Merge pull request #1 from ziejd2/octave_php_separate

Octave php separate

1 files changed, 165 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/SLP/misc/gener_MCAR_net.m b/sourcecodes/bnt-master/SLP/misc/gener_MCAR_net.m
new file mode 100644
index 00000000..5e852a23
--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/misc/gener_MCAR_net.m
@@ -0,0 +1,165 @@
+function bnet_miss = gener_MCAR_net(bnet_orig, base_proba)
+% function bnet_miss = gener_MCAR_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 MCAR dataset
+%
+% Francois.Olivier.C.H@gmail.com
+
+%%%%%%%%%%%% INIT
+%bnet_miss = gener_MCAR_net(bnet_orig, base_proba, upd, bnet_biss)
+%if nargin<4, upd = 0; else upd = 1; end
+%if nargin<3, manual = 0; end
+if nargin<2, error('Not enougth arguments'); end
+
+% création du réseau
+dag = bnet_orig.dag;
+N = size(dag,2);
+ns = bnet_orig.node_sizes;
+
+%if nargin<4,
+  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(N+1:2*N,N+1:2*N)=mk_rnd_dag(N,N-ceil(rand*N/2)); %dag_miss(N+1:2*N,N+1:2*N)=mk_rnd_dag(N,2);
+  for i=1:N, dag_miss(i,2*N+i)=1; dag_miss(N+i,2*N+i)=1; 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
+%  else
+%    ns_miss = bnet_miss.node_sizes;
+%    dag_miss = bnet_miss.dag;
+%  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
+
+%  if manual, % manual generation
+%  
+%  if upd==1;
+%  
+%   fprintf('Base probability of a data to be missing is %1.4f',base_proba);
+%  
+%   b=-1;
+%   while ~(b==0 | b==1), b = input('Would you like to change a probability of a node to be missing (1 for yes, 0 for no) ?   '); end
+%  
+%   %%%%%%%%%%%% Update CPT with MCAR process
+%   while b
+%    fprintf('Nodes are from 1 to %d. ',N);
+%    i=0;
+%    while i<1 | i>N | round(i)~=i, i = input('Which node ?   '); end
+%    fam = find(dag(:,i)==1)'; 
+%    fam_miss = find(dag_miss(:,N+i)==1)';
+%    cas = -ones(1, length(fam_miss)+1);
+%    familly = [fam_miss, N+i];
+%        p=-1;
+%        while p<0 | p>1, p = input(' - A priori probability for this node to be missing ?   ');end
+%        semisize = prod(ns_miss(fam_miss));
+%        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);
+%    b=-1;
+%    while ~(b==0 | b==1), b = input('Would you like to change a probability of a node to be missing (1 for yes, 0 for no) ?   ');end
+%   end
+%  end
+%  
+%  
+%  else % automatic generation
+
+%%%%%%%%%%%% creating BETAs
+%      %% To use multiple of 5 percent in probs
+%      if N<=25,
+%          BETA = gener_problist(base_proba, N);
+%      else
+%          nboucles = floor(N/25);
+%          BETA = [];
+%          for i=1:nboucles
+%              BETA1 = gener_problist(base_proba, 25);
+%              BETA = [BETA, BETA1];
+%          end
+%          nreste = rem(N,25);
+%          BETA1 = gener_problist(base_proba, nreste);
+%          BETA = [BETA, BETA1];
+%      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=1:N, npar(end+1)=length(parents(missdagtmp,i));end,
+     [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
+
+    %node = N+order(i)
+    %for k=1:semisize
+    %  XI(k) = eval_xi(bnet_miss, N+order(i), k);
+    %  %XI(k+semisize)=1-XI(k);
+    %end
+    %MUi1 = zeros(1,semisize);
+    %MUi1 = gener_mu(p, semisize, XI);
+
+        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
+end
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