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, 114 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/cmp_inference_static.m b/sourcecodes/bnt-master/BNT/examples/static/cmp_inference_static.m
new file mode 100644
index 00000000..4eeb22d8
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/cmp_inference_static.m
@@ -0,0 +1,114 @@
+function [time, engine] = cmp_inference_static(bnet, engine, varargin)
+% CMP_INFERENCE Compare several inference engines on a BN
+% function [time, engine] = cmp_inference_static(bnet, engine, ...)
+%
+% engine{i} is the i'th inference engine.
+% time(e) = elapsed time for doing inference with engine e
+%
+% The list below gives optional arguments [default value in brackets].
+%
+% exact - specifies which engines do exact inference [ 1:length(engine) ]
+% singletons_only - if 1, we only call marginal_nodes, else this  and marginal_family [0]
+% maximize - 1 means we do max-propagation, 0 means sum-propagation [0]
+% check_ll - 1 means we check that the log-likelihoods are correct [1]
+% observed - list of the observed ndoes [ bnet.observed ]
+% check_converged - list of loopy engines that should be checked for convergence [ [] ]
+%    If an engine has converged, it is added to the exact list.
+
+
+% set default params
+exact = 1:length(engine);
+singletons_only = 0;
+maximize = 0;
+check_ll = 1;
+observed = bnet.observed;
+check_converged = [];
+
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'exact', exact = args{i+1};
+   case 'singletons_only', singletons_only = args{i+1};
+   case 'maximize', maximize = args{i+1};
+   case 'check_ll', check_ll = args{i+1};
+   case 'observed', observed = args{i+1};
+   case 'check_converged', check_converged = args{i+1};
+   otherwise,
+    error(['unrecognized argument ' args{i}])
+  end
+end
+
+E = length(engine);
+ref = exact(1); % reference
+
+N = length(bnet.dag);
+ev = sample_bnet(bnet);
+evidence = cell(1,N);
+evidence(observed) = ev(observed);
+%celldisp(evidence(observed))
+
+for i=1:E
+  tic;
+  if check_ll
+    [engine{i}, ll(i)] = enter_evidence(engine{i}, evidence, 'maximize', maximize);
+  else
+    engine{i} = enter_evidence(engine{i}, evidence, 'maximize', maximize);
+  end
+  time(i)=toc;
+end
+
+for i=check_converged(:)'
+  niter = loopy_converged(engine{i});
+  if niter > 0
+    fprintf('loopy engine %d  converged in %d iterations\n', i, niter);
+%    exact = myunion(exact, i);
+  else
+    fprintf('loopy engine %d has not converged\n', i);
+  end
+end
+
+cmp = exact(2:end);
+if check_ll
+  for i=cmp(:)'
+    assert(approxeq(ll(ref), ll(i)));
+  end
+end
+
+hnodes = mysetdiff(1:N, observed);
+
+if ~singletons_only
+  get_marginals(engine, hnodes, exact, 0);
+end
+get_marginals(engine, hnodes, exact, 1);
+
+%%%%%%%%%%
+
+function get_marginals(engine, hnodes, exact, singletons)
+
+bnet = bnet_from_engine(engine{1});
+N = length(bnet.dag);
+cnodes_bitv = zeros(1,N);
+cnodes_bitv(bnet.cnodes) = 1;
+ref = exact(1); % reference
+cmp = exact(2:end);
+E = length(engine);
+
+for n=hnodes(:)'
+  for e=1:E
+    if singletons
+      m{e} = marginal_nodes(engine{e}, n);
+    else
+      m{e} = marginal_family(engine{e}, n);
+    end
+  end
+  for e=cmp(:)'
+    if cnodes_bitv(n)
+      assert(approxeq(m{ref}.mu, m{e}.mu))
+      assert(approxeq(m{ref}.Sigma, m{e}.Sigma))
+    else
+      assert(approxeq(m{ref}.T, m{e}.T))
+    end
+    assert(isequal(m{e}.domain, m{ref}.domain));
+  end
+end