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

Inheritance diagram for NeighborSearch< SortPolicy, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType >:

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Public Types
typedef TreeType< MetricType, NeighborSearchStat< SortPolicy >, MatType >	Tree
	Convenience typedef. More...

Public Member Functions
	NeighborSearch (MatType referenceSet, const NeighborSearchMode mode=DUAL_TREE_MODE, const double epsilon=0, const MetricType metric=MetricType())
	Initialize the NeighborSearch object, passing a reference dataset (this is the dataset which is searched). More...

	NeighborSearch (Tree referenceTree, const NeighborSearchMode mode=DUAL_TREE_MODE, const double epsilon=0, const MetricType metric=MetricType())
	Initialize the NeighborSearch object with a copy of the given pre-constructed reference tree (this is the tree built on the points that will be searched). More...

	NeighborSearch (const NeighborSearchMode mode=DUAL_TREE_MODE, const double epsilon=0, const MetricType metric=MetricType())
	Create a NeighborSearch object without any reference data. More...

	NeighborSearch (const NeighborSearch &other)
	Construct the NeighborSearch object by copying the given NeighborSearch object. More...

	NeighborSearch (NeighborSearch &&other)
	Construct the NeighborSearch object by taking ownership of the given NeighborSearch object. More...

	~NeighborSearch ()
	Delete the NeighborSearch object. More...

size_t	BaseCases () const
	Return the total number of base case evaluations performed during the last search. More...

double	Epsilon () const
	Access the relative error to be considered in approximate search. More...

double &	Epsilon ()
	Modify the relative error to be considered in approximate search. More...

NeighborSearch &	operator= (const NeighborSearch &other)
	Copy the given NeighborSearch object. More...

NeighborSearch &	operator= (NeighborSearch &&other)
	Take ownership of the given NeighborSearch object. More...

const MatType &	ReferenceSet () const
	Access the reference dataset. More...

const Tree &	ReferenceTree () const
	Access the reference tree. More...

Tree &	ReferenceTree ()
	Modify the reference tree. More...

size_t	Scores () const
	Return the number of node combination scores during the last search. More...

void	Search (const MatType &querySet, const size_t k, arma::Mat< size_t > &neighbors, arma::mat &distances)
	For each point in the query set, compute the nearest neighbors and store the output in the given matrices. More...

void	Search (Tree &queryTree, const size_t k, arma::Mat< size_t > &neighbors, arma::mat &distances, bool sameSet=false)
	Given a pre-built query tree, search for the nearest neighbors of each point in the query tree, storing the output in the given matrices. More...

void	Search (const size_t k, arma::Mat< size_t > &neighbors, arma::mat &distances)
	Search for the nearest neighbors of every point in the reference set. More...

NeighborSearchMode	SearchMode () const
	Access the search mode. More...

NeighborSearchMode &	SearchMode ()
	Modify the search mode. More...

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

void	Train (MatType referenceSet)
	Set the reference set to a new reference set, and build a tree if necessary. More...

void	Train (Tree referenceTree)
	Set the reference tree to a new reference tree. More...

Static Public Member Functions
static double	EffectiveError (arma::mat &foundDistances, arma::mat &realDistances)
	Calculate the average relative error (effective error) between the distances calculated and the true distances provided. More...

static double	Recall (arma::Mat< size_t > &foundNeighbors, arma::Mat< size_t > &realNeighbors)
	Calculate the recall (% of neighbors found) given the list of found neighbors and the true set of neighbors. More...

Detailed Description

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::EuclideanDistance, typename MatType = arma::mat, template< typename TreeMetricType, typename TreeStatType, typename TreeMatType > class TreeType = tree::KDTree, template< typename RuleType > class DualTreeTraversalType = TreeType<MetricType, NeighborSearchStat<SortPolicy>, MatType>::template DualTreeTraverser, template< typename RuleType > class SingleTreeTraversalType = TreeType<MetricType, NeighborSearchStat<SortPolicy>, MatType>::template SingleTreeTraverser>
class mlpack::neighbor::NeighborSearch< SortPolicy, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType >

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

It takes a query dataset and a reference dataset (or just a reference dataset) and, for each point in the query dataset, finds the k neighbors in the reference dataset which have the 'best' distance according to a given sorting policy. A constructor is given which takes only a reference dataset, and if that constructor is used, the given reference dataset is also used as the query dataset.

