KernelLogisticRegression.cpp

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#include <shark/LinAlg/Base.h>
#include <shark/Core/Random.h>
#include <shark/Data/Dataset.h>
#include <shark/Data/DataDistribution.h>
#include <shark/Models/Kernels/KernelExpansion.h>
#include <shark/Models/Kernels/GaussianRbfKernel.h>
#include <shark/Algorithms/Trainers/KernelSGDTrainer.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>
#include <shark/ObjectiveFunctions/Loss/CrossEntropy.h>
#include <iostream>


using namespace shark;


// data generating distribution for our toy
// multi-category classification problem
/// @cond EXAMPLE_SYMBOLS
class Problem : public LabeledDataDistribution<RealVector, unsigned int>
{
public:
    void draw(RealVector& input, unsigned int& label)const
    {
        label = random::discrete(random::globalRng, 0, 4);
        input.resize(1);
        input(0) = random::gauss(random::globalRng) + 3.0 * label;
    }
};
/// @endcond

int main()
{
    std::cout << "kernel logistic regression example program" << std::endl;

    // experiment settings
    unsigned int ell = 1000;
    unsigned int tests = 1000;
    double C = 10.0;
    double gamma = 0.5;

    // generate a very simple dataset with a little noise
    Problem problem;
    ClassificationDataset training = problem.generateDataset(ell);
    ClassificationDataset test = problem.generateDataset(tests);

    // kernel function
    GaussianRbfKernel<> kernel(gamma);

    // classifier model
    KernelClassifier<RealVector> classifier;

    // loss measuring classification errors
    ZeroOneLoss<unsigned int> loss;

    // loss measuring training errors
    CrossEntropy crossentropy;

    // machine training
    KernelSGDTrainer<RealVector> trainer(&kernel, &crossentropy, C, false);
    trainer.train(classifier, training);

    // evaluation
    Data<unsigned int> output = classifier(training.inputs());
    double train_error = loss.eval(training.labels(), output);
    std::cout << "training error: " << train_error << std::endl;
    output = classifier(test.inputs());
    double test_error = loss.eval(test.labels(), output);
    std::cout << "    test error: " << test_error << std::endl;
}