diff 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

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/private/extract_params_from_gbn.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,38 @@
+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
+
+
+