LearnFDA

 [model] = LearnFDA(k,NumbVar,Card,SelPop,AuxFunVal,learning_params)
 LearnFDA:  Creates a factorized model from the structure 
 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) = Cliques
    Each row of Cliques is a clique. The first value is the number of neighbor for variable i. 
    The second, is the number of new variables (one new variable, i).
    Then, neighbor variables are listed and  finally new variables
    (variable i) are listed
 OUTPUTS
 model: Markov network model containing the structure (model{1} = Cliques)
        and the parameters (model{2} = Tables)
        Cliques is the structure of the model in a list of cliques that defines the  
        Tables: Probability tables for each variable conditioned on its neighbors

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

0001 function[model] = LearnFDA(k,NumbVar,Card,SelPop,AuxFunVal,learning_params)
0002 % [model] = LearnFDA(k,NumbVar,Card,SelPop,AuxFunVal,learning_params)
0003 % LearnFDA:  Creates a factorized model from the structure
0004 % k: Current generation
0005 % NumbVar: Number of variables
0006 % Card: Vector with the dimension of all the variables.
0007 % SelPop:  Population from which the model is learned
0008 % AuxFunVal: Evaluation of the data set (required for some learning algorithms, not for this one)
0009 % learning_params{1}(1) = Cliques
0010 %    Each row of Cliques is a clique. The first value is the number of neighbor for variable i.
0011 %    The second, is the number of new variables (one new variable, i).
0012 %    Then, neighbor variables are listed and  finally new variables
0013 %    (variable i) are listed
0014 % OUTPUTS
0015 % model: Markov network model containing the structure (model{1} = Cliques)
0016 %        and the parameters (model{2} = Tables)
0017 %        Cliques is the structure of the model in a list of cliques that defines the
0018 %        Tables: Probability tables for each variable conditioned on its neighbors
0019 %
0020 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)
0021 
0022 
0023 Cliques =  cell2num(learning_params{1}(1)); 
0024 Tables = LearnFDAParameters(Cliques,SelPop,NumbVar,Card);
0025  
0026 model{1} = Cliques;
0027 model{2} = Tables;
0028 return;
0029