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

Inheritance diagram for RASearch< SortPolicy, MetricType, MatType, TreeType >:

[legend]

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

Public Member Functions
	RASearch (MatType referenceSet, const bool naive=false, const bool singleMode=false, const double tau=5, const double alpha=0.95, const bool sampleAtLeaves=false, const bool firstLeafExact=false, const size_t singleSampleLimit=20, const MetricType metric=MetricType())
	Initialize the RASearch object, passing both a reference dataset (this is the dataset that will be searched). More...

	RASearch (Tree *referenceTree, const bool singleMode=false, const double tau=5, const double alpha=0.95, const bool sampleAtLeaves=false, const bool firstLeafExact=false, const size_t singleSampleLimit=20, const MetricType metric=MetricType())
	Initialize the RASearch object with the given pre-constructed reference tree. More...

	RASearch (const bool naive=false, const bool singleMode=false, const double tau=5, const double alpha=0.95, const bool sampleAtLeaves=false, const bool firstLeafExact=false, const size_t singleSampleLimit=20, const MetricType metric=MetricType())
	Create an RASearch object with no reference data. More...

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

double	Alpha () const
	Get the desired success probability. More...

double &	Alpha ()
	Modify the desired success probability. More...

bool	FirstLeafExact () const
	Get whether or not we traverse to the first leaf without approximation. More...

bool &	FirstLeafExact ()
	Modify whether or not we traverse to the first leaf without approximation. More...

bool	Naive () const
	Get whether or not naive (brute-force) search is used. More...

bool &	Naive ()
	Modify whether or not naive (brute-force) search is used. More...

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

void	ResetQueryTree (Tree *queryTree) const
	This function recursively resets the RAQueryStat of the given query tree to set 'bound' to SortPolicy::WorstDistance and 'numSamplesMade' to 0. More...

bool	SampleAtLeaves () const
	Get whether or not sampling is done at the leaves. More...

bool &	SampleAtLeaves ()
	Modify whether or not sampling is done at the leaves. More...

void	Search (const MatType &querySet, const size_t k, arma::Mat< size_t > &neighbors, arma::mat &distances)
	Compute the rank approximate nearest neighbors of each query point in the query set 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)
	Compute the rank approximate nearest neighbors of each point in the pre-built query tree and store the output in the given matrices. More...

void	Search (const size_t k, arma::Mat< size_t > &neighbors, arma::mat &distances)
	Compute the rank approximate nearest neighbors of each point in the reference set (that is, the query set is taken to be the reference set), and store the output in the given matrices. More...

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

bool	SingleMode () const
	Get whether or not single-tree search is used. More...

bool &	SingleMode ()
	Modify whether or not single-tree search is used. More...

size_t	SingleSampleLimit () const
	Get the limit on the size of a node that can be approximated. More...

size_t &	SingleSampleLimit ()
	Modify the limit on the size of a node that can be approximation. More...

double	Tau () const
	Get the rank-approximation in percentile of the data. More...

double &	Tau ()
	Modify the rank-approximation in percentile of the data. More...

void	Train (MatType referenceSet)
	"Train" the model on the given reference set. More...

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

Detailed Description

template
<
typename
SortPolicy
=
NearestNeighborSort
,
typename
MetricType
=
metric::EuclideanDistance
,
typename
MatType
=
arma::mat
,
template
<
typename
TreeMetricType
,
typename
TreeStatType
,
typename
TreeMatType
>
class
TreeType
=
tree::KDTree
>

class mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >

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

If the 'naive' option is chosen, this rank-approximate search will be done by randomly sampling from the whole set. If the 'naive' option is not chosen, the sampling is done in a stratified manner in the tree as mentioned in the algorithms in Figure 2 of the following paper:

@inproceedings{ram2009rank,
  title={{Rank-Approximate Nearest Neighbor Search: Retaining Meaning and
      Speed in High Dimensions}},
  author={{Ram, P. and Lee, D. and Ouyang, H. and Gray, A. G.}},
  booktitle={{Advances of Neural Information Processing Systems}},
  year={2009}
}

RASearch is currently known to not work with ball trees (#356).

