example_kmeansnn2.cpp

Programa para el testeo de las funciones.Para compilar o código example_kmeansnn2.cpp:

g++ -static -o example_kmeansnn2 example_kmeansnn2.cpp -lpdsmlmm -lpdsramm -lpdsspmm

Para executar o programa:

./example_kmeansnn2

Retornando por consola:

Working Kmeans over ID: 0
┌──────────────────────────────┐
│    Kmeans                    │
│    IterationConf init data   │
├──────────────────────────────┤
│        MinError: 1.0000e-05  │
│         MaxIter: 1e+04       │
└──────────────────────────────┘
┌──────────────────────────────┐
│            Iter: 19          │
│       LastError: 0.0000e+00  │
│ Elapsed time(s): 0.000127    │
└──────────────────────────────┘

Working Kmeans over ID: 1
┌──────────────────────────────┐
│    Kmeans                    │
│    IterationConf init data   │
├──────────────────────────────┤
│        MinError: 1.0000e-05  │
│         MaxIter: 1e+04       │
└──────────────────────────────┘
┌──────────────────────────────┐
│            Iter: 22          │
│       LastError: 0.0000e+00  │
│ Elapsed time(s): 0.000126    │
└──────────────────────────────┘

Working Kmeans over ID: 2
┌──────────────────────────────┐
│    Kmeans                    │
│    IterationConf init data   │
├──────────────────────────────┤
│        MinError: 1.0000e-05  │
│         MaxIter: 1e+04       │
└──────────────────────────────┘
┌──────────────────────────────┐
│            Iter: 16          │
│       LastError: 0.0000e+00  │
│ Elapsed time(s): 0.000108    │
└──────────────────────────────┘

Classification - data

Classification - Confusion matrix

Classification - testing

Código example_kmeansnn2.cpp:

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
 
    
int main(void)
{
    Pds::IterationConf Conf;
    Conf.SetMaxIter(10000); Conf.Show=true;
    Pds::ConfusionMatrix ConfMat;
 
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::MarkerSize=40;
    
    unsigned int K=5; // Number of clusters
    unsigned int C=3; // Numer of classes
    unsigned int L=1000; // Numer of 1s or 0s
    Pds::Matrix X;
    Pds::Vector Y,Yp;
    
    // Generating training data
    Pds::DataSet::Classification::LoadKGaussian(L,C,X,Y);       
    
    Pds::Octave::Plot::ScatterX2DY(X,Y,"testando.m","example_kmeansnn2_data.png");
 
    /********* Training  *********/
    
    // Create KmeansNn
    Pds::KmeansNn KmNn(Conf,X,Y,K);
 
    // Predict "traning" data with Knn=7
    Yp=KmNn.Predict(X,7);
    
    ConfMat=Pds::ConfusionMatrix::Calculate(Yp,Y);
 
    Pds::Octave::Plot::ConfMatrix(ConfMat.Data,ConfMat.Id,"testando.m","example_kmeansnn2_confmatrix.png");
    
    /********* Testing  *********/
    
    // Predict "testing" data with Knn=7
    Pds::Matrix Min=X.MinInCols();
    Pds::Matrix Max=X.MaxInCols();
    Pds::Matrix X1=Pds::Matrix(10*L,1); X1.FillRandU(Min.Get(0),Max.Get(0));
    Pds::Matrix X2=Pds::Matrix(10*L,1); X2.FillRandU(Min.Get(1),Max.Get(1));
    X=Pds::MergeHor({X1,X2});
    
    Yp=KmNn.Predict(X,7);
    
    Pds::Octave::Plot::ScatterX2DY(X,Yp,"testando.m","example_kmeansnn2_testing.png");
    
 
    return 0;
}