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, 27 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMtools/mk_stochastic.m b/sourcecodes/bnt-master/KPMtools/mk_stochastic.m
new file mode 100644
index 00000000..68aac6ab
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMtools/mk_stochastic.m
@@ -0,0 +1,27 @@
+function [T,Z] = mk_stochastic(T)
+% MK_STOCHASTIC Ensure the argument is a stochastic matrix, i.e., the sum over the last dimension is 1.
+% [T,Z] = mk_stochastic(T)
+%
+% If T is a vector, it will sum to 1.
+% If T is a matrix, each row will sum to 1.
+% If T is a 3D array, then sum_k T(i,j,k) = 1 for all i,j.
+
+% Set zeros to 1 before dividing
+% This is valid since S(j) = 0 iff T(i,j) = 0 for all j
+
+if (ndims(T)==2) && (size(T,1)==1 || size(T,2)==1) % isvector
+  [T,Z] = normalise(T);
+elseif ndims(T)==2 % matrix
+  Z = sum(T,2); 
+  S = Z + (Z==0);
+  norm = repmat(S, 1, size(T,2));
+  T = T ./ norm;
+else % multi-dimensional array
+  ns = size(T);
+  T = reshape(T, prod(ns(1:end-1)), ns(end));
+  Z = sum(T,2);
+  S = Z + (Z==0);
+  norm = repmat(S, 1, ns(end));
+  T = T ./ norm;
+  T = reshape(T, ns);
+end