diff toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/learn_params.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/CPDs/@gaussian_CPD/learn_params.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,31 @@
+function CPD = learn_params(CPD, fam, data, ns, cnodes)
+%function CPD = learn_params(CPD, fam, data, ns, cnodes)
+% LEARN_PARAMS Compute the maximum likelihood estimate of the params of a gaussian CPD given complete data
+% CPD = learn_params(CPD, fam, data, ns, cnodes)
+%
+% data(i,m) is the value of node i in case m (can be cell array).
+% We assume this node has a maximize_params method.
+
+ncases = size(data, 2);
+CPD = reset_ess(CPD);
+% make a fully observed joint distribution over the family
+fmarginal.domain = fam;
+fmarginal.T = 1;
+fmarginal.mu = [];
+fmarginal.Sigma = [];
+if ~iscell(data)
+  cases = num2cell(data);
+else
+  cases = data;
+end
+hidden_bitv = zeros(1, max(fam));
+for m=1:ncases
+  % specify (as a bit vector) which elements in the family domain are hidden
+  hidden_bitv = zeros(1, max(fmarginal.domain));
+  ev = cases(:,m);
+  hidden_bitv(find(isempty(ev)))=1;
+  CPD = update_ess(CPD, fmarginal, ev, ns, cnodes, hidden_bitv);
+end
+CPD = maximize_params(CPD);
+
+