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/KPMtools/normalize.m b/sourcecodes/bnt-master/KPMtools/normalize.m
new file mode 100644
index 00000000..c6585921
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMtools/normalize.m
@@ -0,0 +1,34 @@
+function [M, z] = normalise(A, dim)
+% NORMALISE Make the entries of a (multidimensional) array sum to 1
+% [M, c] = normalise(A)
+% c is the normalizing constant
+%
+% [M, c] = normalise(A, dim)
+% If dim is specified, we normalise the specified dimension only,
+% otherwise we normalise the whole array.
+
+if nargin < 2
+  z = sum(A(:));
+  % Set any zeros to one before dividing
+  % This is valid, since c=0 => all i. A(i)=0 => the answer should be 0/1=0
+  s = z + (z==0);
+  M = A / s;
+elseif dim==1 % normalize each column
+  z = sum(A);
+  s = z + (z==0);
+  %M = A ./ (d'*ones(1,size(A,1)))';
+  M = A ./ repmatC(s, size(A,1), 1);
+else
+  % Keith Battocchi - v. slow because of repmat
+  z=sum(A,dim);
+  s = z + (z==0);
+  L=size(A,dim);
+  d=length(size(A));
+  v=ones(d,1);
+  v(dim)=L;
+  %c=repmat(s,v);
+  c=repmat(s,v');
+  M=A./c;
+end
+
+