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1 function bnet = mk_limid(dag, node_sizes, varargin)
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2 % MK_LIMID Make a limited information influence diagram
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3 %
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4 % BNET = MK_LIMID(DAG, NODE_SIZES, ...)
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5 % DAG is the adjacency matrix for a directed acyclic graph.
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6 % The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary.
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7 % For decision nodes, the parents must explicitely include all nodes
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8 % on which it can depends, in contrast to the implicit no-forgetting assumption of influence diagrams.
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9 % (For details, see "Representing and solving decision problems with limited information",
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10 % Lauritzen and Nilsson, Management Science, 2001.)
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11 %
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12 % node_sizes(i) is the number of values node i can take on,
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13 % or the length of node i if i is a continuous-valued vector.
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14 % node_sizes(i) = 1 if i is a utility node.
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15 %
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16 % The list below gives optional arguments [default value in brackets].
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17 %
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18 % chance - the list of nodes which are random variables [1:N]
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19 % decision - the list of nodes which are decision nodes [ [] ]
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20 % utility - the list of nodes which are utility nodes [ [] ]
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21 % equiv_class - equiv_class(i)=j means node i gets its params from CPD{j} [1:N]
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22 %
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23 % e.g., limid = mk_limid(dag, ns, 'chance', [1 3], 'utility', [2])
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24
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25 n = length(dag);
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26
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27 % default values for parameters
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28 bnet.chance_nodes = 1:n;
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29 bnet.equiv_class = 1:n;
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30 bnet.utility_nodes = [];
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31 bnet.decision_nodes = [];
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32 bnet.dnodes = 1:n; % discrete
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33
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34 if nargin >= 3
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35 args = varargin;
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36 nargs = length(args);
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37 if ~isstr(args{1})
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38 if nargs >= 1, bnet.dnodes = args{1}; end
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39 if nargs >= 2, bnet.equiv_class = args{2}; end
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40 else
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41 for i=1:2:nargs
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42 switch args{i},
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43 case 'equiv_class', bnet.equiv_class = args{i+1};
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44 case 'chance', bnet.chance_nodes = args{i+1};
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45 case 'utility', bnet.utility_nodes = args{i+1};
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46 case 'decision', bnet.decision_nodes = args{i+1};
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47 case 'discrete', bnet.dnodes = args{i+1};
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48 otherwise,
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49 error(['invalid argument name ' args{i}]);
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50 end
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51 end
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52 end
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53 end
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54
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55 bnet.limid = 1;
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56
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57 bnet.dag = dag;
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58 bnet.node_sizes = node_sizes(:)';
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59
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60 bnet.cnodes = mysetdiff(1:n, bnet.dnodes);
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61 % too many functions refer to cnodes to rename it to cts_nodes -
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62 % We hope it won't be confused with chance nodes!
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63
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64 bnet.parents = cell(1,n);
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65 for i=1:n
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66 bnet.parents{i} = parents(dag, i);
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67 end
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68
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69 E = max(bnet.equiv_class);
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70 mem = cell(1,E);
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71 for i=1:n
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72 e = bnet.equiv_class(i);
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73 mem{e} = [mem{e} i];
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74 end
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75 bnet.members_of_equiv_class = mem;
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76
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77 bnet.CPD = cell(1, E);
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78
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79 % for e=1:E
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80 % i = bnet.members_of_equiv_class{e}(1); % pick arbitrary member
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81 % switch type{e}
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82 % case 'tabular', bnet.CPD{e} = tabular_CPD(bnet, i);
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83 % case 'gaussian', bnet.CPD{e} = gaussian_CPD(bnet, i);
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84 % otherwise, error(['unrecognized CPD type ' type{e}]);
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85 % end
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86 % end
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87
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88 directed = 1;
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89 if ~acyclic(dag,directed)
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90 error('graph must be acyclic')
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91 end
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92
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93 bnet.order = topological_sort(bnet.dag);
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