diff toolboxes/FullBNT-1.0.7/HMM/dhmm_em_online_demo.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/dhmm_em_online_demo.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,93 @@
+% Example of online EM applied to a simple POMDP with fixed action seq
+
+clear all
+
+% Create a really easy model to learn
+rand('state', 1);
+O = 2;
+S = 2;
+A = 2;
+prior0 = [1 0]';
+transmat0 = cell(1,A);
+transmat0{1} = [0.9 0.1; 0.1 0.9]; % long runs of 1s and 2s
+transmat0{2} = [0.1 0.9; 0.9 0.1]; % short runs
+obsmat0 = eye(2);
+	   
+%prior0 = normalise(rand(S,1));
+%transmat0 = mk_stochastic(rand(S,S));
+%obsmat0 = mk_stochastic(rand(S,O));
+
+T = 10;
+act = [1*ones(1,25) 2*ones(1,25) 1*ones(1,25) 2*ones(1,25)];
+data = pomdp_sample(prior0, transmat0, obsmat0, act);
+%data = sample_dhmm(prior0, transmat0, obsmat0, T, 1);
+
+% Initial guess of params
+rand('state', 2); % different seed!
+transmat1 = cell(1,A);
+for a=1:A
+  transmat1{a} = mk_stochastic(rand(S,S));
+end
+obsmat1 = mk_stochastic(rand(S,O));
+prior1 = prior0; % so it labels states the same way
+
+% Uniformative Dirichlet prior (expected sufficient statistics / pseudo counts)
+e = 0.001;
+ess_trans = cell(1,A);
+for a=1:A
+  ess_trans{a} = repmat(e, S, S);
+end
+ess_emit = repmat(e, S, O);
+
+% Params
+w = 2;
+decay_sched = [0.1:0.1:0.9];
+
+% Initialize
+LL1 = zeros(1,T);
+t = 1;
+y = data(t);
+data_win = y;
+act_win = [1]; % arbitrary initial value
+[prior1, LL1(1)] = normalise(prior1 .* obsmat1(:,y));
+
+% Iterate
+for t=2:T
+  y = data(t);
+  a = act(t);
+  if t <= w
+    data_win = [data_win y];
+    act_win = [act_win a];
+  else
+    data_win = [data_win(2:end) y];
+    act_win = [act_win(2:end) a];
+    prior1 = gamma(:, 2);
+  end
+  d = decay_sched(min(t, length(decay_sched)));
+  [transmat1, obsmat1, ess_trans, ess_emit, gamma, ll] = dhmm_em_online(...
+      prior1, transmat1, obsmat1, ess_trans, ess_emit, d, data_win, act_win);
+  bel = gamma(:, end);
+  LL1(t) = ll/length(data_win);
+  %fprintf('t=%d, ll=%f\n', t, ll);
+end
+
+LL1(1) = LL1(2); % since initial likelihood is for 1 slice
+plot(1:T, LL1, 'rx-');
+
+
+% compare with offline learning
+
+if 0
+rand('state', 2); % same seed as online learner
+transmat2 = cell(1,A);
+for a=1:A
+  transmat2{a} = mk_stochastic(rand(S,S));
+end
+obsmat2 = mk_stochastic(rand(S,O));
+prior2 = prior0;
+[LL2, prior2, transmat2, obsmat2] = dhmm_em(data, prior2, transmat2, obsmat2, ....
+					       'max_iter', 10, 'thresh', 1e-3, 'verbose', 1, 'act', act);
+
+LL2 = LL2 / T
+
+end