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, 34 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@noisyor_CPD/CPD_to_CPT.m b/sourcecodes/bnt-master/BNT/CPDs/@noisyor_CPD/CPD_to_CPT.m
new file mode 100644
index 00000000..93a6dcef
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@noisyor_CPD/CPD_to_CPT.m
@@ -0,0 +1,34 @@
+function CPT = CPD_to_CPT(CPD)
+% CPD_TO_CPT Convert the discrete CPD to tabular form (noisyor)
+% CPT = CPD_to_CPT(CPD)
+%
+% CPT(U1,...,Un, X) = Pr(X|U1,...,Un) where the Us are the parents (excluding leak).
+
+if ~isempty(CPD.CPT)
+  CPT = CPD.CPT; % remember to flush cache if params change (e.g., during learning)
+  return;
+end
+
+q = [CPD.leak_inhibit CPD.inhibit(:)'];
+% q(i) is the prob. that the i'th parent will be inhibited (flipped from 1 to 0).
+% q(1) is the leak inhibition probability, and length(q) = n + 1.
+
+if length(q)==1
+  CPT = [q  1-q];
+  return;
+end
+
+n = length(q);
+Bn = ind2subv(2*ones(1,n), 1:(2^n))-1;  % all n bit vectors, with the left most column toggling fastest (LSB)
+CPT = zeros(2^n, 2);
+% Pr(X=0 | U_1 .. U_n) = prod_{i: U_i = on} q_i =  prod_i q_i ^ U_i = exp(u' * log(q_i))
+% This method is problematic when q contains zeros
+
+Q = repmat(q(:)', 2^n, 1);
+Q(logical(~Bn)) = 1;
+CPT(:,1) = prod(Q,2);
+CPT(:,2) = 1-CPT(:,1);
+
+CPT = reshape(CPT(2:2:end), 2*ones(1,n)); % skip cases in which the leak is off       
+
+CPD.CPT = CPT;