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annotate toolboxes/FullBNT-1.0.7/bnt/inference/static/@cond_gauss_inf_engine/cond_gauss_inf_engine.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function engine = cond_gauss_inf_engine(bnet)
wolffd@0 2 % COND_GAUSS_INF_ENGINE Conditional Gaussian inference engine
wolffd@0 3 % engine = cond_gauss_inf_engine(bnet)
wolffd@0 4 %
wolffd@0 5 % Enumerates all the discrete roots, and runs jtree on the remaining Gaussian nodes.
wolffd@0 6
wolffd@0 7 dnodes = mysetdiff(1:length(bnet.dag), bnet.cnodes);
wolffd@0 8
wolffd@0 9 %onodes = dnodes; % all the discrete ndoes will be observed
wolffd@0 10 %engine.sub_engine = jtree_inf_engine(bnet, onodes);
wolffd@0 11 bnet2 = bnet;
wolffd@0 12 bnet2.observed = dnodes;
wolffd@0 13 engine.sub_engine = jtree_inf_engine(bnet2);
wolffd@0 14
wolffd@0 15 % This is where we will store the results between enter_evidence and marginal_nodes
wolffd@0 16 engine.T = [];
wolffd@0 17 engine.mu = [];
wolffd@0 18 engine.Sigma = [];
wolffd@0 19 engine.joint_dmarginal = [];
wolffd@0 20 engine.onodes = []; % needed for marginal_nodes
wolffd@0 21 engine.evidence = []; % needed for marginal_nodes add_ev
wolffd@0 22
wolffd@0 23 engine = class(engine, 'cond_gauss_inf_engine', inf_engine(bnet));