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, 88 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/update_ess.m b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/update_ess.m
new file mode 100644
index 00000000..3b58c02e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/update_ess.m
@@ -0,0 +1,88 @@
+function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+% UPDATE_ESS Update the Expected Sufficient Statistics of a Gaussian node
+% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+
+%if nargin < 6
+%  hidden_bitv = zeros(1, max(fmarginal.domain));
+%  hidden_bitv(find(isempty(evidence)))=1;
+%end
+
+dom = fmarginal.domain;
+self = dom(end);
+ps = dom(1:end-1);
+cps = myintersect(ps, cnodes);
+dps = mysetdiff(ps, cps);
+
+CPD.nsamples = CPD.nsamples + 1;            
+[ss cpsz dpsz] = size(CPD.weights); % ss = self size
+[ss dpsz] = size(CPD.mean);
+
+% Let X be the cts parent (if any), Y be the cts child (self).
+
+if ~hidden_bitv(self) && ~any(hidden_bitv(cps)) && all(hidden_bitv(dps))
+  % Speedup for the common case that all cts nodes are observed, all discrete nodes are hidden
+  % Since X and Y are observed, SYY = 0, SXX = 0, SXY = 0
+  % Since discrete parents are hidden, we do not need to add evidence to w.
+  w = fmarginal.T(:);
+  CPD.Wsum = CPD.Wsum + w;
+  y = evidence{self};
+  Cyy = y*y';
+  if ~CPD.useC
+     WY = repmat(w(:)',ss,1); % WY(y,i) = w(i)
+     WYY = repmat(reshape(WY, [ss 1 dpsz]), [1 ss 1]); % WYY(y,y',i) = w(i)
+     %CPD.WYsum = CPD.WYsum +  WY .* repmat(y(:), 1, dpsz);
+     CPD.WYsum = CPD.WYsum +  y(:) * w(:)';
+     CPD.WYYsum = CPD.WYYsum + WYY  .* repmat(reshape(Cyy, [ss ss 1]), [1 1 dpsz]);
+  else
+     W = w(:)';
+     W2 = reshape(W, [1 1 dpsz]);
+     CPD.WYsum = CPD.WYsum +  rep_mult(W, y(:), size(CPD.WYsum)); 
+     CPD.WYYsum = CPD.WYYsum + rep_mult(W2, Cyy, size(CPD.WYYsum));
+  end
+  if cpsz > 0 % X exists
+    x = cat(1, evidence{cps}); x = x(:);
+    Cxx = x*x';
+    Cxy = x*y';
+    WX = repmat(w(:)',cpsz,1); % WX(x,i) = w(i)
+    WXX = repmat(reshape(WX, [cpsz 1 dpsz]), [1 cpsz 1]); % WXX(x,x',i) = w(i)
+    WXY = repmat(reshape(WX, [cpsz 1 dpsz]), [1 ss 1]); % WXY(x,y,i) = w(i)
+    if ~CPD.useC
+      CPD.WXsum = CPD.WXsum + WX .* repmat(x(:), 1, dpsz);
+      CPD.WXXsum = CPD.WXXsum + WXX .* repmat(reshape(Cxx, [cpsz cpsz 1]), [1 1 dpsz]);
+      CPD.WXYsum = CPD.WXYsum + WXY .* repmat(reshape(Cxy, [cpsz ss 1]), [1 1 dpsz]);
+    else
+      CPD.WXsum = CPD.WXsum + rep_mult(W, x(:), size(CPD.WXsum));
+      CPD.WXXsum = CPD.WXXsum + rep_mult(W2, Cxx, size(CPD.WXXsum));
+      CPD.WXYsum = CPD.WXYsum + rep_mult(W2, Cxy, size(CPD.WXYsum));
+    end
+  end
+  return;
+end
+
+% general (non-vectorized) case
+fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes); % slow!
+
+if dpsz == 1 % no discrete parents
+  w = 1;
+else
+  w = fullm.T(:);
+end
+
+CPD.Wsum = CPD.Wsum + w;
+xi = 1:cpsz;
+yi = (cpsz+1):(cpsz+ss);
+for i=1:dpsz
+  muY = fullm.mu(yi, i);
+  SYY = fullm.Sigma(yi, yi, i);
+  CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY;
+  CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y]
+  if cpsz > 0
+    muX = fullm.mu(xi, i);
+    SXX = fullm.Sigma(xi, xi, i);
+    SXY = fullm.Sigma(xi, yi, i);
+    CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX;
+    CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX');
+    CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY');
+  end
+end                
+