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| author | ziejd2 | 2018-03-14 23:23:33 -0500 |
|---|---|---|
| committer | GitHub | 2018-03-14 23:23:33 -0500 |
| commit | 1ff6baa44e22b91eefb48aea6f3befa078c0489b (patch) | |
| tree | e0fd79d2e32fd2aedda2eadaed0f19af3514c520 /sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/log_prob_node.m | |
| parent | 6882395afdadf4e982b25b5215071a0932730950 (diff) | |
| parent | c80226899f5cdd9f11c163817d59445213f5bef0 (diff) | |
| download | BNW-1ff6baa44e22b91eefb48aea6f3befa078c0489b.tar.gz | |
Merge pull request #1 from ziejd2/octave_php_separate
Octave php separate
Diffstat (limited to 'sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/log_prob_node.m')
| -rw-r--r-- | sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/log_prob_node.m | 49 |
1 files changed, 49 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/log_prob_node.m b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/log_prob_node.m new file mode 100644 index 00000000..ac10f8a3 --- /dev/null +++ b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/log_prob_node.m @@ -0,0 +1,49 @@ +function L = log_prob_node(CPD, self_ev, pev) +% LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (gaussian) +% L = log_prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +use_log = 1; +ncases = length(self_ev); +nparents = length(CPD.sizes)-1; +assert(ncases == size(pev, 2)); + +if ncases == 0 + L = 0; + return; +end + +L = 0; +for m=1:ncases + if isempty(CPD.dps) + i = 1; + else + if usecell + dpvals = cat(1, pev{CPD.dps, m}); + else + dpvals = pev(CPD.dps, m); + end + i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)'); + end + if usecell + y = self_ev{m}; + else + y = self_ev(m); + end + if length(CPD.cps) == 0 + L = L + gaussian_prob(y, CPD.mean(:,i), CPD.cov(:,:,i), use_log); + else + if usecell + x = cat(1, pev{CPD.cps, m}); + else + x = pev(CPD.cps, m); + end + L = L + gaussian_prob(y, CPD.mean(:,i) + CPD.weights(:,:,i)*x, CPD.cov(:,:,i), use_log); + end +end + |
