comparison toolboxes/FullBNT-1.0.7/HMM/transmat_train_observed.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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));
+
+