#include <kmeans_index.h>

Inheritance diagram for rtflann::KMeansIndex< Distance >:

Classes
struct	Node

struct	PointInfo

Public Types
typedef NNIndex< Distance >	BaseClass

typedef Distance::ResultType	DistanceType

typedef Distance::ElementType	ElementType

typedef bool	needs_vector_space_distance

Public Types inherited from rtflann::NNIndex< Distance >
typedef Distance::ResultType	DistanceType

typedef Distance::ElementType	ElementType

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

BaseClass *	clone () const

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

int	getClusterCenters (Matrix< DistanceType > &centers)

flann_algorithm_t	getType () const

void	initCenterChooser ()

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

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

	KMeansIndex (const KMeansIndex &other)

void	loadIndex (FILE *stream)

KMeansIndex &	operator= (KMeansIndex other)

void	saveIndex (FILE *stream)

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

void	set_cb_index (float index)

int	usedMemory () const

virtual	~KMeansIndex ()

Public Member Functions inherited from rtflann::NNIndex< Distance >
virtual void	buildIndex ()

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

IndexParams	getParameters () const

virtual ElementType *	getPoint (size_t id)

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...

virtual 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

	NNIndex (Distance d)

	NNIndex (const IndexParams &params, Distance d)

	NNIndex (const NNIndex &other)

virtual 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

virtual 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

size_t	removedCount () const

virtual void	removePoint (size_t id)

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

size_t	size () const

size_t	sizeAtBuild () const

size_t	veclen () const

virtual	~NNIndex ()

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

Protected Member Functions
void	buildIndexImpl ()

Protected Member Functions inherited from rtflann::NNIndex< Distance >
void	cleanRemovedPoints ()

void	extendDataset (const Matrix< ElementType > &new_points)

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	swap (NNIndex &other)

Private Types
typedef BranchStruct< NodePtr, DistanceType >	BranchSt

typedef Node *	NodePtr

Private Member Functions
void	addPointToTree (NodePtr node, size_t index, DistanceType dist_to_pivot)

void	computeClustering (NodePtr node, int *indices, int indices_length, int branching)

void	computeNodeStatistics (NodePtr node, const std::vector< int > &indices)

void	copyTree (NodePtr &dst, const NodePtr &src)

int	exploreNodeBranches (NodePtr node, const ElementType q, Heap< BranchSt > heap) const

template<bool with_removed>
void	findExactNN (NodePtr node, ResultSet< DistanceType > &result, const ElementType *vec) const

template<bool with_removed>
void	findNeighborsWithRemoved (ResultSet< DistanceType > &result, const ElementType *vec, const SearchParams &searchParams) const

template<bool with_removed>
void	findNN (NodePtr node, ResultSet< DistanceType > &result, const ElementType vec, int &checks, int maxChecks, Heap< BranchSt > heap) const

void	freeIndex ()

void	getCenterOrdering (NodePtr node, const ElementType *q, std::vector< int > &sort_indices) const

DistanceType	getDistanceToBorder (DistanceType p, DistanceType c, DistanceType *q) const

int	getMinVarianceClusters (NodePtr root, std::vector< NodePtr > &clusters, int clusters_length, DistanceType &varianceValue) const

void	swap (KMeansIndex &other)

Private Attributes
int	branching_

float	cb_index_

flann_centers_init_t	centers_init_

CenterChooser< Distance > *	chooseCenters_

int	iterations_

int	memoryCounter_

PooledAllocator	pool_

NodePtr	root_

Additional Inherited Members
Protected Attributes inherited from rtflann::NNIndex< Distance >
ElementType *	data_ptr_

Distance	distance_

std::vector< size_t >	ids_

IndexParams	index_params_

size_t	last_id_

std::vector< ElementType * >	points_

bool	removed_

size_t	removed_count_

DynamicBitset	removed_points_

size_t	size_

size_t	size_at_build_

size_t	veclen_

Detailed Description

template<typename Distance>
class rtflann::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

template<typename Distance>

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

Definition at line 89 of file kmeans_index.h.

template<typename Distance>

typedef BranchStruct<NodePtr, DistanceType> rtflann::KMeansIndex< Distance >::BranchSt

private

Alias definition for a nicer syntax.

