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1 function gdl = mk_gdl_graph(G, domains, node_sizes, kernels, varargin)
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2 % MK_GDL_GRAPH Make a GDL (generalized distributed law) graph
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3 % gdl = mk_gdl_graph(G, domains, node_sizes, kernels, ...)
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4 %
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5 % A GDL graph is like a moralized, but untriangulated, Bayes net:
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6 % each "node" represents a domain with a corresponding kernel function.
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7 % For details, see "The Generalized Distributive Law", Aji and McEliece,
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8 % IEEE Trans. Info. Theory, 46(2): 325--343, 2000
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9 %
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10 % G(i,j) = 1 if there is an (undirected) edge between domains i,j
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11 %
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12 % domains{i} is the domain of node i
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13 %
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14 % node_sizes(i) is the number of values node i can take on,
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15 % or the length of node i if i is a continuous-valued vector.
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16 % node_sizes(i) = 1 if i is a utility node.
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17 %
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18 % kernels is the list of kernel functions
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19 %
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20 % The list below gives optional arguments [default value in brackets].
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21 %
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22 % equiv_class - equiv_class(i)=j means factor node i gets its params from factors{j} [1:F]
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23 % discrete - the list of nodes which are discrete random variables [1:N]
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24 % chance - the list of nodes which are random variables [1:N]
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25 % decision - the list of nodes which are decision nodes [ [] ]
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26 % utility - the list of nodes which are utility nodes [ [] ]
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27
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28
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29 ns = node_sizes;
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30 N = length(domains);
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31 vars = [];
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32 for i=1:N
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33 vars = myunion(vars, domains{i});
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34 end
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35 Nvars = length(vars);
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36
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37 gdl.equiv_class = 1:length(kernels);
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38 gdl.chance_nodes = 1:Nvars;
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39 gdl.utility_nodes = [];
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40 gdl.decision_nodes = [];
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41 gdl.dnodes = 1:Nvars;
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42
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43 if nargin >= 5
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44 args = varargin;
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45 nargs = length(args);
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46 for i=1:2:nargs
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47 switch args{i},
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48 case 'equiv_class', bnet.equiv_class = args{i+1};
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49 case 'chance', bnet.chance_nodes = args{i+1};
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50 case 'utility', bnet.utility_nodes = args{i+1};
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51 case 'decision', bnet.decision_nodes = args{i+1};
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52 case 'discrete', bnet.dnodes = args{i+1};
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53 otherwise,
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54 error(['invalid argument name ' args{i}]);
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55 end
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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 gdl.G = G;
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61 gdl.vars = vars;
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62 gdl.doms = domains;
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63 gdl.node_sizes = node_sizes;
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64 gdl.cnodes = mysetdiff(vars, gdl.dnodes);
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65 gdl.kernels = kernels;
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66 gdl.type = 'gdl';
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67
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68 % Compute a bit vector representation of the set of domains
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69 % dom_bitv(i,j) = 1 iff variable j occurs in domain i
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70 gdl.dom_bitv = zeros(N, length(vars));
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71 for i=1:N
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72 gdl.dom_bitv(i, domains{i}) = 1;
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73 end
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74
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75 % compute the interesection of the domains on either side of each edge (separating set)
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76 gdl.sepset = cell(N, N);
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77 gdl.nbrs = cell(1,N);
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78 for i=1:N
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79 nbrs = neighbors(G, i);
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80 gdl.nbrs{i} = nbrs;
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81 for j = nbrs(:)'
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82 gdl.sepset{i,j} = myintersect(domains{i}, domains{j});
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83 end
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84 end
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85
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86
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