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, 68 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/general/solve_limid.m b/sourcecodes/bnt-master/BNT/general/solve_limid.m
new file mode 100644
index 00000000..0009cd7c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/general/solve_limid.m
@@ -0,0 +1,68 @@
+function [strategy, MEU, niter] = solve_limid(engine, varargin)
+% SOLVE_LIMID Find the (locally) optimal strategy for a LIMID
+% [strategy, MEU, niter] = solve_limid(inf_engine, ...)
+%
+% strategy{d} = stochastic policy for node d (a decision node)
+% MEU = maximum expected utility
+% niter = num iterations used
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% [default in brackets]
+%
+% max_iter - max. num. iterations [ 1 ]
+% tol - tolerance required of consecutive MEU values, used to assess convergence [1e-3]
+% order - order in which decision nodes are optimized [ reverse numerical order ]
+%
+% e.g., solve_limid(engine, 'tol', 1e-2, 'max_iter', 10)
+
+bnet = bnet_from_engine(engine);
+
+% default values
+max_iter = 1;
+tol = 1e-3;
+D = bnet.decision_nodes;
+order = D(end:-1:1);
+
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'max_iter', max_iter  = args{i+1}; 
+   case 'tol',      tol = args{i+1}; 
+   case 'order',    order = args{i+1}; 
+   otherwise,  
+    error(['invalid argument name ' args{i}]);       
+  end
+end
+
+CPDs = bnet.CPD;
+ns = bnet.node_sizes;
+N = length(ns);
+evidence = cell(1,N);
+strategy = cell(1, N);
+
+iter = 1;
+converged = 0;
+oldMEU = 0;
+while ~converged & (iter <= max_iter)
+  for d=order(:)'
+    engine = enter_evidence(engine, evidence, 'exclude', d);
+    [m, pot] = marginal_family(engine, d);
+    %pot = marginal_family_pot(engine, d);
+    [policy, score] = upot_to_opt_policy(pot);    
+    e = bnet.equiv_class(d);
+    CPDs{e} = set_fields(CPDs{e}, 'policy', policy);
+    engine = update_engine(engine, CPDs);
+    strategy{d} = policy;
+  end  
+  engine = enter_evidence(engine, evidence);
+  [m, pot] = marginal_nodes(engine, []);
+  %pot = marginal_family_pot(engine, []);
+  [dummy, MEU] = upot_to_opt_policy(pot);    
+  if approxeq(MEU, oldMEU, tol)
+    converged = 1;
+  end
+  oldMEU = MEU;
+  iter = iter + 1;
+end
+niter = iter - 1;