Classes
class	DrusillaSelect

class	FurthestNS
	This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class. More...

class	LeafSizeNSWrapper
	LeafSizeNSWrapper wraps any NeighborSearch types that take a leaf size for tree construction. More...

class	LeafSizeRAWrapper
	LeafSizeRAWrapper wraps any RASearch type that needs to be able to take the leaf size into account when building trees. More...

class	LSHSearch
	The LSHSearch class; this class builds a hash on the reference set and uses this hash to compute the distance-approximate nearest-neighbors of the given queries. More...

class	NearestNS
	This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class. More...

class	NeighborSearch
	The NeighborSearch class is a template class for performing distance-based neighbor searches. More...

class	NeighborSearchRules
	The NeighborSearchRules class is a template helper class used by NeighborSearch class when performing distance-based neighbor searches. More...

class	NeighborSearchStat
	Extra data for each node in the tree. More...

class	NSModel
	The NSModel class provides an easy way to serialize a model, abstracts away the different types of trees, and also reflects the NeighborSearch API. More...

class	NSWrapper
	NSWrapper is a wrapper class for most NeighborSearch types. More...

class	NSWrapperBase
	NSWrapperBase is a base wrapper class for holding all NeighborSearch types supported by NSModel. More...

class	QDAFN

class	RAModel
	The RAModel class provides an abstraction for the RASearch class, abstracting away the TreeType parameter and allowing it to be specified at runtime in this class. More...

class	RAQueryStat
	Extra data for each node in the tree. More...

class	RASearch
	The RASearch class: This class provides a generic manner to perform rank-approximate search via random-sampling. More...

class	RASearchRules
	The RASearchRules class is a template helper class used by RASearch class when performing rank-approximate search via random-sampling. More...

class	RAUtil

class	RAWrapper
	RAWrapper is a wrapper class for most RASearch types. More...

class	RAWrapperBase
	RAWrapperBase is a base wrapper class for holding all RASearch types supported by RAModel. More...

class	SpillNSWrapper
	The SpillNSWrapper class wraps the NeighborSearch class when the spill tree is used. More...

Typedefs
template < template < typename TreeMetricType , typename TreeStatType , typename TreeMatType > class TreeType = tree::SPTree >
using	DefeatistKNN = NeighborSearch< NearestNeighborSort, metric::EuclideanDistance, arma::mat, TreeType, TreeType< metric::EuclideanDistance, NeighborSearchStat< NearestNeighborSort >, arma::mat >::template DefeatistDualTreeTraverser, TreeType< metric::EuclideanDistance, NeighborSearchStat< NearestNeighborSort >, arma::mat >::template DefeatistSingleTreeTraverser >
	The DefeatistKNN class is the k-nearest-neighbors method considering defeatist search. More...

using	FurthestNeighborSort = FurthestNS

typedef NeighborSearch< FurthestNeighborSort, metric::EuclideanDistance >	KFN
	The KFN class is the k-furthest-neighbors method. More...

typedef NeighborSearch< NearestNeighborSort, metric::EuclideanDistance >	KNN
	The KNN class is the k-nearest-neighbors method. More...

typedef RASearch< FurthestNeighborSort >	KRAFN
	The KRAFN class is the k-rank-approximate-farthest-neighbors method. More...

typedef RASearch	KRANN
	The KRANN class is the k-rank-approximate-nearest-neighbors method. More...

using	NearestNeighborSort = NearestNS

typedef DefeatistKNN< tree::SPTree >	SpillKNN
	The SpillKNN class is the k-nearest-neighbors method considering defeatist search on SPTree. More...

Enumerations
enum	NeighborSearchMode { NAIVE_MODE , SINGLE_TREE_MODE , DUAL_TREE_MODE , GREEDY_SINGLE_TREE_MODE }
	NeighborSearchMode represents the different neighbor search modes available. More...

Functions
void	Unmap (const arma::Mat< size_t > &neighbors, const arma::mat &distances, const std::vector< size_t > &referenceMap, const std::vector< size_t > &queryMap, arma::Mat< size_t > &neighborsOut, arma::mat &distancesOut, const bool squareRoot=false)
	Assuming that the datasets have been mapped using the referenceMap and the queryMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors. More...

void	Unmap (const arma::Mat< size_t > &neighbors, const arma::mat &distances, const std::vector< size_t > &referenceMap, arma::Mat< size_t > &neighborsOut, arma::mat &distancesOut, const bool squareRoot=false)
	Assuming that the datasets have been mapped using referenceMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors. More...

