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, 32 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMstats/dirichletrnd.m b/sourcecodes/bnt-master/KPMstats/dirichletrnd.m
new file mode 100644
index 00000000..f11074db
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+++ b/sourcecodes/bnt-master/KPMstats/dirichletrnd.m
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+function x = dirichletrnd(alpha)
+%DIRICHLETRND Random vector from a dirichlet distribution.
+%   x = dirichletrnd(alpha) returns a vector randomly selected
+%   from the Dirichlet distribution with parameter vector alpha.
+%
+%   The algorithm used is the following:
+%   For each alpha(i), generate a value s(i) with distribution
+%   Gamma(alpha(i),1).  Now x(i) = s(i) / sum_j s(j).
+%   
+%   The above algorithm was recounted to me by Radford Neal, but
+%   a reference would be appreciated...
+%   Do the gamma parameters always have to be 1?
+%
+%   Author: David Ross
+%   $Id: dirichletrnd.m,v 1.1.1.1 2005/05/22 23:32:12 yozhik Exp $
+
+%-------------------------------------------------
+% Check the input
+%-------------------------------------------------
+error(nargchk(1,1,nargin));
+
+if min(size(alpha)) ~= 1 | length(alpha) < 2
+    error('alpha must be a vector of length at least 2');
+end
+
+
+%-------------------------------------------------
+% Main
+%-------------------------------------------------
+gamma_vals = gamrnd(alpha, ones(size(alpha)), size(alpha));
+denom = sum(gamma_vals);
+x = gamma_vals / denom;
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