Gaussian Mixture Models - Implementation. More...

#include "gmm.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "mathop_avx.h"
#include "shuffle-def.h"

Functions
VlGMM *	vl_gmm_new (vl_type dataType, vl_size dimension, vl_size numComponents)
	Create a new GMM object.
void	vl_gmm_reset (VlGMM *self)
	Reset state.
void	vl_gmm_delete (VlGMM *self)
	Deletes a GMM object.
vl_type	vl_gmm_get_data_type (VlGMM const *self)
	Get data type.
vl_size	vl_gmm_get_num_clusters (VlGMM const *self)
	Get the number of clusters.
vl_size	vl_gmm_get_num_data (VlGMM const *self)
	Get the number of data points.
double	vl_gmm_get_loglikelihood (VlGMM const *self)
	Get the log likelihood of the current mixture.
int	vl_gmm_get_verbosity (VlGMM const *self)
	Get verbosity level.
void	vl_gmm_set_verbosity (VlGMM *self, int verbosity)
	Set verbosity level.
void const *	vl_gmm_get_means (VlGMM const *self)
	Get means.
void const *	vl_gmm_get_covariances (VlGMM const *self)
	Get covariances.
void const *	vl_gmm_get_priors (VlGMM const *self)
	Get priors.
void const *	vl_gmm_get_posteriors (VlGMM const *self)
	Get posteriors.
vl_size	vl_gmm_get_max_num_iterations (VlGMM const *self)
	Get maximum number of iterations.
void	vl_gmm_set_max_num_iterations (VlGMM *self, vl_size maxNumIterations)
	Set maximum number of iterations.
void	vl_gmm_set_num_repetitions (VlGMM *self, vl_size numRepetitions)
	Set maximum number of repetitions.
vl_size	vl_gmm_get_dimension (VlGMM const *self)
	Get data dimension.
VlGMMInitialization	vl_gmm_get_initialization (VlGMM const *self)
	Get initialization algorithm.
void	vl_gmm_set_initialization (VlGMM *self, VlGMMInitialization init)
	Set initialization algorithm.
VlKMeans *	vl_gmm_get_kmeans_init_object (VlGMM const *self)
	Get KMeans initialization object.
void	vl_gmm_set_kmeans_init_object (VlGMM self, VlKMeans kmeans)
	Set KMeans initialization object.
double const *	vl_gmm_get_covariance_lower_bounds (VlGMM const *self)
	Get the lower bound on the diagonal covariance values.
void	vl_gmm_set_covariance_lower_bounds (VlGMM self, double const bounds)
	Set the lower bounds on diagonal covariance values.
void	vl_gmm_set_covariance_lower_bound (VlGMM *self, double bound)
	Set the lower bounds on diagonal covariance values.
VlGMM *	vl_gmm_new_copy (VlGMM const *self)
	Create a new GMM object by copy.
void	vl_gmm_init_with_rand_data (VlGMM self, void const data, vl_size numData)
	Initialize mixture before EM takes place using random initialization.
void	vl_gmm_init_with_kmeans (VlGMM self, void const data, vl_size numData, VlKMeans *kmeansInit)
	Initializes the GMM using KMeans.
double	vl_gmm_cluster (VlGMM self, void const data, vl_size numData)
	Run GMM clustering - includes initialization and EM.
double	vl_gmm_em (VlGMM self, void const data, vl_size numData)
	Invoke the EM algorithm.
void	vl_gmm_set_means (VlGMM self, void const means)
	Explicitly set the initial means for EM.
void	vl_gmm_set_covariances (VlGMM self, void const covariances)
	Explicitly set the initial sigma diagonals for EM.
void	vl_gmm_set_priors (VlGMM self, void const priors)
	Explicitly set the initial priors of the gaussians.
Get parameters
vl_size	vl_gmm_get_num_repetitions (VlGMM const *self)
	Get maximum number of repetitions.

Detailed Description

Author:: David Novotny; Andrea Vedaldi

Function Documentation

double vl_gmm_cluster	(	VlGMM *	self,
		void const *	data,
		vl_size	numData
	)

Parameters:

self	GMM object instance.
data	data points which should be clustered.
dimension	dimensionality of the data points.
numData	number of data points.
numClusters	number of gaussians which should be estimated.

void vl_gmm_delete ( VlGMM * self )

Parameters:

self	GMM object instance.

The function deletes the GMM object instance created by vl_gmm_new.

double vl_gmm_em	(	VlGMM *	self,
		void const *	data,
		vl_size	numData
	)

Parameters:

self	GMM object instance.
data	data points which should be clustered.
numData	number of data points.

double const* vl_gmm_get_covariance_lower_bounds ( VlGMM const * self )

Parameters:

self object

Returns:: lower bound on covariances.

void const* vl_gmm_get_covariances ( VlGMM const * self )

Parameters:

self object

Returns:: diagonals of cluster covariance matrices.

vl_type vl_gmm_get_data_type ( VlGMM const * self )

Parameters:

self object

Returns:: data type.

vl_size vl_gmm_get_dimension ( VlGMM const * self )

Parameters:

self object

Returns:: data dimension.

