function mix = gmmunpak(mix, p)%GMMUNPAK Separates a vector of Gaussian mixture model parameters into its components.%%	Description%	MIX = GMMUNPAK(MIX, P) takes a GMM data structure MIX and  a single%	row vector of parameters P and returns a mixture data structure%	identical to the input MIX, except that the mixing coefficients%	PRIORS, centres CENTRES and covariances COVARS  (and, for PPCA, the%	lambdas and U (PCA sub-spaces)) are all set to the corresponding%	elements of P.%%	See also%	GMM, GMMPAK%%	Copyright (c) Ian T Nabney (1996-2001)errstring = consist(mix, 'gmm');if ~errstring  error(errstring);endif mix.nwts ~= length(p)  error('Invalid weight vector length')endmark1 = mix.ncentres;mark2 = mark1 + mix.ncentres*mix.nin;mix.priors = reshape(p(1:mark1), 1, mix.ncentres);mix.centres = reshape(p(mark1 + 1:mark2), mix.ncentres, mix.nin);switch mix.covar_type  case 'spherical'    mark3 = mix.ncentres*(2 + mix.nin);    mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres);  case 'diag'    mark3 = mix.ncentres*(1 + mix.nin + mix.nin);    mix.covars = reshape(p(mark2 + 1:mark3), mix.ncentres, mix.nin);  case 'full'    mark3 = mix.ncentres*(1 + mix.nin + mix.nin*mix.nin);    mix.covars = reshape(p(mark2 + 1:mark3), mix.nin, mix.nin, ...      mix.ncentres);  case 'ppca'    mark3 = mix.ncentres*(2 + mix.nin);    mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres);    % Now also extract k and eigenspaces    mark4 = mark3 + mix.ncentres*mix.ppca_dim;    mix.lambda = reshape(p(mark3 + 1:mark4), mix.ncentres, ...      mix.ppca_dim);    mix.U = reshape(p(mark4 + 1:end), mix.nin, mix.ppca_dim, ...      mix.ncentres);  otherwise    error(['Unknown covariance type ', mix.covar_type]);end