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/BNT/CPDs/@tabular_CPD/Old/prob_node.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/Old/prob_node.m
new file mode 100644
index 00000000..2764e6c1
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/Old/prob_node.m
@@ -0,0 +1,40 @@
+function p = prob_node(CPD, self_ev, pev)
+% PROB_NODE Compute P(y|pa(y), theta) (tabular)
+% p = prob_node(CPD, self_ev, pev)
+%
+% self_ev{m} is the evidence on this node in case m
+% pev{i,m} is the evidence on the i'th parent in case m
+% If there is a single case, self_ev can be a scalar instead of a cell array
+
+ncases = size(pev, 2);
+
+%assert(~any(isemptycell(pev))); % slow
+%assert(~any(isemptycell(self_ev))); % slow
+
+CPT = CPD_to_CPT(CPD);  
+sz = mysize(CPT);
+nparents = length(sz)-1;
+assert(nparents == size(pev, 1));
+
+if ncases==1 
+  x = cat(1, pev{:});
+  if iscell(y)
+    y = self_ev{1};
+  else
+    y = self_ev;
+  end
+  switch nparents
+   case 0, p = CPT(y);
+   case 1, p = CPT(x(1), y);
+   case 2, p = CPT(x(1), x(2), y);
+   case 3, p = CPT(x(1), x(2), x(3), y);
+   otherwise,
+    ind = subv2ind(CPD.sizes, [x y]);
+    p = CPT(ind);
+  end
+else
+  x = num2cell(pev)'; % each row is a case
+  y = cat(1, self_ev{:})';
+  ind = subv2ind(CPD.sizes, [x y]);
+  p = CPT(ind);
+end