example_kmeansperceptron.cpp

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

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

Para executar o programa:

./example_kmeansperceptron

Retornando por consola:

┌──────────────────────────────┐
│    Kmeans                    │
│    IterationConf init data   │
├──────────────────────────────┤
│        MinError: 1.0000e-05  │
│         MaxIter: 1e+04       │
└──────────────────────────────┘
┌──────────────────────────────┐
│            Iter: 42          │
│       LastError: 0.0000e+00  │
│ Elapsed time(s): 0.001047    │
└──────────────────────────────┘
┌──────────────────────────────┐
│    Kmeans                    │
│    IterationConf init data   │
├──────────────────────────────┤
│        MinError: 1.0000e-05  │
│         MaxIter: 1e+04       │
└──────────────────────────────┘
┌──────────────────────────────┐
│            Iter: 33          │
│       LastError: 0.0000e+00  │
│ Elapsed time(s): 0.000635    │
└──────────────────────────────┘
Trained cluster 39 of 39 with 184 samples.        

Metrics of training:
╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.5             ║
║          Samples: 8000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 3856    113     ║
║ Pred.[1]/Act.[*]: 144     3887    ║
╠═══════════════════════════════════╣
║         Accuracy: 96.79 %         ║
║        Precision: 96.43 %         ║
║           Recall: 97.17 %         ║
╠═══════════════════════════════════╣
║           FScore: 96.8  %         ║
╚═══════════════════════════════════╝

Metrics of testing:
╔═══════════════════════════════════╗
║    ClassificationMetrics data     ║
╠═══════════════════════════════════╣
║        Threshold: 0.5             ║
║          Samples: 8000            ║
╠═══════════════════════════════════╣
║ Pred.[0]/Act.[*]: 3811    116     ║
║ Pred.[1]/Act.[*]: 189     3884    ║
╠═══════════════════════════════════╣
║         Accuracy: 96.19 %         ║
║        Precision: 95.36 %         ║
║           Recall: 97.1  %         ║
╠═══════════════════════════════════╣
║           FScore: 96.22 %         ║
╚═══════════════════════════════════╝

Classification - data

Classification - training

Classification - testing

Código example_kmeansperceptron.cpp:

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
 
    
int main(void)
{
    Pds::ClassificationMetrics Metrics;
    Pds::IterationConf Conf;
    Conf.SetMaxIter(10000); Conf.Show=true;
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    unsigned int K=40; // Number of clusters
    unsigned int L=4000; // Numer of 1s or 0s
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::Vector Yp;
 
    
    /********* Training *********/
    
    // Generating training data
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y);
    
    Pds::Octave::Plot::PointsX2DY(X,Y,"testando.m","example_kmeansperceptron_data.png");
 
    // Create and training an KmeansPerceptron
    Pds::KmeansPerceptron KP(Conf,X,Y,K);
    
    // Predict training data
    Yp=KP.Predict(X);
    
    // Metrics of training
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Y);
    Metrics.Print("\nMetrics of training:\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_kmeansperceptron_training.png");
    
    /********* Testing  *********/
    
    // Generating testing data
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y);
    
    // Predict testing data
    Yp=KP.Predict(X);
    
    // Metrics testing
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Y);
    Metrics.Print("\nMetrics of testing:\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_kmeansperceptron_testing.png");
    return 0;
}