wolffd@0: function [initState, transmat, mu, Sigma] = gausshmm_train_observed(obsData, hiddenData, ...
wolffd@0: 						  nstates, varargin)
wolffd@0: % GAUSSHMM_TRAIN_OBSERVED  Estimate params of HMM with Gaussian output from fully observed sequences
wolffd@0: % [initState, transmat, mu, Sigma] = gausshmm_train_observed(obsData, hiddenData, nstates,...)
wolffd@0: %
wolffd@0: % INPUT
wolffd@0: % If all sequences have the same length
wolffd@0: % obsData(:,t,ex) 
wolffd@0: % hiddenData(ex,t)  - must be ROW vector if only one sequence
wolffd@0: % If sequences have different lengths, we use cell arrays
wolffd@0: % obsData{ex}(:,t) 
wolffd@0: % hiddenData{ex}(t)
wolffd@0: %
wolffd@0: % Optional argumnets
wolffd@0: % dirichletPriorWeight - for smoothing transition matrix counts
wolffd@0: %
wolffd@0: % Optional parameters from mixgauss_Mstep:
wolffd@0: % 'cov_type' - 'full', 'diag' or 'spherical' ['full']
wolffd@0: % 'tied_cov' - 1 (Sigma) or 0 (Sigma_i) [0]
wolffd@0: % 'clamped_cov' - pass in clamped value, or [] if unclamped [ [] ]
wolffd@0: % 'clamped_mean' - pass in clamped value, or [] if unclamped [ [] ]
wolffd@0: % 'cov_prior' - Lambda_i, added to YY(:,:,i) [0.01*eye(d,d,Q)]
wolffd@0: %
wolffd@0: % Output
wolffd@0: % mu(:,q)
wolffd@0: % Sigma(:,:,q) 
wolffd@0: 
wolffd@0: [dirichletPriorWeight, other] = process_options(...
wolffd@0:     varargin, 'dirichletPriorWeight', 0);
wolffd@0: 
wolffd@0: [transmat, initState] = transmat_train_observed(hiddenData, nstates, ...
wolffd@0: 						'dirichletPriorWeight', dirichletPriorWeight);
wolffd@0: 
wolffd@0: % convert to obsData(:,t*nex)
wolffd@0: if ~iscell(obsData)
wolffd@0:   [D T Nex] = size(obsData);
wolffd@0:   obsData = reshape(obsData, D, T*Nex);
wolffd@0: else
wolffd@0:   obsData = cat(2, obsData{:});
wolffd@0:   hiddenData = cat(2,hiddenData{:});
wolffd@0: end
wolffd@0: [mu, Sigma] = condgaussTrainObserved(obsData, hiddenData(:), nstates, varargin{:});
wolffd@0: 
wolffd@0: