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

Public Types
typedef tree::TraversalInfo< TreeType >	TraversalInfoType

Public Member Functions
	RASearchRules (const arma::mat &referenceSet, const arma::mat &querySet, const size_t k, MetricType &metric, const double tau=5, const double alpha=0.95, const bool naive=false, const bool sampleAtLeaves=false, const bool firstLeafExact=false, const size_t singleSampleLimit=20, const bool sameSet=false)
	Construct the RASearchRules object. More...

double	BaseCase (const size_t queryIndex, const size_t referenceIndex)
	Get the distance from the query point to the reference point. More...

void	GetResults (arma::Mat< size_t > &neighbors, arma::mat &distances)
	Store the list of candidates for each query point in the given matrices. More...

size_t	MinimumBaseCases () const
	Get the minimum number of base cases that must be performed for each query point for an acceptable result. More...

size_t	NumDistComputations ()

size_t	NumEffectiveSamples ()

double	Rescore (const size_t queryIndex, TreeType &referenceNode, const double oldScore)
	Re-evaluate the score for recursion order. More...

double	Rescore (TreeType &queryNode, TreeType &referenceNode, const double oldScore)
	Re-evaluate the score for recursion order. More...

double	Score (const size_t queryIndex, TreeType &referenceNode)
	Get the score for recursion order. More...

double	Score (const size_t queryIndex, TreeType &referenceNode, const double baseCaseResult)
	Get the score for recursion order. More...

double	Score (TreeType &queryNode, TreeType &referenceNode)
	Get the score for recursion order. More...

double	Score (TreeType &queryNode, TreeType &referenceNode, const double baseCaseResult)
	Get the score for recursion order, passing the base case result (in the situation where it may be needed to calculate the recursion order). More...

const TraversalInfoType &	TraversalInfo () const

TraversalInfoType &	TraversalInfo ()

Detailed Description

template
<
typename
SortPolicy
,
typename
MetricType
,
typename
TreeType
>

class mlpack::neighbor::RASearchRules< SortPolicy, MetricType, TreeType >

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

Template Parameters

SortPolicy	The sort policy for distances.
MetricType	The metric to use for computation.
TreeType	The tree type to use; must adhere to the TreeType API.

Definition at line 33 of file ra_search_rules.hpp.

Member Typedef Documentation

◆ TraversalInfoType

typedef tree::TraversalInfo<TreeType> TraversalInfoType

Definition at line 239 of file ra_search_rules.hpp.

Constructor & Destructor Documentation

◆ RASearchRules()

RASearchRules	(	const arma::mat &	referenceSet,
		const arma::mat &	querySet,
		const size_t	k,
		MetricType &	metric,
		const double	tau = `5`,
		const double	alpha = `0.95`,
		const bool	naive = `false`,
		const bool	sampleAtLeaves = `false`,
		const bool	firstLeafExact = `false`,
		const size_t	singleSampleLimit = `20`,
		const bool	sameSet = `false`
	)

Construct the RASearchRules object.

This is usually done from within the RASearch class at search time.

Parameters

referenceSet	Set of reference data.
querySet	Set of query data.
k	Number of neighbors to search for.
metric	Instantiated metric.
tau	The rank-approximation in percentile of the data.
alpha	The desired success probability.
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.
sampleAtLeaves	Sample at leaves for faster but less accurate computation.
firstLeafExact	Traverse to the first leaf without approximation.
singleSampleLimit	The limit on the largest node that can be approximated by sampling.
sameSet	If true, the query and reference set are taken to be the same, and a query point will not return itself in the results.

Member Function Documentation

◆ BaseCase()

double BaseCase	(	const size_t	queryIndex,
		const size_t	referenceIndex
	)

Get the distance from the query point to the reference point.

This will update the list of candidates with the new point if appropriate.

Parameters

queryIndex	Index of query point.
referenceIndex	Index of reference point.

◆ GetResults()

void GetResults	(	arma::Mat< size_t > &	neighbors,
		arma::mat &	distances
	)

Store the list of candidates for each query point in the given matrices.

Parameters

neighbors	Matrix storing lists of neighbors for each query point.
distances	Matrix storing distances of neighbors for each query point.

◆ MinimumBaseCases()

size_t MinimumBaseCases ( ) const

inline

Get the minimum number of base cases that must be performed for each query point for an acceptable result.

This is only needed in defeatist search mode.

Definition at line 247 of file ra_search_rules.hpp.

References RASearchRules< SortPolicy, MetricType, TreeType >::Score().

◆ NumDistComputations()

size_t NumDistComputations ( )

inline

Definition at line 230 of file ra_search_rules.hpp.

◆ NumEffectiveSamples()

size_t NumEffectiveSamples ( )

inline

Definition at line 231 of file ra_search_rules.hpp.

◆ Rescore() [1/2]

double Rescore	(	const size_t	queryIndex,
		TreeType &	referenceNode,
		const double	oldScore
	)

Re-evaluate the score for recursion order.

A low score indicates priority for recursion, while DBL_MAX indicates that the node should not be recursed into at all (it should be pruned). This is used when the score has already been calculated, but another recursion may have modified the bounds for pruning. So the old score is checked against the new pruning bound.

For rank-approximation, it also checks if the number of samples left for a query to satisfy the rank constraint is small enough at this point of the algorithm, then this node is approximated by sampling and given a new score of 'DBL_MAX'.

Parameters

queryIndex	Index of query point.
referenceNode	Candidate node to be recursed into.
oldScore	Old score produced by Score() (or Rescore()).

◆ Rescore() [2/2]

double Rescore	(	TreeType &	queryNode,
		TreeType &	referenceNode,
		const double	oldScore
	)

Re-evaluate the score for recursion order.

