A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces. More...

#include <cover_tree.hpp>

Classes
class	DualTreeTraverser
	A dual-tree cover tree traverser; see dual_tree_traverser.hpp. More...

class	SingleTreeTraverser
	A single-tree cover tree traverser; see single_tree_traverser.hpp for implementation. More...

Public Types
typedef MatType	Mat
	So that other classes can access the matrix type.

typedef MatType::elem_type	ElemType
	The type held by the matrix type.

template<typename RuleType >
using	BreadthFirstDualTreeTraverser = DualTreeTraverser< RuleType >

Public Member Functions
	CoverTree (const MatType &dataset, const ElemType base=2.0, MetricType *metric=NULL)
	Create the cover tree with the given dataset and given base. More...

	CoverTree (const MatType &dataset, MetricType &metric, const ElemType base=2.0)
	Create the cover tree with the given dataset and the given instantiated metric. More...

	CoverTree (MatType &&dataset, const ElemType base=2.0)
	Create the cover tree with the given dataset, taking ownership of the dataset. More...

	CoverTree (MatType &&dataset, MetricType &metric, const ElemType base=2.0)
	Create the cover tree with the given dataset and the given instantiated metric, taking ownership of the dataset. More...

	CoverTree (const MatType &dataset, const ElemType base, const size_t pointIndex, const int scale, CoverTree *parent, const ElemType parentDistance, arma::Col< size_t > &indices, arma::vec &distances, size_t nearSetSize, size_t &farSetSize, size_t &usedSetSize, MetricType &metric=NULL)
	Construct a child cover tree node. More...

	CoverTree (const MatType &dataset, const ElemType base, const size_t pointIndex, const int scale, CoverTree parent, const ElemType parentDistance, const ElemType furthestDescendantDistance, MetricType metric=NULL)
	Manually construct a cover tree node; no tree assembly is done in this constructor, and children must be added manually (use Children()). More...

	CoverTree (const CoverTree &other)
	Create a cover tree from another tree. More...

	CoverTree (CoverTree &&other)
	Move constructor for a Cover Tree, possess all the members of the given tree. More...

CoverTree &	operator= (const CoverTree &other)
	Copy the given Cover Tree. More...

CoverTree &	operator= (CoverTree &&other)
	Take ownership of the given Cover Tree. More...

template<typename Archive >
	CoverTree (Archive &ar, const typename std::enable_if_t< cereal::is_loading< Archive >()> *=0)
	Create a cover tree from a cereal archive.

	~CoverTree ()
	Delete this cover tree node and its children.

const MatType &	Dataset () const
	Get a reference to the dataset.

size_t	Point () const
	Get the index of the point which this node represents.

size_t	Point (const size_t) const
	For compatibility with other trees; the argument is ignored.

bool	IsLeaf () const

size_t	NumPoints () const

const CoverTree &	Child (const size_t index) const
	Get a particular child node.

CoverTree &	Child (const size_t index)
	Modify a particular child node.

CoverTree *&	ChildPtr (const size_t index)

size_t	NumChildren () const
	Get the number of children.

const std::vector< CoverTree * > &	Children () const
	Get the children.

std::vector< CoverTree * > &	Children ()
	Modify the children manually (maybe not a great idea).

size_t	NumDescendants () const
	Get the number of descendant points. More...

size_t	Descendant (const size_t index) const
	Get the index of a particular descendant point. More...

int	Scale () const
	Get the scale of this node.

int &	Scale ()
	Modify the scale of this node. Be careful...

ElemType	Base () const
	Get the base.

ElemType &	Base ()
	Modify the base; don't do this, you'll break everything.

const StatisticType &	Stat () const
	Get the statistic for this node.

StatisticType &	Stat ()
	Modify the statistic for this node.

template<typename VecType >
size_t	GetNearestChild (const VecType &point, typename std::enable_if_t< IsVector< VecType >::value > *=0)
	Return the index of the nearest child node to the given query point. More...

template<typename VecType >
size_t	GetFurthestChild (const VecType &point, typename std::enable_if_t< IsVector< VecType >::value > *=0)
	Return the index of the furthest child node to the given query point. More...

size_t	GetNearestChild (const CoverTree &queryNode)
	Return the index of the nearest child node to the given query node. More...

size_t	GetFurthestChild (const CoverTree &queryNode)
	Return the index of the furthest child node to the given query node. More...

