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, 61 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@hhmmF_CPD/Old/update_ess.m b/sourcecodes/bnt-master/BNT/CPDs/@hhmmF_CPD/Old/update_ess.m
new file mode 100644
index 00000000..18f7057e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@hhmmF_CPD/Old/update_ess.m
@@ -0,0 +1,61 @@
+function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmmF node.
+% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+
+% Figure out the node numbers associated with each parent
+% so we extract evidence from the right place
+dom = fmarginal.domain; % Q(1) .. Q(d) F(d+1) F(d)
+Qps = fmarginal.domain(1:end-2);
+Q = Qps(end);
+Qps = Qps(1:end-1);
+
+Qsz = CPD.Qsizes(CPD.Q);
+Qpsz = prod(CPD.Qsizes(CPD.Qps)); % may be 1
+
+% We assume the F node are always hidden, but allow some of the Q nodes
+% to be observed. We do case analysis for speed.
+%We only extract prob from fmarginal.T when F(d+1)=2 i.e., model below has finished.
+% wrong -> % We sum over the possibilities that F(d+1) = 1 or 2
+
+obs_self = ~hidden_bitv(Q);
+if obs_self
+  self_val = evidence{Q};
+end
+
+if isempty(Qps) % independent of parent context
+  counts = zeros(Qsz, 2);
+  %fmarginal.T(Q(d), F(d+1), F(d))
+  if obs_self
+    marg = myreshape(fmarginal.T, [1 2 2]);
+    counts(self_val,:) = marg(1,2,:);
+    %counts(self_val,:) = marg(1,1,:) + marg(1,2,:);
+  else
+    marg = myreshape(fmarginal.T, [Qsz 2 2]);
+    counts = squeeze(marg(:,2,:));
+    %counts = squeeze(marg(:,2,:)) + squeeze(marg(:,1,:));
+  end
+else
+  counts = zeros(Qpsz, Qsz, 2);
+  %fmarginal.T(Q(1:d-1), Q(d), F(d+1), F(d))
+  obs_Qps = ~any(hidden_bitv(Qps));  % we assume that all or none of the Q  parents are observed
+  if obs_Qps
+    Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps}));
+  end
+  if obs_self & obs_Qps
+    marg = myreshape(fmarginal.T, [1 1 2 2]);
+    counts(Qps_val, self_val, :) = squeeze(marg(1,1,2,:));
+    %counts(Qps_val, self_val, :) = squeeze(marg(1,1,2,:)) + squeeze(marg(1,1,1,:));
+  elseif ~obs_self & obs_Qps
+    marg = myreshape(fmarginal.T, [1 Qsz 2 2]);
+    counts(Qps_val, :, :) = squeeze(marg(1,:,2,:));
+    %counts(Qps_val, :, :) = squeeze(marg(1,:,2,:)) + squeeze(marg(1,:,1,:));
+  elseif obs_self & ~obs_Qps
+    error('not yet implemented')
+  else
+    marg = myreshape(fmarginal.T, [Qpsz Qsz 2 2]);
+    counts(:, :, :) = squeeze(marg(:,:,2,:));
+    %counts(:, :, :) = squeeze(marg(:,:,2,:)) + squeeze(marg(:,:,1,:));
+  end    
+end
+
+CPD.sub_CPD_term = update_ess_simple(CPD.sub_CPD_term, counts);