Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/enter_evidence.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
---|---|
date | Tue, 10 Feb 2015 15:05:51 +0000 |
parents | |
children |
line wrap: on
line source
function [engine, loglik] = enter_evidence(engine, evidence, varargin) % ENTER_EVIDENCE Add the specified evidence to the network (gaussian_inf_engine) % [engine, loglik] = enter_evidence(engine, evidence, ...) % % evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) bnet = bnet_from_engine(engine); ns = bnet.node_sizes; O = find(~isemptycell(evidence)); H = find(isemptycell(evidence)); vals = cat(1, evidence{O}); % Compute Pr(H|o) [Hmu, HSigma, loglik] = condition_gaussian(engine.mu, engine.Sigma, H, O, vals(:), ns); engine.Hmu = Hmu; engine.HSigma = HSigma; engine.hnodes = H; %%%%%%%% function [mu2, Sigma2, loglik] = condition_gaussian(mu, Sigma, X, Y, y, ns) % CONDITION_GAUSSIAN Compute Pr(X|Y=y) where X and Y are jointly Gaussian. % [mu2, Sigma2, ll] = condition_gaussian(mu, Sigma, X, Y, y, ns) if isempty(y) mu2 = mu; Sigma2 = Sigma; loglik = 0; return; end use_log = 1; if length(Y)==length(mu) % instantiating every variable mu2 = y; Sigma2 = zeros(length(y)); loglik = gaussian_prob(y, mu, Sigma, use_log); return; end [muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns); K = SXY*inv(SYY); mu2 = muX + K*(y-muY); Sigma2 = SXX - K*SYX; loglik = gaussian_prob(y, muY, SYY, use_log);