ElemType	MinDistance (const CoverTree &other) const
	Return the minimum distance to another node.

ElemType	MinDistance (const CoverTree &other, const ElemType distance) const
	Return the minimum distance to another node given that the point-to-point distance has already been calculated. More...

ElemType	MinDistance (const arma::vec &other) const
	Return the minimum distance to another point.

ElemType	MinDistance (const arma::vec &other, const ElemType distance) const
	Return the minimum distance to another point given that the distance from the center to the point has already been calculated. More...

ElemType	MaxDistance (const CoverTree &other) const
	Return the maximum distance to another node.

ElemType	MaxDistance (const CoverTree &other, const ElemType distance) const
	Return the maximum distance to another node given that the point-to-point distance has already been calculated. More...

ElemType	MaxDistance (const arma::vec &other) const
	Return the maximum distance to another point.

ElemType	MaxDistance (const arma::vec &other, const ElemType distance) const
	Return the maximum distance to another point given that the distance from the center to the point has already been calculated. More...

math::RangeType< ElemType >	RangeDistance (const CoverTree &other) const
	Return the minimum and maximum distance to another node.

math::RangeType< ElemType >	RangeDistance (const CoverTree &other, const ElemType distance) const
	Return the minimum and maximum distance to another node given that the point-to-point distance has already been calculated. More...

math::RangeType< ElemType >	RangeDistance (const arma::vec &other) const
	Return the minimum and maximum distance to another point.

math::RangeType< ElemType >	RangeDistance (const arma::vec &other, const ElemType distance) const
	Return the minimum and maximum distance to another point given that the point-to-point distance has already been calculated. More...

CoverTree *	Parent () const
	Get the parent node.

CoverTree *&	Parent ()
	Modify the parent node.

ElemType	ParentDistance () const
	Get the distance to the parent.

ElemType &	ParentDistance ()
	Modify the distance to the parent.

ElemType	FurthestPointDistance () const
	Get the distance to the furthest point. This is always 0 for cover trees.

ElemType	FurthestDescendantDistance () const
	Get the distance from the center of the node to the furthest descendant.

ElemType &	FurthestDescendantDistance ()
	Modify the distance from the center of the node to the furthest descendant. More...

ElemType	MinimumBoundDistance () const
	Get the minimum distance from the center to any bound edge (this is the same as furthestDescendantDistance). More...

void	Center (arma::vec &center) const
	Get the center of the node and store it in the given vector.

MetricType &	Metric () const
	Get the instantiated metric.

template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the tree. More...

size_t	DistanceComps () const

size_t &	DistanceComps ()

Protected Member Functions
	CoverTree ()
	A default constructor. More...

Friends
class	cereal::access
	Friend access is given for the default constructor.

Detailed Description

template<typename MetricType = metric::LMetric<2, true>, typename StatisticType = EmptyStatistic, typename MatType = arma::mat, typename RootPointPolicy = FirstPointIsRoot>
class mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >

A cover tree is a tree specifically designed to speed up nearest-neighbor computation in high-dimensional spaces.

Each non-leaf node references a point and has a nonzero number of children, including a "self-child" which references the same point. A leaf node represents only one point.

The tree can be thought of as a hierarchy with the root node at the top level and the leaf nodes at the bottom level. Each level in the tree has an assigned 'scale' i. The tree follows these two invariants:

nesting: the level C_i is a subset of the level C_{i - 1}.
covering: all node in level C_{i - 1} have at least one node in the level C_i with distance less than or equal to b^i (exactly one of these is a parent of the point in level C_{i - 1}.

Note that in the cover tree paper, there is a third invariant (the 'separation invariant'), but that does not apply to our implementation, because we have relaxed the invariant.

The value 'b' refers to the base, which is a parameter of the tree. These three properties make the cover tree very good for fast, high-dimensional nearest-neighbor search.

The theoretical structure of the tree contains many 'implicit' nodes which only have a "self-child" (a child referencing the same point, but at a lower scale level). This practical implementation only constructs explicit nodes – non-leaf nodes with more than one child. A leaf node has no children, and its scale level is INT_MIN.

