KernelRegression.cpp

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//===========================================================================
/*!
 * 
 *
 * \brief       Kernel-based regression methods example program.
 * 
 * 
 *
 * \author      T. Glasmachers
 * \date        -
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
//===========================================================================
 
#include <shark/LinAlg/Base.h>
#include <shark/Core/Random.h>
#include <shark/Models/Kernels/GaussianRbfKernel.h>
#include <shark/Algorithms/Trainers/EpsilonSvmTrainer.h>
#include <shark/Algorithms/Trainers/RegularizationNetworkTrainer.h>
#include <shark/ObjectiveFunctions/Loss/SquaredLoss.h>
#include <shark/Data/Dataset.h>
#include <shark/Data/DataDistribution.h>
 
 
using namespace shark;
 
 
int main()
{
    // experiment settings
    unsigned int ell = 200;
    unsigned int tests = 10000;
    double C = 10.0;
    double gamma = 1.0 / C;
    double epsilon = 0.03;
 
    GaussianRbfKernel<> kernel(0.1);
    SquaredLoss<> loss;
 
    // generate dataset
    Wave problem;
    RegressionDataset training = problem.generateDataset(ell);
    RegressionDataset test = problem.generateDataset(tests);
 
    // define the machines
    KernelExpansion<RealVector> svm[2] = {
        KernelExpansion<RealVector>(),
        KernelExpansion<RealVector>()
    };
 
    // define the corresponding trainers
    AbstractTrainer<KernelExpansion<RealVector> >* trainer[2];
    trainer[0] = new EpsilonSvmTrainer<RealVector>(&kernel, C, epsilon);
    trainer[1] = new RegularizationNetworkTrainer<RealVector>(&kernel, gamma);
 
    for (unsigned int i=0; i<2; i++)
    {
        std::cout<<"METHOD"<<(i+1) <<" "<< trainer[i]->name().c_str()<<std::endl;
        std::cout<<"training ..."<<std::flush;
        trainer[i]->train(svm[i], training);
        std::cout<<"done"<<std::endl;
 
        Data<RealVector> output = svm[i](training.inputs());
        double train_error = loss.eval(training.labels(), output);
        std::cout<<"training error: "<<train_error<<std::endl;
        output = svm[i](test.inputs());
        double test_error = loss.eval(test.labels(), output);
        std::cout<<"    test error: "<<test_error<<"\n\n";
    }
 
    delete trainer[0];
    delete trainer[1];
}