Typedef Documentation

◆ DefeatistKNN

using DefeatistKNN = NeighborSearch< NearestNeighborSort, metric::EuclideanDistance, arma::mat, TreeType, TreeType<metric::EuclideanDistance, NeighborSearchStat<NearestNeighborSort>, arma::mat>::template DefeatistDualTreeTraverser, TreeType<metric::EuclideanDistance, NeighborSearchStat<NearestNeighborSort>, arma::mat>::template DefeatistSingleTreeTraverser>

The DefeatistKNN class is the k-nearest-neighbors method considering defeatist search.

It returns L2 distances (Euclidean distances) for each of the k nearest neighbors found.

Template Parameters

TreeType The tree type to use; must adhere to the TreeType API, and implement Defeatist Traversers.

Definition at line 60 of file typedef.hpp.

◆ FurthestNeighborSort

using FurthestNeighborSort = FurthestNS

Definition at line 201 of file furthest_neighbor_sort.hpp.

◆ KFN

typedef NeighborSearch<FurthestNeighborSort, metric::EuclideanDistance> KFN

The KFN class is the k-furthest-neighbors method.

It returns L2 distances (Euclidean distances) for each of the k furthest neighbors.

Definition at line 38 of file typedef.hpp.

◆ KNN

typedef NeighborSearch<NearestNeighborSort, metric::EuclideanDistance> KNN

The KNN class is the k-nearest-neighbors method.

It returns L2 distances (Euclidean distances) for each of the k nearest neighbors.

Definition at line 32 of file typedef.hpp.

◆ KRAFN

typedef RASearch<FurthestNeighborSort> KRAFN

The KRAFN class is the k-rank-approximate-farthest-neighbors method.

It returns L2 distances for each of the k rank-approximate farthest-neighbors.

The approximation is controlled with two parameters (see allkrann_main.cpp) which can be specified at search time. So the tree building is done only once while the search can be performed multiple times with different approximation levels.

Definition at line 47 of file ra_typedef.hpp.

◆ KRANN

typedef RASearch KRANN

The KRANN class is the k-rank-approximate-nearest-neighbors method.

It returns L2 distances for each of the k rank-approximate nearest-neighbors.

The approximation is controlled with two parameters (see allkrann_main.cpp) which can be specified at search time. So the tree building is done only once while the search can be performed multiple times with different approximation levels.

Definition at line 36 of file ra_typedef.hpp.

◆ NearestNeighborSort

using NearestNeighborSort = NearestNS

Definition at line 200 of file nearest_neighbor_sort.hpp.

◆ SpillKNN

typedef DefeatistKNN<tree::SPTree> SpillKNN

The SpillKNN class is the k-nearest-neighbors method considering defeatist search on SPTree.

It returns L2 distances (Euclidean distances) for each of the k nearest neighbors found.

Definition at line 67 of file typedef.hpp.

Enumeration Type Documentation

◆ NeighborSearchMode

enum NeighborSearchMode

NeighborSearchMode represents the different neighbor search modes available.

Enumerator
NAIVE_MODE
SINGLE_TREE_MODE
DUAL_TREE_MODE
GREEDY_SINGLE_TREE_MODE

Definition at line 43 of file neighbor_search.hpp.

Function Documentation

◆ Unmap() [1/2]

void mlpack::neighbor::Unmap	(	const arma::Mat< size_t > &	neighbors,
		const arma::mat &	distances,
		const std::vector< size_t > &	referenceMap,
		const std::vector< size_t > &	queryMap,
		arma::Mat< size_t > &	neighborsOut,
		arma::mat &	distancesOut,
		const bool	squareRoot = `false`
	)

Assuming that the datasets have been mapped using the referenceMap and the queryMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors.

This is useful for the dual-tree case.

Parameters

neighbors	Matrix of neighbors resulting from neighbor search.
distances	Matrix of distances resulting from neighbor search.
referenceMap	Mapping of reference set to old points.
queryMap	Mapping of query set to old points.
neighborsOut	Matrix to store unmapped neighbors into.
distancesOut	Matrix to store unmapped distances into.
squareRoot	If true, take the square root of the distances.

◆ Unmap() [2/2]

void mlpack::neighbor::Unmap	(	const arma::Mat< size_t > &	neighbors,
		const arma::mat &	distances,
		const std::vector< size_t > &	referenceMap,
		arma::Mat< size_t > &	neighborsOut,
		arma::mat &	distancesOut,
		const bool	squareRoot = `false`
	)

Assuming that the datasets have been mapped using referenceMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors.

This is useful for the single-tree case.