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{:});