The template parameters SortPolicy and Metric define the sort function used and the metric (distance function) used. More information on those classes can be found in the NearestNeighborSort class and the kernel::ExampleKernel class.

Template Parameters

SortPolicy	The sort policy for distances; see NearestNeighborSort.
MetricType	The metric to use for computation.
MatType	The type of data matrix.
TreeType	The tree type to use; must adhere to the TreeType API.
DualTreeTraversalType	The type of dual tree traversal to use (defaults to the tree's default traverser).
SingleTreeTraversalType	The type of single tree traversal to use (defaults to the tree's default traverser).

Definition at line 88 of file neighbor_search.hpp.

Member Typedef Documentation

◆ Tree

typedef TreeType<MetricType, NeighborSearchStat<SortPolicy>, MatType> Tree

Convenience typedef.

Definition at line 92 of file neighbor_search.hpp.

Constructor & Destructor Documentation

◆ NeighborSearch() [1/5]

NeighborSearch	(	MatType	referenceSet,
		const NeighborSearchMode	mode = `DUAL_TREE_MODE`,
		const double	epsilon = `0`,
		const MetricType	metric = `MetricType()`
	)

Initialize the NeighborSearch object, passing a reference dataset (this is the dataset which is searched).

Optionally, perform the computation in a different mode. An initialized distance metric can be given, for cases where the metric has internal data (i.e. the distance::MahalanobisDistance class).

This method will move the matrices to internal copies, which are rearranged during tree-building. You can avoid creating an extra copy by pre-constructing the trees, passing std::move(yourReferenceSet).

Parameters

referenceSet	Set of reference points.
mode	Neighbor search mode.
epsilon	Relative approximate error (non-negative).
metric	An optional instance of the MetricType class.

◆ NeighborSearch() [2/5]

NeighborSearch	(	Tree	referenceTree,
		const NeighborSearchMode	mode = `DUAL_TREE_MODE`,
		const double	epsilon = `0`,
		const MetricType	metric = `MetricType()`
	)

Initialize the NeighborSearch object with a copy of the given pre-constructed reference tree (this is the tree built on the points that will be searched).

Optionally, choose to use single-tree mode. Naive mode is not available as an option for this constructor. Additionally, an instantiated distance metric can be given, for cases where the distance metric holds data.

This method will copy the given tree. When copies must absolutely be avoided, you can avoid this copy, while taking ownership of the given tree, by passing std::move(yourReferenceTree)

Note: Mapping the points of the matrix back to their original indices is not done when this constructor is used, so if the tree type you are using maps points (like BinarySpaceTree), then you will have to perform the re-mapping manually.

Parameters

referenceTree	Pre-built tree for reference points.
mode	Neighbor search mode.
epsilon	Relative approximate error (non-negative).
metric	Instantiated distance metric.

◆ NeighborSearch() [3/5]

NeighborSearch	(	const NeighborSearchMode	mode = `DUAL_TREE_MODE`,
		const double	epsilon = `0`,
		const MetricType	metric = `MetricType()`
	)

Create a NeighborSearch object without any reference data.

If Search() is called before a reference set is set with Train(), an exception will be thrown.

Parameters

mode	Neighbor search mode.
epsilon	Relative approximate error (non-negative).
metric	Instantiated metric.

◆ NeighborSearch() [4/5]

NeighborSearch ( const NeighborSearch< SortPolicy, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType > & other )

Construct the NeighborSearch object by copying the given NeighborSearch object.

Parameters

other NeighborSearch object to copy.

