diff toolboxes/FullBNT-1.0.7/bnt/learning/learn_params_dbn.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_params_dbn.m	Tue Feb 10 15:05:51 2015 +0000
@@ -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
+