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, 71 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/convert_to_pot.m b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/convert_to_pot.m
new file mode 100644
index 00000000..6afe8d1d
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/convert_to_pot.m
@@ -0,0 +1,71 @@
+function pot = convert_to_pot(CPD, pot_type, domain, evidence)
+% CONVERT_TO_POT Convert a Gaussian CPD to one or more potentials
+% pot = convert_to_pot(CPD, pot_type, domain, evidence)
+
+sz = CPD.sizes;
+ns = zeros(1, max(domain));
+ns(domain) = sz;
+
+odom = domain(~isemptycell(evidence(domain)));
+ps = domain(1:end-1);
+cps = ps(CPD.cps);
+dps = ps(CPD.dps);
+self = domain(end);
+cdom = [cps(:)' self];
+ddom = dps;
+cnodes = cdom;
+  
+switch pot_type
+ case 'u',
+  error('gaussian utility potentials not yet supported');
+ 
+ case 'd',
+  T = convert_to_table(CPD, domain, evidence);
+  ns(odom) = 1;
+  pot = dpot(domain, ns(domain), T);          
+
+ case {'c','g'},
+  [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence);
+  pot = linear_gaussian_to_cpot(m, C, W, domain, ns, cnodes, evidence);
+
+ case 'cg',
+  [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence);
+  % Convert each conditional Gaussian to a canonical potential
+  cobs = myintersect(cdom, odom);
+  dobs = myintersect(ddom, odom);
+  ens = ns; % effective node size
+  ens(cobs) = 0;
+  ens(dobs) = 1;
+  dpsize = prod(ens(dps));
+  can = cell(1, dpsize);
+  for i=1:dpsize
+    if isempty(W)
+      can{i} = linear_gaussian_to_cpot(m(:,i), C(:,:,i), [], cdom, ns, cnodes, evidence);
+    else
+      can{i} = linear_gaussian_to_cpot(m(:,i), C(:,:,i), W(:,:,i), cdom, ns, cnodes, evidence);
+    end
+  end
+  pot = cgpot(ddom, cdom, ens, can);
+
+ case 'scg',
+  [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence);
+  cobs = myintersect(cdom, odom);
+  dobs = myintersect(ddom, odom);
+  ens = ns; % effective node size
+  ens(cobs) = 0;
+  ens(dobs) = 1;
+  dpsize = prod(ens(dps));
+  cpsize = size(W, 2); % cts parents size
+  ss = size(m, 1); % self size
+  cheaddom = self;
+  ctaildom = cps(:)';
+  pot_array = cell(1, dpsize);
+  for i=1:dpsize
+    pot_array{i} = scgcpot(ss, cpsize, 1, m(:,i), W(:,:,i), C(:,:,i));
+  end
+  pot = scgpot(ddom, cheaddom, ctaildom, ens, pot_array);
+
+ otherwise,
+  error(['unrecognized pot_type' pot_type])
+end
+