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, 76 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMstats/beta_sample.m b/sourcecodes/bnt-master/KPMstats/beta_sample.m
new file mode 100644
index 00000000..82bacbe4
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMstats/beta_sample.m
@@ -0,0 +1,76 @@
+function r = betarnd(a,b,m,n);
+%BETARND Random matrices from beta distribution.
+%   R = BETARND(A,B) returns a matrix of random numbers chosen   
+%   from the beta distribution with parameters A and B.
+%   The size of R is the common size of A and B if both are matrices.
+%   If either parameter is a scalar, the size of R is the size of the other
+%   parameter. Alternatively, R = BETARND(A,B,M,N) returns an M by N matrix. 
+
+%   Reference:
+%      [1]  L. Devroye, "Non-Uniform Random Variate Generation", 
+%      Springer-Verlag, 1986
+
+%   Copyright (c) 1993-98 by The MathWorks, Inc.
+%   $Revision: 1.1.1.1 $  $Date: 2005/04/26 02:29:18 $
+
+if nargin < 2, 
+    error('Requires at least two input arguments'); 
+end 
+
+if nargin == 2
+    [errorcode rows columns] = rndcheck(2,2,a,b);
+end
+
+if nargin == 3
+    [errorcode rows columns] = rndcheck(3,2,a,b,m);
+end
+
+if nargin == 4
+    [errorcode rows columns] = rndcheck(4,2,a,b,m,n);
+end
+
+if errorcode > 0
+    error('Size information is inconsistent.');
+end
+
+r = zeros(rows,columns);
+
+% Use Theorem 4.1, case A (Devroye, page 430) to derive beta
+%   random numbers as a ratio of gamma random numbers.
+if prod(size(a)) == 1
+    a1 = a(ones(rows,1),ones(columns,1));
+    g1 = gamrnd(a1,1);
+else
+    g1 = gamrnd(a,1);
+end
+if prod(size(b)) == 1
+    b1 = b(ones(rows,1),ones(columns,1));
+    g2 = gamrnd(b1,1);
+else
+    g2 = gamrnd(b,1);
+end
+r = g1 ./ (g1 + g2);
+
+% Return NaN if b is not positive.
+if any(any(b <= 0));
+    if prod(size(b) == 1)
+        tmp = NaN;
+        r = tmp(ones(rows,columns));
+    else
+        k = find(b <= 0);
+        tmp = NaN;
+        r(k) = tmp(ones(size(k)));
+    end
+end
+
+% Return NaN if a is not positive.
+if any(any(a <= 0));
+    if prod(size(a) == 1)
+        tmp = NaN;
+        r = tmp(ones(rows,columns));
+    else
+        k = find(a <= 0);
+        tmp = NaN;
+        r(k) = tmp(ones(size(k)));
+    end
+end