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, 44 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@discrete_CPD/Old/convert_to_pot.m b/sourcecodes/bnt-master/BNT/CPDs/@discrete_CPD/Old/convert_to_pot.m
new file mode 100644
index 00000000..3f178e1c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@discrete_CPD/Old/convert_to_pot.m
@@ -0,0 +1,44 @@
+function pot = convert_to_pot(CPD, pot_type, domain, evidence)
+% CONVERT_TO_POT Convert a tabular CPD to one or more potentials
+% pots = convert_to_pot(CPD, pot_type, domain, evidence)
+%
+% pots{i} = CPD evaluated using evidence(domain(:,i))
+% If 'domains' is a single row vector, pots will be an object, not a cell array.
+
+ncases = size(domain,2);
+assert(ncases==1); % not yet vectorized
+
+sz = dom_sizes(CPD);
+ns = zeros(1, max(domain));
+ns(domain) = sz;
+
+local_ev = evidence(domain);
+obs_bitv = ~isemptycell(local_ev);
+odom = domain(obs_bitv);
+T = convert_to_table(CPD, domain, local_ev, obs_bitv);
+
+switch pot_type
+ case 'u',
+  pot = upot(domain, sz, T, 0*myones(sz));  
+ case 'd',
+  ns(odom) = 1;
+  pot = dpot(domain, ns(domain), T);          
+ case {'c','g'},
+  % Since we want the output to be a Gaussian, the whole family must be observed.
+  % In other words, the potential is really just a constant.
+  p = T;
+  %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1)));
+  ns(domain) = 0;
+  pot = cpot(domain, ns(domain), log(p));       
+ case 'cg',
+  T = T(:);
+  ns(odom) = 1;
+  can = cell(1, length(T));
+  for i=1:length(T)
+    can{i} = cpot([], [], log(T(i)));
+  end
+  pot = cgpot(domain, [], ns, can);   
+ otherwise,
+  error(['unrecognized pot type ' pot_type])
+end
+