LearnGaussianUnivModel

 [model] = LearnGaussianUnivModel(k,NumbVar,Card,AuxPop,AuxFunVal,learning_params)
 LearnMargProdModel:     Learns a Gaussian univariate marginal product model 
 INPUTS
 k: Current generation
 NumbVar: Number of variables
 Card: Vector with the dimension of all the variables. 
 AuxPop:  Population from which the model is learned  the factorization 
 AuxFunVal: Evaluation of the data set (required for some learning algorithms, not for this one)
 OUTPUTS
 model: model{1} = mean of the variables
        model{2} = variances of the variables 

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

0001 function [model] = LearnGaussianUnivModel(k,NumbVar,Card,AuxPop,AuxFunVal,learning_params)
0002 % [model] = LearnGaussianUnivModel(k,NumbVar,Card,AuxPop,AuxFunVal,learning_params)
0003 % LearnMargProdModel:     Learns a Gaussian univariate marginal product model
0004 % INPUTS
0005 % k: Current generation
0006 % NumbVar: Number of variables
0007 % Card: Vector with the dimension of all the variables.
0008 % AuxPop:  Population from which the model is learned  the factorization
0009 % AuxFunVal: Evaluation of the data set (required for some learning algorithms, not for this one)
0010 % OUTPUTS
0011 % model: model{1} = mean of the variables
0012 %        model{2} = variances of the variables
0013 %
0014 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)
0015  
0016        
0017      model{1} =  mean(AuxPop);   % Vector of means
0018      model{2} =  std(AuxPop);    % Vector of variances
0019       
0020      return;
0021        
0022        
0023