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1 function [engine, loglik] = enter_evidence(engine, evidence, varargin)
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2 % ENTER_EVIDENCE Add the specified evidence to the network (jtree)
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3 % [engine, loglik] = enter_evidence(engine, evidence, ...)
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4 %
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5 % evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector).
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6 %
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7 % The following optional arguments can be specified in the form of name/value pairs:
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8 % [default value in brackets]
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9 %
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10 % maximize - if 1, does max-product instead of sum-product [0]
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11 % soft - a cell array of soft/virtual evidence;
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12 % soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ]
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13 %
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14 % e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev)
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15 %
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16 % For backwards compatibility with BNT2, you can also specify the parameters in the following order
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17 % engine = enter_evidence(engine, ev, soft_ev)
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18
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19 bnet = bnet_from_engine(engine);
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20 ns = bnet.node_sizes(:);
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21 N = length(bnet.dag);
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22
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23 % set default params
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24 exclude = [];
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25 soft_evidence = cell(1,N);
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26 maximize = 0;
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27
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28 % parse optional params
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29 args = varargin;
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30 nargs = length(args);
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31 if nargs > 0
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32 if iscell(args{1})
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33 soft_evidence = args{1};
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34 else
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35 for i=1:2:nargs
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36 switch args{i},
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37 case 'soft', soft_evidence = args{i+1};
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38 case 'maximize', maximize = args{i+1};
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39 otherwise,
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40 error(['invalid argument name ' args{i}]);
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41 end
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42 end
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43 end
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44 end
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45
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46 engine.maximize = maximize;
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47
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48 onodes = find(~isemptycell(evidence));
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49 hnodes = find(isemptycell(evidence));
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50 pot_type = determine_pot_type(bnet, onodes);
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51 if strcmp(pot_type, 'cg')
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52 check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag);
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53 end
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54
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55
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56 hard_nodes = 1:N;
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57 soft_nodes = find(~isemptycell(soft_evidence));
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58 S = length(soft_nodes);
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59 if S > 0
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60 assert(pot_type == 'd');
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61 assert(mysubset(soft_nodes, bnet.dnodes));
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62 end
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63
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64 % Evaluate CPDs with evidence, and convert to potentials
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65 pot = cell(1, N+S);
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66 for n=1:N
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67 fam = family(bnet.dag, n);
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68 e = bnet.equiv_class(n);
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69 pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence);
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70 end
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71
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72 for i=1:S
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73 n = soft_nodes(i);
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74 pot{N+i} = dpot(n, ns(n), soft_evidence{n});
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75 end
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76
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77 %clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]);
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78 %[clpot, loglik] = enter_soft_evidence(engine, clqs, pot, onodes, pot_type);
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79 %engine.clpot = clpot; % save the results for marginal_nodes
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80
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81
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82 clique = engine.clq_ass_to_node([hard_nodes soft_nodes]);
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83 potential = pot;
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84
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85
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86 % Set the clique potentials to all 1s
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87 C = length(engine.cliques);
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88 for i=1:C
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89 engine.clpot{i} = mk_initial_pot(pot_type, engine.cliques{i}, ns, bnet.cnodes, onodes);
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90 end
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91
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92 % Multiply on specified potentials
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93 for i=1:length(clique)
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94 c = clique(i);
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95 engine.clpot{c} = multiply_by_pot(engine.clpot{c}, potential{i});
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96 end
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97
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98 root = 1; % arbitrary
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99 engine = collect_evidence(engine, root);
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100 engine = distribute_evidence(engine, root);
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101
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102 ll = zeros(1, C);
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103 for i=1:C
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104 [engine.clpot{i}, ll(i)] = normalize_pot(engine.clpot{i});
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105 end
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106 loglik = ll(1); % we can extract the likelihood from any clique
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107
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