example_perceptron_kernel_poly_circle.cpp

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

g++ -static -o example_perceptron_kernel_poly_circle example_perceptron_kernel_poly_circle.cpp -lpdsmlmm 

Para executar o programa:

./example_perceptron_kernel_poly_circle

Retornando por consola:

Neurona:
-3.9290636877995        0.17121159083447        -0.040841236223358      8.4118052572778 -0.094905185116023      8.3201343003554
Optimal:
╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.14            ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 996     5       ║
║ Pred.[1]/Act.[*]: 4       995     ║
╠═══════════════════════════════════╣
║         Accuracy: 99.55 %         ║
║        Precision: 99.6  %         ║
║           Recall: 99.5  %         ║
╠═══════════════════════════════════╣
║           FScore: 99.55 %         ║
╚═══════════════════════════════════╝
Testing:
╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.14            ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 989     7       ║
║ Pred.[1]/Act.[*]: 11      993     ║
╠═══════════════════════════════════╣
║         Accuracy: 99.1  %         ║
║        Precision: 98.9  %         ║
║           Recall: 99.3  %         ║
╠═══════════════════════════════════╣
║           FScore: 99.1  %         ║
╚═══════════════════════════════════╝

Clasification - data

Clasification - training

Clasification - testing

Código example_perceptron_kernel_poly_circle.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=2;
    
    // Generating data
    unsigned int L=1000;
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::DataSet::Classification::LoadDataCircle(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_circle_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("Optimal:\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_perceptron_kernel_poly_circle_training.png");
        
    // Evaluate testing data
    Pds::DataSet::Classification::LoadDataCircle(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("Testing:\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_perceptron_kernel_poly_circle_testing.png");
    return 0;
}