_tree (const PointRange &points, Splitter s=Splitter(), const Traits t=Traits())
| Constructs a `k-d` tree on the elements from the range `points` 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 >
std::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() [1/2]

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`.

## ◆ Kd_tree() [2/2]

template<typename Traits , typename Splitter , typename UseExtendedNode , typename EnablePointsCache >
template<class PointRange >

CGAL::Kd_tree< Traits, Splitter, UseExtendedNode, EnablePointsCache >::Kd_tree | ( | const PointRange & | points,
|  | Splitter | s = `Splitter()`,
|  | const Traits | t = `Traits()`
| ) |  |

Constructs a `k-d` tree on the elements from the range `points` using the splitting rule implemented by `s`.

The value type of elements in `PointRange` 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 been 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 >

std::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`.