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Diffstat (limited to 'sourcecodes/bnt-master/BNT/learning/learn_params_dbn.m')
| -rw-r--r-- | sourcecodes/bnt-master/BNT/learning/learn_params_dbn.m | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/BNT/learning/learn_params_dbn.m b/sourcecodes/bnt-master/BNT/learning/learn_params_dbn.m new file mode 100644 index 00000000..4d0dc501 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/learning/learn_params_dbn.m @@ -0,0 +1,36 @@ +function bnet = learn_params_dbn(bnet, data) +% LEARN_PARAM_DBN Estimate params of a DBN for a fully observed model +% bnet = learn_params_dbn(bnet, data) +% +% data(i,t) is the value of node i in slice t (can be a cell array) +% We currently assume there is a single time series +% +% We set bnet.CPD{i} to its ML/MAP estimate. +% +% Currently we assume each node in the first 2 slices has its own CPD (no param tying); +% all nodes in slices >2 share their params with slice 2 as usual. + +[ss T] = size(data); + +% slice 1 +for j=1:ss + if adjustable_CPD(bnet.CPD{j}) + fam = family(bnet.dag,j); + bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam,1)); + end +end + + +% slices 2:T +% data2(:,t) contains [data(:,t-1); data(:,t)]. +% Then we extract out the rows corresponding to the parents in the current and previous slice. +data2 = [data(:,1:T-1); + data(:,2:T)]; +for j=1:ss + j2 = j+ss; + if adjustable_CPD(bnet.CPD{j2}) + fam = family(bnet.dag,j2); + bnet.CPD{j2} = learn_params(bnet.CPD{j2}, data2(fam,:)); + end +end + |