#include <kmeans_index.h>

Inheritance diagram for flann::KMeansIndex< Distance >:

Collaboration diagram for flann::KMeansIndex< Distance >:

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Public Types
typedef Distance::ElementType	ElementType

typedef Distance::ResultType	DistanceType

typedef NNIndex< Distance >	BaseClass

typedef bool	needs_vector_space_distance

Public Types inherited from flann::NNIndex< Distance >
typedef Distance::ElementType	ElementType

typedef Distance::ResultType	DistanceType

Public Member Functions
flann_algorithm_t	getType () const

	KMeansIndex (const Matrix< ElementType > &inputData, const IndexParams &params=KMeansIndexParams(), Distance d=Distance())

	KMeansIndex (const IndexParams &params=KMeansIndexParams(), Distance d=Distance())

	KMeansIndex (const KMeansIndex &other)

KMeansIndex &	operator= (KMeansIndex other)

void	initCenterChooser ()

virtual	~KMeansIndex ()

BaseClass *	clone () const

void	set_cb_index (float index)

int	usedMemory () const

void	addPoints (const Matrix< ElementType > &points, float rebuild_threshold=2)
	Incrementally add points to the index. More...

template<typename Archive >
void	serialize (Archive &ar)

void	saveIndex (FILE *stream)

void	loadIndex (FILE *stream)

void	findNeighbors (ResultSet< DistanceType > &result, const ElementType *vec, const SearchParams &searchParams) const

int	getClusterCenters (Matrix< DistanceType > &centers)

virtual void	buildIndex ()

virtual void	buildIndex (const Matrix< ElementType > &dataset)

Public Member Functions inherited from flann::NNIndex< Distance >
	NNIndex (Distance d)

	NNIndex (const IndexParams &params, Distance d)

	NNIndex (const NNIndex &other)

virtual	~NNIndex ()

virtual void	buildIndex (const Matrix< ElementType > &dataset)

virtual void	removePoint (size_t id)

virtual ElementType *	getPoint (size_t id)

size_t	size () const

size_t	veclen () const

IndexParams	getParameters () const

template<typename Archive >
void	serialize (Archive &ar)

virtual int	knnSearch (const Matrix< ElementType > &queries, Matrix< size_t > &indices, Matrix< DistanceType > &dists, size_t knn, const SearchParams &params) const
	Perform k-nearest neighbor search. More...

int	knnSearch (const Matrix< ElementType > &queries, Matrix< int > &indices, Matrix< DistanceType > &dists, size_t knn, const SearchParams &params) const

int	knnSearch (const Matrix< ElementType > &queries, std::vector< std::vector< size_t > > &indices, std::vector< std::vector< DistanceType > > &dists, size_t knn, const SearchParams &params) const
	Perform k-nearest neighbor search. More...

int	knnSearch (const Matrix< ElementType > &queries, std::vector< std::vector< int > > &indices, std::vector< std::vector< DistanceType > > &dists, size_t knn, const SearchParams &params) const

int	radiusSearch (const Matrix< ElementType > &queries, Matrix< size_t > &indices, Matrix< DistanceType > &dists, float radius, const SearchParams &params) const
	Perform radius search. More...

int	radiusSearch (const Matrix< ElementType > &queries, Matrix< int > &indices, Matrix< DistanceType > &dists, float radius, const SearchParams &params) const

int	radiusSearch (const Matrix< ElementType > &queries, std::vector< std::vector< size_t > > &indices, std::vector< std::vector< DistanceType > > &dists, float radius, const SearchParams &params) const
	Perform radius search. More...

int	radiusSearch (const Matrix< ElementType > &queries, std::vector< std::vector< int > > &indices, std::vector< std::vector< DistanceType > > &dists, float radius, const SearchParams &params) const

Public Member Functions inherited from flann::IndexBase
virtual	~IndexBase ()

Protected Member Functions
void	buildIndexImpl ()

Protected Member Functions inherited from flann::NNIndex< Distance >
size_t	id_to_index (size_t id)

void	indices_to_ids (const size_t in, size_t out, size_t size) const

void	setDataset (const Matrix< ElementType > &dataset)

void	extendDataset (const Matrix< ElementType > &new_points)

void	cleanRemovedPoints ()

void	swap (NNIndex &other)

Additional Inherited Members
Protected Attributes inherited from flann::NNIndex< Distance >
Distance	distance_

size_t	last_id_

size_t	size_

size_t	size_at_build_

size_t	veclen_

IndexParams	index_params_

bool	removed_

DynamicBitset	removed_points_

size_t	removed_count_

std::vector< size_t >	ids_

std::vector< ElementType * >	points_

ElementType *	data_ptr_

Detailed Description

template<typename Distance>
class flann::KMeansIndex< Distance >

Hierarchical kmeans index

Contains a tree constructed through a hierarchical kmeans clustering and other information for indexing a set of points for nearest-neighbour matching.

