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, 40 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMtools/sample_discrete.m b/sourcecodes/bnt-master/KPMtools/sample_discrete.m
new file mode 100644
index 00000000..d8f6b687
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMtools/sample_discrete.m
@@ -0,0 +1,40 @@
+function M = sample_discrete(prob, r, c)
+% SAMPLE_DISCRETE Like the built in 'rand', except we draw from a non-uniform discrete distrib.
+% M = sample_discrete(prob, r, c)
+%
+% Example: sample_discrete([0.8 0.2], 1, 10) generates a row vector of 10 random integers from {1,2},
+% where the prob. of being 1 is 0.8 and the prob of being 2 is 0.2.
+
+n = length(prob);
+
+if nargin == 1
+  r = 1; c = 1;
+elseif nargin == 2
+  c == r;
+end
+
+R = rand(r, c);
+M = ones(r, c);
+cumprob = cumsum(prob(:));
+
+if n < r*c
+  for i = 1:n-1
+    M = M + (R > cumprob(i));
+  end
+else
+  % loop over the smaller index - can be much faster if length(prob) >> r*c
+  cumprob2 = cumprob(1:end-1);
+  for i=1:r
+    for j=1:c
+      M(i,j) = sum(R(i,j) > cumprob2)+1;
+    end
+  end
+end
+
+
+% Slower, even though vectorized
+%cumprob = reshape(cumsum([0 prob(1:end-1)]), [1 1 n]);
+%M = sum(R(:,:,ones(n,1)) > cumprob(ones(r,1),ones(c,1),:), 3);
+
+% convert using a binning algorithm
+%M=bindex(R,cumprob);