The RangeSearch class is a template class for performing range searches. More...

Inheritance diagram for RangeSearch< MetricType, MatType, TreeType >:

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

Public Member Functions
	RangeSearch (MatType referenceSet, const bool naive=false, const bool singleMode=false, const MetricType metric=MetricType())
	Initialize the RangeSearch object with a given reference dataset (this is the dataset which is searched). More...

	RangeSearch (Tree *referenceTree, const bool singleMode=false, const MetricType metric=MetricType())
	Initialize the RangeSearch object with the given pre-constructed reference tree (this is the tree built on the reference set, which is the set that is searched). More...

	RangeSearch (const bool naive=false, const bool singleMode=false, const MetricType metric=MetricType())
	Initialize the RangeSearch object without any reference data. More...

	RangeSearch (const RangeSearch &other)
	Construct the RangeSearch model as a copy of the given model. More...

	RangeSearch (RangeSearch &&other)
	Construct the RangeSearch model by taking ownership of the given model. More...

	~RangeSearch ()
	Destroy the RangeSearch object. More...

size_t	BaseCases () const
	Get the number of base cases during the last search. More...

bool	Naive () const
	Get whether naive search is being used. More...

bool &	Naive ()
	Modify whether naive search is being used. More...

RangeSearch &	operator= (const RangeSearch &other)
	Deep copy the given RangeSearch model. More...

RangeSearch &	operator= (RangeSearch &&other)
	Move the given RangeSearch model. More...

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

Tree *	ReferenceTree ()
	Return the reference tree (or NULL if in naive mode). More...

size_t	Scores () const
	Get the number of scores during the last search. More...

void	Search (const MatType &querySet, const math::Range &range, std::vector< std::vector< size_t >> &neighbors, std::vector< std::vector< double >> &distances)
	Search for all reference points in the given range for each point in the query set, returning the results in the neighbors and distances objects. More...

void	Search (Tree *queryTree, const math::Range &range, std::vector< std::vector< size_t >> &neighbors, std::vector< std::vector< double >> &distances)
	Given a pre-built query tree, search for all reference points in the given range for each point in the query set, returning the results in the neighbors and distances objects. More...

void	Search (const math::Range &range, std::vector< std::vector< size_t >> &neighbors, std::vector< std::vector< double >> &distances)
	Search for all points in the given range for each point in the reference set (which was passed to the constructor), returning the results in the neighbors and distances objects. More...

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

bool	SingleMode () const
	Get whether single-tree search is being used. More...

bool &	SingleMode ()
	Modify whether single-tree search is being used. 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...

Detailed Description

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

class mlpack::range::RangeSearch< MetricType, MatType, TreeType >

The RangeSearch class is a template class for performing range searches.

It is implemented in the style of a generalized tree-independent dual-tree algorithm; for more details on the actual algorithm, see the RangeSearchRules class.

Template Parameters

MetricType	Metric to use for range search calculations.
MatType	Type of data to use.
TreeType	Type of tree to use; must satisfy the TreeType policy API.

Definition at line 45 of file range_search.hpp.

Member Typedef Documentation

◆ Tree

typedef TreeType<MetricType, RangeSearchStat, MatType> Tree

Convenience typedef.

Definition at line 49 of file range_search.hpp.

Constructor & Destructor Documentation

◆ RangeSearch() [1/5]

RangeSearch	(	MatType	referenceSet,
		const bool	naive = `false`,
		const bool	singleMode = `false`,
		const MetricType	metric = `MetricType()`
	)

Initialize the RangeSearch object with a given reference dataset (this is the dataset which is searched).

Optionally, perform the computation in naive mode or single-tree mode. Additionally, an instantiated metric can be given, for cases where the distance metric holds data.

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 and passing them in using std::move.

Parameters

referenceSet	Reference dataset.
naive	Whether the computation should be done in O(n^2) naive mode.
singleMode	Whether single-tree computation should be used (as opposed to dual-tree computation).
metric	Instantiated distance metric.

◆ RangeSearch() [2/5]

RangeSearch	(	Tree *	referenceTree,
		const bool	singleMode = `false`,
		const MetricType	metric = `MetricType()`
	)

Initialize the RangeSearch object with the given pre-constructed reference tree (this is the tree built on the reference set, which is the set that is searched).

Optionally, choose to use single-tree mode, which will not build a tree on query points. 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.

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.

Note: Because tree-building (at least with BinarySpaceTree) modifies the ordering of a matrix, be aware that mapping of the points back to their original indices is not done when this constructor is used.

Parameters

referenceTree	Pre-built tree for reference points.
singleMode	Whether single-tree computation should be used (as opposed to dual-tree computation).
metric	Instantiated distance metric.

◆ RangeSearch() [3/5]

RangeSearch	(	const bool	naive = `false`,
		const bool	singleMode = `false`,
		const MetricType	metric = `MetricType()`
	)

Initialize the RangeSearch object without any reference data.

If the monochromatic Search() is called before a reference set is set with Train(), no results will be returned (since the reference set is empty).

Parameters

naive	Whether to use naive search.
singleMode	Whether single-tree computation should be used (as opposed to dual-tree computation).
metric	Instantiated metric.

