function mix = gmm(dim, ncentres, covar_type)%GMM	Creates a Gaussian mixture model with specified architecture.%%	Description%	 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%%	See also%	GMMPAK, GMMUNPAK, GMMSAMP, GMMINIT, GMMEM, GMACTIV, GMPOST, %	GMPROB%%	Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)mix.type = 'gmm';mix.nin = dim;mix.ncentres = ncentres;mix.nparams = mix.ncentres + mix.ncentres*mix.nin + mix.ncentres;vartypes = {'spherical', 'diag', 'full'};if sum(strcmp(covar_type, vartypes)) == 0  error('Undefined covariance type')else  mix.covar_type = covar_type;end% Initialise priors to be equal and summing to onemix.priors = ones(1,mix.ncentres) ./ mix.ncentres;% Initialise centresmix.centres = randn(mix.ncentres, mix.nin);% Initialise all the variances to unityswitch mix.covar_type  case 'spherical'    mix.covars = ones(1, mix.ncentres);  case 'diag'    % Store diagonals of covariance matrices as rows in a matrix    mix.covars =  ones(mix.ncentres, mix.nin);  case 'full'    % Store covariance matrices in a row vector of matrices    mix.covars = repmat(eye(mix.nin), [1 1 mix.ncentres]);    end