LearnFDAParameters

 [Tables] = LearnFDAParameters(Cliques,SelPop,NumbVar,Card)
 LearnFDAParameter: The parameters of the factorized  model represented in Cliques 
                    are learned from data
 INPUTS
 Cliques: Structure of the model in a list of cliques that defines the junction graph. 
          Each row of Cliques is a clique. The first value is the number of overlapping variables. 
          The second, is the number of new variables.
          Then, overlapping variables are listed and  finally new variables are listed.
 NumbVar: Number of variables
 Card: Vector with the dimension of all the variables. 
 SelPop:  Population from which the model is learned         
 OUTPUTS
 Tables: Probability tables for each variable conditioned on its neighbors

 Last version 8/26/2008. Roberto Santana and Siddarta Shakya (roberto.santana@ehu.es)

0001 function[Tables] = LearnFDAParameters(Cliques,SelPop,NumbVar,Card)
0002 % [Tables] = LearnFDAParameters(Cliques,SelPop,NumbVar,Card)
0003 % LearnFDAParameter: The parameters of the factorized  model represented in Cliques
0004 %                    are learned from data
0005 % INPUTS
0006 % Cliques: Structure of the model in a list of cliques that defines the junction graph.
0007 %          Each row of Cliques is a clique. The first value is the number of overlapping variables.
0008 %          The second, is the number of new variables.
0009 %          Then, overlapping variables are listed and  finally new variables are listed.
0010 % NumbVar: Number of variables
0011 % Card: Vector with the dimension of all the variables.
0012 % SelPop:  Population from which the model is learned
0013 % OUTPUTS
0014 % Tables: Probability tables for each variable conditioned on its neighbors
0015 %
0016 % Last version 8/26/2008. Roberto Santana and Siddarta Shakya (roberto.santana@ehu.es)
0017 
0018 
0019 NumberCliques = size(Cliques,1);
0020 
0021 
0022 %%%%%%%%%%%%%%%%%%%%%%  First step  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0023 
0024 % The tables of all the cliques are filled
0025 
0026 
0027 for i=1:size(Cliques,1)
0028 
0029   sizeCliqOther = Cliques(i,2);
0030   sizeCliqSolap = Cliques(i,1);
0031 
0032   CliqOther = Cliques(i,Cliques(i,1)+3:Cliques(i,1)+Cliques(i,2)+2);
0033   AccCardOther = FindAccCard(sizeCliqOther,Card(CliqOther));
0034   dimOther =   NumconvertCard(Card(CliqOther)-1,sizeCliqOther,AccCardOther)+1;
0035 
0036   if(sizeCliqSolap > 0)
0037     CliqSolap = Cliques(i,3:(Cliques(i,1)+2));
0038     AccCardSolap = FindAccCard(sizeCliqSolap,Card(CliqSolap));
0039     dimSolap =   NumconvertCard(Card(CliqSolap)-1,sizeCliqSolap,AccCardSolap)+1;
0040     aux=zeros(dimSolap,dimOther);
0041   else 
0042     AccCardSolap = [];
0043     CliqSolap = [];
0044     aux=zeros(1,dimOther);
0045     dimSolap = 1;
0046   end
0047    
0048   AllVars = [CliqSolap,CliqOther];
0049 
0050  
0051  for j=1:dimSolap
0052     if (sizeCliqSolap>0) 
0053       solapval = IndexconvertCard(j-1,sizeCliqSolap,AccCardSolap);
0054     else
0055       solapval=[];
0056     end
0057 
0058     for k=1:dimOther
0059      auxSelPop=SelPop(:,[CliqSolap,CliqOther]);
0060      otherval =  IndexconvertCard(k-1,sizeCliqOther,AccCardOther);
0061      allvarvalues = [solapval,otherval];
0062     
0063      if(size(allvarvalues,2)==1)
0064        aux(j,k) =  sum((auxSelPop==repmat(allvarvalues,size(SelPop,1),1))');
0065      else 
0066        aux(j,k)=sum( sum((auxSelPop==repmat(allvarvalues,size(SelPop,1),1))') == size(allvarvalues,2)); 
0067      end
0068     end 
0069    %aux(j,:) = (aux(j,:))/(sum(aux(j,:)));
0070    aux(j,:) = (aux(j,:)+1)/(sum(aux(j,:))+dimOther); % Laplace Estimator
0071   end
0072  %aux=aux/sum(sum(aux)); % Normalization
0073  
0074  
0075  % En Table i se guardan las probabilidades del clique i
0076 
0077  eval(['Tables{',num2str(i),'}=aux;']);
0078          
0079 end
0080