Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/learning/learn_params.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 bnet = learn_params(bnet, data) % LEARN_PARAMS Find the maximum likelihood params for a fully observed model % bnet = learn_params(bnet, data) % % data(i,m) is the value of node i in case m (can be a cell array) % % We set bnet.CPD{i} to its ML/MAP estimate. % % Currently we assume no param tying % AND THAT EACH DATA POINT IS A SCALAR - no longer assumed %if iscell(data) % data=cell2num(data); %end [n ncases] = size(data); for j=1:n e = bnet.equiv_class(j); assert(e==j); if adjustable_CPD(bnet.CPD{e}) fam = family(bnet.dag,j); %bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam,:)); bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, bnet.node_sizes, bnet.cnodes); end end