www.pudn.com > HMM1.zip > gausshmm_train_observed.m

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{:});