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

function [transmat, initState] = transmat_train_observed(labels,  nstates, varargin) 
% transmat_train_observed ML estimation from fully observed data 
% function [transmat, initState] = transmat_train_observed(labels,  nstates, varargin) 
% 
% If all sequences have the same length 
% labels(ex,t) 
% If sequences have different lengths, we use cell arrays 
% labels{ex}(t) 
 
[dirichletPriorWeight, mkSymmetric, other] = process_options(... 
    varargin, 'dirichletPriorWeight', 0, 'mkSymmetric', 0); 
 
if ~iscell(labels) 
  [numex T] = size(labels); 
  if T==1 
    labels = labels'; 
  end 
  %fprintf('T=%d, numex=%d\n', T, numex); 
  labels = num2cell(labels,2); % each row gets its own cell 
end 
numex = length(labels); 
 
counts = zeros(nstates, nstates); 
counts1 = zeros(nstates,1); 
for s=1:numex 
  labs = labels{s}; labs = labs(:)'; 
  dat = [labs(1:end-1); labs(2:end)]; 
  counts = counts + compute_counts(dat, [nstates nstates]); 
  q = labs(1); 
  counts1(q) = counts1(q) + 1; 
end 
pseudo_counts = dirichletPriorWeight*ones(nstates, nstates); 
if mkSymmetric 
  counts = counts + counts'; 
end 
transmat = mk_stochastic(counts + pseudo_counts); 
initState = normalize(counts1 + dirichletPriorWeight*ones(nstates,1));