comparison toolboxes/FullBNT-1.0.7/bnt/general/log_marg_lik_complete.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function L = log_marg_lik_complete(bnet, cases, clamped)
+% LOG_MARG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m)) for a completely observed data set
+% L = log_marg_lik_complete(bnet, cases, clamped)
+%
+% This differs from log_lik_complete because we integrate out the parameters.   
+% If there is a missing data, you must use an inference engine.
+% cases(i,m) is the value assigned to node i in case m.
+% (If there are vector-valued nodes, cases should be a cell array.)
+% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros)
+% Clamped nodes contribute a factor of 1.0 to the likelihood.
+%
+% If there is a single case, clamped is a list of the clamped nodes, not a bit vector.
+
+if iscell(cases), usecell = 1; else usecell = 0; end
+
+n = length(bnet.dag);
+ncases = size(cases, 2);
+if n ~= size(cases, 1)
+  error('data should be of size nnodes * ncases');
+end
+
+if ncases == 1
+  if nargin < 3, clamped = []; end
+  clamp_set = clamped;
+  clamped = zeros(n,1);
+  clamped(clamp_set) = 1;
+else
+  if nargin < 3, clamped = zeros(n,ncases); end
+end
+
+L = 0;
+for i=1:n
+  ps = parents(bnet.dag, i);
+  e = bnet.equiv_class(i);
+  u = find(clamped(i,:)==0);
+  L = L + log_marg_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u));
+end
+
+
+