BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

5 files changed, 69 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Entries b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Entries
new file mode 100644
index 00000000..9a351a87
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Entries
@@ -0,0 +1,3 @@
+/dbn_to_lds.m/1.1.1.1/Wed May 29 15:59:56 2002//
+/extract_params_from_gbn.m/1.1.1.1/Wed May 29 15:59:56 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Repository b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Repository
new file mode 100644
index 00000000..3f67aee0
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/inference/dynamic/@kalman_inf_engine/private
diff --git a/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Root b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/dbn_to_lds.m b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/dbn_to_lds.m
new file mode 100644
index 00000000..6249ac0d
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/dbn_to_lds.m
@@ -0,0 +1,26 @@
+function [trans_mat, trans_cov, obs_mat, obs_cov, init_state, init_cov] = dbn_to_lds(bnet)
+% DBN_TO_LDS Compute the Linear Dynamical System parameters from the Gaussian DBN.
+% [trans_mat, trans_cov, obs_mat, obs_cov, init_state, init_cov] = dbn_to_lds(bnet)
+
+onodes = bnet.observed;
+ss = length(bnet.intra);
+num_nodes = ss*2;
+assert(isequal(bnet.cnodes_slice, 1:ss));
+[W,D,mu] = extract_params_from_gbn(bnet);
+
+hnodes = mysetdiff(1:ss, onodes);
+bs = bnet.node_sizes(:); % block sizes
+
+obs_mat = W(block(hnodes,bs), block(onodes,bs))';
+u = block(onodes,bs);
+obs_cov = D(u,u);
+
+trans_mat = W(block(hnodes,bs), block(hnodes + ss, bs))';
+u = block(hnodes + ss, bs);
+trans_cov = D(u,u);
+
+u = block(hnodes,bs);
+init_cov = D(u,u);
+init_state = mu(u);
+
+
diff --git a/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/extract_params_from_gbn.m b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/extract_params_from_gbn.m
new file mode 100644
index 00000000..86345830
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/dynamic/@kalman_inf_engine/private/extract_params_from_gbn.m
@@ -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
+
+
+