example_perceptron_kernel_gaussian_gmm.cpp

 
#include <iostream>
#include <Pds/Ra>
#include <Pds/Ml>
 
    
int main(void)
{
    Pds::ClassificationMetrics Metrics;
    Pds::Vector Yp;
    Pds::Matrix F;
    Pds::IterationConf Conf;
    Conf.SetMaxIter(10000); Conf.Show=false;
    unsigned int K=10;
    
    // Generating data
    unsigned int L=1000;
    Pds::Matrix X;
    Pds::Vector Y;
    Pds::Matrix G;
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y);
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DY(X,Y,"testando.m","example_perceptron_kernel_gaussian_gmm_data.png");
    
    std::vector<Pds::Matrix> Block=X.FindRowsBlock(Y);
    Pds::DataGMM Dat0=Pds::Clustering::GMM(Conf,Block[0],K,1.0);
    Pds::DataGMM Dat1=Pds::Clustering::GMM(Conf,Block[1],K,1.0);
    Dat1.Mu.insert  (Dat1.Mu.end()  , Dat0.Mu.begin()  , Dat0.Mu.end());
    Dat1.Sinv.insert(Dat1.Sinv.end(), Dat0.Sinv.begin(), Dat0.Sinv.end());
    
    // Create Perceptron
    F=Pds::Kernel::Gaussian(X,Dat1.Mu,Dat1.Sinv);
 
    Pds::Perceptron Neurona(Conf,F,Y);
    Neurona.Print("\nNeurona:\n");
    
    // Predict training data
    Yp=Neurona.Predict(F);
    
    // Metrics of training
    Metrics = Pds::ClassificationMetrics::Calculate(0.5,Yp,Y);
    Metrics.Print("\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_perceptron_kernel_gaussian_gmm_training.png");
        
    // Predict testing data
    Pds::DataSet::Classification::LoadDataYinYang(L,X,Y); // Load new data
    
    F=Pds::Kernel::Gaussian(X,Dat1.Mu,Dat1.Sinv);
    Yp=Neurona.Predict(F);
    
    // Metrics testing
    Metrics = Pds::ClassificationMetrics::Calculate(Metrics.Threshold,Yp,Y);
    Metrics.Print("\n");
    
    Pds::Octave::Plot::PointsX2DY(X,Yp,"testando.m","example_perceptron_kernel_gaussian_gmm_testing.png");
    return 0;
}