NAME

get_full_GMM - Finds a Gaussian mixture model (GMM) for the correlated features.

SYNOPSIS

#include "r2/r2_gmm_em.h"

Example compile flags (system dependent):
  -DLINUX_X86_64 -DLINUX_X86_64_OPTERON  
   -I/home/kobus/include
   -L/misc/load/linux_x86_64_opteron -L/usr/lib
  -lKJB                               -lfftw3 -lgandalf  -ltiff -ltiff -lpng -lgsl -lgslcblas -ljpeg  -lSVM -lstdc++  -lGL -lGLU -lX11 -lglut -lGL -lGLU -lpthread -lSLATEC -lg2c  -ltiff  -lacml -lacml_mv -lg2c     -lX11 -lncursesw 


int get_full_GMM
(
	int num_clusters,
	const Matrix *feature_mp,
	const Matrix *covariance_mask_mp,
	Vector **a_vpp,
	Matrix **u_mpp,
	Matrix_vector **S_mvpp,
	Matrix **P_mpp
);

DESCRIPTION

This routine finds a Gaussian mixture model (GMM) for the data under the assumption that the features are not independent. The model is fit with EM. Some features are controlled via the set facility. The argument num_clusters is the number of requested mixture compoenent (clusters), K. The data matrix feature_mp is an N by M matrix where N is the number of data points, and M is the number of features. The covariance matrix The model parameters are put into *a_vpp, *u_mpp, and *S_mvpp. Any of a_vpp, u_mpp, or S_mvpp is NULL if that value is not needed. If P_mpp, is not NULL, then the soft clustering (cluster membership) for each data point is returned. In that case, *P_mpp will be N by K.

RETURNS

If the routine fails (due to storage allocation), then ERROR is returned with an error message being set. Otherwise NO_ERROR is returned.

DISCLAIMER

This software is not adequatedly tested. It is recomended that results are checked independantly where appropriate.

AUTHOR

Kobus Barnard

DOCUMENTER

Kobus Barnard

SEE ALSO

get_full_GMM_2 , get_full_GMM_3 , get_independent_GMM , get_independent_GMM_2 , get_independent_GMM_3 , get_independent_GMM_2_with_missing_data , get_independent_GMM_3_mt , create_independent_GMM_thread