example_mncknn.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=5; // Number of clusters
    unsigned int L=1000; // Numer of 1s or 0s
    Pds::Matrix X;
    Pds::Vector Y,Yp;
    
    // Generating training data
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y);
    
    Pds::Octave::Plot::PointsX2DY(X,Y,"testando.m","example_mncknn_data.png");
 
    /********* Training  *********/
    
    // Create MncKnn
    Pds::MncKnn MKnn(Conf,X,Y,K);
    
    // Predict "training" data with Knn=7
    Yp=MKnn.Predict(X,3,7);
    
    // 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_mncknn_training.png");
    
    /********* Testing  *********/
    
    // Generating testing data
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y);
    
    // Predict testing data with Knn=7
    Yp=MKnn.Predict(X,3,7);
    
    // 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_mncknn_testing.png");
    return 0;
}