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1 function mpe = find_mpe(engine, evidence, varargin)
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2 % FIND_MPE Find the most probable explanation of the data (belprop_fg)
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3 % function mpe = find_mpe(engine, evidence,...)
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4 %
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5 % evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector).
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6 %
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7 % This finds the marginally most likely value for each hidden node,
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8 % and may give the wrong results even if the graph is acyclic,
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9 % unless you set break_ties = 1.
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10 %
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11 % The following optional arguments can be specified in the form of name/value pairs:
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12 % [default value in brackets]
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13 %
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14 % break_ties is optional. If 1, we will force ties to be broken consistently
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15 % by calling enter_evidence N times. (see Jensen96, p106) Default = 1.
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16
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17 break_ties = 1;
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18
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19 % parse optional params
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20 args = varargin;
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21 nargs = length(args);
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22 for i=1:2:nargs
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23 switch args{i},
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24 case 'break_ties', break_ties = args{i+1};
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25 otherwise,
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26 error(['invalid argument name ' args{i}]);
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27 end
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28 end
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29
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30 engine = enter_evidence(engine, evidence, 'maximize', 1);
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31
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32 observed = ~isemptycell(evidence);
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33 evidence = evidence(:); % hack to handle unrolled DBNs
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34 N = length(evidence);
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35 mpe = cell(1,N);
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36 for i=1:N
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37 m = marginal_nodes(engine, i);
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38 % observed nodes are all set to 1 inside the inference engine, so we must undo this
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39 if observed(i)
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40 mpe{i} = evidence{i};
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41 else
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42 mpe{i} = argmax(m.T);
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43 if break_ties
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44 evidence{i} = mpe{i};
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45 [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1);
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46 end
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47 end
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48 end
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49
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