ikmeans.c File Reference

Integer K-Means clustering - Definition. More...

#include "ikmeans.h"
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "assert.h"
#include "ikmeans_init.tc"
#include "ikmeans_lloyd.tc"
#include "ikmeans_elkan.tc"

Functions
VlIKMFilt *	vl_ikm_new (int method)
	Create a new IKM quantizer. More...
void	vl_ikm_delete (VlIKMFilt *f)
	Delete IKM quantizer. More...
int	vl_ikm_train (VlIKMFilt *f, vl_uint8 const *data, vl_size N)
	Train clusters. More...
void	vl_ikm_push (VlIKMFilt *f, vl_uint32 *asgn, vl_uint8 const *data, vl_size N)
	Project data to clusters. More...
vl_uint32	vl_ikm_push_one (vl_ikmacc_t const *centers, vl_uint8 const *data, vl_size M, vl_size K)
	Project one datum to clusters. More...
vl_size	vl_ikm_get_ndims (VlIKMFilt const *f)
	Get data dimensionality. More...
vl_size	vl_ikm_get_K (VlIKMFilt const *f)
	Get the number of centers K. More...
int	vl_ikm_get_verbosity (VlIKMFilt const *f)
	Get verbosity level. More...
vl_size	vl_ikm_get_max_niters (VlIKMFilt const *f)
	Get maximum number of iterations. More...
vl_ikmacc_t const *	vl_ikm_get_centers (VlIKMFilt const *f)
	Get maximum number of iterations. More...
void	vl_ikm_set_verbosity (VlIKMFilt *f, int verb)
	Set verbosity level. More...
void	vl_ikm_set_max_niters (VlIKMFilt *f, vl_size max_niters)
	Set maximum number of iterations. More...

Detailed Description

Author

Brian Fulkerson

Andrea Vedaldi

Function Documentation

vl_ikm_delete()

void vl_ikm_delete

(

VlIKMFilt *

f

)

Parameters

f	IKM quantizer.

vl_ikm_get_centers()

vl_ikmacc_t const* vl_ikm_get_centers

(

VlIKMFilt const *

f

)

Parameters

f	IKM filter.

Returns

maximum number of iterations.

vl_ikm_get_K()

vl_size vl_ikm_get_K

(

VlIKMFilt const *

f

)

Parameters

f	IKM filter.

Returns

number of centers K.

vl_ikm_get_max_niters()

vl_size vl_ikm_get_max_niters

(

VlIKMFilt const *

f

)

Parameters

f	IKM filter.

Returns

maximum number of iterations.

vl_ikm_get_ndims()

vl_size vl_ikm_get_ndims

(

VlIKMFilt const *

f

)

Parameters

f	IKM filter.

Returns

data dimensionality.

vl_ikm_get_verbosity()

int vl_ikm_get_verbosity

(

VlIKMFilt const *

f

)

Parameters

f	IKM filter.

Returns

verbosity level.

vl_ikm_new()

VlIKMFilt* vl_ikm_new

(

int

method

)

Parameters

method

Clustering algorithm.

Returns

new IKM quantizer.

The function allocates initializes a new IKM quantizer to operate based algorithm method.

method has values in the enumerations VlIKMAlgorithms.

vl_ikm_push()

void vl_ikm_push	(	VlIKMFilt *	f,
		vl_uint32 *	asgn,
		vl_uint8 const *	data,
		vl_size	N
	)

Parameters

f	IKM quantizer.
asgn	Assignments (out).
data	data.
N	number of data (N >= 1).

The function projects the data data on the integer K-means clusters specified by the IKM quantizer f. Notice that the quantizer must be initialized.

vl_ikm_push_one()

vl_uint32 vl_ikm_push_one	(	vl_ikmacc_t const *	centers,
		vl_uint8 const *	data,
		vl_size	M,
		vl_size	K
	)

Parameters

centers	centers.
data	datum to project.
K	number of centers.
M	dimensionality of the datum.

Returns

the cluster index.

The function projects the specified datum data on the clusters specified by the centers centers.

vl_ikm_set_max_niters()

void vl_ikm_set_max_niters	(	VlIKMFilt *	f,
		vl_size	max_niters
	)

Parameters

f	IKM filter.
max_niters	maximum number of iterations.

vl_ikm_set_verbosity()

void vl_ikm_set_verbosity	(	VlIKMFilt *	f,
		int	verb
	)

Parameters

f	IKM filter.
verb	verbosity level.

vl_ikm_train()

int vl_ikm_train	(	VlIKMFilt *	f,
		vl_uint8 const *	data,
		vl_size	N
	)

Parameters

f	IKM quantizer.
data	data.
N	number of data (N >= 1).

Returns

-1 if an overflow may have occurred.