Regularizer.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Regularizer
 * 
 * 
 *
 * \author      T. Glasmachers
 * \date        2010-2011
 *
 *
 * \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_OBJECTIVEFUNCTIONS_REGULARIZER_H
#define SHARK_OBJECTIVEFUNCTIONS_REGULARIZER_H
 
 
#include <shark/ObjectiveFunctions/AbstractObjectiveFunction.h>
 
namespace shark {
 
 
///
/// \brief One-norm of the input as an objective function
///
/// \par
/// The OneNormRegularizer is intended to be used together with other
/// objective functions within a CombinedObjectiveFunction, in order to
/// obtain a more smooth and more sparse solution.
/// \ingroup objfunctions
template<class SearchPointType = RealVector>
class OneNormRegularizer : public AbstractObjectiveFunction< SearchPointType, double >
{
public:
 
    /// Constructor
    OneNormRegularizer(std::size_t numVariables = 0):m_numberOfVariables(numVariables){
        this->m_features |= this->HAS_FIRST_DERIVATIVE;
    }
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "OneNormRegularizer"; }
 
    std::size_t numberOfVariables()const{
        return m_numberOfVariables;
    }
    
    bool hasScalableDimensionality()const{
        return true;
    }
 
    void setNumberOfVariables( std::size_t numberOfVariables ){
        m_numberOfVariables = numberOfVariables;
    }
 
    void setMask(SearchPointType const& mask){
        m_mask = mask;
    }
    SearchPointType const& mask() const{
        return m_mask;
    }
    /// Evaluates the objective function.
    double eval( SearchPointType const& input ) const{
        if(m_mask.empty()){
            return norm_1(input);
        }else{
            return norm_1(input * m_mask);
        }
    }
 
    /// Evaluates the objective function
    /// and calculates its gradient.
    double evalDerivative( SearchPointType const& input, SearchPointType& derivative ) const {
        SIZE_CHECK(m_mask.empty() || m_mask.size() == input.size());
        std::size_t ic = input.size();
        derivative.resize(ic);
        
        for (std::size_t i = 0; i != ic; i++){
            derivative(i) = boost::math::sign(input(i));
        }
        if(!m_mask.empty()){
            derivative *= m_mask;
        }
        return eval(input);
    }
private:
    SearchPointType m_mask;
    std::size_t m_numberOfVariables;
};
 
 
///
/// \brief Two-norm of the input as an objective function
///
/// \par
/// The TwoNormRegularizer is intended to be used together with other
/// objective functions within a CombinedObjectiveFunction, in order to
/// obtain a more smooth solution.
/// \ingroup objfunctions
template<class SearchPointType = RealVector>
class TwoNormRegularizer : public AbstractObjectiveFunction< SearchPointType, double >
{
public:
    typedef AbstractObjectiveFunction< SearchPointType, double > base_type;
 
    /// Constructor
    TwoNormRegularizer(std::size_t numVariables = 0):m_numberOfVariables(numVariables){
        this->m_features |=  base_type::HAS_FIRST_DERIVATIVE;
    }
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "TwoNormRegularizer"; }
 
    std::size_t numberOfVariables()const{
        return m_numberOfVariables;
    }
    
    bool hasScalableDimensionality()const{
        return true;
    }
 
    void setNumberOfVariables( std::size_t numberOfVariables ){
        m_numberOfVariables = numberOfVariables;
    }
    
    void setMask(SearchPointType const& mask){
        m_mask = mask;
    }
    SearchPointType const& mask()const{
        return m_mask;
    }
 
    /// Evaluates the objective function.
    double eval( SearchPointType const& input ) const
    { 
        if(m_mask.empty()){
            return 0.5*norm_sqr(input);
        }else{
            return 0.5 * sum(m_mask*sqr(input));
        }
    }
 
    /// Evaluates the objective function
    /// and calculates its gradient.
    double evalDerivative( SearchPointType const& input, SearchPointType & derivative ) const {
        if(m_mask.empty()){
            derivative = input;
        }else{
            derivative = m_mask * input;
        }
        return eval(input);
    }
private:
    std::size_t m_numberOfVariables;
    SearchPointType m_mask;
};
 
 
}
#endif // SHARK_CORE_REGULARIZER_H