Definition at line 83 of file kmeans_index.h.

Member Typedef Documentation

◆ BaseClass

template<typename Distance >

typedef NNIndex<Distance> flann::KMeansIndex< Distance >::BaseClass

Definition at line 89 of file kmeans_index.h.

◆ DistanceType

template<typename Distance >

typedef Distance::ResultType flann::KMeansIndex< Distance >::DistanceType

Definition at line 87 of file kmeans_index.h.

◆ ElementType

template<typename Distance >

typedef Distance::ElementType flann::KMeansIndex< Distance >::ElementType

Definition at line 86 of file kmeans_index.h.

◆ needs_vector_space_distance

template<typename Distance >

typedef bool flann::KMeansIndex< Distance >::needs_vector_space_distance

Definition at line 91 of file kmeans_index.h.

Constructor & Destructor Documentation

◆ KMeansIndex() [1/3]

template<typename Distance >

flann::KMeansIndex< Distance >::KMeansIndex	(	const Matrix< ElementType > &	inputData,
		const IndexParams &	params = `KMeansIndexParams()`,
		Distance	d = `Distance()`
	)

inline

Index constructor

Params: inputData = dataset with the input features params = parameters passed to the hierarchical k-means algorithm

Definition at line 107 of file kmeans_index.h.

References FLANN_CENTERS_RANDOM, flann::get_param(), flann::KMeansIndex< Distance >::initCenterChooser(), max(), params, and flann::NNIndex< Distance >::setDataset().

Referenced by flann::KMeansIndex< Distance >::clone().

◆ KMeansIndex() [2/3]

template<typename Distance >

flann::KMeansIndex< Distance >::KMeansIndex	(	const IndexParams &	params = `KMeansIndexParams()`,
		Distance	d = `Distance()`
	)

inline

Index constructor

Params: inputData = dataset with the input features params = parameters passed to the hierarchical k-means algorithm

Definition at line 131 of file kmeans_index.h.

References FLANN_CENTERS_RANDOM, flann::get_param(), flann::KMeansIndex< Distance >::initCenterChooser(), max(), and params.

◆ KMeansIndex() [3/3]

template<typename Distance >

flann::KMeansIndex< Distance >::KMeansIndex ( const KMeansIndex< Distance > & other )

inline

Definition at line 146 of file kmeans_index.h.

References flann::KMeansIndex< Distance >::initCenterChooser().

◆ ~KMeansIndex()

template<typename Distance >

virtual flann::KMeansIndex< Distance >::~KMeansIndex ( )

inlinevirtual

Index destructor.

Release the memory used by the index.

Definition at line 187 of file kmeans_index.h.

Member Function Documentation

◆ addPoints()

template<typename Distance >

void flann::KMeansIndex< Distance >::addPoints	(	const Matrix< ElementType > &	points,
		float	rebuild_threshold = `2`
	)

inlinevirtual

Incrementally add points to the index.

Parameters

points	Matrix with points to be added
rebuild_threshold

Reimplemented from flann::NNIndex< Distance >.

Definition at line 215 of file kmeans_index.h.

References flann::KMeansIndex< Distance >::buildIndex(), dist(), flann::NNIndex< Distance >::distance_, flann::NNIndex< Distance >::extendDataset(), points, flann::NNIndex< Distance >::size_, flann::NNIndex< Distance >::size_at_build_, and flann::NNIndex< Distance >::veclen_.

◆ buildIndex() [1/2]

template<typename Distance >

virtual void flann::NNIndex< Distance >::buildIndex

inlinevirtual

Builds the index

Reimplemented from flann::NNIndex< Distance >.

Definition at line 125 of file nn_index.h.

Referenced by flann::KMeansIndex< Distance >::addPoints(), and flann::hierarchicalClustering().

◆ buildIndex() [2/2]

template<typename Distance >

virtual void flann::NNIndex< Distance >::buildIndex

inlinevirtual

Builds the index using the specified dataset

Parameters

dataset the dataset to use

Reimplemented from flann::NNIndex< Distance >.

Definition at line 141 of file nn_index.h.

◆ buildIndexImpl()

template<typename Distance >

void flann::KMeansIndex< Distance >::buildIndexImpl ( )

inlineprotectedvirtual

Builds the index

Implements flann::NNIndex< Distance >.

Definition at line 329 of file kmeans_index.h.

References flann::NNIndex< Distance >::size_, and flann::NNIndex< Distance >::veclen_.

◆ clone()

template<typename Distance >

BaseClass* flann::KMeansIndex< Distance >::clone ( ) const

inlinevirtual

Implements flann::NNIndex< Distance >.