Definition at line 454 of file kmeans_index.h.

template<typename Distance>

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

Definition at line 87 of file kmeans_index.h.

template<typename Distance>

typedef Distance::ElementType rtflann::KMeansIndex< Distance >::ElementType

Definition at line 86 of file kmeans_index.h.

template<typename Distance>

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

Definition at line 91 of file kmeans_index.h.

template<typename Distance>

typedef Node* rtflann::KMeansIndex< Distance >::NodePtr

private

Definition at line 449 of file kmeans_index.h.

Constructor & Destructor Documentation

template<typename Distance>

rtflann::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.

template<typename Distance>

rtflann::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.

template<typename Distance>

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

inline

Definition at line 146 of file kmeans_index.h.

template<typename Distance>

virtual rtflann::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

template<typename Distance>

void rtflann::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 rtflann::NNIndex< Distance >.

Definition at line 215 of file kmeans_index.h.

template<typename Distance>

void rtflann::KMeansIndex< Distance >::addPointToTree	(	NodePtr	node,
		size_t	index,
		DistanceType	dist_to_pivot
	)

inlineprivate

Definition at line 969 of file kmeans_index.h.

template<typename Distance>

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

inlineprotectedvirtual

Builds the index

Implements rtflann::NNIndex< Distance >.

Definition at line 329 of file kmeans_index.h.

template<typename Distance>

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

inlinevirtual

Implements rtflann::NNIndex< Distance >.

Definition at line 193 of file kmeans_index.h.

template<typename Distance>

void rtflann::KMeansIndex< Distance >::computeClustering	(	NodePtr	node,
		int *	indices,
		int	indices_length,
		int	branching
	)

inlineprivate

The method responsible with actually doing the recursive hierarchical clustering

Params: node = the node to cluster indices = indices of the points belonging to the current node branching = the branching factor to use in the clustering

TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)

Definition at line 542 of file kmeans_index.h.

template<typename Distance>

void rtflann::KMeansIndex< Distance >::computeNodeStatistics	(	NodePtr	node,
		const std::vector< int > &	indices
	)

inlineprivate

Computes the statistics of a node (mean, radius, variance).

Params: node = the node to use indices = the indices of the points belonging to the node

Definition at line 495 of file kmeans_index.h.

template<typename Distance>

void rtflann::KMeansIndex< Distance >::copyTree	(	NodePtr &	dst,
		const NodePtr &	src
	)

inlineprivate

Definition at line 467 of file kmeans_index.h.

template<typename Distance>

int rtflann::KMeansIndex< Distance >::exploreNodeBranches	(	NodePtr	node,
		const ElementType *	q,
		Heap< BranchSt > *	heap
	)		const

inlineprivate

Helper function that computes the nearest childs of a node to a given query point. Params: node = the node q = the query point distances = array with the distances to each child node. Returns:

Definition at line 802 of file kmeans_index.h.

template<typename Distance>

template<bool with_removed>

void rtflann::KMeansIndex< Distance >::findExactNN	(	NodePtr	node,
		ResultSet< DistanceType > &	result,
		const ElementType *	vec
	)		const

inlineprivate

Function the performs exact nearest neighbor search by traversing the entire tree.

Definition at line 835 of file kmeans_index.h.

template<typename Distance>

void rtflann::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 rtflann::NNIndex< Distance >.

Definition at line 283 of file kmeans_index.h.

template<typename Distance>

template<bool with_removed>

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

inlineprivate

Definition at line 715 of file kmeans_index.h.

template<typename Distance>

template<bool with_removed>

void rtflann::KMeansIndex< Distance >::findNN	(	NodePtr	node,
		ResultSet< DistanceType > &	result,
		const ElementType *	vec,
		int &	checks,
		int	maxChecks,
		Heap< BranchSt > *	heap
	)		const

inlineprivate

Performs one descent in the hierarchical k-means tree. The branches not visited are stored in a priority queue.

Params: node = node to explore result = container for the k-nearest neighbors found vec = query points checks = how many points in the dataset have been checked so far maxChecks = maximum dataset points to checks

Definition at line 755 of file kmeans_index.h.

template<typename Distance>

void rtflann::KMeansIndex< Distance >::freeIndex ( )

inlineprivatevirtual

Helper function

Implements rtflann::NNIndex< Distance >.

