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1 function [time, engine] = cmp_inference_static(bnet, engine, varargin)
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2 % CMP_INFERENCE Compare several inference engines on a BN
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3 % function [time, engine] = cmp_inference_static(bnet, engine, ...)
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4 %
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5 % engine{i} is the i'th inference engine.
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6 % time(e) = elapsed time for doing inference with engine e
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7 %
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8 % The list below gives optional arguments [default value in brackets].
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9 %
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10 % exact - specifies which engines do exact inference [ 1:length(engine) ]
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11 % singletons_only - if 1, we only call marginal_nodes, else this and marginal_family [0]
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12 % maximize - 1 means we do max-propagation, 0 means sum-propagation [0]
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13 % check_ll - 1 means we check that the log-likelihoods are correct [1]
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14 % observed - list of the observed ndoes [ bnet.observed ]
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15 % check_converged - list of loopy engines that should be checked for convergence [ [] ]
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16 % If an engine has converged, it is added to the exact list.
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17
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18
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19 % set default params
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20 exact = 1:length(engine);
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21 singletons_only = 0;
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22 maximize = 0;
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23 check_ll = 1;
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24 observed = bnet.observed;
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25 check_converged = [];
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26
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27 args = varargin;
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28 nargs = length(args);
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29 for i=1:2:nargs
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30 switch args{i},
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31 case 'exact', exact = args{i+1};
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32 case 'singletons_only', singletons_only = args{i+1};
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33 case 'maximize', maximize = args{i+1};
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34 case 'check_ll', check_ll = args{i+1};
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35 case 'observed', observed = args{i+1};
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36 case 'check_converged', check_converged = args{i+1};
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37 otherwise,
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38 error(['unrecognized argument ' args{i}])
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39 end
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40 end
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41
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42 E = length(engine);
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43 ref = exact(1); % reference
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44
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45 N = length(bnet.dag);
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46 ev = sample_bnet(bnet);
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47 evidence = cell(1,N);
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48 evidence(observed) = ev(observed);
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49 %celldisp(evidence(observed))
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50
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51 for i=1:E
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52 tic;
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53 if check_ll
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54 [engine{i}, ll(i)] = enter_evidence(engine{i}, evidence, 'maximize', maximize);
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55 else
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56 engine{i} = enter_evidence(engine{i}, evidence, 'maximize', maximize);
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57 end
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58 time(i)=toc;
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59 end
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60
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61 for i=check_converged(:)'
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62 niter = loopy_converged(engine{i});
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63 if niter > 0
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64 fprintf('loopy engine %d converged in %d iterations\n', i, niter);
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65 % exact = myunion(exact, i);
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66 else
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67 fprintf('loopy engine %d has not converged\n', i);
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68 end
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69 end
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70
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71 cmp = exact(2:end);
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72 if check_ll
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73 for i=cmp(:)'
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74 assert(approxeq(ll(ref), ll(i)));
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75 end
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76 end
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77
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78 hnodes = mysetdiff(1:N, observed);
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79
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80 if ~singletons_only
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81 get_marginals(engine, hnodes, exact, 0);
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82 end
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83 get_marginals(engine, hnodes, exact, 1);
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84
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85 %%%%%%%%%%
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86
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87 function get_marginals(engine, hnodes, exact, singletons)
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88
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89 bnet = bnet_from_engine(engine{1});
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90 N = length(bnet.dag);
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91 cnodes_bitv = zeros(1,N);
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92 cnodes_bitv(bnet.cnodes) = 1;
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93 ref = exact(1); % reference
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94 cmp = exact(2:end);
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95 E = length(engine);
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96
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97 for n=hnodes(:)'
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98 for e=1:E
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99 if singletons
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100 m{e} = marginal_nodes(engine{e}, n);
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101 else
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102 m{e} = marginal_family(engine{e}, n);
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103 end
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104 end
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105 for e=cmp(:)'
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106 if cnodes_bitv(n)
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107 assert(approxeq(m{ref}.mu, m{e}.mu))
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108 assert(approxeq(m{ref}.Sigma, m{e}.Sigma))
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109 else
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110 assert(approxeq(m{ref}.T, m{e}.T))
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111 end
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112 assert(isequal(m{e}.domain, m{ref}.domain));
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113 end
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114 end
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