example_logisticregression_meansquare_hard.cpp

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

g++ -static -o example_logisticregression_meansquare_hard example_logisticregression_meansquare_hard.cpp -lpdsmlmm 

Para executar o programa:

./example_logisticregression_meansquare_hard

Retornando por consola:

W_ms_conf:
0.54735453715447        4.5966020377534 -13.467304051768
W_ms:
0.54735453715474        4.5966020377536 -13.467304051769
W_ms_conf:
12.840026069741 9.1677022674658 -27.478493922404
W_ms_w:
12.840026069741 9.1677022674659 -27.478493922405

Clasification - Mean square conf

Clasification - Mean square

Clasification - W mean square conf

Clasification - W mean square

Código example_logisticregression_meansquare_hard.cpp:

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
 
        
int main(void)
{
    // Generating data
    Pds::Matrix X(Pds::Ra::TextFormat,"../test/hard2-data-x.txt");
    Pds::Vector Y(Pds::Ra::TextFormat,"../test/hard2-data-y.txt");
    
    
    Pds::Vector W;
    
    Pds::IterationConf Conf;
    Pds::Vector d=Y.BalancedBinaryWeight(0.5);
    
    
    // Create W using training data
    Conf.Show=true; Conf.SetMinError(1e-07);    Conf.SetAlpha(0.1);
    
    W=Pds::LogisticModel::FittingLogitMeanSquare(Conf,X,Y,0.0001);
    W.T().Print("W_ms_conf:\n");
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DYW(X,Y,W,"testando.m","example_logisticregression_meansquare_hard_ms-conf.png");
    
    // Create W using training data
    W=Pds::LogisticModel::FittingLogitMeanSquare(X,Y,0.0001);
    W.T().Print("W_ms:\n");
    
    Pds::Octave::Plot::PointsX2DYW(X,Y,W,"testando.m","example_logisticregression_meansquare_hard_ms.png");
    
    
    // Create W using training data
    Conf.Show=true; Conf.SetMinError(1e-07);    Conf.SetAlpha(0.1);
    
    W=Pds::LogisticModel::FittingLogitWeightedMeanSquare(Conf,X,Y,d,0.0001);
    W.T().Print("W_ms_conf:\n");
    
    Pds::Octave::Plot::PointsX2DYW(X,Y,W,"testando.m","example_logisticregression_meansquare_hard_wms-conf.png");
    
    // Create W using training data
    W=Pds::LogisticModel::FittingLogitWeightedMeanSquare(X,Y,d,0.0001);
    W.T().Print("W_ms_w:\n");
    
    Pds::Octave::Plot::PointsX2DYW(X,Y,W,"testando.m","example_logisticregression_meansquare_hard_wms.png");
 
    
    return 0;
}