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, 72 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/log_nextcase_prob_node.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/log_nextcase_prob_node.m
new file mode 100644
index 00000000..c946de69
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/log_nextcase_prob_node.m
@@ -0,0 +1,72 @@
+function L = log_nextcase_prob_node(CPD, self_ev, pev, test_self_ev, test_pev)
+% LOG_NEXTCASE_PROB_NODE compute the joint distribution of a node (tabular) of a new case given
+% completely observed data.
+%
+% The input arguments are mainly similar with log_marg_prob_node(CPD, self_ev, pev, usecell),
+% but add test_self_ev, test_pev, and without usecell
+% test_self_ev(m) is the evidence on this node in a test case.
+% test_pev(i) is the evidence on the i'th parent in the test case (if there are any parents).
+%
+% Written by qian.diao@intel.com
+
+ncases = length(self_ev);
+sz = CPD.sizes;
+nparents = length(sz)-1;
+assert(ncases == size(pev, 2)); 
+
+if nargin < 6
+  %usecell = 0;
+  if iscell(self_ev)
+    usecell = 1;
+  else
+    usecell = 0;
+  end
+end
+
+
+if ncases==0
+  L = 0;
+  return;
+elseif ncases==1  % speedup the sequential learning case; here need correction!!!
+  CPT = CPD.CPT;
+  % We assume the CPTs are already set to the mean of the posterior (due to bayes_update_params)
+  if usecell
+    x = cat(1, pev{:})';
+    y = self_ev{1};
+  else
+    %x = pev(:)';
+    x = pev;
+    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(sz, [x y]);
+    p = CPT(ind);
+  end
+  L = log(p);
+else
+  % We ignore the CPTs here and assume the prior has not been changed
+  
+  % We arrange the data as in the following example.
+  % Let there be 2 parents and 3 cases. Let p(i,m) be parent i in case m,
+  % and y(m) be the child in case m. Then we create the data matrix
+  % 
+  % p(1,1) p(1,2) p(1,3)
+  % p(2,1) p(2,2) p(2,3)
+  % y(1)   y(2)   y(3)
+  if usecell
+    data = [cell2num(pev); cell2num(self_ev)]; 
+  else
+    data = [pev; self_ev];
+  end
+  counts = compute_counts(data, sz);
+  
+  % compute the (N_ijk'+ N_ijk)/(N_ij' + N_ij) under the condition of 1_m+1,ijk = 1 
+  L = predict_family(counts, CPD.prior, test_self_ev, test_pev);
+end
+
+