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/BNT/general/sample_bnet.m b/sourcecodes/bnt-master/BNT/general/sample_bnet.m
new file mode 100644
index 00000000..3c219c24
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/general/sample_bnet.m
@@ -0,0 +1,34 @@
+function sample = sample_bnet(bnet, varargin)
+% SAMPLE_BNET Generate a random sample from a Bayes net.
+% SAMPLE = SAMPLE_BNET(BNET, ...)
+%
+% sample{i} contains the value of the i'th node.
+% i.e., the result is an Nx1 cell array.
+% Nodes are sampled in the order given by bnet.order.
+%
+% Optional arguments:
+%
+% evidence - initial evidence; if evidence{i} is non-empty, node i won't be sampled.
+
+% set defauly params
+n = length(bnet.dag);
+sample = cell(n,1);
+
+% get optional params
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'evidence',    sample = args{i+1}(:);
+   otherwise, error(['unrecognized argument ' args{i}])
+  end
+end
+
+for j=bnet.order(:)'
+  if isempty(sample{j})
+    %ps = parents(bnet.dag, j);
+    ps = bnet.parents{j};
+    e = bnet.equiv_class(j);
+    sample{j} = sample_node(bnet.CPD{e}, sample(ps));
+  end
+end