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

first hg version after svn

+% Sigmoid Belief Net
+
+clear all
+clc
+dum1 = 1;
+dum2 = 2;
+dum3 = 3;
+Q1 = 4;
+Q2 = 5;
+Y = 6;
+dag = zeros(6,6);
+dag(dum1,[Q1 Y]) = 1;
+dag(dum2, Q2)=1;
+dag(dum3, [Q1 Q2])=1;
+dag(Q1,[Q2 Y]) = 1;
+dag(Q2, Y)=1;
+
+ns = [2 2 3 3 4 3];
+dnodes = [1:6];
+bnet = mk_bnet(dag,ns, dnodes);
+
+rand('state',0); randn('state',0);
+n_iter=10;
+clamped=0;
+
+bnet.CPD{1} = tabular_CPD(bnet, 1);
+bnet.CPD{2} = tabular_CPD(bnet, 2);
+bnet.CPD{3} = tabular_CPD(bnet, 3);
+% CPD = dsoftmax_CPD(bnet, self, dummy_pars, w, b, clamped, max_iter, verbose, wthresh,...
+%    llthresh, approx_hess)
+bnet.CPD{4} = softmax_CPD(bnet, 4, 'discrete', [1 3]);
+bnet.CPD{5} = softmax_CPD(bnet, 5, 'discrete', [2 3]);
+bnet.CPD{6} = softmax_CPD(bnet, 6, 'discrete', [1 4]);
+
+T=5;
+cases = cell(6, T);
+cases(1,:)=num2cell(round(rand(1,T)*1)+1);
+%cases(2,:)=num2cell(round(rand(1,T)*1)+1);
+cases(3,:)=num2cell(round(rand(1,T)*2)+1);
+cases(4,:)=num2cell(round(rand(1,T)*2)+1); 
+%cases(5,:)=num2cell(round(rand(1,T)*3)+1);
+cases(6,:)=num2cell(round(rand(1,T)*2)+1);
+
+engine = jtree_inf_engine(bnet);
+
+[engine, loglik] = enter_evidence(engine, cases);
+
+disp('learning-------------------------------------------')
+[bnet2, LL2, eng2] = learn_params_em(engine, cases, n_iter);