example_logisticregression_kmeanstest.cpp

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

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

Para executar o programa:

./example_logisticregression_kmeanstest

Retornando por consola:

W:
0.045411802310072 0.090577312222274 0.090494055170035 
Accuracy: 0.7585

example_logisticregression_kmeanstest_y.png

example_logisticregression_kmeanstest_yp.png

Código example_logisticregression_kmeanstest.cpp:

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
 
        
int main(void)
{
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::XLimits=true;
    Pds::Octave::YLimits=true;
    
    // Generating data
    Pds::Matrix X;
    Pds::Vector Y;
    unsigned int L=1000;
    Pds::DataSet::Classification::LoadDataBand(L,X,Y);
    
    Pds::Octave::Plot::PointsX2DY(X,Y,"testando.m","example_logisticregression_kmeanstest_y.png");
    
    Pds::Vector W;
    Pds::Vector Yp;
    
    Pds::IterationConf Conf;
    
    // Create W using training data
    W=Pds::LogisticModel::FittingKmeansLogitMeanSquare(Conf,X,Y,0.001);
    W.T().Print("W:\n");
    
    Yp=Pds::LogisticModel::Classify(W,X);
    Pds::Octave::Plot::PointsX2DYW(X,Yp,W,"testando.m","example_logisticregression_kmeanstest_yp.png");
    
    std::cout<<"Accuracy: "<<Pds::Accuracy(Y,Yp)<<"\n";
    
    return 0;
}