For more information on cover trees, see

@inproceedings{
  author = {Beygelzimer, Alina and Kakade, Sham and Langford, John},
  title = {Cover trees for nearest neighbor},
  booktitle = {Proceedings of the 23rd International Conference on Machine
    Learning},
  series = {ICML '06},
  year = {2006},
  pages = {97--104]
}

For information on runtime bounds of the nearest-neighbor computation using cover trees, see the following paper, presented at NIPS 2009:

@inproceedings{
  author = {Ram, P., and Lee, D., and March, W.B., and Gray, A.G.},
  title = {Linear-time Algorithms for Pairwise Statistical Problems},
  booktitle = {Advances in Neural Information Processing Systems 22},
  editor = {Y. Bengio and D. Schuurmans and J. Lafferty and C.K.I. Williams
    and A. Culotta},
  pages = {1527--1535},
  year = {2009}
}

The CoverTree class offers three template parameters; a custom metric type can be used with MetricType (this class defaults to the L2-squared metric). The root node's point can be chosen with the RootPointPolicy; by default, the FirstPointIsRoot policy is used, meaning the first point in the dataset is used. The StatisticType policy allows you to define statistics which can be gathered during the creation of the tree.

Template Parameters

MetricType	Metric type to use during tree construction.
RootPointPolicy	Determines which point to use as the root node.
StatisticType	Statistic to be used during tree creation.
MatType	Type of matrix to build the tree on (generally mat or sp_mat).

Constructor & Destructor Documentation

◆ CoverTree() [1/9]

template<typename MetricType, typename StatisticType , typename MatType, typename RootPointPolicy >

mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::CoverTree	(	const MatType &	dataset,
		const ElemType	base = `2.0`,
		MetricType *	metric = `NULL`
	)

Create the cover tree with the given dataset and given base.

The dataset will not be modified during the building procedure (unlike BinarySpaceTree).

The last argument will be removed in mlpack 1.1.0 (see #274 and #273).

Parameters

dataset	Reference to the dataset to build a tree on.
base	Base to use during tree building (default 2.0).
metric	Metric to use (default NULL).

◆ CoverTree() [2/9]

template<typename MetricType, typename StatisticType , typename MatType, typename RootPointPolicy >

mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::CoverTree	(	const MatType &	dataset,
		MetricType &	metric,
		const ElemType	base = `2.0`
	)

Create the cover tree with the given dataset and the given instantiated metric.

Optionally, set the base. The dataset will not be modified during the building procedure (unlike BinarySpaceTree).

Parameters

dataset	Reference to the dataset to build a tree on.
metric	Instantiated metric to use during tree building.
base	Base to use during tree building (default 2.0).

◆ CoverTree() [3/9]

template<typename MetricType, typename StatisticType , typename MatType, typename RootPointPolicy >

mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::CoverTree	(	MatType &&	dataset,
		const ElemType	base = `2.0`
	)

Create the cover tree with the given dataset, taking ownership of the dataset.

Optionally, set the base.

Parameters

dataset	Reference to the dataset to build a tree on.
base	Base to use during tree building (default 2.0).

◆ CoverTree() [4/9]

template<typename MetricType, typename StatisticType , typename MatType, typename RootPointPolicy >

mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::CoverTree	(	MatType &&	dataset,
		MetricType &	metric,
		const ElemType	base = `2.0`
	)

Create the cover tree with the given dataset and the given instantiated metric, taking ownership of the dataset.

Optionally, set the base.

Parameters

dataset	Reference to the dataset to build a tree on.
metric	Instantiated metric to use during tree building.
base	Base to use during tree building (default 2.0).

◆ CoverTree() [5/9]

template<typename MetricType, typename StatisticType , typename MatType, typename RootPointPolicy >

mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::CoverTree	(	const MatType &	dataset,
		const ElemType	base,
		const size_t	pointIndex,
		const int	scale,
		CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > *	parent,
		const ElemType	parentDistance,
		arma::Col< size_t > &	indices,
		arma::vec &	distances,
		size_t	nearSetSize,
		size_t &	farSetSize,
		size_t &	usedSetSize,
		MetricType &	metric = `NULL`
	)

Construct a child cover tree node.

This constructor is not meant to be used externally, but it could be used to insert another node into a tree. This procedure uses only one vector for the near set, the far set, and the used set (this is to prevent unnecessary memory allocation in recursive calls to this constructor). Therefore, the size of the near set, far set, and used set must be passed in. The near set will be entirely used up, and some of the far set may be used. The value of usedSetSize will be set to the number of points used in the construction of this node, and the value of farSetSize will be modified to reflect the number of points in the far set after the construction of this node.

