function mix = gmm(dim, ncentres, covar_type, ppca_dim)%GMM	Creates a Gaussian mixture model with specified architecture.%%	Description%	 MIX = GMM(DIM, NCENTRES, COVARTYPE) 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%	  'ppca' = probabilistic PCA: stored as principal components (in a 3d array%	    and associated variances and off-subspace noise%	 MIX = GMM(DIM, NCENTRES, COVARTYPE, PPCA_DIM) also sets the%	dimension of the PPCA sub-spaces: the default value is one.%%	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%	  covartype = string for type of variance model%	  priors = mixing coefficients%	  centres = means of Gaussians: stored as rows of a matrix%	  covars = covariances of Gaussians%	 The additional fields for mixtures of PPCA are%	  U = principal component subspaces%	  lambda = in-space covariances: stored as rows of a matrix%	 The off-subspace noise is stored in COVARS.%%	See also%	GMMPAK, GMMUNPAK, GMMSAMP, GMMINIT, GMMEM, GMACTIV, GMPOST, %	GMPROB%%	Copyright (c) Ian T Nabney (1996-2001)if ncentres < 1  error('Number of centres must be greater than zero')endmix.type = 'gmm';mix.nin = dim;mix.ncentres = ncentres;vartypes = {'spherical', 'diag', 'full', 'ppca'};if sum(strcmp(covar_type, vartypes)) == 0  error('Undefined covariance type')else  mix.covar_type = covar_type;end% Make default dimension of PPCA subspaces one.if strcmp(covar_type, 'ppca')  if nargin < 4    ppca_dim = 1;  end  if ppca_dim > dim    error('Dimension of PPCA subspaces must be less than data.')  end  mix.ppca_dim = ppca_dim;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_typecase 'spherical'  mix.covars = ones(1, mix.ncentres);  mix.nwts = mix.ncentres + mix.ncentres*mix.nin + mix.ncentres;case 'diag'  % Store diagonals of covariance matrices as rows in a matrix  mix.covars =  ones(mix.ncentres, mix.nin);  mix.nwts = mix.ncentres + mix.ncentres*mix.nin + ...    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]);  mix.nwts = mix.ncentres + mix.ncentres*mix.nin + ...    mix.ncentres*mix.nin*mix.nin;case 'ppca'  % This is the off-subspace noise: make it smaller than  % lambdas  mix.covars = 0.1*ones(1, mix.ncentres);  % Also set aside storage for principal components and  % associated variances  init_space = eye(mix.nin);  init_space = init_space(:, 1:mix.ppca_dim);  init_space(mix.ppca_dim+1:mix.nin, :) = ...    ones(mix.nin - mix.ppca_dim, mix.ppca_dim);  mix.U = repmat(init_space , [1 1 mix.ncentres]);  mix.lambda = ones(mix.ncentres, mix.ppca_dim);  % Take account of additional parameters  mix.nwts = mix.ncentres + mix.ncentres*mix.nin + ...    mix.ncentres + mix.ncentres*mix.ppca_dim + ...    mix.ncentres*mix.nin*mix.ppca_dim;otherwise  error(['Unknown covariance type ', mix.covar_type]);               end