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, 55 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/bayes_update_params.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/bayes_update_params.m
new file mode 100644
index 00000000..0de0f8b8
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/bayes_update_params.m
@@ -0,0 +1,55 @@
+function CPD = bayes_update_params(CPD, self_ev, pev)
+% UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (tabular)
+% CPD = update_params_complete(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 are any parents).
+% These can be arrays or cell arrays.
+%
+% We update the Dirichlet pseudo counts and set the CPT to the mean of the posterior.
+
+if iscell(self_ev), usecell = 1; else usecell = 0; end
+
+ncases = length(self_ev);
+sz = CPD.sizes;
+nparents = length(sz)-1;
+assert(nparents == size(pev,1));
+
+if ncases == 0 | ~adjustable_CPD(CPD)
+  return;
+elseif ncases == 1 % speedup the sequential learning case by avoiding normalization of the whole array
+  if usecell
+    x = cat(1, pev{:})';
+    y = self_ev{1};
+  else
+    x = pev(:)';
+    y = self_ev;
+  end
+  switch nparents
+   case 0,
+    CPD.dirichlet(y) = CPD.dirichlet(y)+1;
+    CPD.CPT = CPD.dirichlet / sum(CPD.dirichlet);
+   case 1,
+    CPD.dirichlet(x(1), y) = CPD.dirichlet(x(1), y)+1;
+    CPD.CPT(x(1), :) = CPD.dirichlet(x(1), :) ./ sum(CPD.dirichlet(x(1), :));
+   case 2,
+    CPD.dirichlet(x(1), x(2), y) = CPD.dirichlet(x(1), x(2), y)+1;
+    CPD.CPT(x(1), x(2), :) = CPD.dirichlet(x(1), x(2), :) ./ sum(CPD.dirichlet(x(1), x(2), :));
+   case 3,
+    CPD.dirichlet(x(1), x(2), x(3), y) = CPD.dirichlet(x(1), x(2), x(3), y)+1;
+    CPD.CPT(x(1), x(2), x(3), :) = CPD.dirichlet(x(1), x(2), x(3), :) ./ sum(CPD.dirichlet(x(1), x(2), x(3), :));
+   otherwise,
+    ind = subv2ind(sz, [x y]);
+    CPD.dirichlet(ind) = CPD.dirichlet(ind) + 1;
+    CPD.CPT = mk_stochastic(CPD.dirichlet);
+  end
+else  
+  if usecell
+    data = [cell2num(pev); cell2num(self_ev)]; 
+  else
+    data = [pev; self_ev];
+  end
+  counts = compute_counts(data, sz);
+  CPD.dirichlet = CPD.dirichlet + counts;
+  CPD.CPT = mk_stochastic(CPD.dirichlet);
+end