◆ RangeSearch() [4/5]

RangeSearch ( const RangeSearch< MetricType, MatType, TreeType > & other )

size_t Scores ( ) const

inline

Get the number of scores during the last search.

Definition at line 284 of file range_search.hpp.

◆ Search() [1/3]

void Search	(	const MatType &	querySet,
		const math::Range &	range,
		std::vector< std::vector< size_t >> &	neighbors,
		std::vector< std::vector< double >> &	distances
	)

Search for all reference points in the given range for each point in the query set, returning the results in the neighbors and distances objects.

Each entry in the external vector corresponds to a query point. Each of these entries holds a vector which contains the indices and distances of the reference points falling into the given range.

That is:

neighbors.size() and distances.size() both equal the number of query points.
neighbors[i] contains the indices of all the points in the reference set which have distances inside the given range to query point i.
distances[i] contains all of the distances corresponding to the indices contained in neighbors[i].
neighbors[i] and distances[i] are not sorted in any particular order.

Parameters

querySet	Set of query points to search with.
range	Range of distances in which to search.
neighbors	Object which will hold the list of neighbors for each point which fell into the given range, for each query point.
distances	Object which will hold the list of distances for each point which fell into the given range, for each query point.

◆ Search() [2/3]

void Search	(	Tree *	queryTree,
		const math::Range &	range,
		std::vector< std::vector< size_t >> &	neighbors,
		std::vector< std::vector< double >> &	distances
	)

Given a pre-built query tree, search for all reference points in the given range for each point in the query set, returning the results in the neighbors and distances objects.

Each entry in the external vector corresponds to a query point. Each of these entries holds a vector which contains the indices and distances of the reference points falling into the given range.

That is:

neighbors.size() and distances.size() both equal the number of query points.
neighbors[i] contains the indices of all the points in the reference set which have distances inside the given range to query point i.
distances[i] contains all of the distances corresponding to the indices contained in neighbors[i].
neighbors[i] and distances[i] are not sorted in any particular order.

If either naive or singleMode are set to true, this will throw an invalid_argument exception; passing in a query tree implies dual-tree search.

If you want to use the reference tree as the query tree, instead call the overload of Search() that does not take a query set.

Parameters

queryTree	Tree built on query points.
range	Range of distances in which to search.
neighbors	Object which will hold the list of neighbors for each point which fell into the given range, for each query point.
distances	Object which will hold the list of distances for each point which fell into the given range, for each query point.

◆ Search() [3/3]

void Search	(	const math::Range &	range,
		std::vector< std::vector< size_t >> &	neighbors,
		std::vector< std::vector< double >> &	distances
	)

Search for all points in the given range for each point in the reference set (which was passed to the constructor), returning the results in the neighbors and distances objects.

This means that the query set and the reference set are the same.

Each entry in the external vector corresponds to a query point. Each of these entries holds a vector which contains the indices and distances of the reference points falling into the given range.

That is:

neighbors.size() and distances.size() both equal the number of query points.
neighbors[i] contains the indices of all the points in the reference set which have distances inside the given range to query point i.
distances[i] contains all of the distances corresponding to the indices contained in neighbors[i].
neighbors[i] and distances[i] are not sorted in any particular order.

Parameters

range	Range of distances in which to search.
neighbors	Object which will hold the list of neighbors for each point which fell into the given range, for each query point.
distances	Object which will hold the list of distances for each point which fell into the given range, for each query point.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t	version
	)

Serialize the model.

Referenced by RangeSearch< metric::EuclideanDistance, arma::mat, TreeType >::Scores().

◆ SingleMode() [1/2]

bool SingleMode ( ) const

inline

Get whether single-tree search is being used.

Definition at line 272 of file range_search.hpp.

◆ SingleMode() [2/2]

bool& SingleMode ( )

inline

Modify whether single-tree search is being used.

Definition at line 274 of file range_search.hpp.

◆ Train() [1/2]

void Train ( MatType referenceSet )

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

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.

Use std::move to pass in the reference set if the old copy is no longer needed.

Parameters

referenceSet New set of reference data.

◆ 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/range_search/range_search.hpp

Public Types

Public Member Functions

Detailed Description

template<typenameMetricType=metric::EuclideanDistance,typenameMatType=arma::mat,template<typenameTreeMetricType,typenameTreeStatType,typenameTreeMatType>classTreeType=tree::KDTree> class mlpack::range::RangeSearch< MetricType, MatType, TreeType >

Member Typedef Documentation

◆ Tree

Constructor & Destructor Documentation

◆ RangeSearch() [1/5]

◆ RangeSearch() [2/5]

◆ RangeSearch() [3/5]

◆ RangeSearch() [4/5]

◆ RangeSearch() [5/5]

◆ ~RangeSearch()

Member Function Documentation

◆ BaseCases()

◆ Naive() [1/2]

◆ Naive() [2/2]

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ ReferenceSet()

◆ ReferenceTree()

◆ Scores()

◆ Search() [1/3]

◆ Search() [2/3]

◆ Search() [3/3]

◆ serialize()

◆ SingleMode() [1/2]

◆ SingleMode() [2/2]

◆ Train() [1/2]

◆ Train() [2/2]

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

class mlpack::range::RangeSearch< MetricType, MatType, TreeType >