VlGMMInitialization vl_gmm_get_initialization ( VlGMM const * self )

Parameters:

self object

Returns:: initialization algorithm.

VlKMeans* vl_gmm_get_kmeans_init_object ( VlGMM const * self )

Parameters:

self object

Returns:: kmeans initialization object.

double vl_gmm_get_loglikelihood ( VlGMM const * self )

Parameters:

self object

Returns:: loglikelihood.

vl_size vl_gmm_get_max_num_iterations ( VlGMM const * self )

Parameters:

self object

Returns:: maximum number of iterations.

void const* vl_gmm_get_means ( VlGMM const * self )

Parameters:

self object

Returns:: cluster means.

vl_size vl_gmm_get_num_clusters ( VlGMM const * self )

Parameters:

self object

Returns:: number of clusters.

vl_size vl_gmm_get_num_data ( VlGMM const * self )

Parameters:

self object

Returns:: number of data points.

vl_size vl_gmm_get_num_repetitions ( VlGMM const * self )

Parameters:

self object

Returns:: current number of repretitions for quantization.

void const* vl_gmm_get_posteriors ( VlGMM const * self )

Parameters:

self object

Returns:: posterior probabilities of cluster memberships.

void const* vl_gmm_get_priors ( VlGMM const * self )

Parameters:

self object

Returns:: priors of cluster gaussians.

int vl_gmm_get_verbosity ( VlGMM const * self )

Parameters:

self object

Returns:: verbosity level.

void vl_gmm_init_with_kmeans	(	VlGMM *	self,
		void const *	data,
		vl_size	numData,
		VlKMeans *	kmeansInit
	)

Parameters:

self	GMM object instance.
data	data points which should be clustered.
dimension	dimensionality of the data points.
numData	number of data points.
numClusters	number of gaussians which should be estimated.
kmeansInit	KMeans object to use.

void vl_gmm_init_with_rand_data	(	VlGMM *	self,
		void const *	data,
		vl_size	numData
	)

Parameters:

self	GMM object instance.
data	data points which should be clustered.
dimension	dimensionality of the data points.
numData	number of data points.
numClusters	number of gaussians which should be estimated.

VlGMM* vl_gmm_new	(	vl_type	dataType,
		vl_size	dimension,
		vl_size	numComponents
	)

Parameters:

dataType type of data (VL_TYPE_FLOAT or VL_TYPE_DOUBLE)

Returns:: new GMM object instance.

VlGMM* vl_gmm_new_copy ( VlGMM const * self )

Parameters:

self object.

Returns:: new copy.

Most parameters, including the cluster priors, means, and covariances are copied. Data posteriors (available after initalization or EM) are not; nor is the KMeans object used for initialization, if any.

void vl_gmm_reset ( VlGMM * self )

Parameters:

self object.

The function reset the state of the GMM object. It deletes any stored posterior and other internal state variables.

void vl_gmm_set_covariance_lower_bound	(	VlGMM *	self,
		double	bound
	)

Parameters:

self	object.
bound	bound.

While there is one lower bound per dimension, this function sets all of them to the specified scalar. Use ::vl:gmm_set_covariance_lower_bounds to set them individually.

void vl_gmm_set_covariance_lower_bounds	(	VlGMM *	self,
		double const *	bounds
	)

Parameters:

self	object.
bounds	bounds.

There is one lower bound per dimension. Use ::vl:gmm_set_covariance_lower_bound to set all of them to a given scalar.

void vl_gmm_set_covariances	(	VlGMM *	self,
		void const *	covariances
	)

Parameters:

self	GMM object instance.
covariances	initial values of covariance matrix diagonals.

void vl_gmm_set_initialization	(	VlGMM *	self,
		VlGMMInitialization	init
	)

Parameters:

self	object
init	initialization algorithm.

void vl_gmm_set_kmeans_init_object	(	VlGMM *	self,
		VlKMeans *	kmeans
	)

Parameters:

self	object
kmeans	initialization KMeans object.

void vl_gmm_set_max_num_iterations	(	VlGMM *	self,
		vl_size	maxNumIterations
	)

Parameters:

self	VlGMM filter.
maxNumIterations	maximum number of iterations.

void vl_gmm_set_means	(	VlGMM *	self,
		void const *	means
	)

Parameters:

self	GMM object instance.
means	initial values of means.

void vl_gmm_set_num_repetitions	(	VlGMM *	self,
		vl_size	numRepetitions
	)

Parameters:

self	object
numRepetitions	maximum number of repetitions. The number of repetitions cannot be smaller than 1.

void vl_gmm_set_priors	(	VlGMM *	self,
		void const *	priors
	)

Parameters:

self	GMM object instance.
priors	initial values of the gaussian priors.

void vl_gmm_set_verbosity	(	VlGMM *	self,
		int	verbosity
	)

Parameters:

self	object
verbosity	verbosity level.

VLFeat.org

Functions

Detailed Description

Function Documentation