From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001
From: ziejd2
Date: Thu, 28 Sep 2017 15:04:40 -0500
Subject: 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
---
 sourcecodes/bnt-master/KPMstats/gamma_sample.m | 126 +++++++++++++++++++++++++
 1 file changed, 126 insertions(+)
 create mode 100644 sourcecodes/bnt-master/KPMstats/gamma_sample.m

(limited to 'sourcecodes/bnt-master/KPMstats/gamma_sample.m')

diff --git a/sourcecodes/bnt-master/KPMstats/gamma_sample.m b/sourcecodes/bnt-master/KPMstats/gamma_sample.m
new file mode 100644
index 00000000..e622df40
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMstats/gamma_sample.m
@@ -0,0 +1,126 @@
+function r = gamrnd(a,b,m,n);
+%GAMRND Random matrices from gamma distribution.
+%   R = GAMRND(A,B) returns a matrix of random numbers chosen   
+%   from the gamma 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 = GAMRND(A,B,M,N) returns an M by N matrix. 
+%
+%   Some references refer to the gamma distribution
+%   with a single parameter. This corresponds to GAMRND
+%   with B = 1. (See Devroye, pages 401-402.)
+
+%   GAMRND uses a rejection or an inversion method depending on the
+%   value of A. 
+
+%   References:
+%      [1]  L. Devroye, "Non-Uniform Random Variate Generation", 
+%      Springer-Verlag, 1986
+
+%   B.A. Jones 2-1-93
+%   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
+
+% Initialize r to zero.
+lth = rows*columns;
+r = zeros(lth,1);
+a = a(:); b = b(:);
+
+scalara = (length(a) == 1);
+if scalara 
+   a = a*ones(lth,1);
+end
+
+scalarb = (length(b) == 1);
+if scalarb 
+   b = b*ones(lth,1);
+end
+
+% If a == 1, then gamma is exponential. (Devroye, page 405).
+k = find(a == 1);
+if any(k)
+   r(k) = -b(k) .* log(rand(size(k)));
+end 
+
+
+k = find(a < 1 & a > 0);
+% (Devroye, page 418 Johnk's generator)
+if any(k)
+  c = zeros(lth,1);
+  d = zeros(lth,1);
+  c(k) = 1 ./ a(k);
+  d(k) = 1 ./ (1 - a(k));
+  accept = k;
+  while ~isempty(accept)
+    u = rand(size(accept));
+    v = rand(size(accept));
+    x = u .^ c(accept);
+    y = v .^ d(accept);
+    k1 = find((x + y) <= 1); 
+    if ~isempty(k1)
+      e = -log(rand(size(k1))); 
+      r(accept(k1)) = e .* x(k1) ./ (x(k1) + y(k1));
+      accept(k1) = [];
+    end
+  end
+  r(k) = r(k) .* b(k);
+end
+
+% Use a rejection method for a > 1.
+k = find(a > 1);
+% (Devroye, page 410 Best's algorithm)
+bb = zeros(size(a));
+c  = bb;
+if any(k)
+  bb(k) = a(k) - 1;
+  c(k) = 3 * a(k) - 3/4;
+  accept = k; 
+  count = 1;
+  while ~isempty(accept)
+    m = length(accept);
+    u = rand(m,1);
+    v = rand(m,1);
+    w = u .* (1 - u);
+    y = sqrt(c(accept) ./ w) .* (u - 0.5);
+    x = bb(accept) + y;
+    k1 = find(x >= 0);
+    if ~isempty(k1)
+      z = 64 * (w .^ 3) .* (v .^ 2);
+      k2 = (z(k1) <= (1 - 2 * (y(k1) .^2) ./ x(k1)));
+      k3 = k1(find(k2));
+      r(accept(k3)) = x(k3); 
+      k4 = k1(find(~k2));
+      k5 = k4(find(log(z(k4)) <= (2*(bb(accept(k4)).*log(x(k4)./bb(accept(k4)))-y(k4)))));
+      r(accept(k5)) = x(k5);
+      omit = [k3; k5];
+      accept(omit) = [];
+    end
+  end
+  r(k) = r(k) .* b(k);
+end
+
+% Return NaN if a or b is not positive.
+r(b <= 0 | a <= 0) = NaN;
+
+r = reshape(r,rows,columns);
-- 
cgit 1.4.1