diff toolboxes/FullBNT-1.0.7/HMM/mhmm_sample.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/HMM/mhmm_sample.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,31 @@
+function [obs, hidden] = mhmm_sample(T, numex, initial_prob, transmat, mu, Sigma, mixmat)
+% SAMPLE_MHMM Generate random sequences from an HMM with (mixtures of) Gaussian output.
+% [obs, hidden] = sample_mhmm(T, numex, initial_prob, transmat, mu, Sigma, mixmat)
+%
+% INPUTS:
+% T - length of each sequence
+% numex - num. sequences
+% init_state_prob(i) = Pr(Q(1) = i)
+% transmat(i,j) = Pr(Q(t+1)=j | Q(t)=i)
+% mu(:,j,k) = mean of Y(t) given Q(t)=j, M(t)=k
+% Sigma(:,:,j,k) = cov. of Y(t) given Q(t)=j, M(t)=k
+% mixmat(j,k) = Pr(M(t)=k | Q(t)=j) : set to ones(Q,1) or omit if single mixture
+%
+% OUTPUT:
+% obs(:,t,l) = observation vector at time t for sequence l
+% hidden(t,l) = the hidden state at time t for sequence l
+
+Q = length(initial_prob);
+if nargin < 7, mixmat = ones(Q,1); end
+O = size(mu,1);
+hidden = zeros(T, numex);
+obs = zeros(O, T, numex);
+
+hidden = mc_sample(initial_prob, transmat, T, numex)';
+for i=1:numex
+  for t=1:T
+    q = hidden(t,i);
+    m = sample_discrete(mixmat(q,:), 1, 1);
+    obs(:,t,i) =  gaussian_sample(mu(:,q,m), Sigma(:,:,q,m), 1);
+  end
+end