example_pwc_2d.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
    
int main(void)
{
    unsigned int L=16;
    unsigned int K=3;
    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::LoadKGaussian(L,K,X,Y);
    
    // Plor de dataset X with label Y.
    Pds::Octave::Plot::ScatterX2DY(X,Y,"testando3d.m","example_pwc_2d_data.png");
 
    // Calculate the Pwc classifier.
    Pds::PWC Pwc(X,Y);
    Pwc.Print("----- PWC :\n");
 
    // Test of training
    Ys=Pwc.Predict(X,0.5);
 
    // Confusion matrix
    CM=Pds::ConfusionMatrix::Calculate(Ys,Y);
    Pds::Octave::Plot::ConfMatrix(CM.Data,Pwc.GetLabel(),"testando.m","example_pwc_2d_ConfMatrix.png");
 
    // Load new dataset Xt.
    Pds::Matrix Min=X.MinInCols();
    Pds::Matrix Max=X.MaxInCols();
    Pds::Matrix X1=Pds::Matrix(100*L,1); X1.FillRandU(Min.Get(0),Max.Get(0));
    Pds::Matrix X2=Pds::Matrix(100*L,1); X2.FillRandU(Min.Get(1),Max.Get(1));
    X=Pds::MergeHor({X1,X2});
    
    // Predict the datset Xt.
    Ys=Pwc.Predict(X,0.5);
    
    // Print the result of classification.
    Pds::Octave::Plot::ScatterX2DY(X,Ys,"testando3d.m","example_pwc_2d_class.png");
    
    Pwc.ExportFile("salida.txt");
    Pds::PWC Pwc2("salida.txt");
    Pwc.Print("----- PWC2 :\n");
 
    return 0;
}