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, 48 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/general/mk_fgraph_given_ev.m b/sourcecodes/bnt-master/BNT/general/mk_fgraph_given_ev.m
new file mode 100644
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+++ b/sourcecodes/bnt-master/BNT/general/mk_fgraph_given_ev.m
@@ -0,0 +1,48 @@
+function fg = mk_fgraph_given_ev(G, node_sizes, factors, ev_CPD, evidence, varargin)
+% MK_FGRAPH_GIVEN_EV Make a factor graph where each node has its own private evidence term
+% fg = mk_fgraph(G, node_sizes, factors, ev_CPD, evidence, ...)
+%
+% G, node_sizes and factors are as in mk_fgraph, but they refer to the hidden nodes.
+% ev_CPD{i} is a CPD for the i'th hidden node; this will be converted into a factor
+% for node i using evidence{i}.
+% We currently assume all hidden nodes are discrete, for simplicity.
+%
+% The list below gives optional arguments [default value in brackets].
+% 
+% equiv_class - equiv_class(i)=j  means factor node i gets its params from factors{j} [1:F]
+% ev_equiv_class - ev_equiv_class(i)=j  means evidence node i gets its params from ev_CPD{j} [1:N]
+
+
+N = length(node_sizes);
+nfactors = length(factors);
+
+% default values for parameters
+eclass = 1:nfactors;
+ev_eclass = 1:N;
+
+if nargin >= 6
+  args = varargin;
+  nargs = length(args);
+  for i=1:2:nargs
+    switch args{i},
+     case 'equiv_class', eclass = args{i+1}; 
+     case 'ev_equiv_class', ev_eclass = args{i+1}; 
+     otherwise,  
+      error(['invalid argument name ' args{i}]);       
+    end
+  end
+end
+
+pot_type = 'd';
+for x=1:N
+  ev = cell(1,2); % cell 1 is the hidden parent, cell 2 is the observed child
+  ev(2) = evidence(x); 
+  dom = 1:2;
+  F = convert_to_pot(ev_CPD{ev_eclass(x)}, pot_type, dom(:), ev);
+  M = pot_to_marginal(F);
+  %factors{end+1} = tabular_CPD('self', 1, 'ps', [], 'sz', node_sizes(x), 'CPT', M.T);
+  factors{end+1} = mk_isolated_tabular_CPD(node_sizes(x), {'CPT', M.T});
+end
+
+E = max(eclass);
+fg = mk_fgraph([G eye(N)], node_sizes, factors, 'equiv_class', [eclass E+1:E+N]);