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

first hg version after svn

+function [initState, transmat, mu, Nproto, pick] = mhmmParzen_train_observed(obsData, hiddenData, ...
+						  nstates, maxNproto, varargin)
+% mhmmParzentrain_observed  with mixture of Gaussian outputs from fully observed sequences
+% function [initState, transmat, mu, Nproto] = mhmm_train_observed_parzen(obsData, hiddenData, ...
+%						  nstates, maxNproto)
+%
+%
+% 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
+% mkSymmetric
+%
+% Output
+% mu(:,q)
+% Nproto(q) is the number of prototypes (mixture components) chosen for state q
+
+[transmat, initState] = transmat_train_observed(...
+    hiddenData, nstates, varargin{:});
+
+% 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, Nproto, pick] = parzen_fit_select_unif(obsData, hiddenData(:), maxNproto);
+
+