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

1 files changed, 62 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/CPD_to_lambda_msg.m b/sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/CPD_to_lambda_msg.m
new file mode 100644
index 00000000..c323e8e5
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/CPD_to_lambda_msg.m
@@ -0,0 +1,62 @@
+function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence)
+% CPD_TO_LAMBDA_MSG Compute lambda message (gmux)
+% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence)
+% Pearl p183 eq 4.52
+
+% Let Y be this node, X1..Xn be the cts parents and M the discrete switch node.
+% e.g., for n=3, M=1
+%
+%  X1 X2 X3 M
+%   \
+%    \
+%      Y
+%
+% So the only case in which we send an informative message is if p=1=M.
+% To the other cts parents, we send the "know nothing" message.
+
+switch msg_type
+ case 'd',
+  error('gaussian_CPD can''t create discrete msgs')
+ case 'g',
+  cps = ps(CPD.cps);
+  cpsizes = CPD.sizes(CPD.cps);
+  self_size = CPD.sizes(end);
+  i = find_equiv_posns(p, cps); % p is n's i'th cts parent
+  psz = cpsizes(i);
+  dps = ps(CPD.dps);
+  M = evidence{dps};
+  if isempty(M)
+    error('gmux node must have observed discrete parent')
+  end
+  P = msg{n}.lambda.precision;
+  if all(P == 0) | (cps(M) ~= p) % if we know nothing, or are sending to a disconnected parent
+    lam_msg.precision = zeros(psz, psz);
+    lam_msg.info_state = zeros(psz, 1);
+    return;
+  end
+  % We are sending a message to the only effectively connected parent.
+  % There are no other incoming pi messages.
+  Bmu = CPD.mean(:,M);
+  BSigma = CPD.cov(:,:,M);
+  Bi = CPD.weights(:,:,M);
+  if (det(P) > 0) | isinf(P) 
+    if isinf(P) % Y is observed
+      Sigma_lambda = zeros(self_size, self_size); % infinite precision => 0 variance
+      mu_lambda = msg{n}.lambda.mu; % observed_value;
+    else
+      Sigma_lambda = inv(P);
+      mu_lambda = Sigma_lambda * msg{n}.lambda.info_state;
+    end
+    C = inv(Sigma_lambda + BSigma);
+    lam_msg.precision = Bi' * C * Bi;
+    lam_msg.info_state = Bi' * C * (mu_lambda - Bmu);
+  else
+    % method that uses matrix inversion lemma
+    A = inv(P + inv(BSigma));
+    C = P - P*A*P;
+    lam_msg.precision = Bi' * C * Bi;
+    D = eye(self_size) - P*A;
+    z = msg{n}.lambda.info_state;
+    lam_msg.info_state = Bi' * (D*z - D*P*Bmu);
+  end
+end