Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/find_mpe.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function mpe = find_mpe(engine, evidence, varargin) % FIND_MPE Find the most probable explanation of the data (belprop) % function mpe = find_mpe(engine, evidence,...) % % evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). % % This finds the marginally most likely value for each hidden node, % and may give the wrong results even if the graph is acyclic, % unless you set break_ties = 1. % % The following optional arguments can be specified in the form of name/value pairs: % [default value in brackets] % % break_ties is optional. If 1, we will force ties to be broken consistently % by calling enter_evidence N times. (see Jensen96, p106) Default = 1. break_ties = 1; % parse optional params args = varargin; nargs = length(args); for i=1:2:nargs switch args{i}, case 'break_ties', break_ties = args{i+1}; otherwise, error(['invalid argument name ' args{i}]); end end engine = enter_evidence(engine, evidence, 'maximize', 1); observed = ~isemptycell(evidence); evidence = evidence(:); % hack to handle unrolled DBNs N = length(evidence); mpe = cell(1,N); for i=1:N m = marginal_nodes(engine, i); % observed nodes are all set to 1 inside the inference engine, so we must undo this if observed(i) mpe{i} = evidence{i}; else mpe{i} = argmax(m.T); if break_ties evidence{i} = mpe{i}; [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); end end end