example_logisticregression_ig2.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
        
int main(void)
{
    Pds::IterationConf Conf;    
    Conf.Show=false; Conf.SetMinError(1e-07);    Conf.SetAlpha(0.1);
    
    // Generating data
    Pds::Vector Y="0    1       1       0       0       0       1       0       1       1       1       1       0       0       1       1";
 
    Pds::Matrix X=
    "2  4       2       2       4       4       2       2       2       2       2       4       4       4       4       4\n"
    "6  6       6       6       6       6       6       6       6       6       6       6       6       6       6       6\n"
    "1500       1500    1500    1500    1500    1500    1500    1500    1500    1500    1500    1500    1500    1500    1500    1500\n"
    "22 23      28      28      35      39      22      26      26      28      35      28      30      35      35      38";
 
    X=X.T();
    Y=Y.T();
    
    Pds::MergeHor({X,Y}).Print("{X,Y}\n");
    
    Pds::Vector Yp;
    Pds::Vector W;
    
    std::cout<<"\n\n";    
    // Create W using training data
    
    // Método ortogonal
    W=Pds::LogisticModel::FittingOrtogonalIG(X,Y,1);
 
    W.T().Print("    W_ig: ");
    std::cout<< " Cost(W): "<<Pds::LogisticModel::CostInformationGain(W,X,Y)<<"\n\n";    
    
    Yp=Pds::LogisticModel::Classify(W,X);
    
    // Método de gradiente
    //W=Pds::LogisticModel::GetW0MeanMethod(X);   
    W=Pds::LogisticModel::FittingGradientIG(Conf,X,Y,W);
    
    W.T().Print("    W_ig: ");
    std::cout<< " Cost(W): "<<Pds::LogisticModel::CostInformationGain(W,X,Y)<<"\n"; 
    
    Yp=Pds::LogisticModel::Classify(W,X);
    
    return 0;
}