Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/private/extract_params_from_gbn.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function [B,D,mu] = extract_params_from_gbn(bnet) % Extract all the local parameters of each Gaussian node, and collect them into global matrices. % [B,D,mu] = extract_params_from_gbn(bnet) % % B(i,j) is a block matrix that contains the transposed weight matrix from node i to node j. % D(i,i) is a block matrix that contains the noise covariance matrix for node i. % mu(i) is a block vector that contains the shifted noise mean for node i. % In Shachter's model, the mean of each node in the global gaussian is % the same as the node's local unconditional mean. % In Alag's model (which we use), the global mean gets shifted. num_nodes = length(bnet.dag); bs = bnet.node_sizes(:); % bs = block sizes N = sum(bs); % num scalar nodes B = zeros(N,N); D = zeros(N,N); mu = zeros(N,1); for i=1:num_nodes % in topological order ps = parents(bnet.dag, i); e = bnet.equiv_class(i); %[m, Sigma, weights] = extract_params_from_CPD(bnet.CPD{e}); s = struct(bnet.CPD{e}); % violate privacy of object m = s.mean; Sigma = s.cov; weights = s.weights; if length(ps) == 0 mu(block(i,bs)) = m; else mu(block(i,bs)) = m + weights * mu(block(ps,bs)); end B(block(ps,bs), block(i,bs)) = weights'; D(block(i,bs), block(i,bs)) = Sigma; end