comparison toolboxes/FullBNT-1.0.7/HMM/gausshmm_train_observed.m @ 0:e9a9cd732c1e tip

first hg version after svn

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