SampleMixtureofFullGaussianModels

  [NewPop] = SampleMixtureofFullGaussianModels(NumbVar,model,RangeValues,AuxPop,AuxFunVal,sampling_params)
 SampleMixtureFullGaussianModels:         Samples a population of individuals from
                                          a mixture of full multivariate Gaussian  model
 INPUTS
 NumbVar: Number of variables
 model: model{1,i} = mean of the variables for solutions in  cluster i
        model{2,i} = covariance of the variables for solutions in cluster i
        model{3,i} = coefficient associated to component i of the mixture
 RangeValues: Matrix of two vectors with the minimum and maximum real values 
              each variable can take 
 AuxPop: Auxiliary (selected) population (May be use for partial sampling or resampling)
 AuxFunVal: Evaluation of the data set (required for some sampling algorithms, not for this one)
 sampling_params{1,1} = PopSize: Number of generated individuals 
 sampling_params{1,2} = var_scaling: Scaling for the covariance values. A
                        very low variance tends to move the algorith to stagnation. Scaling is a partial 
                        solution to this problem. Dynamic scaling should
                        be implemented as a more robust solution
 OUTPUTS
 NewPop: Sampled individuals

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

0001 function [NewPop] =  SampleMixtureofFullGaussianModels(NumbVar,model,RangeValues,AuxPop,AuxFunVal,sampling_params)
0002 %  [NewPop] = SampleMixtureofFullGaussianModels(NumbVar,model,RangeValues,AuxPop,AuxFunVal,sampling_params)
0003 % SampleMixtureFullGaussianModels:         Samples a population of individuals from
0004 %                                          a mixture of full multivariate Gaussian  model
0005 % INPUTS
0006 % NumbVar: Number of variables
0007 % model: model{1,i} = mean of the variables for solutions in  cluster i
0008 %        model{2,i} = covariance of the variables for solutions in cluster i
0009 %        model{3,i} = coefficient associated to component i of the mixture
0010 % RangeValues: Matrix of two vectors with the minimum and maximum real values
0011 %              each variable can take
0012 % AuxPop: Auxiliary (selected) population (May be use for partial sampling or resampling)
0013 % AuxFunVal: Evaluation of the data set (required for some sampling algorithms, not for this one)
0014 % sampling_params{1,1} = PopSize: Number of generated individuals
0015 % sampling_params{1,2} = var_scaling: Scaling for the covariance values. A
0016 %                        very low variance tends to move the algorith to stagnation. Scaling is a partial
0017 %                        solution to this problem. Dynamic scaling should
0018 %                        be implemented as a more robust solution
0019 % OUTPUTS
0020 % NewPop: Sampled individuals
0021 %
0022 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)
0023 
0024 PopSize = cell2num(sampling_params{1}(1)); 
0025 var_scaling = cell2num(sampling_params{1}(2)); 
0026 
0027 nclusters = size(model,2);
0028 for j=1:nclusters
0029  prob_vector(j) = model{3,j};
0030 end
0031 partialsum=cumsum(prob_vector);
0032 
0033 Index=sus(PopSize,partialsum);  % Individuals to be selected from each cluster
0034 
0035 for i=1:PopSize,
0036  vars_means = model{1,Index(i)};
0037  vars_cov =   var_scaling*model{2,Index(i)};
0038  NewPop(i,:) = mvnrnd(vars_means,vars_cov); 
0039 end  
0040   
0041 return;