www.pudn.com > HMM1.zip > mhmmParzen_train_observed.m
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);