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

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 % Sigmoid Belief Net
wolffd@0 2
wolffd@0 3 clear all
wolffd@0 4 clc
wolffd@0 5 dum1 = 1;
wolffd@0 6 dum2 = 2;
wolffd@0 7 dum3 = 3;
wolffd@0 8 Q1 = 4;
wolffd@0 9 Q2 = 5;
wolffd@0 10 Y = 6;
wolffd@0 11 dag = zeros(6,6);
wolffd@0 12 dag(dum1,[Q1 Y]) = 1;
wolffd@0 13 dag(dum2, Q2)=1;
wolffd@0 14 dag(dum3, [Q1 Q2])=1;
wolffd@0 15 dag(Q1,[Q2 Y]) = 1;
wolffd@0 16 dag(Q2, Y)=1;
wolffd@0 17
wolffd@0 18 ns = [2 2 3 3 4 3];
wolffd@0 19 dnodes = [1:6];
wolffd@0 20 bnet = mk_bnet(dag,ns, dnodes);
wolffd@0 21
wolffd@0 22 rand('state',0); randn('state',0);
wolffd@0 23 n_iter=10;
wolffd@0 24 clamped=0;
wolffd@0 25
wolffd@0 26 bnet.CPD{1} = tabular_CPD(bnet, 1);
wolffd@0 27 bnet.CPD{2} = tabular_CPD(bnet, 2);
wolffd@0 28 bnet.CPD{3} = tabular_CPD(bnet, 3);
wolffd@0 29 % CPD = dsoftmax_CPD(bnet, self, dummy_pars, w, b, clamped, max_iter, verbose, wthresh,...
wolffd@0 30 % llthresh, approx_hess)
wolffd@0 31 bnet.CPD{4} = softmax_CPD(bnet, 4, 'discrete', [1 3]);
wolffd@0 32 bnet.CPD{5} = softmax_CPD(bnet, 5, 'discrete', [2 3]);
wolffd@0 33 bnet.CPD{6} = softmax_CPD(bnet, 6, 'discrete', [1 4]);
wolffd@0 34
wolffd@0 35 T=5;
wolffd@0 36 cases = cell(6, T);
wolffd@0 37 cases(1,:)=num2cell(round(rand(1,T)*1)+1);
wolffd@0 38 %cases(2,:)=num2cell(round(rand(1,T)*1)+1);
wolffd@0 39 cases(3,:)=num2cell(round(rand(1,T)*2)+1);
wolffd@0 40 cases(4,:)=num2cell(round(rand(1,T)*2)+1);
wolffd@0 41 %cases(5,:)=num2cell(round(rand(1,T)*3)+1);
wolffd@0 42 cases(6,:)=num2cell(round(rand(1,T)*2)+1);
wolffd@0 43
wolffd@0 44 engine = jtree_inf_engine(bnet);
wolffd@0 45
wolffd@0 46 [engine, loglik] = enter_evidence(engine, cases);
wolffd@0 47
wolffd@0 48 disp('learning-------------------------------------------')
wolffd@0 49 [bnet2, LL2, eng2] = learn_params_em(engine, cases, n_iter);