example_perceptron_kernel_poly_sine.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
 
    
int main(void)
{
    Pds::ClassificationMetrics Metrics;
    Pds::Vector Yp;
    Pds::Matrix F;
    Pds::IterationConf Conf;
    unsigned int M=8;
    
    // Generating data
    unsigned int L=1000;
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::DataSet::Classification::LoadDataSine(L,X,Y);
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DY(X,Y,"testando.m","example_perceptron_kernel_poly_sine_data.png");
        
    // Create Perceptron
    F=Pds::Kernel::Polynomial(X,M);
    Conf.SetMaxIter(10000);
    Pds::Perceptron Neurona(Conf,F,Y);
    Neurona.Print("\nNeurona:\n");
    
    // Evaluate training data
    Yp=Neurona.Evaluate(F);
    
    // Metrics of training
    Metrics = Pds::ClassificationMetrics::Optimal(Yp,Y);
    Metrics.Print("\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_perceptron_kernel_poly_sine_training.png");
        
    // Evaluate testing data
    Pds::DataSet::Classification::LoadDataSine(L,X,Y); // Load new data
    F=Pds::Kernel::Polynomial(X,M);
    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_poly_sine_testing.png");
    return 0;
}