LearnMargProdModel

 [model] = LearnMargProdModel(k,NumbVar,Card,SelPop,AuxFunVal,learning_params)
 LearnMargProdModel:     Learns a marginal product model using affinity
                         propagation on the matrix of mutual information
 INPUTS
 k: Current generation
 NumbVar: Number of variables
 Card: Vector with the dimension of all the variables. 
 SelPop:  Population from which the model is learned 
 AuxFunVal: Evaluation of the data set (required for some learning algorithms, not for this one)
 learning_params{1}(1) = sizeconstraint: Maximum size of the cliques in
                                         the factorization 
 OUTPUTS
 model: Markov network model containing the structure (model{1} = Cliques)
        and the parameters (model{2} = Tables)

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

0001 function [model] = LearnMargProdModel(k,NumbVar,Card,SelPop,AuxFunVal,learning_params)
0002 % [model] = LearnMargProdModel(k,NumbVar,Card,SelPop,AuxFunVal,learning_params)
0003 % LearnMargProdModel:     Learns a marginal product model using affinity
0004 %                         propagation on the matrix of mutual information
0005 % INPUTS
0006 % k: Current generation
0007 % NumbVar: Number of variables
0008 % Card: Vector with the dimension of all the variables.
0009 % SelPop:  Population from which the model is learned
0010 % AuxFunVal: Evaluation of the data set (required for some learning algorithms, not for this one)
0011 % learning_params{1}(1) = sizeconstraint: Maximum size of the cliques in
0012 %                                         the factorization
0013 % OUTPUTS
0014 % model: Markov network model containing the structure (model{1} = Cliques)
0015 %        and the parameters (model{2} = Tables)
0016 %
0017 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)
0018 
0019         sizeconstraint =  cell2num(learning_params{1}(1)); 
0020 
0021         % Univariate and Bivariate probabilities are learned
0022         [UnivProb,BivProb]= FindMargProb(SelPop,NumbVar,Card);
0023        
0024        % The Matrix of Mutual information is learned
0025          MI = IntMutualInfFromMargProb(NumbVar,Card,UnivProb,BivProb);
0026 
0027          %[idx,cent] = kmeans(MI,NumbVar/3,'Distance','correlation');
0028          
0029          
0030          % The cliques are learned from the matrix of mutual information
0031          auxperm = randperm(NumbVar);
0032          [Cliques,auxval]=FactAffinityElim(MI(auxperm,auxperm),auxperm,sizeconstraint,median(MI(auxperm)),1);
0033          %[Cliques,auxval]=FactAffinityElim(MI,[1:NumbVar],sizeconstraint,median(MI),1)
0034  
0035          % The parameters of the model are learned
0036          [Tables] = LearnFDAParameters(Cliques,SelPop,NumbVar,Card);
0037          
0038        
0039      model{1} = Cliques;
0040      model{2} = Tables;
0041      
0042      return;
0043        
0044 
0045