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

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

[legend]

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

Public Member Functions
	RASearch (const 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 (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 unsigned int)
	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 (const MatType &referenceSet)
	"Train" the model on the given reference set. More...

void	Train (MatType &&referenceSet)
	"Train" the model on the given reference set, taking ownership of the data matrix. More...

Private Attributes
double	alpha
	The desired success probability (between 0 and 1). More...

bool	firstLeafExact
	If true, we will traverse to the first leaf without approximation. More...

MetricType	metric
	Instantiation of kernel. More...

bool	naive
	Indicates if naive random sampling on the set is being used. More...

std::vector< size_t >	oldFromNewReferences
	Permutations of reference points during tree building. More...

const MatType *	referenceSet
	Reference dataset. In some situations we may own this dataset. More...

Tree *	referenceTree
	Pointer to the root of the reference tree. More...

bool	sampleAtLeaves
	Whether or not sampling is done at the leaves. Faster, but less accurate. More...

bool	setOwner
	If true, we are responsible for deleting the dataset. More...

bool	singleMode
	Indicates if single-tree search is being used (opposed to dual-tree). More...

size_t	singleSampleLimit
	The limit on the number of points in the largest node that can be approximated by sampling. More...

double	tau
	The rank-approximation in percentile of the data (between 0 and 100). More...

bool	treeOwner
	If true, this object created the trees and is responsible for them. 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:

{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 71 of file ra_search.hpp.

Member Typedef Documentation

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

typedef TreeType<MetricType, RAQueryStat<SortPolicy>, MatType> mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Tree

Convenience typedef.

Definition at line 75 of file ra_search.hpp.

Constructor & Destructor Documentation

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

mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::RASearch	(	const 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. You can avoid this extra copy by pre-constructing the trees and using the appropriate constructor, or by using the constructor that takes an rvalue reference to the data with std::move().

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.

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

mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::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 take ownership of the given reference set, avoiding a copy. If you need to use the reference set for other purposes, too, consider using the constructor that takes a const reference.

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.

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

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

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

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

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

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

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

double mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Alpha ( ) const

inline

Get the desired success probability.

Definition at line 398 of file ra_search.hpp.

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

double& mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Alpha ( )

inline

Modify the desired success probability.

Definition at line 400 of file ra_search.hpp.

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::FirstLeafExact ( ) const

inline

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

Definition at line 408 of file ra_search.hpp.

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

bool& mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::FirstLeafExact ( )

inline

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

Definition at line 410 of file ra_search.hpp.

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Naive ( ) const

inline

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

Definition at line 383 of file ra_search.hpp.

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

bool& mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Naive ( )

inline

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

Definition at line 385 of file ra_search.hpp.

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

const MatType& mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::ReferenceSet ( ) const

inline

Access the reference set.

Definition at line 380 of file ra_search.hpp.

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

void mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::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.

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::SampleAtLeaves ( ) const

inline

Get whether or not sampling is done at the leaves.

Definition at line 403 of file ra_search.hpp.

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

bool& mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::SampleAtLeaves ( )

inline

Modify whether or not sampling is done at the leaves.

Definition at line 405 of file ra_search.hpp.

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

void mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::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.

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

void mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::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.

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

void mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::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.

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

template<typename Archive >

void mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the object.

Referenced by mlpack::neighbor::RASearch< tree::RStarTree >::SingleSampleLimit().

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::SingleMode ( ) const

inline

Get whether or not single-tree search is used.

Definition at line 388 of file ra_search.hpp.

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

bool& mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::SingleMode ( )

inline

Modify whether or not single-tree search is used.

Definition at line 390 of file ra_search.hpp.

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

size_t mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::SingleSampleLimit ( ) const

inline

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

Definition at line 413 of file ra_search.hpp.

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

size_t& mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::SingleSampleLimit ( )

inline

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

Definition at line 415 of file ra_search.hpp.

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

double mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Tau ( ) const

inline

Get the rank-approximation in percentile of the data.

Definition at line 393 of file ra_search.hpp.

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

double& mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Tau ( )

inline

Modify the rank-approximation in percentile of the data.

Definition at line 395 of file ra_search.hpp.

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

void mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Train ( const MatType & referenceSet )

"Train" the model on the given reference set.

If tree-based search is being used (if Naive() is false), this means rebuilding the reference tree. This particular method will make a copy of the given reference data. To avoid that copy, use the Train() method that takes an rvalue reference with std::move().

Parameters

referenceSet New reference set to use.

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

void mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::Train ( MatType && referenceSet )

"Train" the model on the given reference set, taking ownership of the data matrix.

If tree-based search is being used (if Naive() is false), this also means rebuilding the reference tree. If you need to keep a copy of the reference data, use the Train() method that takes a const reference to the data.

Parameters

referenceSet New reference set to use.

Member Data Documentation

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

double mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::alpha

private

The desired success probability (between 0 and 1).

Definition at line 442 of file ra_search.hpp.

Referenced by mlpack::neighbor::RASearch< tree::RStarTree >::Alpha().

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::firstLeafExact

private

If true, we will traverse to the first leaf without approximation.

Definition at line 446 of file ra_search.hpp.

Referenced by mlpack::neighbor::RASearch< tree::RStarTree >::FirstLeafExact().

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

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

private

Instantiation of kernel.

Definition at line 452 of file ra_search.hpp.

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::naive

private

Indicates if naive random sampling on the set is being used.

Definition at line 435 of file ra_search.hpp.

Referenced by mlpack::neighbor::RASearch< tree::RStarTree >::Naive().

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

std::vector<size_t> mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::oldFromNewReferences

private

Permutations of reference points during tree building.

Definition at line 423 of file ra_search.hpp.

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

const MatType* mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::referenceSet

private

Reference dataset. In some situations we may own this dataset.

Definition at line 427 of file ra_search.hpp.

Referenced by mlpack::neighbor::RASearch< tree::RStarTree >::ReferenceSet().

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

Tree* mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::referenceTree

private

Pointer to the root of the reference tree.

Definition at line 425 of file ra_search.hpp.

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::sampleAtLeaves

private

Whether or not sampling is done at the leaves. Faster, but less accurate.

Definition at line 444 of file ra_search.hpp.

Referenced by mlpack::neighbor::RASearch< tree::RStarTree >::SampleAtLeaves().

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::setOwner

private

If true, we are responsible for deleting the dataset.

Definition at line 432 of file ra_search.hpp.

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::singleMode

private

Indicates if single-tree search is being used (opposed to dual-tree).

Definition at line 437 of file ra_search.hpp.

Referenced by mlpack::neighbor::RASearch< tree::RStarTree >::SingleMode().

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

size_t mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::singleSampleLimit

private

The limit on the number of points in the largest node that can be approximated by sampling.

Definition at line 449 of file ra_search.hpp.

Referenced by mlpack::neighbor::RASearch< tree::RStarTree >::SingleSampleLimit().

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

double mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::tau

private

The rank-approximation in percentile of the data (between 0 and 100).

Definition at line 440 of file ra_search.hpp.

Referenced by mlpack::neighbor::RASearch< tree::RStarTree >::Tau().

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

bool mlpack::neighbor::RASearch< SortPolicy, MetricType, MatType, TreeType >::treeOwner

private

If true, this object created the trees and is responsible for them.

Definition at line 430 of file ra_search.hpp.

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

src/mlpack/methods/rann/ra_search.hpp

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