example_perceptron_kernel_gaussian_yinyang.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
 
    
int main(void)
{
    Pds::ClassificationMetrics Metrics;
    Pds::Vector Yp;
    Pds::Matrix F;
    Pds::IterationConf Conf;
    Conf.SetMaxIter(10000); Conf.Show=false;
    double Sigma=0.1;
    
    // Generating data
    unsigned int L=1000;
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::Matrix G;
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y);
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DY(X,Y,"testando.m","example_perceptron_kernel_gaussian_yinyang_data.png");
    
    // Create Perceptron
    G=Pds::MergeVer( {Pds::Clustering::Kmeans(Conf,X.GetRows(Pds::Find(Y)),60),
                      Pds::Clustering::Kmeans(Conf,X.GetRows(Pds::Find(1-Y)), 60)}
                   ); //Random centroids to Gaussian Kernel
    F=Pds::Kernel::Gaussian(X,G,Sigma);
 
    Pds::Perceptron Neurona(Conf,F,Y);
    Neurona.Print("\nNeurona:\n");
    
    // Evaluate training data
    Yp=Neurona.Evaluate(F);
    
    // Metrics of training
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Y);
    Metrics.Print("\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_perceptron_kernel_gaussian_yinyang_training.png");
        
    // Evaluate testing data
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y); // Load new data
    
    F=Pds::Kernel::Gaussian(X,G,Sigma);
    Yp=Neurona.Evaluate(F);
    
    // Metrics testing
    Metrics = Pds::ClassificationMetrics::Calculate(Metrics.Threshold,Yp,Y);
    Metrics.Print("\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_perceptron_kernel_gaussian_yinyang_testing.png");
    return 0;
}