example_perceptron_predict.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
 
    
int main(void)
{
    Pds::IterationConf Conf; Conf.SetMaxIter(1000); Conf.Show=true;
    Pds::ClassificationMetrics Metrics;
    Pds::Vector Yp;
    
    // Generating data
    unsigned int L=2000;
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::DataSet::Classification::LoadDataLine(L,X,Y);
 
    // Split data set in {Training,Cross-validation,Test}
    Pds::DataSetBlock Dat=Pds::DataSet::Split(X,Y,6,2,2);
    
    // Create Perceptron
    Pds::Ra::Tic();
    Pds::Perceptron Neurona(Conf,Dat.Xtr,Dat.Ytr);
    Pds::Ra::Toc();
    Pds::Vector W=Neurona.GetW();
    Neurona.Print("\nNeurona:\n");
    
    // Predict training
    Yp=Neurona.Predict(Dat.Xtr);
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Dat.Ytr);
    Metrics.Print("\nTraining:\n");
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DYW(Dat.Xtr,Dat.Ytr,W,"testando.m","example_perceptron_predict_training.png");
 
    // Predict cross-validation
    Yp=Neurona.Predict(Dat.Xcv);
    Pds::Ra::Tic();
    Metrics = Pds::ClassificationMetrics::Optimal(Yp,Dat.Ycv);
    Metrics.Print("\nCross-validation:\n");
    Pds::Ra::Toc();
    
    // Predict testing data
    Yp=Neurona.Predict(Dat.Xtt);
    Metrics = Pds::ClassificationMetrics::Calculate(Metrics.Threshold,Yp,Dat.Ytt);
    Metrics.Print("\nTesting:\n");
    
    Pds::Octave::Plot::PointsX2DYW(Dat.Xtt,Dat.Ytt,W,"testando.m","example_perceptron_predict_testing.png");
    
    return 0;
}