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1 function CPD = deterministic_CPD(bnet, self, fname, pfail)
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2 % DETERMINISTIC_CPD Make a tabular CPD representing a (noisy) deterministic function
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3 %
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4 % CPD = deterministic_CPD(bnet, self, fname)
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5 % This calls feval(fname, pvals) for each possible vector of parent values.
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6 % e.g., suppose there are 2 ternary parents, then pvals =
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7 % [1 1], [2 1], [3 1], [1 2], [2 2], [3 2], [1 3], [2 3], [3 3]
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8 % If v = feval(fname, pvals(i)), then
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9 % CPD(x | parents=pvals(i)) = 1 if x==v, and = 0 if x<>v
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10 % e.g., suppose X4 = X2 AND (NOT X3). Then
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11 % bnet.CPD{4} = deterministic_CPD(bnet, 4, inline('((x(1)-1) & ~(x(2)-1)) + 1'));
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12 % Note that x(1) refers pvals(1) = X2, and x(2) refers to pvals(2)=X3
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13 % See also boolean_CPD.
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14 %
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15 % CPD = deterministic_CPD(bnet, self, fname, pfail)
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16 % will put probability mass 1-pfail on f(parents), and distribute pfail over the other values.
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17 % This is useful for simulating noisy deterministic functions.
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18 % If pfail is omitted, it is set to 0.
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19 %
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20
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21
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22 if nargin==0
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23 % This occurs if we are trying to load an object from a file.
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24 CPD = tabular_CPD(bnet, self);
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25 return;
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26 elseif isa(bnet, 'deterministic_CPD')
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27 % This might occur if we are copying an object.
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28 CPD = bnet;
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29 return;
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30 end
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31
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32 if nargin < 4, pfail = 0; end
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33
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34 ps = parents(bnet.dag, self);
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35 ns = bnet.node_sizes;
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36 psizes = ns(ps);
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37 self_size = ns(self);
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38
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39 psucc = 1-pfail;
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40
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41 CPT = zeros(prod(psizes), self_size);
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42 pvals = zeros(1, length(ps));
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43 for i=1:prod(psizes)
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44 pvals = ind2subv(psizes, i);
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45 x = feval(fname, pvals);
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46 %fprintf('%d ', [pvals x]); fprintf('\n');
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47 if psucc == 1
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48 CPT(i, x) = 1;
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49 else
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50 CPT(i, x) = psucc;
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51 rest = mysetdiff(1:self_size, x);
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52 CPT(i, rest) = pfail/length(rest);
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53 end
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54 end
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55 CPT = reshape(CPT, [psizes self_size]);
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56
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57 CPD = tabular_CPD(bnet, self, 'CPT',CPT, 'clamped',1);
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58
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59
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