comparison toolboxes/FullBNT-1.0.7/bnt/examples/static/Brutti/Belief_hme.m @ 0:e9a9cd732c1e tip

first hg version after svn

+% Sigmoid Belief Hierarchical Mixtures of Experts
+
+clear all
+clc
+X = 1;
+Q1 = 2;
+Q2 = 3;
+Y = 4;
+dag = zeros(4,4);
+dag(X,[Q1 Q2 Y]) = 1;
+dag(Q1, [Q2 Y]) = 1;
+dag(Q2,Y)=1;
+ns = [1 3 4 3];
+dnodes = [2 3 4];
+onodes=[1 2 3 4];
+bnet = mk_bnet(dag,ns, dnodes);
+
+rand('state',0); randn('state',0);
+
+bnet.CPD{1} = root_CPD(bnet, 1);
+bnet.CPD{2} = softmax_CPD(bnet, 2, 'max_iter', 3);
+bnet.CPD{3} = softmax_CPD(bnet, 3, 'discrete', [2], 'max_iter', 3);
+bnet.CPD{4} = softmax_CPD(bnet, 4, 'discrete', [2 3], 'max_iter', 3);
+
+T=5;
+cases = cell(4, T);
+cases(1,:)=num2cell(rand(1,T));
+%cases(2,:)=num2cell(round(rand(1,T)*2)+1);
+%cases(3,:)=num2cell(round(rand(1,T)*3)+1);
+cases(4,:)=num2cell(round(rand(1,T)*2)+1);
+
+engine = jtree_inf_engine(bnet, onodes);
+
+[engine, loglik] = enter_evidence(engine, cases);
+
+disp('learning-------------------------------------------')
+[bnet2, LL2] = learn_params_em(engine, cases, 4);