Hierarchical kmeans index. More...

#include <kmeans_index.h>

Public Member Functions
void	chooseCentersRandom (int k, int indices, int indices_length, int centers, int &centers_length)
	Chooses the initial centers in the k-means clustering in a random manner.
void	chooseCentersGonzales (int k, int indices, int indices_length, int centers, int &centers_length)
	Chooses the initial centers in the k-means using Gonzales' algorithm so that the centers are spaced apart from each other.
void	chooseCentersKMeanspp (int k, int indices, int indices_length, int centers, int &centers_length)
	Chooses the initial centers in the k-means using the algorithm proposed in the KMeans++ paper: Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding.
flann_algorithm_t	getType () const
	KMeansIndex (const Matrix< ElementType > &inputData, const IndexParams &params=KMeansIndexParams(), Distance d=Distance())
	Index constructor.
virtual	~KMeansIndex ()
	Index destructor.
size_t	size () const
	Returns size of index.
size_t	veclen () const
	Returns the length of an index feature.
int	usedMemory () const
	Computes the inde memory usage Returns: memory used by the index.
void	buildIndex ()
	Builds the index.
void	saveIndex (FILE *stream)
	Saves the index to a stream.
void	loadIndex (FILE *stream)
	Loads the index from a stream.
void	findNeighbors (ResultSet< DistanceType > &result, const ElementType *vec, const SearchParams &searchParams)
	Find set of nearest neighbors to vec.
int	getClusterCenters (Matrix< DistanceType > &centers)
	Clustering function that takes a cut in the hierarchical k-means tree and return the clusters centers of that clustering.
IndexParams	getParameters () const
virtual void	knnSearch (const Matrix< ElementType > &queries, Matrix< int > &indices, Matrix< DistanceType > &dists, int knn, const SearchParams &params)
	Perform k-nearest neighbor search.
virtual int	radiusSearch (const Matrix< ElementType > &query, Matrix< int > &indices, Matrix< DistanceType > &dists, float radius, const SearchParams &params)
	Perform radius search.
virtual void	findNeighbors (ResultSet< DistanceType > &result, const ElementType *vec, const SearchParams &searchParams)=0
	Method that searches for nearest-neighbours.
Data Fields
centersAlgFunction	chooseCenters
	The function used for choosing the cluster centers.

Detailed Description

template<typename Distance>
class cvflann::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 80 of file kmeans_index.h.

Constructor & Destructor Documentation

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

Index constructor.

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

Definition at line 346 of file kmeans_index.h.

virtual ~KMeansIndex ( ) [virtual]

Index destructor.

Release the memory used by the index.

Definition at line 388 of file kmeans_index.h.

Member Function Documentation

void buildIndex ( ) [virtual]

Builds the index.

Implements NNIndex< Distance >.

Definition at line 432 of file kmeans_index.h.

void chooseCentersGonzales	(	int	k,
		int *	indices,
		int	indices_length,
		int *	centers,
		int &	centers_length
	)

Chooses the initial centers in the k-means using Gonzales' algorithm so that the centers are spaced apart from each other.

Params: k = number of centers vecs = the dataset of points indices = indices in the dataset Returns:

Definition at line 148 of file kmeans_index.h.

void chooseCentersKMeanspp	(	int	k,
		int *	indices,
		int	indices_length,
		int *	centers,
		int &	centers_length
	)

Chooses the initial centers in the k-means using the algorithm proposed in the KMeans++ paper: Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding.

Implementation of this function was converted from the one provided in Arthur's code.

Params: k = number of centers vecs = the dataset of points indices = indices in the dataset Returns:

Definition at line 199 of file kmeans_index.h.

void chooseCentersRandom	(	int	k,
		int *	indices,
		int	indices_length,
		int *	centers,
		int &	centers_length
	)

Chooses the initial centers in the k-means clustering in a random manner.

Params: k = number of centers vecs = the dataset of points indices = indices in the dataset indices_length = length of indices vector

Definition at line 107 of file kmeans_index.h.

virtual void findNeighbors	(	ResultSet< DistanceType > &	result,
		const ElementType *	vec,
		const SearchParams &	searchParams
	)		`[pure virtual, inherited]`

Method that searches for nearest-neighbours.

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

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)

Definition at line 498 of file kmeans_index.h.

int getClusterCenters ( Matrix< DistanceType > & centers )

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 532 of file kmeans_index.h.

IndexParams getParameters ( ) const [virtual]

Returns:: The index parameters

Implements NNIndex< Distance >.

Definition at line 557 of file kmeans_index.h.

flann_algorithm_t getType ( ) const [virtual]

Returns:: The index type (kdtree, kmeans,...)

Implements NNIndex< Distance >.

Definition at line 269 of file kmeans_index.h.

virtual void knnSearch	(	const Matrix< ElementType > &	queries,
		Matrix< int > &	indices,
		Matrix< DistanceType > &	dists,
		int	knn,
		const SearchParams &	params
	)		`[virtual, inherited]`

Perform k-nearest neighbor search.

Parameters:

[in]	queries	The query points for which to find the nearest neighbors
[out]	indices	The indices of the nearest neighbors found
[out]	dists	Distances to the nearest neighbors found
[in]	knn	Number of nearest neighbors to return
[in]	params	Search parameters

Definition at line 68 of file nn_index.h.

void loadIndex ( FILE * stream ) [virtual]

Loads the index from a stream.

Parameters:

stream The stream from which the index is loaded

Implements NNIndex< Distance >.

Definition at line 463 of file kmeans_index.h.

virtual int radiusSearch	(	const Matrix< ElementType > &	query,
		Matrix< int > &	indices,
		Matrix< DistanceType > &	dists,
		float	radius,
		const SearchParams &	params
	)		`[virtual, inherited]`

Perform radius search.

Parameters:

[in]	query	The query point
[out]	indices	The indinces of the neighbors found within the given radius
[out]	dists	The distances to the nearest neighbors found
[in]	radius	The radius used for search
[in]	params	Search parameters

Returns:: Number of neighbors found

Definition at line 102 of file nn_index.h.

void saveIndex ( FILE * stream ) [virtual]

Saves the index to a stream.

Parameters:

stream The stream to save the index to

Implements NNIndex< Distance >.

Definition at line 451 of file kmeans_index.h.

size_t size ( ) const [virtual]

Returns size of index.

Implements NNIndex< Distance >.

Definition at line 401 of file kmeans_index.h.

int usedMemory ( ) const [virtual]

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

Implements NNIndex< Distance >.

Definition at line 424 of file kmeans_index.h.

size_t veclen ( ) const [virtual]

Returns the length of an index feature.

Implements NNIndex< Distance >.

Definition at line 409 of file kmeans_index.h.

Field Documentation

centersAlgFunction chooseCenters

The function used for choosing the cluster centers.

Definition at line 93 of file kmeans_index.h.

Type:	Program
Mbed OS support:	Mbed 2 deprecated
Created:	31 Mar 2016
Imports:	152
Forks:	0
Commits:	1
Dependents:	0
Dependencies:	1
Followers:	2

KMeansIndex< Distance > Class Template Reference

Public Member Functions

Data Fields

Detailed Description

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

Constructor & Destructor Documentation

Member Function Documentation

Field Documentation

Repository toolbox

Repository details

Important Information for this Arm website

KMeansIndex< Distance > Class Template Reference

Public Member Functions

Data Fields

Detailed Description

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

Constructor & Destructor Documentation

Member Function Documentation

Field Documentation

Repository toolbox

Repository details

Important Information for this Arm website

Access Warning

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