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1 % Sigmoid Belief Hidden Markov Decision Tree (Jordan/Gharhamani 1996)
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2 %
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3 clear all;
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4 %clc;
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5 rand('state',0); randn('state',0);
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6 X = 1; Q1 = 2; Q2 = 3; Y = 4;
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7 % intra time-slice graph
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8 intra=zeros(4);
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9 intra(X,[Q1 Q2 Y])=1;
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10 intra(Q1,[Q2 Y])=1;
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11 intra(Q2, Y)=1;
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12 % inter time-slice graph
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13 inter=zeros(4);
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14 inter(Q1,Q1)=1;
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15 inter(Q2,Q2)=1;
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16
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17 ns = [1 2 3 1];
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18 dnodes = [2 3];
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19 eclass1 = [1 2 3 4];
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20 eclass2 = [1 5 6 4];
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21 bnet = mk_dbn(intra, inter, ns, dnodes, eclass1, eclass2);
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22
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23 bnet.CPD{1} = root_CPD(bnet, 1);
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24 % =========================================
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25 bnet.CPD{2} = softmax_CPD(bnet, 2);
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26 bnet.CPD{3} = softmax_CPD(bnet, 3, 'discrete', [2]);
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27 bnet.CPD{5} = softmax_CPD(bnet, 6);
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28 bnet.CPD{6} = softmax_CPD(bnet, 7, 'discrete', [3 6]);
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29 % =========================================
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30 bnet.CPD{4} = gaussian_CPD(bnet, 4);
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31
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32 % make some data
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33 T=20;
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34 cases = cell(4, T);
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35 cases(1,:)=num2cell(round(rand(1,T)*2)+1);
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36 %cases(2,:)=num2cell(round(rand(1,T))+1);
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37 %cases(3,:)=num2cell(round(rand(1,T)*2)+1);
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38 cases(4,:)=num2cell(rand(1,T));
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39
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40 engine = bk_inf_engine(bnet, 'exact', [1 2 3 4]);
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41
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42 % log lik before learning
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43 [engine, loglik] = enter_evidence(engine, cases);
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44
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45 % do learning
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46 ev=cell(1,1);
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47 ev{1}=cases;
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48 [bnet2, LL2] = learn_params_dbn_em(engine, ev, 10); |