Template Parameters

SortPolicy	The sort policy for distances; see NearestNeighborSort.
MetricType	The metric to use for computation.
TreeType	The tree type to use.

Definition at line 77 of file ra_search.hpp.

Member Typedef Documentation

◆ Tree

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

Convenience typedef.

Definition at line 81 of file ra_search.hpp.

Constructor & Destructor Documentation

◆ RASearch() [1/3]

RASearch	(	MatType	referenceSet,
		const bool	naive = `false`,
		const bool	singleMode = `false`,
		const double	tau = `5`,
		const double	alpha = `0.95`,
		const bool	sampleAtLeaves = `false`,
		const bool	firstLeafExact = `false`,
		const size_t	singleSampleLimit = `20`,
		const MetricType	metric = `MetricType()`
	)

Initialize the RASearch object, passing both a reference dataset (this is the dataset that will be searched).

Optionally, perform the computation in naive mode or single-tree 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 copy the matrices to internal copies, which are rearranged during tree-building. If you don't need to keep the reference dataset, you can use std::move() to remove the overhead of making copies. Using std::move() transfers the ownership of the dataset.

tau, the rank-approximation parameter, specifies that we are looking for k neighbors with probability alpha of being in the top tau percent of nearest neighbors. So, as an example, if our dataset has 1000 points, and we want 5 nearest neighbors with 95% probability of being in the top 5% of nearest neighbors (or, the top 50 nearest neighbors), we set k = 5, tau = 5, and alpha = 0.95.

The method will fail (and throw a std::invalid_argument exception) if the value of tau is too low: tau must be set such that the number of points in the corresponding percentile of the data is greater than k. Thus, if we choose tau = 0.1 with a dataset of 1000 points and k = 5, then we are attempting to choose 5 nearest neighbors out of the closest 1 point – this is invalid.

Parameters

referenceSet	Set of reference points.
naive	If true, the rank-approximate search will be performed by directly sampling the whole set instead of using the stratified sampling on the tree.
singleMode	If true, single-tree search will be used (as opposed to dual-tree search). This is useful when Search() will be called with few query points.
metric	An optional instance of the MetricType class.
tau	The rank-approximation in percentile of the data. The default value is 5%.
alpha	The desired success probability. The default value is 0.95.
sampleAtLeaves	Sample at leaves for faster but less accurate computation. This defaults to 'false'.
firstLeafExact	Traverse to the first leaf without approximation. This can ensure that the query definitely finds its (near) duplicate if there exists one. This defaults to 'false' for now.
singleSampleLimit	The limit on the largest node that can be approximated by sampling. This defaults to 20.

◆ RASearch() [2/3]

RASearch	(	Tree *	referenceTree,
		const bool	singleMode = `false`,
		const double	tau = `5`,
		const double	alpha = `0.95`,
		const bool	sampleAtLeaves = `false`,
		const bool	firstLeafExact = `false`,
		const size_t	singleSampleLimit = `20`,
		const MetricType	metric = `MetricType()`
	)

Initialize the RASearch object with the given pre-constructed reference tree.

It is assumed that the points in the tree's dataset correspond to the reference set. Optionally, choose to use single-tree mode. Naive mode is not available as an option for this constructor; instead, to run naive computation, use a different constructor. Additionally, an instantiated distance metric can be given, for cases where the distance metric holds data.

There is no copying of the data matrices in this constructor (because tree-building is not necessary), so this is the constructor to use when copies absolutely must be avoided.

tau, the rank-approximation parameter, specifies that we are looking for k neighbors with probability alpha of being in the top tau percent of nearest neighbors. So, as an example, if our dataset has 1000 points, and we want 5 nearest neighbors with 95% probability of being in the top 5% of nearest neighbors (or, the top 50 nearest neighbors), we set k = 5, tau = 5, and alpha = 0.95.

The method will fail (and throw a std::invalid_argument exception) if the value of tau is too low: tau must be set such that the number of points in the corresponding percentile of the data is greater than k. Thus, if we choose tau = 0.1 with a dataset of 1000 points and k = 5, then we are attempting to choose 5 nearest neighbors out of the closest 1 point – this is invalid.

