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, 119 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m
new file mode 100644
index 00000000..4e054800
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_gs2.m
@@ -0,0 +1,119 @@
+function [dag, best_score, cache] = learn_struct_gs2(data, nodesizes, seeddag, varargin)
+%
+% LEARN_STRUCT_GS2(data,seeddag) learns a structure of Bayesian net by Greedy Search.
+% dag = learn_struct_gs(data, nodesizes, seeddag)
+%
+% dag: the final structure 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
+% cache : data structure used to memorize local score computations 
+%   (cf. SCORE_INIT_CACHE function)
+%
+% by Gang Li @ Deakin University (gli73@hotmail.com)
+% (use mk_nbrs_of_dag_topo, developped by Wei Hu, instead of mk_nbrs_of_dag)
+% (Caching implementation : ofrancois.olivier.c.h@gmail.com, philippe.leray@univ-nantes.fr)
+%
+
+[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';
+cache=[];
+
+% 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');
+    		case 'cache',  cache=args{i+1} ;
+    		end;
+    	end;
+    end;
+end;
+
+done = 0;
+[best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache);
+while ~done
+    [dags,op,nodes] = mk_nbrs_of_dag_topo(seeddag);
+    nbrs = length(dags);
+    [scores cache] = score_dags(data, nodesizes, dags,'scoring_fn',scoring_fn,'cache',cache);
+    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 cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache);
+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 cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache);
+                    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 cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache);
+                   if temp_score > best_score
+                       seeddag = tempdag;
+                       best_score = temp_score;
+                       innercount = innercount +1;
+                   else
+                       tempdag = seeddag;
+                       tempdag(i,j) = 0;
+                       [temp_score cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache);
+                       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 cache] = score_dags(data,nodesizes, {tempdag},'scoring_fn',scoring_fn,'cache',cache);
+                   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;