Classes
class	AlphaVisitor
	Exposes the Alpha() method of the given RAType. More...

class	BiSearchVisitor
	BiSearchVisitor executes a bichromatic neighbor search on the given NSType. More...

class	DeleteVisitor
	DeleteVisitor deletes the given NSType instance. More...

class	DrusillaSelect

class	EpsilonVisitor
	EpsilonVisitor exposes the Epsilon method of the given NSType. More...

class	FirstLeafExactVisitor
	Exposes the FirstLeafExact() method of the given RAType. More...

class	FurthestNS
	This class implements the necessary methods for the SortPolicy template parameter of the NeighborSearch class. 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	MonoSearchVisitor
	MonoSearchVisitor executes a monochromatic neighbor search on the given NSType. More...

class	NaiveVisitor
	NaiveVisitor exposes the Naive() method of the given RAType. 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	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	ReferenceSetVisitor
	ReferenceSetVisitor exposes the referenceSet of the given NSType. More...

class	SampleAtLeavesVisitor
	Exposes the SampleAtLeaves() method of the given RAType. More...

class	SearchModeVisitor
	SearchModeVisitor exposes the SearchMode() method of the given NSType. More...

class	SingleModeVisitor
	Exposes the SingleMode() method of the given RAType. More...

class	SingleSampleLimitVisitor
	Exposes the SingleSampleLimit() method of the given RAType. More...

class	TauVisitor
	Exposes the Tau() method of the given RAType. More...

class	TrainVisitor
	TrainVisitor sets the reference set to a new reference set on the given NSType. 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

template < typename SortPolicy , template < typename TreeMetricType , typename TreeStatType , typename TreeMatType > class TreeType >
using	NSType = NeighborSearch< SortPolicy, metric::EuclideanDistance, arma::mat, TreeType, TreeType< metric::EuclideanDistance, NeighborSearchStat< SortPolicy >, arma::mat >::template DualTreeTraverser >
	Alias template for euclidean neighbor search. More...

template < typename SortPolicy , template < typename TreeMetricType , typename TreeStatType , typename TreeMatType > class TreeType >
using	RAType = RASearch< SortPolicy, metric::EuclideanDistance, arma::mat, TreeType >
	Alias template for RASearch. More...

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.

◆ NSType

using NSType = NeighborSearch<SortPolicy, metric::EuclideanDistance, arma::mat, TreeType, TreeType<metric::EuclideanDistance, NeighborSearchStat<SortPolicy>, arma::mat>::template DualTreeTraverser>

Alias template for euclidean neighbor search.

Definition at line 42 of file ns_model.hpp.

◆ RAType

using RAType = RASearch<SortPolicy, metric::EuclideanDistance, arma::mat, TreeType>

Alias template for RASearch.

Definition at line 37 of file ra_model.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 38 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.