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