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1 % Check that adding soft evidence to a hidden node is equivalent to evaluating its leaf CPD.
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2
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3 % Make an HMM
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4 T = 3; Q = 2; O = 2; cts_obs = 0; param_tying = 0;
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5 bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying);
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6 N = 2*T;
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7 onodes = bnet.observed;
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8 hnodes = mysetdiff(1:N, onodes);
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9 for i=1:N
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10 bnet.CPD{i} = tabular_CPD(bnet, i);
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11 end
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12
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13 ev = sample_bnet(bnet);
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14 evidence = cell(1,N);
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15 evidence(onodes) = ev(onodes);
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16
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17 engine = jtree_inf_engine(bnet);
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18
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19 [engine, ll] = enter_evidence(engine, evidence);
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20 query = 1;
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21 m = marginal_nodes(engine, query);
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22
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23
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24 % Make a Markov chain with the same backbone
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25 bnet2 = mk_markov_chain_bnet(T, Q);
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26 for i=1:T
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27 S = struct(bnet.CPD{hnodes(i)}); % violate object privacy
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28 bnet2.CPD{i} = tabular_CPD(bnet2, i, S.CPT);
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29 end
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30
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31 % Evaluate the observed leaves of the HMM
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32 soft_ev = cell(1,T);
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33 for i=1:T
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34 S = struct(bnet.CPD{onodes(i)}); % violate object privacy
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35 dist = S.CPT(:, evidence{onodes(i)});
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36 soft_ev{i} = dist;
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37 end
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38
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39 % Use the leaf potentials as soft evidence
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40 engine2 = jtree_inf_engine(bnet2);
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41 [engine2, ll2] = enter_evidence(engine2, cell(1,T), 'soft', soft_ev);
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42 m2 = marginal_nodes(engine2, query);
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43
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44 assert(approxeq(m2.T, m.T))
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45 assert(approxeq(ll2, ll))
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46
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47
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48
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49 % marginal on node 1 without evidence
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50 [engine2, ll2] = enter_evidence(engine2, cell(1,T));
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51 m2 = marginal_nodes(engine2, 1);
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52
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53 % add soft evidence
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54 soft_ev=cell(1,T);
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55 soft_ev{1}=[0.7 0.3];
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56 [engine2, ll2] = enter_evidence(engine2, cell(1,T), 'soft', soft_ev);
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57 m3 = marginal_nodes(engine2, 1);
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58
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59 assert(approxeq(normalise(m2.T .* [0.7 0.3]'), m3.T))
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60
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