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/@hhmmQ_CPD/maximize_params.m b/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/maximize_params.m
new file mode 100644
index 00000000..541a50be
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/maximize_params.m
@@ -0,0 +1,40 @@
+function CPD = maximize_params(CPD, temp)
+% MAXIMIZE_PARAMS Set the params of a hhmmQ node to their ML/MAP values.
+% CPD = maximize_params(CPD, temperature)
+
+Qsz = CPD.Qsz;
+Qpsz = CPD.Qpsz;
+
+if ~isempty(CPD.sub_CPD_start)
+  CPD.sub_CPD_start = maximize_params(CPD.sub_CPD_start, temp);
+  S = struct(CPD.sub_CPD_start);
+  CPD.startprob = myreshape(S.CPT, [Qpsz Qsz]);
+  %CPD.startprob = S.CPT;
+end
+
+if 1
+  % If we are in a state that can only go the end state,
+  % we will never see a transition to another (non-end) state,
+  % so counts(i,k,j)=0 (and termprob(k,i)=1).
+  % We set counts(i,k,i)=1 in this case.
+  % This will cause remove_hhmm_end_state to return a
+  % stochastic matrix, but otherwise has no effect on EM.
+  counts = get_field(CPD.sub_CPD_trans, 'counts');
+  counts = reshape(counts, [Qsz Qpsz Qsz]);
+  for k=1:Qpsz
+    for i=1:Qsz
+      if sum(counts(i,k,:))==0 % never witnessed a transition out of i
+	counts(i,k,i)=1; % add self loop 
+	%fprintf('CPDQ d=%d i=%d k=%d\n', CPD.d, i, k);
+      end
+    end
+  end
+  CPD.sub_CPD_trans = set_fields(CPD.sub_CPD_trans, 'counts', counts(:)); 
+end
+ 
+CPD.sub_CPD_trans = maximize_params(CPD.sub_CPD_trans, temp);
+S = struct(CPD.sub_CPD_trans);
+%CPD.transprob = S.CPT;
+CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]);
+
+CPD = update_CPT(CPD);