example_linearmodel_outlier.cpp

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

g++ -static -o example_linearmodel_outlier example_linearmodel_outlier.cpp -lpdsmlmm -lpdsramm -lpdsspmm

Para executar o programa:

./example_linearmodel_outlier

Retornando por consola:

W:
0.16076878997905 0.51976819199234 
Training :: R2 : 0.26923944283949

W:
0.025100329361696 0.91921647375166  
Training :: R2 : 0.035913187011475

Ajuste de curva con meansquare

Ajuste de curva con robust bisquare

Código example_linearmodel_outlier.cpp:

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
        
int main(void)
{
    Pds::IterationConf Conf;
    Pds::Vector W;
    Pds::Vector Ypr;
    Pds::Octave::XLabel="x1";
    Pds::Octave::YLabel="x2";
    Pds::Octave::Legend={"Data","Curve"};    
        
    // Generating data
    unsigned int L=2000;
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::DataSet::Regression::LoadDataLineOutlierBall(L,X,Y,10);
    
    // Training  Pds::LinearModel::FittingNormalMeanSquare()
    W=Pds::LinearModel::FittingNormalMeanSquare(Conf,X,Y);
    W.T().Print("\nW:\n");
    
    // Testing 
    Ypr=Pds::LinearModel::Predict(W,X);
    std::cout<<"Training :: R2 : "<<Y.R2(Ypr)<<std::endl;
    
    Pds::Octave::Plot::CurveXYXY(X,Y,X,Ypr,"testando.m","example_linearmodel_meansquare_outlier.png");
    
 
    // Training  Pds::LinearModel::FittingRobustBisquare()
    W=Pds::LinearModel::FittingRobustBisquare(Conf,X,Y,10);
    W.T().Print("\nW:\n");
    
    // Testing 
    Ypr=Pds::LinearModel::Predict(W,X);
    std::cout<<"Training :: R2 : "<<Y.R2(Ypr)<<std::endl;
    
    Pds::Octave::Plot::CurveXYXY(X,Y,X,Ypr,"testando.m","example_linearmodel_bisquare_outlier.png");
    
    return 0;
}