◆ NeighborSearch() [5/5]

NeighborSearch ( NeighborSearch< SortPolicy, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType > && other )

Construct the NeighborSearch object by taking ownership of the given NeighborSearch object.

Parameters

other NeighborSearch object to take ownership of.

◆ ~NeighborSearch()

~NeighborSearch ( )

Delete the NeighborSearch object.

The tree is the only member we are responsible for deleting. The others will take care of themselves.

Member Function Documentation

◆ BaseCases()

size_t BaseCases ( ) const

inline

Return the total number of base case evaluations performed during the last search.

Definition at line 314 of file neighbor_search.hpp.

◆ EffectiveError()

static double EffectiveError	(	arma::mat &	foundDistances,
		arma::mat &	realDistances
	)

static

Calculate the average relative error (effective error) between the distances calculated and the true distances provided.

The input matrices must have the same size.

Cases where the true distance is zero (the same point) or the calculated distance is SortPolicy::WorstDistance() (didn't find enough points) will be ignored.

Parameters

foundDistances	Matrix storing lists of calculated distances for each query point.
realDistances	Matrix storing lists of true best distances for each query point.

Returns: Average relative error.

◆ Epsilon() [1/2]

double Epsilon ( ) const

inline

Access the relative error to be considered in approximate search.

Definition at line 325 of file neighbor_search.hpp.

◆ Epsilon() [2/2]

double& Epsilon ( )

inline

Modify the relative error to be considered in approximate search.

Definition at line 327 of file neighbor_search.hpp.

◆ operator=() [1/2]

NeighborSearch& operator= ( const NeighborSearch< SortPolicy, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType > & other )

Copy the given NeighborSearch object.

Parameters

other NeighborSearch object to copy.

◆ operator=() [2/2]

NeighborSearch& operator= ( NeighborSearch< SortPolicy, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType > && other )

Take ownership of the given NeighborSearch object.

Parameters

other NeighborSearch object to take ownership of.

◆ Recall()

static double Recall	(	arma::Mat< size_t > &	foundNeighbors,
		arma::Mat< size_t > &	realNeighbors
	)

static

Calculate the recall (% of neighbors found) given the list of found neighbors and the true set of neighbors.

The recall returned will be in the range [0, 1].

Parameters

foundNeighbors	Matrix storing lists of calculated neighbors for each query point.
realNeighbors	Matrix storing lists of true best neighbors for each query point.

Returns: Recall.

◆ ReferenceSet()

const MatType& ReferenceSet ( ) const

inline

Access the reference dataset.

Definition at line 330 of file neighbor_search.hpp.

◆ ReferenceTree() [1/2]

const Tree& ReferenceTree ( ) const

inline

Access the reference tree.

Definition at line 333 of file neighbor_search.hpp.

◆ ReferenceTree() [2/2]

Tree& ReferenceTree ( )

inline

Modify the reference tree.

Definition at line 335 of file neighbor_search.hpp.

◆ Scores()

size_t Scores ( ) const

inline

Return the number of node combination scores during the last search.

Definition at line 317 of file neighbor_search.hpp.

◆ Search() [1/3]

void Search	(	const MatType &	querySet,
		const size_t	k,
		arma::Mat< size_t > &	neighbors,
		arma::mat &	distances
	)

For each point in the query set, compute the nearest neighbors and store the output in the given matrices.

The matrices will be set to the size of n columns by k rows, where n is the number of points in the query dataset and k is the number of neighbors being searched for.

If querySet contains only a few query points, the extra cost of building a tree on the points for dual-tree search may not be warranted, and it may be worthwhile to set singleMode = false (either in the constructor or with SingleMode()).

Parameters

querySet	Set of query points (can be just one point).
k	Number of neighbors to search for.
neighbors	Matrix storing lists of neighbors for each query point.
distances	Matrix storing distances of neighbors for each query point.

Referenced by LMetricSearch< TPower >::Search(), CosineSearch::Search(), and PearsonSearch::Search().

