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

4 files changed, 63 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/CVS/Entries b/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/CVS/Entries
new file mode 100644
index 00000000..eb6be4f0
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/CVS/Entries
@@ -0,0 +1,2 @@
+/deterministic_CPD.m/1.1.1.1/Mon Oct  7 13:26:36 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/CVS/Repository b/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/CVS/Repository
new file mode 100644
index 00000000..fe1e84b5
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/CPDs/@deterministic_CPD
diff --git a/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/CVS/Root b/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/deterministic_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/deterministic_CPD.m
new file mode 100644
index 00000000..f44b6545
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@deterministic_CPD/deterministic_CPD.m
@@ -0,0 +1,59 @@
+function CPD = deterministic_CPD(bnet, self, fname, pfail)
+% DETERMINISTIC_CPD Make a tabular CPD representing a (noisy) deterministic function
+%
+% CPD = deterministic_CPD(bnet, self, fname)
+% This calls feval(fname, pvals) for each possible vector of parent values.
+% e.g., suppose there are 2 ternary parents, then pvals = 
+%  [1 1], [2 1], [3 1],   [1 2], [2 2], [3 2],   [1 3], [2 3], [3 3]
+% If v = feval(fname, pvals(i)), then
+%  CPD(x | parents=pvals(i)) = 1 if x==v, and = 0 if x<>v
+% e.g., suppose X4 = X2 AND (NOT X3). Then
+%    bnet.CPD{4} = deterministic_CPD(bnet, 4, inline('((x(1)-1) & ~(x(2)-1)) + 1'));  
+% Note that x(1) refers pvals(1) = X2, and x(2) refers to pvals(2)=X3
+% See also boolean_CPD.
+%
+% CPD = deterministic_CPD(bnet, self, fname, pfail)
+% will put probability mass 1-pfail on f(parents), and distribute pfail over the other values.
+% This is useful for simulating noisy deterministic functions.
+% If pfail is omitted, it is set to 0.
+%
+
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = tabular_CPD(bnet, self);
+  return;
+elseif isa(bnet, 'deterministic_CPD')
+  % This might occur if we are copying an object.
+  CPD = bnet;
+  return;
+end
+
+if nargin < 4, pfail = 0; end
+
+ps = parents(bnet.dag, self);
+ns = bnet.node_sizes;
+psizes = ns(ps);
+self_size = ns(self);
+
+psucc = 1-pfail;
+
+CPT = zeros(prod(psizes), self_size);
+pvals = zeros(1, length(ps));
+for i=1:prod(psizes)
+  pvals = ind2subv(psizes, i);
+  x = feval(fname, pvals);
+  %fprintf('%d ', [pvals x]); fprintf('\n');
+  if psucc == 1
+    CPT(i, x) = 1;
+  else
+    CPT(i, x) = psucc;
+    rest = mysetdiff(1:self_size, x);
+    CPT(i, rest) = pfail/length(rest);
+  end
+end
+CPT = reshape(CPT, [psizes self_size]);  
+
+CPD = tabular_CPD(bnet, self, 'CPT',CPT, 'clamped',1);
+
+