Definition at line 460 of file kmeans_index.h.

template<typename Distance>

void rtflann::KMeansIndex< Distance >::getCenterOrdering	(	NodePtr	node,
		const ElementType *	q,
		std::vector< int > &	sort_indices
	)		const

inlineprivate

Helper function.

I computes the order in which to traverse the child nodes of a particular node.

Definition at line 880 of file kmeans_index.h.

template<typename Distance>

int rtflann::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.

template<typename Distance>

DistanceType rtflann::KMeansIndex< Distance >::getDistanceToBorder	(	DistanceType *	p,
		DistanceType *	c,
		DistanceType *	q
	)		const

inlineprivate

Method that computes the squared distance from the query point q from inside region with center c to the border between this region and the region with center p

Definition at line 902 of file kmeans_index.h.

template<typename Distance>

int rtflann::KMeansIndex< Distance >::getMinVarianceClusters	(	NodePtr	root,
		std::vector< NodePtr > &	clusters,
		int	clusters_length,
		DistanceType &	varianceValue
	)		const

inlineprivate

Helper function the descends in the hierarchical k-means tree by spliting those clusters that minimize the overall variance of the clustering. Params: root = root node clusters = array with clusters centers (return value) varianceValue = variance of the clustering (return value) Returns:

Definition at line 926 of file kmeans_index.h.

template<typename Distance>

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

inlinevirtual

Implements rtflann::IndexBase.

Definition at line 95 of file kmeans_index.h.

template<typename Distance>

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

inline

Definition at line 165 of file kmeans_index.h.

template<typename Distance>

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

inlinevirtual

Implements rtflann::IndexBase.

Definition at line 266 of file kmeans_index.h.

template<typename Distance>

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

inline

Definition at line 158 of file kmeans_index.h.

template<typename Distance>

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

inlinevirtual

Implements rtflann::IndexBase.

Definition at line 260 of file kmeans_index.h.

template<typename Distance>

template<typename Archive >

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

inline

Definition at line 234 of file kmeans_index.h.

template<typename Distance>

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

inline

Definition at line 199 of file kmeans_index.h.

template<typename Distance>

void rtflann::KMeansIndex< Distance >::swap ( KMeansIndex< Distance > & other )

inlineprivate

Definition at line 1010 of file kmeans_index.h.

template<typename Distance>

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

inlinevirtual

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

Implements rtflann::IndexBase.

Definition at line 208 of file kmeans_index.h.

Member Data Documentation

template<typename Distance>

int rtflann::KMeansIndex< Distance >::branching_

private

The branching factor used in the hierarchical k-means clustering

Definition at line 1025 of file kmeans_index.h.

template<typename Distance>

float rtflann::KMeansIndex< Distance >::cb_index_

private

Cluster border index. This is used in the tree search phase when determining the closest cluster to explore next. A zero value takes into account only the cluster centres, a value greater then zero also take into account the size of the cluster.

Definition at line 1039 of file kmeans_index.h.

template<typename Distance>

flann_centers_init_t rtflann::KMeansIndex< Distance >::centers_init_

private

Algorithm for choosing the cluster centers

Definition at line 1031 of file kmeans_index.h.

template<typename Distance>

CenterChooser<Distance>* rtflann::KMeansIndex< Distance >::chooseCenters_

private

Algorithm used to choose initial centers

Definition at line 1059 of file kmeans_index.h.

template<typename Distance>

int rtflann::KMeansIndex< Distance >::iterations_

private

Maximum number of iterations to use when performing k-means clustering

Definition at line 1028 of file kmeans_index.h.

template<typename Distance>

int rtflann::KMeansIndex< Distance >::memoryCounter_

private

Memory occupied by the index.

Definition at line 1054 of file kmeans_index.h.

template<typename Distance>

PooledAllocator rtflann::KMeansIndex< Distance >::pool_

private

Pooled memory allocator.

Definition at line 1049 of file kmeans_index.h.

template<typename Distance>

NodePtr rtflann::KMeansIndex< Distance >::root_

private

The root node in the tree.

Definition at line 1044 of file kmeans_index.h.

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

kmeans_index.h

Classes

Public Types

Public Member Functions

Protected Member Functions

Private Types

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

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

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

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