◆ Search() [2/3]

void Search	(	Tree &	queryTree,
		const size_t	k,
		arma::Mat< size_t > &	neighbors,
		arma::mat &	distances,
		bool	sameSet = `false`
	)

Given a pre-built query tree, search for the nearest neighbors of each point in the query tree, storing the output in the given matrices.

The matrices will be set to the size of n columns by k rows, where n is the number of points in the query dataset and k is the number of neighbors being searched for.

Note that if you are calling Search() multiple times with a single query tree, you need to reset the bounds in the statistic of each query node, otherwise the result may be wrong! You can do this by calling TreeType::Stat().Reset() on each node in the query tree.

Parameters

queryTree	Tree built on query points.
k	Number of neighbors to search for.
neighbors	Matrix storing lists of neighbors for each query point.
distances	Matrix storing distances of neighbors for each query point.
sameSet	Denotes whether or not the reference and query sets are the same.

◆ Search() [3/3]

void Search	(	const size_t	k,
		arma::Mat< size_t > &	neighbors,
		arma::mat &	distances
	)

Search for the nearest neighbors of every point in the reference set.

This is basically equivalent to calling any other overload of Search() with the reference set as the query set; so, this lets you do all-k-nearest-neighbors search. The results are stored in the given matrices. The matrices will be set to the size of n columns by k rows, where n is the number of points in the query dataset and k is the number of neighbors being searched for.

Parameters

k	Number of neighbors to search for.
neighbors	Matrix storing lists of neighbors for each query point.
distances	Matrix storing distances of neighbors for each query point.

◆ SearchMode() [1/2]

NeighborSearchMode SearchMode ( ) const

inline

Access the search mode.

Definition at line 320 of file neighbor_search.hpp.

◆ SearchMode() [2/2]

NeighborSearchMode& SearchMode ( )

inline

Modify the search mode.

Definition at line 322 of file neighbor_search.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t	version
	)

Serialize the NeighborSearch model.

Referenced by NeighborSearch< neighbor::NearestNeighborSort, metric::LMetric< TPower, true > >::ReferenceTree().

◆ Train() [1/2]

void Train ( MatType referenceSet )

Set the reference set to a new reference set, and build a tree if necessary.

The dataset is copied by default, but the copy can be avoided by transferring the ownership of the dataset using std::move(). This method is called 'Train()' in order to match the rest of the mlpack abstractions, even though calling this "training" is maybe a bit of a stretch.

Parameters

referenceSet New set of reference data.

Referenced by CosineSearch::CosineSearch(), and PearsonSearch::PearsonSearch().

◆ Train() [2/2]

void Train ( Tree referenceTree )

Set the reference tree to a new reference tree.

The tree is copied by default, but the copy can be avoided by using std::move() to transfer the ownership of the tree. This method is called 'Train()' in order to match the rest of the mlpack abstractions, even though calling this "training" is maybe a bit of a stretch.

Parameters

referenceTree Pre-built tree for reference points.

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/neighbor_search/neighbor_search.hpp

Public Types

Public Member Functions

Static Public Member Functions

Detailed Description

Member Typedef Documentation

◆ Tree

Constructor & Destructor Documentation

◆ NeighborSearch() [1/5]

◆ NeighborSearch() [2/5]

◆ NeighborSearch() [3/5]

◆ NeighborSearch() [4/5]

◆ NeighborSearch() [5/5]

◆ ~NeighborSearch()

Member Function Documentation

◆ BaseCases()

◆ EffectiveError()

◆ Epsilon() [1/2]

◆ Epsilon() [2/2]

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ Recall()

◆ ReferenceSet()

◆ ReferenceTree() [1/2]

◆ ReferenceTree() [2/2]

◆ Scores()

◆ Search() [1/3]

◆ Search() [2/3]

◆ Search() [3/3]

◆ SearchMode() [1/2]

◆ SearchMode() [2/2]

◆ serialize()

◆ Train() [1/2]

◆ Train() [2/2]