refman-0.1.1/html/example_perceptron_kernel_gaussian_circle_8cpp-example.html

#include <iostream>

#include <Pds/Ra>

#include <Pds/Ml>


int main(void)

{

    Pds::ClassificationMetrics Metrics;

    Pds::Vector Yp;

    Pds::Matrix F;

    Pds::Matrix G;

    Pds::IterationConf Conf;    Conf.SetMaxIter(10000);

    double Sigma=0.3;


    // Generating data

    unsigned int L=200;

    Pds::Matrix X;

    Pds::Vector Y;

    Pds::DataSet::Classification::LoadDataCircle(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_circle_data.png");


    // Create Perceptron

    G=X;

    F=Pds::Kernel::Gaussian(X,G,Sigma);


    Pds::Perceptron Neurona(Conf,F,Y);

    Neurona.Print("\nNeurona:\n");


    // Evaluate training data

    Yp=Neurona.Evaluate(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_circle_training.png");


    // Evaluate testing data

    Pds::DataSet::Classification::LoadDataCircle(L,X,Y); // Load new data

    F=Pds::Kernel::Gaussian(X,G,Sigma);

    Yp=Neurona.Evaluate(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_circle_testing.png");

    return 0;

}

Pds::ClassificationMetrics
La clase tipo Pds::ClassificationMetrics . Esta clase genera un bloque de datos para analizar curvas ...
Definition: ClassificationMetrics.hpp:50

Pds::ClassificationMetrics::Threshold
double Threshold
Definition: ClassificationMetrics.hpp:69

Pds::IterationConf
La clase tipo Pds::IterationConf . Esta clase genera una matriz de Nlin lineas y 1 columna....
Definition: IterationConf.hpp:56

Pds::Perceptron
La clase tipo Pds::Perceptron . Esta clase genera una matriz de Nlin lineas y 1 columna....
Definition: Perceptron.hpp:63

Pds::ClassificationMetrics::Calculate
static Pds::ClassificationMetrics Calculate(double Threshold, const Pds::Vector &Ypredict, const Pds::Vector &Yactual)
Crea un objeto Dat de tipo Pds::ClassificationMetrics.

Pds::ClassificationMetrics::Print
void Print(std::string str="")
Imprime en pantalla los datos de la estructura tipo Pds::ClassificationMetrics.

Pds::IterationConf::SetMaxIter
bool SetMaxIter(unsigned int MaxIter)
Coloca el valor MaxIter.

Pds::DataSet::Classification::LoadDataCircle
void LoadDataCircle(unsigned int L, Pds::Matrix &X, Pds::Vector &Y)
Clasificacion de datos separados por una curva.

Pds::Kernel::Gaussian
Pds::Matrix Gaussian(const Pds::Matrix &X, const Pds::Matrix &G, double Sigma=1.0)
Crea una nueva matriz de "features" kernelizando de forma gaussiana.

Pds::Octave::YLabel
std::string YLabel
Texto correspondente a ylabel.

Pds::Octave::XLabel
std::string XLabel
Texto correspondente a xlabel.

Pds::Octave::Plot::PointsX2DY
bool PointsX2DY(const Pds::Matrix &X, const Pds::Vector &Y, const std::string &octfilename, const std::string &imgfilename)
Plot de muestras en 2D en las columnas X, clasificadas de forma binaria con Y>=0.5,...

Pds::Perceptron::Print
void Print(std::string str="") const
Imprime en pantalla el contenido del vector de pesos después del texto indicado en str.

Pds::Perceptron::Evaluate
double Evaluate(const std::initializer_list< double > list) const
Evalua el objeto de tipo Pds::Perceptron.