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, 59 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/CPD_to_lambda_msg.m b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/CPD_to_lambda_msg.m
new file mode 100644
index 00000000..340ebe5c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/CPD_to_lambda_msg.m
@@ -0,0 +1,59 @@
+function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence)
+% CPD_TO_LAMBDA_MSG Compute lambda message (gaussian)
+% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence)
+% Pearl p183 eq 4.52
+
+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);
+  if all(msg{n}.lambda.precision == 0) % no info to send on
+    lam_msg.precision = zeros(psz, psz);
+    lam_msg.info_state = zeros(psz, 1);
+    return;
+  end
+  [m, Q, W] = gaussian_CPD_params_given_dps(CPD, [ps n], evidence);
+  Bmu = m;
+  BSigma = Q;
+  for k=1:length(cps) % only get pi msgs from cts parents
+    pk = cps(k);
+    if pk ~= p
+      %bk = block(k, cpsizes);
+      bk = CPD.cps_block_ndx{k};
+      Bk = W(:, bk);
+      m = msg{n}.pi_from_parent{k}; 
+      BSigma = BSigma + Bk * m.Sigma * Bk';
+      Bmu = Bmu + Bk * m.mu;
+    end
+  end
+  % BSigma = Q + sum_{k \neq i} B_k Sigma_k B_k'
+  %bi = block(i, cpsizes);
+  bi = CPD.cps_block_ndx{i};
+  Bi = W(:,bi);
+  P = msg{n}.lambda.precision;
+  if (rcond(P) > 1e-3) || 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 to avoid inverting P
+    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