example_logisticregression_ce_svm_hard.cpp

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

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

Para executar o programa:

./example_logisticregression_ce_svm_hard

Retornando por consola:

  W0: 0.0037733819812223        -1      -0.020282310732759

┌──────────────────────────────┐
│    CE                        │
│    IterationConf init data   │
├──────────────────────────────┤
│           Alpha: 0.1         │
│          Lambda: 0.1         │
│   MinError ΔW/W: 1.0000e-07  │
│         MaxIter: 1000        │
└──────────────────────────────┘
┌──────────────────────────────┐
│            Iter: 1000        │
│           Alpha: 0.001       │
│  LastError ΔW/W: 6.8187e-06  │
└──────────────────────────────┘
┌──────────────────────────────┐
│    CE                        │
│    IterationConf end data    │
├──────────────────────────────┤
│           Alpha: 0.001       │
│  LastError ΔW/W: 6.8187e-06  │
│        LastIter: 1000        │
└──────────────────────────────┘
 W_ce: 0.00029598207052271      -0.036805352866569      -0.028390896649131

┌──────────────────────────────┐
│    SVM                       │
│    IterationConf init data   │
├──────────────────────────────┤
│           Alpha: 0.1         │
│          Lambda: 0.1         │
│   MinError ΔW/W: 1.0000e-07  │
│         MaxIter: 1000        │
└──────────────────────────────┘
┌──────────────────────────────┐
│            Iter: 1000        │
│           Alpha: 0.001       │
│  LastError ΔW/W: 4.9802e-05  │
│ Cost Func Error: 1.0357e+00  │
└──────────────────────────────┘
┌──────────────────────────────┐
│    SVM                       │
│    IterationConf end data    │
├──────────────────────────────┤
│           Alpha: 0.001       │
│  LastError ΔW/W: 4.9802e-05  │
│        LastIter: 1000        │
└──────────────────────────────┘
W_svm: 0.0039810069812223       -0.85608162230586       -0.041708055765726


 IG_ce: 0.00026042863760706
IG_svm: 5.8431398858694e-05

Classification - cross entropy

Classification - svm

Código example_logisticregression_ce_svm_hard.cpp:

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
        
int main(void)
{
    
    Pds::Matrix X;
    Pds::Vector Y;
    unsigned int L=1000;
    Pds::DataSet::Classification::LoadDataBand2(L,X,Y);
    
    Pds::Vector Yp;
    Pds::Vector W0(X.Ncol()+1), W;
    
    
    // Create W0 using training data
    W0=Pds::LogisticModel::GetW0MeanSquareMethod(X);
    W0.T().Print("  W0: "); 
    std::cout<<std::endl;   
 
    Pds::Octave::YLimits=true;
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DYW(X,Y,W0,"testando_dat.m","example_logisticregression_ce_svm_hard_dat.png");    
    
    // Create W using Pds::LogisticModel::FittingGradientCrossEntropy()
    Pds::IterationConf Conf;
    Conf.Show=true; Conf.SetMinError(1e-06);    Conf.SetAlpha(0.1); Conf.SetLambda(0.1);
    
    W=Pds::LogisticModel::FittingGradientCrossEntropy(Conf,X,Y,W0);
    W.T().Print(" W_ce: ");
    std::cout<<std::endl;
    
    Yp=Pds::LogisticModel::Classify(W,X);
    double IG_ce=Pds::InformationGain(Y,Y.FindRowsBlock(Yp));
 
    Pds::Octave::YLimits=true;
    Pds::Octave::Plot::PointsX2DYW(X,Y,W,"testando.m","example_logisticregression_ce_svm_hard_ce.png");
    
    // Create W using Pds::LogisticModel::FittingGradientSVM()
    Conf.Show=true; Conf.SetMinError(1e-07);    Conf.SetAlpha(0.1); Conf.SetLambda(0.1);
    
    W=Pds::LogisticModel::FittingGradientSVM(Conf,X,Y,W0);
    W.T().Print("W_svm: ");
    std::cout<<std::endl;
    
    Yp=Pds::LogisticModel::Classify(W,X);
    double IG_svm=Pds::InformationGain(Y,Y.FindRowsBlock(Yp));
    
    Pds::Octave::YLimits=true;
    Pds::Octave::Plot::PointsX2DYW(X,Y,W,"testando.m","example_logisticregression_ce_svm_hard_svm.png");
    
    // Information gain
    std::cout<<std::endl;
    std::cout<<" IG_ce: "<<IG_ce<<std::endl;
    std::cout<<"IG_svm: "<<IG_svm<<std::endl;
    
    return 0;
}