If you are calling this manually, be careful that the given scale is as small as possible, or you may be creating an implicit node in your tree.

Parameters

dataset	Reference to the dataset to build a tree on.
base	Base to use during tree building.
pointIndex	Index of the point this node references.
scale	Scale of this level in the tree.
parent	Parent of this node (NULL indicates no parent).
parentDistance	Distance to the parent node.
indices	Array of indices, ordered [ nearSet \| farSet \| usedSet ]; will be modified to [ farSet \| usedSet ].
distances	Array of distances, ordered the same way as the indices. These represent the distances between the point specified by pointIndex and each point in the indices array.
nearSetSize	Size of the near set; if 0, this will be a leaf.
farSetSize	Size of the far set; may be modified (if this node uses any points in the far set).
usedSetSize	The number of points used will be added to this number.
metric	Metric to use (default NULL).

◆ CoverTree() [6/9]

template<typename MetricType, typename StatisticType , typename MatType, typename RootPointPolicy >

mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::CoverTree	(	const MatType &	dataset,
		const ElemType	base,
		const size_t	pointIndex,
		const int	scale,
		CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > *	parent,
		const ElemType	parentDistance,
		const ElemType	furthestDescendantDistance,
		MetricType *	metric = `NULL`
	)

Manually construct a cover tree node; no tree assembly is done in this constructor, and children must be added manually (use Children()).

This constructor is useful when the tree is being "imported" into the CoverTree class after being created in some other manner.

Parameters

dataset	Reference to the dataset this node is a part of.
base	Base that was used for tree building.
pointIndex	Index of the point in the dataset which this node refers to.
scale	Scale of this node's level in the tree.
parent	Parent node (NULL indicates no parent).
parentDistance	Distance to parent node point.
furthestDescendantDistance	Distance to furthest descendant point.
metric	Instantiated metric (optional).

◆ CoverTree() [7/9]

template<typename MetricType, typename StatisticType , typename MatType, typename RootPointPolicy >

mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::CoverTree ( const CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > & other )

Create a cover tree from another tree.

Be careful! This may use a lot of memory and take a lot of time. This will also make a copy of the dataset.

Parameters

other Cover tree to copy from.

◆ CoverTree() [8/9]

template<typename MetricType, typename StatisticType , typename MatType, typename RootPointPolicy >

mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::CoverTree ( CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > && other )

Move constructor for a Cover Tree, possess all the members of the given tree.

Parameters

other Cover Tree to move.

◆ CoverTree() [9/9]

template<typename MetricType, typename StatisticType , typename MatType, typename RootPointPolicy >

mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::CoverTree ( )

protected

A default constructor.

Default constructor, only for use with cereal.

This is meant to only be used with cereal, which is allowed with the friend declaration below. This does not return a valid tree! This method must be protected, so that the serialization shim can work with the default constructor.

Member Function Documentation

◆ Descendant()

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

size_t mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::Descendant ( const size_t index ) const

inline

Get the index of a particular descendant point.

Return the index of a particular descendant point.

◆ FurthestDescendantDistance()

template<typename MetricType = metric::LMetric<2, true>, typename StatisticType = EmptyStatistic, typename MatType = arma::mat, typename RootPointPolicy = FirstPointIsRoot>

ElemType& mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::FurthestDescendantDistance ( )

inline

Modify the distance from the center of the node to the furthest descendant.

◆ GetFurthestChild() [1/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

template<typename VecType >

size_t mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::GetFurthestChild	(	const VecType &	point,
		typename std::enable_if_t< IsVector< VecType >::value > *	= `0`
	)

Return the index of the furthest child node to the given query point.

If this is a leaf node, it will return NumChildren() (invalid index).

◆ GetFurthestChild() [2/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

size_t mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::GetFurthestChild ( const CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > & queryNode )

Return the index of the furthest child node to the given query node.

If it can't decide, it will return NumChildren() (invalid index).

◆ GetNearestChild() [1/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

template<typename VecType >

size_t mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::GetNearestChild	(	const VecType &	point,
		typename std::enable_if_t< IsVector< VecType >::value > *	= `0`
	)

Return the index of the nearest child node to the given query point.

If this is a leaf node, it will return NumChildren() (invalid index).

