example_logisticregression_robust_meansquare.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::Ra::TextFormat,"../test/hard-data-x.txt");
    Pds::Vector Y(Pds::Ra::TextFormat,"../test/hard-data-y.txt");
    */
    
    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_robust_meansquare_y.png");
    
    Pds::Vector W;
    Pds::Vector Yp;
    
    Pds::IterationConf Conf;
    Conf.Show=true; Conf.SetMinError(1e-07);    Conf.SetAlpha(0.1);
    unsigned int N=2;
    
    // Create W using training data
    W=Pds::LogisticModel::FittingRobustLogitMeanSquare(X,Y,0.001,N,0.01);
    W.T().Print("W:\n");
    
    Yp=Pds::LogisticModel::Classify(W,X);
    Pds::Octave::Plot::PointsX2DYW(X,Yp,W,"testando.m","example_logisticregression_robust_meansquare_yp.png");
    
    // Create W using training data
    W=Pds::LogisticModel::FittingRobustLogitMeanSquare(Conf,X,Y,0.001,N,0.01);
    W.T().Print("W:\n");
    
    Yp=Pds::LogisticModel::Classify(W,X);
    Pds::Octave::Plot::PointsX2DYW(X,Yp,W,"testando.m","example_logisticregression_robust_meansquare_ypc.png");
    
    return 0;
}