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, 171 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst_EM.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst_EM.m
new file mode 100644
index 00000000..7dcf531e
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_mwst_EM.m
@@ -0,0 +1,171 @@
+function [bnet0, Sbest, Obest] = learn_struct_mwst_EM(data, discrete, node_sizes, prior, nbloopmax, thresh)
+% LEARN_STRUCT_MWST_EM Learn an oriented tree using the MSWT algorithm
+% [bnet, Ebic] = learn_struct_mwstem(data, discrete, node_sizes, prior,
+% nbloopmax, thresh)
+%
+% Input : 
+%   data{i,m} a cell where the node i in the case m, 
+%   discrete = [ 1 if discret-node 0 if not ],       (1:N) 
+%   node_sizes = 1 if gaussian node,                 (max on complete samples)
+%   prior = 1 to use uniform Dirichlet prior         (0) 
+%   nbloopmax = max loop number                      (ceil(log(N*log(N))))
+%   thresh = the convergence test's threshold        (1e-3)
+%
+% Output :
+%	bnet = the output bayesian network
+%       Ebic = the espected BIC score of bnet given the data
+%
+% francois.olivier.c.h@gmail.com
+
+%%%%%%%%%%%%%
+%fprintf('-- INITIALIZATION\n');
+%%%%%%%%%%%%%
+[N, m]=size(data);
+ rand('state',sum(100*clock))
+
+if nargin<6, thresh = 1e-4; end
+if nargin<5, nbloopmax = 15; end
+if nargin<4, prior = 0; end
+if nargin<3, 
+    misv = -9999;
+    data_mat = bnt_to_mat(data,misv);
+    node_sizes = max(data_mat'), 
+end
+if nargin<2, discrete = ones(1,N); end
+
+nbloop = 1; last=0;
+max_iter = 6; % for learn_struct_params
+
+%%%%%%%%%%%%%
+%fprintf('     Choice of the first bnet                           ');tic
+%%%%%%%%%%%%%
+% Random Chain like DAG
+T = diag(ones(N-1,1),1); T=T+T';
+order=randperm(N); root=randperm(N); root=root(1);
+[tmp order2]=sort(order); order2;
+T2 = full(mk_rooted_tree(T,order2(root)));
+torder=topological_sort(T2(order2,order2));
+[tmp torder2]=sort(torder); torder2;
+ordre = 1:N;torder=ordre;torder2=ordre;order2=ordre;
+%figure(2), subplot(3,2,1), draw_graph(T2(order2,order2));drawnow
+
+bnet0 = mk_bnet(T2(order2(torder),order2(torder)), node_sizes(torder));
+%for i=1:N, bnet0.CPD{i} = tabular_CPD(bnet0, i); end % probleme de log of zeros si tous les cas ne sont pas repr�sent�s dans la base
+for i=1:N, bnet0.CPD{i} = tabular_CPD(bnet0, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end % a priori -> change les espected counts
+%tmp = toc;fprintf('  : %6.2f seconds\n',tmp);
+
+Sbest = -Inf; Bbest = bnet0; fini=0;T3=T2;
+while not(fini)
+    %%%%%%%%%%%%%
+    %fprintf('Loop %d : learning parameters...\n',nbloop);tic
+%    engine0=jtree_sparse_inf_engine(bnet0);  
+    engine0=jtree_inf_engine(bnet0);
+    [bnet1, LL1, engine1] = learn_params_em(engine0, data(torder,:), max_iter, thresh);
+    %tmp = toc;
+    %fprintf('     Parameters learning                                  : %6.2f seconds\n',tmp);
+
+    BIC0=0;
+    for i=1:N, 
+        xxx=struct(bnet1.CPD{i}); 
+        BIC0=BIC0+bic_score_family(xxx.counts, xxx.CPT, xxx.nsamples);
+    end
+    %fprintf('%d ',torder), fprintf('%5.2f\n',BIC0);
+    %figure(2), subplot(3,2,nbloop), title(sprintf('%5.2f',BIC0));
+
+    if BIC0 < Sbest+ thresh*abs(Sbest) | nbloop>nbloopmax
+        fini=1; 
+    else