◆ GetNearestChild() [2/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

size_t mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::GetNearestChild ( const CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > & queryNode )

Return the index of the nearest child node to the given query node.

If it can't decide, it will return NumChildren() (invalid index).

◆ MaxDistance() [1/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::ElemType mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::MaxDistance	(	const CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > &	other,
		const ElemType	distance
	)		const

Return the maximum distance to another node given that the point-to-point distance has already been calculated.

◆ MaxDistance() [2/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::ElemType mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::MaxDistance	(	const arma::vec &	other,
		const ElemType	distance
	)		const

Return the maximum distance to another point given that the distance from the center to the point has already been calculated.

◆ MinDistance() [1/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::ElemType mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::MinDistance	(	const CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > &	other,
		const ElemType	distance
	)		const

Return the minimum distance to another node given that the point-to-point distance has already been calculated.

◆ MinDistance() [2/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::ElemType mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::MinDistance	(	const arma::vec &	other,
		const ElemType	distance
	)		const

Return the minimum distance to another point given that the distance from the center to the point has already been calculated.

◆ MinimumBoundDistance()

template<typename MetricType = metric::LMetric<2, true>, typename StatisticType = EmptyStatistic, typename MatType = arma::mat, typename RootPointPolicy = FirstPointIsRoot>

ElemType mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::MinimumBoundDistance ( ) const

inline

Get the minimum distance from the center to any bound edge (this is the same as furthestDescendantDistance).

◆ NumDescendants()

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

size_t mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::NumDescendants ( ) const

inline

Get the number of descendant points.

Return the number of descendant points.

◆ operator=() [1/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > & mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::operator= ( const CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > & other )

Copy the given Cover Tree.

Parameters

other The tree to be copied.

◆ operator=() [2/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > & mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::operator= ( CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > && other )

Take ownership of the given Cover Tree.

Parameters

other The tree to take ownership of.

◆ RangeDistance() [1/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

math::RangeType< typename CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::ElemType > mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::RangeDistance	(	const CoverTree< MetricType, StatisticType, MatType, RootPointPolicy > &	other,
		const ElemType	distance
	)		const

Return the minimum and maximum distance to another node given that the point-to-point distance has already been calculated.

◆ RangeDistance() [2/2]

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

math::RangeType< typename CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::ElemType > mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::RangeDistance	(	const arma::vec &	other,
		const ElemType	distance
	)		const

Return the minimum and maximum distance to another point given that the point-to-point distance has already been calculated.

◆ serialize()

template<typename MetricType , typename StatisticType , typename MatType , typename RootPointPolicy >

template<typename Archive >

void mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >::serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the tree.

Serialize to/from a cereal archive.

The documentation for this class was generated from the following files:

src/mlpack/core/tree/cover_tree/cover_tree.hpp
src/mlpack/core/tree/cover_tree/cover_tree_impl.hpp

Classes

Public Types

Public Member Functions

Protected Member Functions

Friends

Detailed Description

template<typename MetricType = metric::LMetric<2, true>, typename StatisticType = EmptyStatistic, typename MatType = arma::mat, typename RootPointPolicy = FirstPointIsRoot> class mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >

Constructor & Destructor Documentation

◆ CoverTree() [1/9]

◆ CoverTree() [2/9]

◆ CoverTree() [3/9]

◆ CoverTree() [4/9]

◆ CoverTree() [5/9]

◆ CoverTree() [6/9]

◆ CoverTree() [7/9]

◆ CoverTree() [8/9]

◆ CoverTree() [9/9]

Member Function Documentation

◆ Descendant()

◆ FurthestDescendantDistance()

◆ GetFurthestChild() [1/2]

◆ GetFurthestChild() [2/2]

◆ GetNearestChild() [1/2]

◆ GetNearestChild() [2/2]

◆ MaxDistance() [1/2]

◆ MaxDistance() [2/2]

◆ MinDistance() [1/2]

◆ MinDistance() [2/2]

◆ MinimumBoundDistance()

◆ NumDescendants()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ RangeDistance() [1/2]

◆ RangeDistance() [2/2]

◆ serialize()

template<typename MetricType = metric::LMetric<2, true>, typename StatisticType = EmptyStatistic, typename MatType = arma::mat, typename RootPointPolicy = FirstPointIsRoot>
class mlpack::tree::CoverTree< MetricType, StatisticType, MatType, RootPointPolicy >