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

first hg version after svn

+% Sigmoid Belief Hidden Markov Decision Tree    (Jordan/Gharhamani 1996)
+% 
+clear all;
+%clc;
+rand('state',0); randn('state',0);
+X = 1; Q1 = 2; Q2 = 3; Y = 4;
+% intra time-slice graph
+intra=zeros(4);
+intra(X,[Q1 Q2 Y])=1;
+intra(Q1,[Q2 Y])=1;
+intra(Q2, Y)=1;
+% inter time-slice graph
+inter=zeros(4);
+inter(Q1,Q1)=1;
+inter(Q2,Q2)=1;
+
+ns = [1 2 3 1]; 
+dnodes = [2 3]; 
+eclass1 = [1 2 3 4];
+eclass2 = [1 5 6 4];
+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{3} = softmax_CPD(bnet, 3, 'discrete', [2]);
+bnet.CPD{5} = softmax_CPD(bnet, 6);
+bnet.CPD{6} = softmax_CPD(bnet, 7, 'discrete', [3 6]);
+% =========================================
+bnet.CPD{4} = gaussian_CPD(bnet, 4);
+
+% make some data
+T=20;
+cases = cell(4, T);
+cases(1,:)=num2cell(round(rand(1,T)*2)+1);
+%cases(2,:)=num2cell(round(rand(1,T))+1);
+%cases(3,:)=num2cell(round(rand(1,T)*2)+1);
+cases(4,:)=num2cell(rand(1,T));
+
+engine = bk_inf_engine(bnet, 'exact', [1 2 3 4]);
+
+% 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, 10);