example_lda_2d.cpp

Programa para el testeo de las funciones.Para compilar o código example_lda_2d.cpp:

g++ -static -o example_lda_2d example_lda_2d.cpp -lpdsmlmm -lpdsramm -lpdsspmm

Para executar o programa:

./example_lda_2d

Retornando por consola:

----- LDA :
Lambda:
9.4017204746525 9.792373894639  
V:
0.48750693238305  0.87302817868768  
0.8731191160881   -0.48766976451004 
W[0]:
-1.6002717696647   1.0014438993629    -0.016525001345288 
W[1]:
-1.5922959815084  -0.51853946729161 0.84772731034858  
W[2]:
-1.603368542761   -0.49287682343256 -0.87554761815149 
Label[0]:       0
Label[1]:       1
Label[2]:       2

Tarining data:

Data of axes {0,1,2}

Discriminant functions:


Decision boundaries:

Regions

Código 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;
}