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