Note: Tree-building may (at least with BinarySpaceTree) modify the ordering of a matrix, so be aware that the results you get from Search() will correspond to the modified matrix.

Parameters

referenceTree	Pre-built tree for reference points.
singleMode	Whether single-tree computation should be used (as opposed to dual-tree computation).
tau	The rank-approximation in percentile of the data. The default value is 5%.
alpha	The desired success probability. The default value is 0.95.
sampleAtLeaves	Sample at leaves for faster but less accurate computation. This defaults to 'false'.
firstLeafExact	Traverse to the first leaf without approximation. This can ensure that the query definitely finds its (near) duplicate if there exists one. This defaults to 'false' for now.
singleSampleLimit	The limit on the largest node that can be approximated by sampling. This defaults to 20.
metric	Instantiated distance metric.

◆ RASearch() [3/3]

RASearch	(	const bool	naive = `false`,
		const bool	singleMode = `false`,
		const double	tau = `5`,
		const double	alpha = `0.95`,
		const bool	sampleAtLeaves = `false`,
		const bool	firstLeafExact = `false`,
		const size_t	singleSampleLimit = `20`,
		const MetricType	metric = `MetricType()`
	)

Create an RASearch object with no reference data.

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

Parameters

naive	Whether naive (brute-force) search should be used.
singleMode	Whether single-tree computation should be used (as opposed to dual-tree computation).
tau	The rank-approximation in percentile of the data. The default value is 5%.
alpha	The desired success probability. The default value is 0.95.
sampleAtLeaves	Sample at leaves for faster but less accurate computation. This defaults to 'false'.
firstLeafExact	Traverse to the first leaf without approximation. This can ensure that the query definitely finds its (near) duplicate if there exists one. This defaults to 'false' for now.
singleSampleLimit	The limit on the largest node that can be approximated by sampling. This defaults to 20.
metric	Instantiated distance metric.

◆ ~RASearch()

~RASearch ( )

Delete the RASearch object.

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

Member Function Documentation

◆ Alpha() [1/2]

double Alpha ( ) const

inline

Get the desired success probability.

Definition at line 342 of file ra_search.hpp.

◆ Alpha() [2/2]

double& Alpha ( )

inline

Modify the desired success probability.

Definition at line 344 of file ra_search.hpp.

◆ FirstLeafExact() [1/2]

bool FirstLeafExact ( ) const

inline

Get whether or not we traverse to the first leaf without approximation.

Definition at line 352 of file ra_search.hpp.

◆ FirstLeafExact() [2/2]

bool& FirstLeafExact ( )

inline

Modify whether or not we traverse to the first leaf without approximation.

Definition at line 354 of file ra_search.hpp.

◆ Naive() [1/2]

bool Naive ( ) const

inline

Get whether or not naive (brute-force) search is used.

Definition at line 327 of file ra_search.hpp.

◆ Naive() [2/2]

bool& Naive ( )

inline

Modify whether or not naive (brute-force) search is used.

Definition at line 329 of file ra_search.hpp.

◆ ReferenceSet()

const MatType& ReferenceSet ( ) const

inline

Access the reference set.

Definition at line 324 of file ra_search.hpp.

◆ ResetQueryTree()

void ResetQueryTree ( Tree * queryTree ) const

This function recursively resets the RAQueryStat of the given query tree to set 'bound' to SortPolicy::WorstDistance and 'numSamplesMade' to 0.

This allows a user to perform multiple searches with the same query tree, possibly with different levels of approximation without requiring to build a new pair of trees for every new (approximate) search.

If Search() is called multiple times with the same query tree without calling ResetQueryTree(), the results may not satisfy the theoretical guarantees provided by the rank-approximate neighbor search algorithm.

Parameters

queryTree Tree whose statistics should be reset.

◆ SampleAtLeaves() [1/2]

bool SampleAtLeaves ( ) const

inline

Get whether or not sampling is done at the leaves.

Definition at line 347 of file ra_search.hpp.

