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1 function fg = mk_fgraph_given_ev(G, node_sizes, factors, ev_CPD, evidence, varargin)
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2 % MK_FGRAPH_GIVEN_EV Make a factor graph where each node has its own private evidence term
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3 % fg = mk_fgraph(G, node_sizes, factors, ev_CPD, evidence, ...)
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4 %
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5 % G, node_sizes and factors are as in mk_fgraph, but they refer to the hidden nodes.
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6 % ev_CPD{i} is a CPD for the i'th hidden node; this will be converted into a factor
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7 % for node i using evidence{i}.
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8 % We currently assume all hidden nodes are discrete, for simplicity.
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9 %
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10 % The list below gives optional arguments [default value in brackets].
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11 %
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12 % equiv_class - equiv_class(i)=j means factor node i gets its params from factors{j} [1:F]
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13 % ev_equiv_class - ev_equiv_class(i)=j means evidence node i gets its params from ev_CPD{j} [1:N]
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14
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15
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16 N = length(node_sizes);
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17 nfactors = length(factors);
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18
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19 % default values for parameters
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20 eclass = 1:nfactors;
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21 ev_eclass = 1:N;
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22
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23 if nargin >= 6
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24 args = varargin;
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25 nargs = length(args);
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26 for i=1:2:nargs
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27 switch args{i},
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28 case 'equiv_class', eclass = args{i+1};
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29 case 'ev_equiv_class', ev_eclass = args{i+1};
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30 otherwise,
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31 error(['invalid argument name ' args{i}]);
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32 end
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33 end
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34 end
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35
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36 pot_type = 'd';
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37 for x=1:N
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38 ev = cell(1,2); % cell 1 is the hidden parent, cell 2 is the observed child
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39 ev(2) = evidence(x);
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40 dom = 1:2;
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41 F = convert_to_pot(ev_CPD{ev_eclass(x)}, pot_type, dom(:), ev);
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42 M = pot_to_marginal(F);
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43 %factors{end+1} = tabular_CPD('self', 1, 'ps', [], 'sz', node_sizes(x), 'CPT', M.T);
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44 factors{end+1} = mk_isolated_tabular_CPD(node_sizes(x), {'CPT', M.T});
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45 end
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46
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47 E = max(eclass);
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48 fg = mk_fgraph([G eye(N)], node_sizes, factors, 'equiv_class', [eclass E+1:E+N]);
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