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1 function engine = jtree_sparse_2TBN_inf_engine(bnet, varargin)
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2 % JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs.
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3 % engine = jtree_online_inf_engine(bnet, ...)
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4 %
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5 % The following optional arguments can be specified in the form of name/value pairs:
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6 % [default value in brackets]
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7 %
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8 % clusters - specifies variables that must be grouped in the 1.5 slice DBN
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9 % maximize - 1 means do max-product, 0 means sum-product [0]
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10 %
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11 % The same nodes must be observed in every slice.
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12
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13 ss = length(bnet.intra);
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14 clusters = {};
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15 engine.maximize = 0;
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16
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17 args = varargin;
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18 nargs = length(args);
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19 for i=1:2:length(args)
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20 switch args{i},
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21 case 'clusters', clusters = args{i+1};
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22 case 'maximize', engine.maximize = args{i+1};
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23 otherwise, error(['unrecognized argument ' args{i}])
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24 end
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25 end
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26
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27 engine.evidence = [];
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28 engine.node_sizes = [];
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29
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30 int = [];
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31 % include nodes with any outgoing arcs
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32 for u=1:ss
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33 if any(bnet.inter(u,:))
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34 int = [int u];
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35 end
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36 end
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37
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38 engine.interface = int;
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39 engine.nonint = mysetdiff(1:ss, int);
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40
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41 onodes = bnet.observed;
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42
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43 % Create a "1.5 slice" jtree, containing the interface nodes of slice 1
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44 % and all the nodes of slice 2
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45 % To keep the node numbering the same, we simply disconnect the non-interface nodes
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46 % from slice 1, and set their size to 1.
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47 % We do this to speed things up, and so that the likelihood is computed correctly - we do not need to do
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48 % this if we just want to compute marginals (i.e., we can include nodes whose potentials will
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49 % be left as all 1s).
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50 intra15 = bnet.intra;
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51 for i=engine.nonint(:)'
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52 intra15(:,i) = 0;
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53 intra15(i,:) = 0;
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54 assert(~any(bnet.inter(i,:)))
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55 end
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56 dag15 = [intra15 bnet.inter;
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57 zeros(ss) bnet.intra];
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58 ns = bnet.node_sizes(:);
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59 ns(engine.nonint) = 1; % disconnected nodes get size 1
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60 obs_nodes = [onodes(:) onodes(:)+ss];
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61 bnet15 = mk_bnet(dag15, ns, 'discrete', bnet.dnodes, 'equiv_class', bnet.equiv_class(:), ...
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62 'observed', obs_nodes(:));
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63
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64 % use unconstrained elimination,
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65 % but force there to be a clique containing both interfaces
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66 clusters(end+1:end+2) = {int, int+ss};
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67 %engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss);
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68 engine.jtree_engine = jtree_sparse_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss);
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69 jtree_engine = struct(engine.jtree_engine); % violate object privacy
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70
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71 engine.in_clq = clq_containing_nodes(engine.jtree_engine, int);
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72 engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss);
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73 engine.clq_ass_to_node = jtree_engine.clq_ass_to_node;
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74 engine.root = jtree_engine.root_clq;
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75
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76 % Also create an engine just for slice 1
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77 bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes,1:ss), ...
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78 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes);
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79 for i=1:max(bnet1.equiv_class)
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80 bnet1.CPD{i} = bnet.CPD{i};
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81 end
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82 %engine.jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int);
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83 engine.jtree_engine1 = jtree_sparse_inf_engine(bnet1, 'clusters', {int}, 'root', int);
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84 jtree_engine1 = struct(engine.jtree_engine1); % violate object privacy
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85 engine.int_clq1 = clq_containing_nodes(engine.jtree_engine1, int);
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86 engine.clq_ass_to_node1 = jtree_engine1.clq_ass_to_node;
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87 engine.root1 = jtree_engine1.root_clq;
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88
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89 engine.observed = [onodes onodes+ss];
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90 engine.observed1 = onodes;
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91 engine.pot_type = determine_pot_type(bnet, onodes);
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92 engine.slice_size = bnet.nnodes_per_slice;
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93
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94 engine = class(engine, 'jtree_sparse_2TBN_inf_engine', inf_engine(bnet));
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95
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