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"

Include dependency graph for ikmeans.c:

Go to the source code of this file.

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

---

Detailed Description

Integer K-Means clustering - Definition.

Author:

Brian Fulkerson

Andrea Vedaldi

Definition in file ikmeans.c.

---

Function Documentation

void vl_ikm_delete

(

VlIKMFilt *

f

)

Delete IKM quantizer.

Parameters:

f	IKM quantizer.

Definition at line 117 of file ikmeans.c.

vl_ikmacc_t const* vl_ikm_get_centers

(

VlIKMFilt const *

f

)

Get maximum number of iterations.

Parameters:

f	IKM filter.

Returns:

maximum number of iterations.

Definition at line 268 of file ikmeans.c.

vl_size vl_ikm_get_K

(

VlIKMFilt const *

f

)

Get the number of centers K.

Parameters:

f	IKM filter.

Returns:

number of centers K.

Definition at line 235 of file ikmeans.c.

vl_size vl_ikm_get_max_niters

(

VlIKMFilt const *

f

)

Get maximum number of iterations.

Parameters:

f	IKM filter.

Returns:

maximum number of iterations.

Definition at line 257 of file ikmeans.c.

vl_size vl_ikm_get_ndims

(

VlIKMFilt const *

f

)

Get data dimensionality.

Parameters:

f	IKM filter.

Returns:

data dimensionality.

Definition at line 223 of file ikmeans.c.

int vl_ikm_get_verbosity

(

VlIKMFilt const *

f

)

Get verbosity level.

Parameters:

f	IKM filter.

Returns:

verbosity level.

Definition at line 246 of file ikmeans.c.

static void vl_ikm_init_elkan

(

VlIKMFilt *

)

[static]

static void vl_ikm_init_lloyd

(

VlIKMFilt *

)

[static]

VlIKMFilt* vl_ikm_new

(

int

method

)

Create a new IKM quantizer.

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.

Definition at line 104 of file ikmeans.c.

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

Project data to clusters.

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.

Definition at line 164 of file ikmeans.c.

static void vl_ikm_push_elkan	(	VlIKMFilt *	,
		vl_uint32 *	,
		vl_uint8 const *	,
		vl_size
	)		[static]

static void vl_ikm_push_lloyd	(	VlIKMFilt *	,
		vl_uint32 *	,
		vl_uint8 const *	,
		vl_size
	)		[static]

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.

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.

Definition at line 185 of file ikmeans.c.

void vl_ikm_set_max_niters	(	VlIKMFilt *	f,
		vl_size	max_niters
	)

Set maximum number of iterations.

Parameters:

f	IKM filter.
max_niters	maximum number of iterations.

Definition at line 290 of file ikmeans.c.

void vl_ikm_set_verbosity	(	VlIKMFilt *	f,
		int	verb
	)

Set verbosity level.

Parameters:

f	IKM filter.
verb	verbosity level.

Definition at line 279 of file ikmeans.c.

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

Train clusters.

Parameters:

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

Returns:

-1 if an overflow may have occurred.

Definition at line 134 of file ikmeans.c.

static int vl_ikm_train_elkan	(	VlIKMFilt *	,
		vl_uint8 const *	,
		vl_size
	)		[static]

static int vl_ikm_train_lloyd	(	VlIKMFilt *	,
		vl_uint8 const *	,
		vl_size
	)		[static]