+        Sbest = BIC0; 
+        Bbest = bnet1;  
+        Obest = torder2 ;
+        Tbest = T3;
+
+    %%%%%%%%%%%%%
+    %tic;
+    %%%%%%%%%%%%%
+
+    theta_Xi=cell(N,1);
+    evidence = cell(1,N);
+    [engine2, loglik] = enter_evidence(engine1, evidence);
+    for j=1:N,
+        SS= marginal_nodes (engine2,torder2(j)); 
+        theta_Xi{j} = SS.T; 
+    end
+
+    theta_Xj_given_Xi = cell(N,N); 
+    for i=1:N
+        for vali = 1:node_sizes(i)
+            evidence = cell(1,N);  evidence{torder2(i)} = vali;
+            [engine2, loglik] = enter_evidence(engine1, evidence);
+            for j=mysetdiff(1:N,i)
+                SS= marginal_nodes (engine2,torder2(j)); 
+                theta_Xj_given_Xi{j,i} = [theta_Xj_given_Xi{j,i}, SS.T];
+            end
+        end
+    end
+    %celldisp(theta_Xi);
+
+    BIC_mat=zeros(N,N);
+    for i=1:N,
+        BIC_mat(i,i)=bic_score_family(theta_Xi{i}*m,theta_Xi{i},m);
+        for j=mysetdiff(1:N,i)
+            theta_XjXi=(ones(node_sizes(i),1)*theta_Xi{j}').*theta_Xj_given_Xi{i,j};
+            BIC_mat(i,j)= bic_score_family(m*theta_XjXi,theta_Xj_given_Xi{i,j},m);
+        end
+    end
+    BIC_delta = BIC_mat-diag(BIC_mat)*ones(1,N);
+
+    BIC1=0;
+    for i = 1:N
+        j = find(bnet0.dag(:,i)==1);
+        if isempty(j)
+            BIC1 = BIC1 + BIC_mat(torder2(i),torder2(i));
+        else
+            BIC1 = BIC1 + BIC_mat(torder2(i),torder2(j));
+        end
+    end
+    %fprintf('%d ',torder), fprintf('%5.2f (1)\n',BIC1);
+
+    %fprintf('     Creation of the score matrix                       ');
+    %tmp = toc;fprintf('  : %6.2f seconds\n',tmp);
+
+        %%%%%%%%%%%%%
+        %fprintf('     Creation of the new bnet                           ');tic
+        %%%%%%%%%%%%%  
+        T2 = minimum_spanning_tree(-BIC_delta);
+        root=randperm(N); root=root(1);
+        T3 = full(mk_rooted_tree(T2, root));
+        torder=topological_sort(T3);
+        [tmp torder2]=sort(torder); torder2;
+
+        bnet0 = mk_bnet(T3(torder,torder), node_sizes(torder));
+%bnet0.order
+        %BIC=0;
+        for i = 1:N
+            j = find(T3(:,i)==1);
+            if isempty(j)
+                bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xi{i}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif');
+                %BIC = BIC + BIC_mat(i,i);
+            else
+                bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xj_given_Xi{i,j}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif');
+                %BIC=BIC + BIC_mat(i,j);
+            end
+        end
+        nbloop=nbloop+1;
+        %figure(2), subplot(3,2,nbloop), draw_graph(T3); drawnow
+        %tmp = toc;fprintf('  : %6.2f seconds\n',tmp);
+        fprintf('================================================================================\n');
+        %fprintf(' --> BIC score = %6.2f\n\n\n',BIC);
+        theta_Xi_best=theta_Xi;
+        theta_Xj_given_Xi_best=theta_Xj_given_Xi;
+    end
+end
+
+%  Bbest.dag
+%  Tbest
+
+%  bnet = mk_bnet(Tbest, node_sizes);
+%  for i=1:N, bnet.CPD{i} = tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end
+%  engine=jtree_sparse_inf_engine(bnet);
+%  [Bbest, LL1] = learn_params_em(engine, data, max_iter, thresh);
+