McSvmLinear.cpp

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#include <shark/Data/Dataset.h>
#include <shark/Algorithms/Trainers/CSvmTrainer.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>
#include <shark/Data/DataDistribution.h>
#include <shark/Algorithms/Trainers/CSvmTrainer.h>
 
using namespace shark;
 
 
double const noise = 1.0;
typedef CompressedRealVector VectorType;
typedef CompressedRealMatrix MatrixType;
 
 
// data generating distribution for our toy
// multi-category classification problem
/// @cond EXAMPLE_SYMBOLS
class Problem : public LabeledDataDistribution<VectorType, unsigned int>
{
public:
    void draw(VectorType& input, unsigned int& label)const
    {
        label = random::discrete(random::globalRng, 0, 4);
        input.resize(1000002);
        input.set_element(input.end(), 1000000,  noise * random::gauss(random::globalRng) + 3.0 * std::cos((double)label));
        input.set_element(input.end(), 1000001,  noise * random::gauss(random::globalRng) + 3.0 * std::sin((double)label));
    }
};
/// @endcond
 
 
int main(int argc, char** argv)
{
    if (argc != 4)
    {
        std::cout << "required parameters: ell C epsilon" << std::endl;
        return 1;
    }
 
    // experiment settings
    unsigned int ell = std::atoi(argv[1]);
    double C = std::atof(argv[2]);
    double epsilon = std::atof(argv[3]);
    unsigned int tests = 10000;
    std::cout << "ell=" << ell << std::endl;
    std::cout << "C=" << C << std::endl;
    std::cout << "epsilon=" << epsilon << std::endl;
 
    // generate a very simple dataset with a little noise
    Problem problem;
    LabeledData<VectorType, unsigned int> training = problem.generateDataset(ell);
    LabeledData<VectorType, unsigned int> test = problem.generateDataset(tests);
 
    // define the model
    LinearClassifier<VectorType > svm;
 
    // train the machine
    std::cout << "machine training ..." << std::endl;
    LinearCSvmTrainer<VectorType> trainer(C, epsilon);
    trainer.setMcSvmType(McSvm::OVA);
    trainer.train(svm, training);
    std::cout << "done." << std::endl;
 
    // loss measuring classification errors
    ZeroOneLoss<unsigned int> loss;
 
    Data<unsigned int> output = svm(training.inputs());
    double train_error = loss.eval(training.labels(), output);
    output = svm(test.inputs());
    double test_error = loss.eval(test.labels(), output);
    std::cout <<"training error= "<< train_error <<"    test error= "<<  test_error<<std::endl;
}