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/potentials/@cgpot/Old/simple_marginalize_pot.m b/sourcecodes/bnt-master/BNT/potentials/@cgpot/Old/simple_marginalize_pot.m
new file mode 100644
index 00000000..a2774636
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/potentials/@cgpot/Old/simple_marginalize_pot.m
@@ -0,0 +1,86 @@
+function smallpot = marginalize_pot(bigpot, keep)
+% MARGINALIZE_POT Marginalize a cgpot onto a smaller domain.
+% smallpot = marginalize_pot(bigpot, keep)
+
+sumover = mysetdiff(bigpot.domain, keep);
+csumover = myintersect(sumover, bigpot.cdom);
+dsumover = myintersect(sumover, bigpot.ddom);
+dkeep = myintersect(keep, bigpot.ddom);
+ckeep = myintersect(keep, bigpot.cdom);
+%ns = sparse(1, max(bigpot.domain)); % must be full, so I is an integer
+ns = zeros(1, max(bigpot.domain));
+ns(bigpot.ddom) = bigpot.dsizes;
+ns(bigpot.cdom) = bigpot.csizes;
+
+% sum(ns(csumover))==0 is like isempty(csumover) but handles observed nodes.
+% Similarly, prod(ns(dsumover))==1 is like isempty(dsumover)
+
+% Marginalize the cts parts.
+% If we are in canonical form, we stay that way, since moment form might not exist.
+% Besides, we would like to minimize the number of conversions.
+if sum(ns(csumover)) > 0
+  if bigpot.subtype == 'm'
+    for i=1:bigpot.dsize
+      bigpot.mom{i} = marginalize_pot(bigpot.mom{i}, ckeep);
+    end
+  else
+    for i=1:bigpot.dsize
+      bigpot.can{i} = marginalize_pot(bigpot.can{i}, ckeep);
+    end
+  end
+end
+
+% If we are not marginalizing over any discrete nodes, we are done.
+if prod(ns(dsumover))==1
+  smallpot = cgpot(dkeep, ckeep, ns, bigpot.can, bigpot.mom, bigpot.subtype);
+  return;
+end
+
+% To marginalize the discrete parts, we must be in moment form.
+bigpot = cg_can_to_mom(bigpot);
+
+I = prod(ns(dkeep));
+J = prod(ns(dsumover));
+C = sum(ns(ckeep));
+
+% Reshape bigpot into the form mu1(:,j,i), where i is in dkeep, j is in dsumover
+T1 = zeros(I,J);
+mu1 = zeros(C,J,I);
+Sigma1 = zeros(C,C,J,I);
+sum_map = find_equiv_posns(dsumover, bigpot.ddom);
+keep_map = find_equiv_posns(dkeep, bigpot.ddom);
+iv = zeros(1, length(bigpot.ddom)); % index vector
+for i=1:I
+  keep_iv = ind2subv(ns(dkeep), i);
+  iv(keep_map) = keep_iv;
+  for j=1:J
+    sum_iv = ind2subv(ns(dsumover), j);
+    iv(sum_map) = sum_iv;
+    k = subv2ind(ns(bigpot.ddom), iv);
+    mom = struct(bigpot.mom{k}); % violate object privacy
+    T1(i,j) = exp(mom.logp);
+    if C > 0 % so mu1 and Sigma1 are non-empty
+      mu1(:,j,i) = mom.mu;
+      Sigma1(:,:,j,i) = mom.Sigma;
+    end
+  end
+end
+
+% Collapse the mixture of Gaussians
+coef = mk_stochastic(T1); % coef must be convex combination
+T2 = sum(T1,2);
+T2 = T2 + (T2==0)*eps;
+%if C > 0, disp('collapsing onto '); disp(leep); end
+mu = [];
+Sigma = [];
+mom = cell(1,I);
+for i=1:I
+  if C > 0
+    [mu, Sigma] = collapse_mog(mu1(:,:,i), Sigma1(:,:,:,i), coef(i,:));
+  end
+  logp = log(T2(i));
+  mom{i} = mpot(ckeep, ns(ckeep), logp, mu, Sigma);
+end
+
+smallpot = cgpot(dkeep, ckeep, ns, [], mom, 'm');
+