wolffd@0: function mix = gmmunpak(mix, p)
wolffd@0: %GMMUNPAK Separates a vector of Gaussian mixture model parameters into its components.
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %	MIX = GMMUNPAK(MIX, P) takes a GMM data structure MIX and  a single
wolffd@0: %	row vector of parameters P and returns a mixture data structure
wolffd@0: %	identical to the input MIX, except that the mixing coefficients
wolffd@0: %	PRIORS, centres CENTRES and covariances COVARS  (and, for PPCA, the
wolffd@0: %	lambdas and U (PCA sub-spaces)) are all set to the corresponding
wolffd@0: %	elements of P.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	GMM, GMMPAK
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: errstring = consist(mix, 'gmm');
wolffd@0: if ~errstring
wolffd@0:   error(errstring);
wolffd@0: end
wolffd@0: if mix.nwts ~= length(p)
wolffd@0:   error('Invalid weight vector length')
wolffd@0: end
wolffd@0: 
wolffd@0: mark1 = mix.ncentres;
wolffd@0: mark2 = mark1 + mix.ncentres*mix.nin;
wolffd@0: 
wolffd@0: mix.priors = reshape(p(1:mark1), 1, mix.ncentres);
wolffd@0: mix.centres = reshape(p(mark1 + 1:mark2), mix.ncentres, mix.nin);
wolffd@0: switch mix.covar_type
wolffd@0:   case 'spherical'
wolffd@0:     mark3 = mix.ncentres*(2 + mix.nin);
wolffd@0:     mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres);
wolffd@0:   case 'diag'
wolffd@0:     mark3 = mix.ncentres*(1 + mix.nin + mix.nin);
wolffd@0:     mix.covars = reshape(p(mark2 + 1:mark3), mix.ncentres, mix.nin);
wolffd@0:   case 'full'
wolffd@0:     mark3 = mix.ncentres*(1 + mix.nin + mix.nin*mix.nin);
wolffd@0:     mix.covars = reshape(p(mark2 + 1:mark3), mix.nin, mix.nin, ...
wolffd@0:       mix.ncentres);
wolffd@0:   case 'ppca'
wolffd@0:     mark3 = mix.ncentres*(2 + mix.nin);
wolffd@0:     mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres);
wolffd@0:     % Now also extract k and eigenspaces
wolffd@0:     mark4 = mark3 + mix.ncentres*mix.ppca_dim;
wolffd@0:     mix.lambda = reshape(p(mark3 + 1:mark4), mix.ncentres, ...
wolffd@0:       mix.ppca_dim);
wolffd@0:     mix.U = reshape(p(mark4 + 1:end), mix.nin, mix.ppca_dim, ...
wolffd@0:       mix.ncentres);
wolffd@0:   otherwise
wolffd@0:     error(['Unknown covariance type ', mix.covar_type]);
wolffd@0: end
wolffd@0: