example_perceptron_kernel_poly_sine.cpp

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

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

Para executar o programa:

./example_perceptron_kernel_poly_sine

Retornando por consola:

Neurona:
-0.44394896796723       23.369812401582 -5.5990859143574        1.6047345202706 -2.3234824190806        1.9410596273968 -121.38309823869        3.1348051746528 -32.992395188308        -19.520365097443        -0.67356985992054       2.9783538661135 -2.6597861788598        9.1168731797187 -4.7714304618693        145.64641915713 3.1174663505789 156.67697505028 -10.566567730069        -5.1222827984402        47.475062351416 -1.1031438346344        -1.1636195655602        -0.27212944691812       -7.8025071008203        3.5366155020148 -10.551246450729        6.1685389877243 -48.977294633292        -10.180053490278        -101.72465180136        16.716071856782 -35.878358431932        -1.696229395668 12.567109268125 -26.754055525364        0.090377306290623       0.6702065404376 2.3202973668627 0.38919350746505        -0.47738323137392       5.3347170140667 -2.7150870362765        3.50694434439   -2.9032676038638

╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.53            ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 975     33      ║
║ Pred.[1]/Act.[*]: 25      967     ║
╠═══════════════════════════════════╣
║         Accuracy: 97.1  %         ║
║        Precision: 97.48 %         ║
║           Recall: 96.7  %         ║
╠═══════════════════════════════════╣
║           FScore: 97.09 %         ║
╚═══════════════════════════════════╝

╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.53            ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 983     32      ║
║ Pred.[1]/Act.[*]: 17      968     ║
╠═══════════════════════════════════╣
║         Accuracy: 97.55 %         ║
║        Precision: 98.27 %         ║
║           Recall: 96.8  %         ║
╠═══════════════════════════════════╣
║           FScore: 97.53 %         ║
╚═══════════════════════════════════╝

Clasification - data

Clasification - training

Clasification - testing

Código 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;
}