CSvmLinear.cpp

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#include <shark/Data/Dataset.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>
#include <shark/Algorithms/Trainers/CSvmTrainer.h>
#include <shark/Data/DataDistribution.h>


using namespace shark;
using namespace std;


    typedef RealVector VectorType;
    // or:
    // typedef CompressedRealVector VectorType;


int main(int argc, char** argv)
{
    // experiment settings
    unsigned int ell = 500;      // number of training data point
    unsigned int tests = 10000;  // number of test data points
    double C = 1.0;              // regularization parameter

    // generate dataset
    PamiToy problem; // artificial benchmark data
    LabeledData<VectorType, unsigned int> training;
    training = problem.generateDataset(ell);
    LabeledData<VectorType, unsigned int> test;
    test = problem.generateDataset(tests);

    // define the model
    LinearClassifier<VectorType> model;

    // define the machine
    LinearCSvmTrainer<VectorType> trainer(C, false);

    // train the machine
    cout << "Algorithm: " << trainer.name() << "\ntraining ..." << flush; // Shark algorithms know their names
    trainer.train(model, training);
    cout << "\n  number of iterations: " << trainer.solutionProperties().iterations;
    cout << "\n  dual value: " << trainer.solutionProperties().value;
    cout << "\n  training time: " << trainer.solutionProperties().seconds << " seconds\ndone." << endl;

    // evaluate
    ZeroOneLoss<unsigned int> loss;
    Data<unsigned int> output = model(training.inputs());
    double train_error = loss.eval(training.labels(), output);
    cout << "training error:\t" <<  train_error << endl;
    output = model(test.inputs());
    double test_error = loss.eval(test.labels(), output);
    cout << "test error:\t" << test_error << endl;
}