wolffd@0: function [loglik, errors] = mhmm_logprob(data, prior, transmat, mu, Sigma, mixmat)
wolffd@0: % LOG_LIK_MHMM Compute the log-likelihood of a dataset using a (mixture of) Gaussians HMM
wolffd@0: % [loglik, errors] = log_lik_mhmm(data, prior, transmat, mu, sigma, mixmat)
wolffd@0: %
wolffd@0: % data{m}(:,t) or data(:,t,m) if all cases have same length
wolffd@0: % errors  is a list of the cases which received a loglik of -infinity
wolffd@0: %
wolffd@0: % Set mixmat to ones(Q,1) or omit it if there is only 1 mixture component
wolffd@0: 
wolffd@0: Q = length(prior);
wolffd@0: if size(mixmat,1) ~= Q % trap old syntax
wolffd@0:   error('mixmat should be QxM')
wolffd@0: end
wolffd@0: if nargin < 6, mixmat = ones(Q,1); end
wolffd@0: 
wolffd@0: if ~iscell(data)
wolffd@0:   data = num2cell(data, [1 2]); % each elt of the 3rd dim gets its own cell
wolffd@0: end
wolffd@0: ncases = length(data);
wolffd@0: 
wolffd@0: loglik = 0;
wolffd@0: errors = [];
wolffd@0: for m=1:ncases
wolffd@0:   obslik = mixgauss_prob(data{m}, mu, Sigma, mixmat);
wolffd@0:   [alpha, beta, gamma, ll] = fwdback(prior, transmat, obslik, 'fwd_only', 1);
wolffd@0:   if ll==-inf
wolffd@0:     errors = [errors m];
wolffd@0:   end
wolffd@0:   loglik = loglik + ll;
wolffd@0: end