diff toolboxes/FullBNT-1.0.7/bnt/examples/static/Brutti/Belief_IOhmm.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/bnt/examples/static/Brutti/Belief_IOhmm.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,49 @@
+% Sigmoid Belief IOHMM
+% Here is the model
+%
+%  X \  X \
+%  | |  | |
+%  Q-|->Q-|-> ...
+%  | /  | /
+%  Y    Y
+%
+clear all;
+clc;
+rand('state',0); randn('state',0);
+X = 1; Q = 2; Y = 3;
+% intra time-slice graph
+intra=zeros(3);
+intra(X,[Q Y])=1;
+intra(Q,Y)=1;
+% inter time-slice graph
+inter=zeros(3);
+inter(Q,Q)=1;
+
+ns = [1 3 1]; 
+dnodes = [2];
+eclass1 = [1 2 3];
+eclass2 = [1 4 3];
+bnet = mk_dbn(intra, inter, ns, dnodes, eclass1, eclass2);
+bnet.CPD{1} = root_CPD(bnet, 1);
+% ==========================================================
+bnet.CPD{2} = softmax_CPD(bnet, 2);
+bnet.CPD{4} = softmax_CPD(bnet, 5, 'discrete', [2]);
+% ==========================================================
+bnet.CPD{3} = gaussian_CPD(bnet, 3);
+
+% make some data
+T=20;
+cases = cell(3, T);
+cases(1,:)=num2cell(round(rand(1,T)*2)+1);
+%cases(2,:)=num2cell(round(rand(1,T))+1);
+cases(3,:)=num2cell(rand(1,T));
+
+engine = bk_inf_engine(bnet, 'exact', [1 2 3]);
+
+% log lik before learning
+[engine, loglik] = enter_evidence(engine, cases);
+
+% do learning
+ev=cell(1,1);
+ev{1}=cases;
+[bnet2, LL2] = learn_params_dbn_em(engine, ev, 3);
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