LinearRegression.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Linear Regression
 * 
 * \par
 * This implementation is based on a class removed from the
 * LinAlg package, written by M. Kreutz in 1998.
 * 
 * 
 *
 * \author      T. Glasmachers
 * \date        2007-2011
 *
 *
 * \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/>.
 *
 */
//===========================================================================
 
 
#ifndef SHARK_ALGORITHMS_TRAINERS_LINEARREGRESSION_H
#define SHARK_ALGORITHMS_TRAINERS_LINEARREGRESSION_H
 
#include <shark/Core/DLLSupport.h>
#include <shark/Models/LinearModel.h>
#include <shark/Core/IParameterizable.h>
#include <shark/Algorithms/Trainers/AbstractTrainer.h>
 
namespace shark {
 
 
///  \brief Linear Regression
///
///  Linear Regression builds an affine linear model
///  \f$ f(x) = A x + b \f$ minimizing the squared
///  error from a dataset of pairs of vectors (x, y).
///  That is, the error
///  \f$ \sum_i (f(x_i) - y_i)^2 \f$ is minimized.
///  The solution to this problem is found analytically.
/// \ingroup supervised_trainer
class LinearRegression : public AbstractTrainer<LinearModel<> >, public IParameterizable<>
{
public:
    SHARK_EXPORT_SYMBOL LinearRegression(double regularization = 0.0);
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "LinearRegression"; }
 
    double regularization() const{ 
        return m_regularization; 
    }
    void setRegularization(double regularization) {
        RANGE_CHECK(regularization >= 0.0);
        m_regularization = regularization;
    }
 
    RealVector parameterVector() const {
        RealVector param(1);
        param(0) = m_regularization;
        return param;
    }
    void setParameterVector(const RealVector& param) {
        SIZE_CHECK(param.size() == 1);
        m_regularization = param(0);
    }
    size_t numberOfParameters() const{ 
        return 1; 
    }
 
    SHARK_EXPORT_SYMBOL void train(LinearModel<>& model, LabeledData<RealVector, RealVector> const& dataset);
protected:
    double m_regularization;
};
 
 
}
#endif