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-master/SLP/learning/learn_struct_ges.m     | 122 +++++++++++++++++++++
 1 file changed, 122 insertions(+)
 create mode 100644 sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m

(limited to 'sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m')

diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m
new file mode 100644
index 00000000..e985195e
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_ges.m
@@ -0,0 +1,122 @@
+function [cpdag, best_score, cache] = learn_struct_ges(data, nodesizes, varargin)
+%
+% LEARN_STRUCT_GES learns a structure of Bayesian net by Greedy Equivalence Search.
+% cpdag = learn_struct_ges(Data, Nodesizes, 'cache', cache, 'scoring_fn', 'bic', 'verbose', 'yes')
+%
+% cpdag: the final cpdag
+% Data : training data, data(i,m) is the m obsevation of node i
+% Nodesizes: the size array of different nodes
+% cache : data structure used to memorize local score computations
+%   (cf. SCORE_INIT_CACHE function)
+%
+% V1.1 : 28 july 2003 (Ph. Leray - philippe.leray@univ-nantes.fr, O. francois - francois.olivier.c.h@gmail.com)
+%
+% Ref:
+%   Optimal Structure Identification with Greedy Search, Chickering 2002
+%
+
+[N ncases] = size(data);
+seeddag = zeros(N,N);
+
+% set default params
+scoring_fn = 'bayesian';
+verbose  = 0;
+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;
+
+if verbose
+    names=cellstr(int2str((1:N)'));
+    carre=zeros(N,1);
+end
+
+done = 0;
+[best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache);
+cptt=0;
+
+% First step : INSERT
+while ~done
+    cptt=cptt+1;
+    [pdags,nodes] = mk_nbrs_of_pdag_add(seeddag);
+    seedold=seeddag;
+    sold=best_score;
+    nbrs = length(pdags);
+    dags=pdag_to_dag(pdags);
+    [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 = dag_to_cpdag(dags{new(p)});
+        new=new(p);
+        if verbose
+            figure;
+            subplot(1,2,1), [xx yy]=draw_graph(seedold,names,carre);  
+            set(gca,'color',[1 1 0]); 
+            title(sprintf('current CPDAG (Smax=%5.2f)',sold));
+            subplot(1,2,2), draw_graph(seeddag,names,carre,xx,yy);     
+            s=sprintf(' %d',nodes{new,3});
+            title([sprintf('Best in N+ = INSERT(%d, %d,',nodes{new,1},nodes{new,2}) s ')' sprintf('  S=%5.2f',max_score)]);
+            drawnow;
+        end
+
+    else
+        done = 1;
+    end
+
+end;
+
+done = 0;
+%[best_score cache] = score_dags(data,nodesizes, {seeddag},'scoring_fn',scoring_fn,'cache',cache);
+cptt=0;
+
+if sum(sum(seeddag))==0, done=1;end
+
+% Second step : DELETE
+while ~done
+    cptt=cptt+1;
+    [pdags,nodes] = mk_nbrs_of_pdag_del(seeddag);
+    seedold=seeddag; sold=best_score;
+    nbrs = length(pdags);
+    dags=pdag_to_dag(pdags);
+    [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 = dag_to_cpdag(dags{new(p)});
+        new=new(p);
+        if verbose
+            cpdags=dag_to_cpdag(dags);
+            figure; 
+            subplot(1,2,1), [xx yy]=draw_graph(seedold,names,carre);  
+            set(gca,'color',[1 1 0]); 
+            title(sprintf('current CPDAG (Smax=%5.2f)',best_score));
+            subplot(1,2,2), draw_graph(seeddag,names,carre,xx,yy);     
+            s=sprintf('%d',nodes{new,3});
+            title([sprintf('Best in N- = DELETE(%d, %d,',nodes{new,1},nodes{new,2}) s ')' sprintf('  S=%5.2f',max_score)]);
+            drawnow;
+        end
+
+    else
+        done = 1;
+    end
+
+end
+
+cpdag = seeddag;
-- 
cgit 1.4.1