example_nbgmm.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
    
int main(void)
{
    unsigned int L=1000;
    Pds::IterationConf Conf;
    Pds::Octave::MarkerSize=64;
    Pds::Octave::YLimits=true;
    Pds::Octave::XLimits=true;
    Pds::Octave::DAspect=true;
    
    Pds::Matrix X;
    Pds::Vector Y,Ys;
    Pds::ConfusionMatrix CM;
        
    // Load dataset of L samples an K gaussian normal.
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y);
    
    Pds::Octave::Plot::ScatterX2DY(X,Y,"testando3d.m","example_nbgmm_data.png");
 
    // Calculate the NbGmm classifier.
    Pds::NBGMM NbGmm(Conf,X,Y,{8,8});
    NbGmm.Print("\n----- NBGMM :\n");
 
    // Load new dataset Xt.
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y);
    
    // Predict the datset Xt.
    Ys=NbGmm.Predict(X);
    
    // Confusion matrix
    CM=Pds::ConfusionMatrix::Calculate(Ys,Y);
    Pds::Octave::Plot::ConfMatrix(CM.Data,NbGmm.GetLabel(),"testando.m","example_nbgmm_ConfMatrix.png");
    
    // Print the result of classification.
    Pds::Octave::Plot::ScatterX2DY(X,Ys,"testando3d.m","example_nbgmm_class.png");
    
    NbGmm.ExportFile("salida.txt");
    Pds::NBGMM NbGmm2("salida.txt");
    NbGmm.Print("\n----- NBGMM2 :\n");
 
    return 0;
}