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, 86 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/update_ess.m b/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/update_ess.m
new file mode 100644
index 00000000..07dfc72e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@hhmmQ_CPD/update_ess.m
@@ -0,0 +1,86 @@
+function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node.
+% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv)
+%
+% we assume if one of the Qps is observed, all of them are 
+% We assume the F nodes are already hidden 
+
+% Figure out the node numbers associated with each parent
+dom = fmarginal.domain;
+self = dom(end);
+old_self = dom(CPD.old_self_ndx);
+%Fself = dom(CPD.Fself_ndx);
+%Fbelow = dom(CPD.Fbelow_ndx);
+Qps = dom(CPD.Qps_ndx);
+
+Qsz = CPD.Qsz;
+Qpsz = CPD.Qpsz;
+
+
+% hor_counts(old_self, Qps, self),
+% fmarginal(old_self, Fbelow, Fself, Qps, self)
+% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not
+% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset)
+% Since any of i,j,k may be observed, we write
+% hor_counts(i_counts_ndx, kndx, jndx) = fmarginal(i_fmarg_ndx...)
+% where i_fmarg_ndx = 1 and i_counts_ndx = i if old_self is observed to have value i,
+% i_fmarg_ndx = 1:Qsz and i_counts_ndx = 1:Qsz if old_self is hidden, etc.
+
+
+if hidden_bitv(old_self)
+  i_counts_ndx = 1:Qsz;
+  eff_oldQsz = Qsz;
+else
+  i_counts_ndx = evidence{old_self};
+  eff_oldQsz = 1;
+end
+
+if all(hidden_bitv(Qps)) % we assume all are hidden or all are observed
+  k_counts_ndx = 1:Qpsz;
+  eff_Qpsz = Qpsz;
+else
+  k_counts_ndx = subv2ind(Qpsz, cat(1, evidence{Qps}));
+  eff_Qpsz = 1;
+end
+
+if hidden_bitv(self)
+  j_counts_ndx = 1:Qsz;
+  eff_Qsz = Qsz;
+else
+  j_counts_ndx = evidence{self};
+  eff_Qsz = 1;
+end
+
+hor_counts = zeros(Qsz, Qpsz, Qsz);
+ver_counts = zeros(Qpsz, Qsz);
+    
+if ~isempty(CPD.Fbelow_ndx)
+  if ~isempty(CPD.Fself_ndx) % general case
+    fmarg = myreshape(fmarginal.T, [eff_oldQsz 2 2 eff_Qpsz eff_Qsz]);
+    hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) =  fmarg(:, 2, 1, :, :);
+    ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Fbelow and Qold
+	sumv(fmarg(:, :,  2, :, :), [1 2]); % require Fself=2
+  else % no F from self, hence no startprob
+    fmarg = myreshape(fmarginal.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]);
+    hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = ...
+	fmarg(:, 2, :, :); % require Fbelow = 2
+  end
+else % no F signal from below
+  if ~isempty(CPD.Fself_ndx) % self F
+    fmarg = myreshape(fmarginal.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]);
+    hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) =  fmarg(:, 1, :, :);
+    ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Qold
+	squeeze(sum(fmarg(:, 2, :, :), 1)); % Fself=2
+  else % no F from self
+    error('An hhmmQ node without any F parents is just a tabular_CPD')
+  end
+end
+
+
+CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts);
+
+if ~isempty(CPD.sub_CPD_start)
+  CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts);
+end
+
+