annotate toolboxes/FullBNT-1.0.7/bnt/general/log_lik_complete.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function L = log_lik_complete(bnet, cases, clamped)
wolffd@0 2 % LOG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m), theta_i) for a completely observed data set
wolffd@0 3 % L = log_lik_complete(bnet, cases, clamped)
wolffd@0 4 %
wolffd@0 5 % If there is a missing data, you must use an inference engine.
wolffd@0 6 % cases(i,m) is the value assigned to node i in case m.
wolffd@0 7 % (If there are vector-valued nodes, cases should be a cell array.)
wolffd@0 8 % clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros)
wolffd@0 9 % Clamped nodes contribute a factor of 1.0 to the likelihood.
wolffd@0 10
wolffd@0 11 if iscell(cases), usecell = 1; else usecell = 0; end
wolffd@0 12
wolffd@0 13 n = length(bnet.dag);
wolffd@0 14 ncases = size(cases, 2);
wolffd@0 15 if n ~= size(cases, 1)
wolffd@0 16 error('data should be of size nnodes * ncases');
wolffd@0 17 end
wolffd@0 18
wolffd@0 19 if nargin < 3, clamped = zeros(n,ncases); end
wolffd@0 20
wolffd@0 21 L = 0;
wolffd@0 22 for i=1:n
wolffd@0 23 ps = parents(bnet.dag, i);
wolffd@0 24 e = bnet.equiv_class(i);
wolffd@0 25 u = find(clamped(i,:)==0);
wolffd@0 26 ll = log_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u));
wolffd@0 27 if approxeq(exp(ll), 0), fprintf('node %d has very low likelihood\n'); end
wolffd@0 28 L = L + ll;
wolffd@0 29 end
wolffd@0 30