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, 89 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/cmp_learning_dbn.m b/sourcecodes/bnt-master/BNT/examples/dynamic/cmp_learning_dbn.m
new file mode 100644
index 00000000..d6138f9d
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/cmp_learning_dbn.m
@@ -0,0 +1,89 @@
+function [time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, varargin)
+% CMP_LEARNING_DBN Compare a bunch of inference engines by learning a DBN
+% function [time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, exact, T, ncases, max_iter)
+%
+% engine{i} is the i'th inference engine.
+% time(e) = elapsed time for doing inference with engine e
+% CPD{e,c} is the learned CPD for eclass c in engine e
+% LL{e} is the learning curve for engine e
+% cases{i} is the i'th training case
+%
+% The list below gives optional arguments [default value in brackets].
+%
+% exact - specifies which engines do exact inference [ 1:length(engine) ]
+% check_ll - 1 means we check that the log-likelihoods are correct [1]
+% ncases - num. random training cases [2]
+% max_iter - max. num EM iterations [2]
+
+% set default params
+exact = 1:length(engine);
+check_ll = 1;
+ncases = 2;
+max_iter = 2;
+
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'exact', exact = args{i+1};
+   case 'check_ll', check_ll = args{i+1};
+   case 'ncases', ncases = args{i+1};
+   case 'max_iter', max_iter = args{i+1};
+   otherwise,
+    error(['unrecognized argument ' args{i}])
+  end
+end
+
+E = length(engine);
+ss = length(bnet.intra);
+onodes = bnet.observed;
+
+cases = cell(1, ncases);
+for i=1:ncases
+  ev = sample_dbn(bnet, 'length', T);
+  cases{i} = cell(ss,T);
+  cases{i}(onodes,:) = ev(onodes, :);
+end
+
+LL = cell(1,E);
+time = zeros(1,E);
+for i=1:E
+  tic
+  [bnet2{i}, LL{i}] = learn_params_dbn_em(engine{i}, cases, 'max_iter', max_iter);
+  time(i) = toc;
+  fprintf('engine %d took %6.4f seconds\n', i, time(i));
+end
+
+ref = exact(1); % reference
+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
+
+nCPDs = length(bnet.CPD);
+CPD = cell(E, nCPDs);
+tabular = zeros(1, nCPDs);
+for i=1:E
+  temp = bnet2{i};
+  for c=1:nCPDs
+    tabular(c) = isa(temp.CPD{c}, 'tabular_CPD');
+    CPD{i,c} = struct(temp.CPD{c});
+  end
+end
+
+for i=cmp(:)'
+  for c=1:nCPDs
+    if tabular(c)
+      assert(approxeq(CPD{i,c}.CPT, CPD{ref,c}.CPT));
+    else
+      assert(approxeq(CPD{i,c}.mean, CPD{ref,c}.mean));
+      assert(approxeq(CPD{i,c}.cov, CPD{ref,c}.cov));
+      assert(approxeq(CPD{i,c}.weights, CPD{ref,c}.weights));
+    end
+  end
+end
+