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Diffstat (limited to 'sourcecodes/bnt-master/BNT/learning/learn_params.m')
| -rw-r--r-- | sourcecodes/bnt-master/BNT/learning/learn_params.m | 26 |
1 files changed, 26 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/BNT/learning/learn_params.m b/sourcecodes/bnt-master/BNT/learning/learn_params.m new file mode 100644 index 00000000..1bf9c843 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/learning/learn_params.m @@ -0,0 +1,26 @@ +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 + |