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/SLP/learning/learn_struct_gs.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs.m
new file mode 100644
index 00000000..3dd3ef1c
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs.m
@@ -0,0 +1,114 @@
+function [dag,best_score] = learn_struct_gs(data, nodesizes, seeddag, varargin)
+%
+% LEARN_STRUCT_GS(data,seeddag) learns a structure of Bayesian net by Greedy Search.
+% dag = learn_struct_gs(data, nodesizes, seeddag)
+%
+% dag: the final structurre matrix
+% Data : training data, data(i,m) is the m obsevation of node i
+% Nodesizes: the size array of different nodes
+% seeddag: given seed Dag for hill climbing, optional
+%
+%
+% by Gang Li @ Deakin University (gli73@hotmail.com)
+
+[N ncases] = size(data);
+if (nargin < 3 ) 
+    seeddag = zeros(N,N); % mk_rnd_dag(N); %call BNT function
+elseif ~acyclic(seeddag)
+    seeddag = mk_rnd_dag(N); %zeros(N,N);
+end;
+
+% set default params
+scoring_fn = 'bic';
+verbose  = 'yes';
+
+% get params
+args = varargin;
+nargs = length(args);
+if length(args) > 0
+    if isstr(args{1})
+    	for i = 1:2:nargs
+    		switch args{i}
+    		case 'scoring_fn', scoring_fn = args{i+1};
+    		case 'verbose',  verbose  = strcmp(args{i+1},'yes');
+    		end;
+    	end;
+    end;
+end;
+
+done = 0;
+best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn);
+while ~done
+    [dags,op,nodes] = mk_nbrs_of_dag(seeddag);
+    nbrs = length(dags);
+    scores = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn);
+    max_score = max(scores);
+    new = find(scores == max_score );
+    if ~isempty(new) & (max_score > best_score)
+        p = sample_discrete(normalise(ones(1, length(new))));
+        best_score = max_score;
+        seeddag = dags{new(p)};
+    else
+        done = 1;
+    end;
+end;
+
+dag = seeddag;
+
+outcount = 0; 
+best_score = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn);
+while outcount < 2
+    innercount = 0;
+    for i=1:N
+        for j=1:N
+           if i==j, continue;    end;
+           if seeddag(i,j) == 0  % No edge i-->j, then try to add it
+               tempdag = seeddag;
+               tempdag(i,j) = 1;
+               if acyclic(tempdag)
+                    temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn);
+                    if temp_score > best_score
+                        seeddag = tempdag;
+                        best_score= temp_score;
+                        innercount = innercount +1;
+                    end;
+               end
+           else  % exists edge i--j, then try reverse it or remove it
+               tempdag = seeddag;
+               tempdag(i,j) = 0; tempdag(j,i) = 1; 
+               if acyclic(tempdag)
+                   temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn);
+                   if temp_score > best_score
+                       seeddag = tempdag;
+                       best_score = temp_score;
+                       innercount = innercount +1;
+                   else
+                       tempdag = seeddag;
+                       tempdag(i,j) = 0;
+                       temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn);
+                       if temp_score > best_score
+                           seeddag = tempdag;
+                           best_score= temp_score;
+                           innercount = innercount +1;
+                       end;
+                   end;
+               else
+                   tempdag = seeddag;
+                   tempdag(i,j)=0;
+                   temp_score = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn);
+                   if temp_score > best_score
+                       seeddag = tempdag;
+                       best_score= temp_score;
+                       innercount = innercount +1;
+                   end;
+               end;
+           end;
+        end; % end for j
+    end; % end for i
+    if innercount == 0
+        outcount = outcount +1;
+    end;
+end;  % end while
+
+dag = seeddag;
+