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1 function pot = convert_to_pot(CPD, pot_type, domain, evidence)
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2 % CONVERT_TO_POT Convert a discrete CPD to a potential
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3 % pot = convert_to_pot(CPD, pot_type, domain, evidence)
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4 %
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5 % pots = CPD evaluated using evidence(domain)
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6
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7 ncases = size(domain,2);
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8 assert(ncases==1); % not yet vectorized
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9
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10 sz = dom_sizes(CPD);
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11 ns = zeros(1, max(domain));
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12 ns(domain) = sz;
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13
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14 CPT1 = CPD_to_CPT(CPD);
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15 spar = issparse(CPT1);
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16 odom = domain(~isemptycell(evidence(domain)));
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17 if spar
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18 T = convert_to_sparse_table(CPD, domain, evidence);
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19 else
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20 T = convert_to_table(CPD, domain, evidence);
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21 end
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22
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23 switch pot_type
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24 case 'u',
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25 pot = upot(domain, sz, T, 0*myones(sz));
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26 case 'd',
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27 ns(odom) = 1;
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28 pot = dpot(domain, ns(domain), T);
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29 case {'c','g'},
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30 % Since we want the output to be a Gaussian, the whole family must be observed.
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31 % In other words, the potential is really just a constant.
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32 p = T;
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33 %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1)));
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34 ns(domain) = 0;
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35 pot = cpot(domain, ns(domain), log(p));
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36
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37 case 'cg',
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38 T = T(:);
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39 ns(odom) = 1;
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40 can = cell(1, length(T));
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41 for i=1:length(T)
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42 if T(i) == 0
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43 can{i} = cpot([], [], -Inf); % bug fix by Bob Welch 20/2/04
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44 else
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45 can{i} = cpot([], [], log(T(i)));
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46 end;
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47 end
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48 pot = cgpot(domain, [], ns, can);
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49
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50 case 'scg'
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51 T = T(:);
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52 ns(odom) = 1;
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53 pot_array = cell(1, length(T));
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54 for i=1:length(T)
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55 pot_array{i} = scgcpot([], [], T(i));
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56 end
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57 pot = scgpot(domain, [], [], ns, pot_array);
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58
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59 otherwise,
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60 error(['unrecognized pot type ' pot_type])
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61 end
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62
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