Definition at line 193 of file kmeans_index.h.

References flann::KMeansIndex< Distance >::KMeansIndex().

◆ findNeighbors()

template<typename Distance >

void flann::KMeansIndex< Distance >::findNeighbors	(	ResultSet< DistanceType > &	result,
		const ElementType *	vec,
		const SearchParams &	searchParams
	)		const

inlinevirtual

Find set of nearest neighbors to vec. Their indices are stored inside the result object.

Params: result = the result object in which the indices of the nearest-neighbors are stored vec = the vector for which to search the nearest neighbors searchParams = parameters that influence the search algorithm (checks, cb_index)

Implements flann::NNIndex< Distance >.

Definition at line 283 of file kmeans_index.h.

References flann::NNIndex< Distance >::removed_, and result.

◆ getClusterCenters()

template<typename Distance >

int flann::KMeansIndex< Distance >::getClusterCenters ( Matrix< DistanceType > & centers )

inline

Clustering function that takes a cut in the hierarchical k-means tree and return the clusters centers of that clustering. Params: numClusters = number of clusters to have in the clustering computed Returns: number of cluster centers

Definition at line 301 of file kmeans_index.h.

References flann::Logger::info(), flann::Matrix_::rows, and flann::NNIndex< Distance >::veclen_.

Referenced by flann::hierarchicalClustering().

◆ getType()

template<typename Distance >

flann_algorithm_t flann::KMeansIndex< Distance >::getType ( ) const

inlinevirtual

Implements flann::IndexBase.

Definition at line 95 of file kmeans_index.h.

References FLANN_INDEX_KMEANS.

Referenced by flann::KMeansIndex< Distance >::serialize().

◆ initCenterChooser()

template<typename Distance >

void flann::KMeansIndex< Distance >::initCenterChooser ( )

inline

Definition at line 165 of file kmeans_index.h.

References flann::NNIndex< Distance >::distance_, FLANN_CENTERS_GONZALES, FLANN_CENTERS_KMEANSPP, FLANN_CENTERS_RANDOM, and flann::NNIndex< Distance >::points_.

Referenced by flann::KMeansIndex< Distance >::KMeansIndex().

◆ loadIndex()

template<typename Distance >

void flann::KMeansIndex< Distance >::loadIndex ( FILE * stream )

inlinevirtual

Implements flann::IndexBase.

Definition at line 266 of file kmeans_index.h.

◆ operator=()

template<typename Distance >

KMeansIndex& flann::KMeansIndex< Distance >::operator= ( KMeansIndex< Distance > other )

inline

Definition at line 158 of file kmeans_index.h.

◆ saveIndex()

template<typename Distance >

void flann::KMeansIndex< Distance >::saveIndex ( FILE * stream )

inlinevirtual

Implements flann::IndexBase.

Definition at line 260 of file kmeans_index.h.

◆ serialize()

template<typename Distance >

template<typename Archive >

void flann::KMeansIndex< Distance >::serialize ( Archive & ar )

inline

Definition at line 234 of file kmeans_index.h.

References flann::KMeansIndex< Distance >::getType(), and flann::NNIndex< Distance >::index_params_.

◆ set_cb_index()

template<typename Distance >

void flann::KMeansIndex< Distance >::set_cb_index ( float index )

inline

Definition at line 199 of file kmeans_index.h.

◆ usedMemory()

template<typename Distance >

int flann::KMeansIndex< Distance >::usedMemory ( ) const

inlinevirtual

Computes the inde memory usage Returns: memory used by the index

Implements flann::IndexBase.

Definition at line 208 of file kmeans_index.h.

References flann::PooledAllocator::usedMemory, and flann::PooledAllocator::wastedMemory.

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

/root/ACloudViewer/libs/Reconstruction/lib/FLANN/algorithms/kmeans_index.h

Public Types

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

template<typename Distance> class flann::KMeansIndex< Distance >

Member Typedef Documentation

◆ BaseClass

◆ DistanceType

◆ ElementType

◆ needs_vector_space_distance

Constructor & Destructor Documentation

◆ KMeansIndex() [1/3]

◆ KMeansIndex() [2/3]

◆ KMeansIndex() [3/3]

◆ ~KMeansIndex()

Member Function Documentation

◆ addPoints()

◆ buildIndex() [1/2]

◆ buildIndex() [2/2]

◆ buildIndexImpl()

◆ clone()

◆ findNeighbors()

◆ getClusterCenters()

◆ getType()

◆ initCenterChooser()

◆ loadIndex()

◆ operator=()

◆ saveIndex()

◆ serialize()

◆ set_cb_index()

◆ usedMemory()

template<typename Distance>
class flann::KMeansIndex< Distance >