A low score indicates priority for recursion, while DBL_MAX indicates that the node should not be recursed into at all (it should be pruned). This is used when the score has already been calculated, but another recursion may have modified the bounds for pruning. So the old score is checked against the new pruning bound.

For the rank-approximation, we check if the referenceNode can be approximated by sampling. If it can be, enough samples are made for every query in the queryNode. No further query-tree traversal is performed.

The 'NumSamplesMade' query stat is propagated up the tree. And then if pruning occurs (by distance or by sampling), the 'NumSamplesMade' stat is not propagated down the tree. If no pruning occurs, the stat is propagated down the tree.

Parameters

queryNode	Candidate query node to recurse into.
referenceNode	Candidate reference node to recurse into.
oldScore	Old score produced by Socre() (or Rescore()).

◆ Score() [1/4]

double Score	(	const size_t	queryIndex,
		TreeType &	referenceNode
	)

Get the score for recursion order.

A low score indicates priority for recursion, while DBL_MAX indicates that the node should not be recursed into at all (it should be pruned).

For rank-approximation, the scoring function first checks if pruning by distance is possible. If yes, then the node is given the score of 'DBL_MAX' and the expected number of samples from that node are added to the number of samples made for the query.

If no, then the function tries to see if the node can be pruned by approximation. If number of samples required from this node is small enough, then that number of samples are acquired from this node and the score is set to be 'DBL_MAX'.

If the pruning by approximation is not possible either, the algorithm continues with the usual tree-traversal.

Parameters

queryIndex	Index of query point.
referenceNode	Candidate node to be recursed into.

Referenced by RASearchRules< SortPolicy, MetricType, TreeType >::MinimumBaseCases().

◆ Score() [2/4]

double Score	(	const size_t	queryIndex,
		TreeType &	referenceNode,
		const double	baseCaseResult
	)

Get the score for recursion order.

A low score indicates priority for recursion, while DBL_MAX indicates that the node should not be recursed into at all (it should be pruned).

For rank-approximation, the scoring function first checks if pruning by distance is possible. If yes, then the node is given the score of 'DBL_MAX' and the expected number of samples from that node are added to the number of samples made for the query.

If no, then the function tries to see if the node can be pruned by approximation. If number of samples required from this node is small enough, then that number of samples are acquired from this node and the score is set to be 'DBL_MAX'.

If the pruning by approximation is not possible either, the algorithm continues with the usual tree-traversal.

Parameters

queryIndex	Index of query point.
referenceNode	Candidate node to be recursed into.
baseCaseResult	Result of BaseCase(queryIndex, referenceNode).

◆ Score() [3/4]

double Score	(	TreeType &	queryNode,
		TreeType &	referenceNode
	)

Get the score for recursion order.

A low score indicates priority for recursionm while DBL_MAX indicates that the node should not be recursed into at all (it should be pruned).

For the rank-approximation, we check if the referenceNode can be approximated by sampling. If it can be, enough samples are made for every query in the queryNode. No further query-tree traversal is performed.

The 'NumSamplesMade' query stat is propagated up the tree. And then if pruning occurs (by distance or by sampling), the 'NumSamplesMade' stat is not propagated down the tree. If no pruning occurs, the stat is propagated down the tree.

Parameters

queryNode	Candidate query node to recurse into.
referenceNode	Candidate reference node to recurse into.

◆ Score() [4/4]

double Score	(	TreeType &	queryNode,
		TreeType &	referenceNode,
		const double	baseCaseResult
	)

Get the score for recursion order, passing the base case result (in the situation where it may be needed to calculate the recursion order).

A low score indicates priority for recursion, while DBL_MAX indicates that the node should not be recursed into at all (it should be pruned).

For the rank-approximation, we check if the referenceNode can be approximated by sampling. If it can be, enough samples are made for every query in the queryNode. No further query-tree traversal is performed.

The 'NumSamplesMade' query stat is propagated up the tree. And then if pruning occurs (by distance or by sampling), the 'NumSamplesMade' stat is not propagated down the tree. If no pruning occurs, the stat is propagated down the tree.

Parameters

queryNode	Candidate query node to recurse into.
referenceNode	Candidate reference node to recurse into.
baseCaseResult	Result of BaseCase(queryIndex, referenceNode).

◆ TraversalInfo() [1/2]

const TraversalInfoType& TraversalInfo ( ) const

inline

Definition at line 241 of file ra_search_rules.hpp.

◆ TraversalInfo() [2/2]

TraversalInfoType& TraversalInfo ( )

inline

Definition at line 242 of file ra_search_rules.hpp.

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_rules.hpp

Public Types

Public Member Functions

Detailed Description

template<typenameSortPolicy,typenameMetricType,typenameTreeType> class mlpack::neighbor::RASearchRules< SortPolicy, MetricType, TreeType >

Member Typedef Documentation

◆ TraversalInfoType

Constructor & Destructor Documentation

◆ RASearchRules()

Member Function Documentation

◆ BaseCase()

◆ GetResults()

◆ MinimumBaseCases()

◆ NumDistComputations()

◆ NumEffectiveSamples()

◆ Rescore() [1/2]

◆ Rescore() [2/2]

◆ Score() [1/4]

◆ Score() [2/4]

◆ Score() [3/4]

◆ Score() [4/4]

◆ TraversalInfo() [1/2]

◆ TraversalInfo() [2/2]

template
<
typename
SortPolicy
,
typename
MetricType
,
typename
TreeType
>

class mlpack::neighbor::RASearchRules< SortPolicy, MetricType, TreeType >