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, 104 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/misc/gener_MAR_net.m b/sourcecodes/bnt-master/SLP/misc/gener_MAR_net.m
new file mode 100644
index 00000000..64740076
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/misc/gener_MAR_net.m
@@ -0,0 +1,104 @@
+function bnet_miss = gener_MAR_net(bnet_orig, base_proba)
+% function bnet_miss = gener_MAR_net(bnet_orig, base_proba)
+% 
+%   bnet_orig : a bnet
+%   base_proba :  a probability for value to be missing
+%
+%   bnet_miss : a bnet that could be used in gener_data_from_bnet_miss function
+%               to generate incomplete MAR dataset
+%
+% Francois.Olivier.C.H@gmail.com
+
+%%%%%%%%%%%% INIT
+if nargin<2, error('Not enougth arguments'); end
+
+% création du réseau
+dag = bnet_orig.dag;
+N = size(dag,2);
+ns = bnet_orig.node_sizes;
+
+  ns_miss = zeros(1,3*N);
+  ns_miss(1:N) = ns;
+  ns_miss(N+1:2*N) = 2*ones(1,N); % 1= node i-N present, 2= node i-N missing
+  ns_miss(2*N+1:3*N) = ns+1; % 1:ns, absent
+
+  dag_miss = zeros(3*N,3*N);
+  dag_miss(1:N,1:N) = dag;
+%  dag_miss(2*N+1:3*N,N+1:2*N)=mk_rnd_dag(N,N-ceil(rand*N/2)); 
+  lim = 1+(rand>.4)+(rand>.65)+(rand>.9);
+  dag_miss(2*N+1:3*N,N+1:2*N)=mk_rnd_dag(N,lim); 
+
+  for i=1:N, dag_miss(i,2*N+i)=1; dag_miss(N+i,2*N+i)=1; dag_miss(2*N+i,i)=0; dag_miss(2*N+i,N+i)=0; end
+
+  bnet_miss = mk_bnet(dag_miss, ns_miss);
+  CPT = CPT_from_bnet(bnet_orig, 0);
+  for i=1:N
+    bnet_miss.CPD{i} = tabular_CPD (bnet_miss, i, CPT{i});
+  end
+
+  % CPD of nodes M
+  for i=1:N,
+     if find(bnet_miss.order==i)<find(bnet_miss.order==N+i),
+        CPT_M=[];
+        for j=1:ns_miss(i), for l=1:ns_miss(N+i), for k=1:ns_miss(2*N+i),
+          CPT_M=[CPT_M (((j==k)&(l==1))|((k==ns_miss(2*N+i))&(l==2)))];
+        end, end, end
+     else 
+        CPT_M=[];
+        for k=1:ns_miss(2*N+i), for l=1:ns_miss(N+i), for j=1:ns_miss(i), 
+          CPT_M=[CPT_M (((j==k)&(l==1))|((k==ns_miss(2*N+i))&(l==2)))];
+        end, end, end
+     end
+     bnet_miss.CPD{2*N+i} = tabular_CPD (bnet_miss, 2*N+i, CPT_M);
+  end
+
+%%%%%%%%%%%% Base probability of missing value
+p = base_proba;
+ for i=1:N
+  fam = find(dag_miss(:,N+i)==1)';
+  semisize = prod(ns_miss(fam)); % as node N+i is binary to say i is present or missing
+  CPT = zeros(1,2*semisize);
+  CPT(1:semisize) = 1-p;
+  CPT(semisize+1:2*semisize) = p;
+  bnet_miss.CPD{N+i} = tabular_CPD (bnet_miss, N+i, CPT);
+ end
+
+   BETA = gener_discrete_dist(N, base_proba);
+
+   order=[];
+   missdagtmp = bnet_miss.dag; %(N+1:2*N,N+1:2*N);
+   unprocessed = 1:N;
+   while ~isempty(unprocessed)
+     npar=[];
+     for i=N+1:2*N, npar(end+1)=length(parents(missdagtmp,i));end,  % to be verifie from here
+     [npar, ord] = sort(npar);
+     while ~ismember(ord(1),unprocessed)
+       ord=ord(2:end);
+     end
+     order = [order, ord(1)];
+     missdagtmp(ord(1),:)=0;
+     unprocessed = mysetdiff(unprocessed,ord(1));
+   end
+
+%%%%%%%%%%%% Update CPT with MCAR process
+for i=1:length(BETA)
+  fam_miss = find(dag_miss(:,N+order(i))==1)';
+  p=BETA(i);
+  semisize = prod(ns_miss(fam_miss));
+
+  if isempty(fam_miss),
+        CPT = zeros(1,2*semisize);
+        CPT(1:semisize) = 1-p;
+        CPT(semisize+1:2*semisize) = p;
+        bnet_miss.CPD{N+order(i)} = tabular_CPD (bnet_miss, N+order(i), CPT);
+  else
+
+        MUi1k = gener_discrete_dist(semisize, p);
+        CPT = zeros(1,2*semisize);
+        CPT(1:semisize) = 1-MUi1k;
+        CPT(semisize+1:2*semisize) = MUi1k;
+        bnet_miss.CPD{N+order(i)} = tabular_CPD (bnet_miss, N+order(i), CPT);
+
+  end
+
+end