example_decisiontree.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
    
int main(void)
{
    Pds::Octave::Colormap="jet";
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    
    Pds::ClassificationMetrics Metrics;
    
    Pds::IterationConf Conf;
    Conf.Show=false; Conf.SetMinError(1e-07);    Conf.SetAlpha(0.1);
    
    Pds::Vector Yp;
    
 
    // Load training data
    unsigned int L=1000;
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y);
    
    Pds::Octave::Plot::ScatterX2DY(X,Y,"testando.m","example_decisiontree_data.png");
    
    // Creando un arbol
    Pds::DecisionTree::Counter=0;
    Pds::DecisionTree Arbol(Conf,X,Y,0.8,4);
    std::cout<<"Pds::DecisionTree::Counter: "<<Pds::DecisionTree::Counter<<std::endl;
 
    // Exportando arbol en archivo en formato dot.
    Arbol.ExportDotFile("arbol.dot");
    
    // Evaluate training data
    Yp=Arbol.Evaluate(X);
    
    // Metrics of training
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Y);
    Metrics.Print("\nTraining metrics:\n");
    
    Pds::Octave::Plot::ScatterX2DY(X,Yp,"testando.m","example_decisiontree_training.png");
    
    // Load testing data
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y); // Load new data
    
    // Evaluate testing data
    Yp=Arbol.Evaluate(X);
    
    // Metrics of testing
    Metrics = Pds::ClassificationMetrics::Calculate(Metrics.Threshold,Yp,Y);
    Metrics.Print("\nTesting metrics:\n");
    
    Pds::Octave::Plot::ScatterX2DY(X,Yp,"testando.m","example_decisiontree_testing.png");
 
    return 0;
}