hmmhsinit.m

VARHMMBOX, version 1.1, Iead Rezek, Oxford University, MAR 2002 Matlab toolbox for Hidden Markov Mo

function [hmm] = hmmhsinit (X,hmm)

% function [hmm] = hmmhsinit (X,hmm)
%
% Initialise  HMM Chain's hidden state variables
%
% X		N x p data matrix
% hmm		hmm data structure
%
% e.g.   hmm=struct(K,2);
%        hmm=hmmint(X,hmm); 

[T,ndim]=size(X);
if length(X)~=T,
  X=X';
  [T,ndim]=size(X);
end;

% Initialising the parameters
for k=1:hmm.K,
  % Initial state
  Dir_alpha(k)=1;
  hmm.Pi(k)=Dir_alpha(k)./hmm.K;
  % State transitions
  Dir2d_alpha(k,:)=ones(1,hmm.K);
  Dir2d_alpha(k,k)=2; 
  hmm.P(k,:)=Dir2d_alpha(k,:)./sum(Dir2d_alpha(k,:),2);
end;


% define P-priors
defhmmprior=struct('Dir2d_alpha',[],'Dir_alpha',[]);
  
defhmmprior.Dir_alpha=ones(1,hmm.K);
defhmmprior.Dir2d_alpha=ones(hmm.K);

% assigning default priors for hidden states
if ~isfield(hmm,'priors'),
  hmm.priors=defhmmprior;
else
  % priors not specified are set to default
  hmmpriorlist=fieldnames(defhmmprior);
  fldname=fieldnames(hmm.priors);
  misfldname=find(~ismember(hmmpriorlist,fldname));
  for i=1:length(misfldname),
    priorval=getfield(defhmmprior,hmmpriorlist{i});
    hmm.priors=setfield(hmm.priors,hmmpriorlist{i},priorval);
  end;
end;