example_perceptron_kernel_poly_band.cpp

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

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

Para executar o programa:

./example_perceptron_kernel_poly_band

Retornando por consola:

Neurona:
-3.5569770398015        0.79670348005628        -0.63309815814264       15.39272544458  42.416871008276 17.32211476837  -2.1147183718924        1.3921990052308 -1.0786607452332        2.53300873880121.9302026069433  -65.170943476362        -121.7234808035 -62.594282031445        -2.0091214142462        0.9869732278331 -2.3940892940125        1.4250687639271 0.35088834779188        0.82018763562632        -1.8400653603591        -10.539811283958        5.1670012856281 60.022540811669 88.796257352587 57.990546031286 0.56854699090767        -9.183468393062

Optimal:
╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.6             ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 881     6       ║
║ Pred.[1]/Act.[*]: 119     994     ║
╠═══════════════════════════════════╣
║         Accuracy: 93.75 %         ║
║        Precision: 89.31 %         ║
║           Recall: 99.4  %         ║
╠═══════════════════════════════════╣
║           FScore: 94.08 %         ║
╚═══════════════════════════════════╝

Calculate
╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.6             ║
║          Samples: 2000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 891     9       ║
║ Pred.[1]/Act.[*]: 109     991     ║
╠═══════════════════════════════════╣
║         Accuracy: 94.1  %         ║
║        Precision: 90.09 %         ║
║           Recall: 99.1  %         ║
╠═══════════════════════════════════╣
║           FScore: 94.38 %         ║
╚═══════════════════════════════════╝

Classification - data

Classification - training

Classification - testing

Código example_perceptron_kernel_poly_band.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=true;
    unsigned int M=6;
    
    // Generating data
    unsigned int L=1000;
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::DataSet::Classification::LoadDataBand2(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_band_data.png");
        
    // Create Perceptron
    F=Pds::Kernel::Polynomial(X,M);
 
    Pds::Perceptron Neurona(Conf,F,Y);
    Neurona.Print("\nNeurona:\n");
    
    // Predict training data
    Yp=Neurona.Predict(F);
 
    // Metrics of training
    Metrics = Pds::ClassificationMetrics::Optimal(Yp,Y);
    Metrics.Print("\nOptimal:\n");
        
    Pds::Octave::Plot::PointsX2DY(X,Yp.Geq(Metrics.Threshold),"testando.m","example_perceptron_kernel_poly_band_training.png");
        
    // Predict testing data
    Pds::DataSet::Classification::LoadDataBand2(L,X,Y); // Load new data
    F=Pds::Kernel::Polynomial(X,M);
    Yp=Neurona.Predict(F);
    
    // Metrics testing
    Metrics = Pds::ClassificationMetrics::Calculate(Metrics.Threshold,Yp,Y);
    Metrics.Print("\nCalculate\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp.Geq(Metrics.Threshold),"testando.m","example_perceptron_kernel_poly_band_testing.png");
    
    return 0;
}