From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001
From: ziejd2
Date: Thu, 28 Sep 2017 15:04:40 -0500
Subject: 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
---
 .../BNT/examples/dynamic/cmp_inference_dbn.m       | 100 +++++++++++++++++++++
 1 file changed, 100 insertions(+)
 create mode 100644 sourcecodes/bnt-master/BNT/examples/dynamic/cmp_inference_dbn.m

(limited to 'sourcecodes/bnt-master/BNT/examples/dynamic/cmp_inference_dbn.m')

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/cmp_inference_dbn.m b/sourcecodes/bnt-master/BNT/examples/dynamic/cmp_inference_dbn.m
new file mode 100644
index 00000000..da54095f
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/cmp_inference_dbn.m
@@ -0,0 +1,100 @@
+function [time, engine] = cmp_inference_dbn(bnet, engine, T, varargin)
+% CMP_INFERENCE_DBN Compare several inference engines on a DBN
+% function [time, engine] = cmp_inference_dbn(bnet, engine, T, ...)
+%
+% 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]
+% check_ll - 1 means we check that the log-likelihoods are correct [1]
+
+% set default params
+exact = 1:length(engine);
+singletons_only = 0;
+check_ll = 1;
+onodes = bnet.observed;
+
+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 'check_ll', check_ll = args{i+1};
+   case 'observed', onodes = args{i+1};
+   otherwise,
+    error(['unrecognized argument ' args{i}])
+  end
+end
+
+E = length(engine);
+ref = exact(1); % reference
+
+ss = length(bnet.intra);
+ev = sample_dbn(bnet, 'length', T);
+evidence = cell(ss,T);
+evidence(onodes,:) = ev(onodes, :);
+
+for i=1:E
+  tic;
+  [engine{i}, ll(i)] = enter_evidence(engine{i}, evidence);
+  time(i)=toc;
+  fprintf('engine %d took %6.4f seconds\n', i, time(i));
+end
+
+cmp = mysetdiff(exact, ref);
+if check_ll
+  for i=cmp(:)'
+    if ~approxeq(ll(ref), ll(i))
+      error(['engine ' num2str(i) ' has wrong ll'])
+    end
+  end
+end
+ll
+
+hnodes = mysetdiff(1:ss, onodes);
+
+if ~singletons_only
+  get_marginals(engine, hnodes, exact, 0, T);
+end
+get_marginals(engine, hnodes, exact, 1, T);
+
+%%%%%%%%%%
+
+function get_marginals(engine, hnodes, exact, singletons, T)
+
+bnet = bnet_from_engine(engine{1});
+N = length(bnet.intra);
+cnodes_bitv = zeros(1,N);
+cnodes_bitv(bnet.cnodes) = 1;
+ref = exact(1); % reference
+cmp = exact(2:end);
+E = length(engine);
+m = cell(1,E);
+
+for t=1:T
+  for n=1:N
+  %for n=hnodes(:)'
+    for e=1:E
+      if singletons
+	m{e} = marginal_nodes(engine{e}, n, t);
+      else
+	m{e} = marginal_family(engine{e}, n, t);
+      end
+    end
+    for e=cmp(:)'
+      assert(isequal(m{e}.domain, m{ref}.domain));
+      if cnodes_bitv(n) & isfield(m{e}, 'mu') & isfield(m{ref}, 'mu')
+	wrong = ~approxeq(m{ref}.mu, m{e}.mu) | ~approxeq(m{ref}.Sigma, m{e}.Sigma);
+      else
+	wrong = ~approxeq(m{ref}.T(:), m{e}.T(:));
+      end
+      if wrong
+	error(sprintf('engine %d is wrong; n=%d, t=%d, fam=%d', e, n, t, ~singletons))
+      end
+    end
+  end
+end
-- 
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