◆ SampleAtLeaves() [2/2]

bool& SampleAtLeaves ( )

inline

Modify whether or not sampling is done at the leaves.

Definition at line 349 of file ra_search.hpp.

◆ Search() [1/3]

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

Compute the rank approximate nearest neighbors of each query point in the query set 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 is small or only contains one point, it can be faster to do single-tree search; single-tree search can be set with the SingleMode() function or in the constructor.

Parameters

querySet	Set of query points (can be a single 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.

◆ Search() [2/3]

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

Compute the rank approximate nearest neighbors of each point in the pre-built query tree 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 singleMode or naive is enabled, then this method will throw a std::invalid_argument exception; calling this function implies a dual-tree algorithm.

Note: If the tree type you are using modifies the data matrix, be aware that the results returned from this function will be with respect to the modified data matrix.

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.

◆ Search() [3/3]

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

Compute the rank approximate nearest neighbors of each point in the reference set (that is, the query set is taken to be the reference set), 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.

Parameters

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

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the object.

Referenced by RASearch< NearestNeighborSort, metric::EuclideanDistance, arma::mat, TreeType >::SingleSampleLimit().

◆ SingleMode() [1/2]

bool SingleMode ( ) const

inline

Get whether or not single-tree search is used.

Definition at line 332 of file ra_search.hpp.

◆ SingleMode() [2/2]

bool& SingleMode ( )

inline

Modify whether or not single-tree search is used.

Definition at line 334 of file ra_search.hpp.

◆ SingleSampleLimit() [1/2]

size_t SingleSampleLimit ( ) const

inline

Get the limit on the size of a node that can be approximated.

Definition at line 357 of file ra_search.hpp.

◆ SingleSampleLimit() [2/2]

size_t& SingleSampleLimit ( )

inline

Modify the limit on the size of a node that can be approximation.

Definition at line 359 of file ra_search.hpp.

◆ Tau() [1/2]

double Tau ( ) const

inline

Get the rank-approximation in percentile of the data.

Definition at line 337 of file ra_search.hpp.

◆ Tau() [2/2]

double& Tau ( )

inline

Modify the rank-approximation in percentile of the data.

Definition at line 339 of file ra_search.hpp.

◆ Train() [1/2]

void Train ( MatType referenceSet )

"Train" the model on the given reference set.

If tree-based search is being used (if Naive() is false), the reference tree is rebuilt. Thus, a copy of the reference dataset is made. If you don't need to keep the dataset, you can avoid copying by using std::move(). This transfers the ownership of the dataset.

Parameters

referenceSet New reference set to use.

◆ Train() [2/2]

void Train ( Tree * referenceTree )

Set the reference tree to a new reference tree.

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

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

Public Types

Public Member Functions

Detailed Description

template<typenameSortPolicy=NearestNeighborSort,typenameMetricType=metric::EuclideanDistance,typenameMatType=arma::mat,template<typenameTreeMetricType,typenameTreeStatType,typenameTreeMatType>classTreeType=tree::KDTree> class mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >

Member Typedef Documentation

◆ Tree

Constructor & Destructor Documentation

◆ RASearch() [1/3]

◆ RASearch() [2/3]

◆ RASearch() [3/3]

◆ ~RASearch()

Member Function Documentation

◆ Alpha() [1/2]

◆ Alpha() [2/2]

◆ FirstLeafExact() [1/2]

◆ FirstLeafExact() [2/2]

◆ Naive() [1/2]

◆ Naive() [2/2]

◆ ReferenceSet()

◆ ResetQueryTree()

◆ SampleAtLeaves() [1/2]

◆ SampleAtLeaves() [2/2]

◆ Search() [1/3]

◆ Search() [2/3]

◆ Search() [3/3]

◆ serialize()

◆ SingleMode() [1/2]

◆ SingleMode() [2/2]

◆ SingleSampleLimit() [1/2]

◆ SingleSampleLimit() [2/2]

◆ Tau() [1/2]

◆ Tau() [2/2]

◆ Train() [1/2]

◆ Train() [2/2]