mix = gmm(dim, ncentres, covar_type)
mix = gmm(dim, ncentres, covar_type) takes
the dimension of the space dim, the number of centres in the
mixture model and the type of the mixture model, and returns a data
structure mix.
The mixture model type defines the covariance structure of each component
Gaussian:
'spherical' = single variance parameter for each component: stored as a vector 'diag' = diagonal matrix for each component: stored as rows of a matrix 'full' = full matrix for each component: stored as 3d array
The priors are initialised to equal values summing to one, and the covariances
are all the identity matrix (or equivalent). The centres are
initialised randomly from a zero mean unit variance Gaussian. This makes use
of the MATLAB function randn and so the seed for the random weight
initialisation can be set using randn('state', s) where s is the
state value.
The fields in mix are
type = 'gmm' nin = the dimension of the space ncentres = number of mixture components covar_type = string for type of variance model priors = mixing coefficients centres = means of Gaussians: stored as rows of a matrix covars = covariances of Gaussians
mix = gmm(2, 4, 'spherical');This creates a Gaussian mixture model with 4 components in 2 dimensions. The covariance structure is a spherical model.
gmmpak, gmmunpak, gmmsamp, gmminit, gmmem, gmactiv, gmpost, gmprobCopyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)