example_lda_2d.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
    
int main(void)
{
    unsigned int L=300;
    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;
        
    // Load dataset of L samples an K gaussian normal.
    Pds::DataSet::Classification::LoadKNormal(L,K,X,Y);
    
    // Plor de dataset X with label Y.
    Pds::Octave::Plot::ScatterX2DY(X,Y,"testando3d.m","example_lda_2d_data.png");
 
    // Calculate the Lda classifier.
    Pds::LDA Lda(X,Y);
    Lda.Print("----- LDA :\n");
    
    // Plot the discriminant functions in the Lda object.
    std::vector<Pds::Vector> W=Lda.GetW();
    for(unsigned int n=0;n<W.size();n++)
    {
        Pds::Octave::Plot::ScatterX2DYW(X,Y,W[n],"testando3d.m","example_lda_2d_w"+std::to_string(n)+".png");
    }
 
    // Load new dataset Xt.
    Pds::Matrix Xt(10*L,2);
    Xt.FillRandU(-1.5,1.5);
    
    // Predict the datset Xt.
    Ys=Lda.Predict(Xt);
    
    // Print the result of classification.
    Pds::Octave::Plot::ScatterX2DY(Xt,Ys,"testando3d.m","example_lda_2d_class.png");
    
 
    return 0;
}