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, 58 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/CPD_to_scgpot.m b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/CPD_to_scgpot.m
new file mode 100644
index 00000000..90e7cc80
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/CPD_to_scgpot.m
@@ -0,0 +1,58 @@
+function pot = CPD_to_scgpot(CPD, domain, ns, cnodes, evidence)
+% CPD_TO_CGPOT Convert a Gaussian CPD to a CG potential, incorporating any evidence   
+% pot = CPD_to_cgpot(CPD, domain, ns, cnodes, evidence)
+
+self = CPD.self;
+dnodes = mysetdiff(1:length(ns), cnodes);
+odom = domain(~isemptycell(evidence(domain)));
+cdom = myintersect(cnodes, domain);
+cheaddom = myintersect(self, domain);
+ctaildom = mysetdiff(cdom,cheaddom);
+ddom = myintersect(dnodes, domain);
+cobs = myintersect(cdom, odom);
+dobs = myintersect(ddom, odom);
+ens = ns; % effective node size
+ens(cobs) = 0;
+ens(dobs) = 1;
+
+% Extract the params compatible with the observations (if any) on the discrete parents (if any)
+% parents are all but the last domain element
+ps = domain(1:end-1);
+dps = myintersect(ps, ddom);
+dops = myintersect(dps, odom);
+
+map = find_equiv_posns(dops, dps);
+dpvals = cat(1, evidence{dops});
+index = mk_multi_index(length(dps), map, dpvals);
+
+dpsize = prod(ens(dps));
+cpsize = size(CPD.weights(:,:,1), 2); % cts parents size
+ss = size(CPD.mean, 1); % self size
+% the reshape acts like a squeeze
+m = reshape(CPD.mean(:, index{:}), [ss dpsize]);
+C = reshape(CPD.cov(:, :, index{:}), [ss ss dpsize]);
+W = reshape(CPD.weights(:, :, index{:}), [ss cpsize dpsize]);
+
+
+% Convert each conditional Gaussian to a canonical potential
+pot = cell(1, dpsize);
+for i=1:dpsize
+  %pot{i} = linear_gaussian_to_scgcpot(m(:,i), C(:,:,i), W(:,:,i), cdom, ns, cnodes, evidence);
+  pot{i} = scgcpot(ss, cpsize, 1, m(:,i), W(:,:,i), C(:,:,i));
+end
+
+pot = scgpot(ddom, cheaddom, ctaildom, ens, pot);
+
+
+function pot = linear_gaussian_to_scgcpot(mu, Sigma, W, domain, ns, cnodes, evidence)
+% LINEAR_GAUSSIAN_TO_CPOT Convert a linear Gaussian CPD  to a stable conditional potential element.
+% pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence)
+
+p = 1;
+A = mu;
+B = W;
+C = Sigma;
+ns(odom) = 0;
+%pot = scgcpot(, ns(domain), p, A, B, C);
+
+