example_decisiontree_simple2.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
    
int main(void)
{
    Pds::ClassificationMetrics Metrics;
    Pds::Vector Yp;
    
    Pds::IterationConf Conf;
    Conf.Show=true; Conf.SetMinError(1e-07);    Conf.SetAlpha(0.1);
    
    // Load training 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_decisiontree_simple2_data.png");
    
    unsigned int StopSamples=3;
    unsigned int StopDeep=64;
    
    // Creando un arbol
    Pds::DecisionTree::Counter=0;
    Pds::DecisionTree Arbol("k2means",Conf,X,Y,0.8,StopSamples,StopDeep);
    std::cout<<"Pds::DecisionTree::Counter: "<<Pds::DecisionTree::Counter<<std::endl;
 
    Arbol.ExportFile("arbol.txt");
 
    // Exportando arbol en archivo en formato dot.
    Arbol.ExportDotFile("arbol.dot");
    Pds::Ra::System("dot -Tpng -o example_decisiontree_simple2_arbol.png arbol.dot");
    
    // Predict training data
    Yp=Arbol.Predict(X);
    
    // Metrics of training
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Y);
    Metrics.Print("\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_decisiontree_simple2_training.png");
    
    // Load testing data
    Pds::DataSet::Classification::LoadDataBand2(L,X,Y);
    
    // Predict testing data
    Yp=Arbol.Predict(X);
    
    // Metrics of testing
    Metrics = Pds::ClassificationMetrics::Calculate(Metrics.Threshold,Yp,Y);
    Metrics.Print("\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_decisiontree_simple2_testing.png");
 
    return 0;
}