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1 function [engine, loglik] = enter_evidence(engine, evidence, varargin)
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2 % ENTER_EVIDENCE Add the specified evidence to the network (bk_ff_hmm)
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3 % [engine, loglik] = enter_evidence(engine, evidence, ...)
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4 %
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5 % evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector)
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6 %
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7 % The following optional arguments can be specified in the form of name/value pairs:
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8 % [default value in brackets]
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9 %
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10 % maximize - if 1, does max-product (not yet supported), else sum-product [0]
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11 % filter - if 1, do filtering, else smoothing [0]
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12 %
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13 % e.g., engine = enter_evidence(engine, ev, 'maximize', 1)
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14
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15 maximize = 0;
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16 filter = 0;
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17
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18 % parse optional params
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19 args = varargin;
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20 nargs = length(args);
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21 if nargs > 0
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22 for i=1:2:nargs
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23 switch args{i},
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24 case 'maximize', maximize = args{i+1};
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25 case 'filter', filter = args{i+1};
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26 otherwise,
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27 error(['invalid argument name ' args{i}]);
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28 end
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29 end
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30 end
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31
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32 assert(~maximize);
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33
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34 bnet = bnet_from_engine(engine);
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35 ss = length(bnet.intra);
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36 onodes = bnet.observed;
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37 hnodes = mysetdiff(1:ss, onodes);
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38 T = size(evidence, 2);
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39 assertBNT(~any(isemptycell(evidence(onodes,:))));
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40
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41 obslik = mk_hmm_obs_lik_mat(bnet, onodes, evidence);
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42
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43 ns = bnet.node_sizes_slice;
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44 ns(onodes) = 1;
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45
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46 [gamma, loglik, marginals, marginalsT] = bk_ff_fb(engine.prior, engine.transmat, obslik, filter, hnodes, ns);
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47
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48 for t=1:T
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49 for i=hnodes(:)'
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50 engine.marginals{i,t} = pot_to_marginal(marginalsT{i,t});
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51 end
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52 for i=onodes(:)'
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53 m.domain = i + (t-1)*ss;
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54 m.T = 1;
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55 engine.marginals{i,t} = m;
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56 end
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57 end
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58
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59
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60
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