example_predictor_kernel_poly_series.cpp

Programa para el testeo de las funciones.Para compilar o código example_predictor_kernel_poly_series.cpp:

g++ -static -o example_predictor_kernel_poly_series example_predictor_kernel_poly_series.cpp -lpdsnnmm 

Para executar o programa:

./example_predictor_kernel_poly_series

Retornando por consola:

W:
-0.020581071480649      2.8041621211665 -0.4152617004234        -20.71506121128 6.8207470934485 36.0154116593   -31.351458564928        15.913198985684 62.935403852869 -85.312801186344        -57.669342150773        51.374614710382 19.704262465999
Training :: R2 : 0.73993881165304
Testing  :: R2 : 0.74796922783387

Código example_predictor_kernel_poly_series.cpp:

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
 
    
int main(void)
{
    Pds::Vector Yp;
    Pds::IterationConf Conf;
    
    // Generating data
    unsigned int L=1000;
    unsigned int M=12;
    Pds::Matrix X;
    Pds::Matrix F;
    Pds::Vector Y;
    Pds::DataSet::Regression::LoadDataFourierSeries(L,X,Y);    
    
    // Create Predictor
    F=Pds::Kernel::Polynomial(X,M);
    Pds::Predictor Data(Conf,F,Y);
    Data.Print("\nW:\n");
    
    // Evaluate training data
    Yp=Data.Evaluate(F);
    
    // Testing W with training data set
    std::cout<<"Training :: R2 : "<<Y.R2(Yp)<<std::endl;
    
    // Evaluate testing data
    Pds::DataSet::Regression::LoadDataFourierSeries(L,X,Y);
    X.Save("Xtest.txt");
    Y.Save("Ytest.txt");
    
    F=Pds::Kernel::Polynomial(X,M);
    Yp=Data.Evaluate(F);
    Yp.Save("Ypredict.txt");
 
    // Testing W with training data set
    std::cout<<"Testing  :: R2 : "<<Y.R2(Yp)<<std::endl;
    
    return 0;
}