ScaledKernel.h

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//===========================================================================
/*!
 * 
 *
 * \brief       A kernel function that wraps a member kernel and multiplies it by a scalar.
 * 
 * 
 *
 * \author      M. Tuma, T. Glasmachers, O. Krause
 * \date        2012
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <http://shark-ml.org/>
 * 
 * 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_MODELS_KERNELS_SCALED_KERNEL_H
#define SHARK_MODELS_KERNELS_SCALED_KERNEL_H


#include <shark/Models/Kernels/AbstractKernelFunction.h>
namespace shark {


/// \brief Scaled version of a kernel function
///
/// For a positive definite kernel k, the scaled kernel
/// \f[ \tilde k(x_1, x_2) := c k(x_1, x_2) \f]
/// is again a positive definite kernel function as long as \f$ c > 0 \f$.
template<class InputType=RealVector>
class ScaledKernel : public AbstractKernelFunction<InputType>
{
private:
    typedef AbstractKernelFunction<InputType> base_type;
public:
    typedef typename base_type::BatchInputType BatchInputType;
    typedef typename base_type::ConstInputReference ConstInputReference;
    typedef typename base_type::ConstBatchInputReference ConstBatchInputReference;

    ScaledKernel( AbstractKernelFunction<InputType>* base, double factor = 1.0 )
    : m_base( base ),
      m_factor( factor )
    {
        RANGE_CHECK( factor > 0 );
        SHARK_ASSERT( base != NULL );
        if ( m_base->hasFirstInputDerivative() ) 
            this->m_features|=base_type::HAS_FIRST_INPUT_DERIVATIVE;
        if ( m_base->hasFirstParameterDerivative() ) 
            this->m_features|=base_type::HAS_FIRST_PARAMETER_DERIVATIVE;
    }

    /// \brief From INameable: return the class name.
    std::string name() const
    { return "ScaledKernel"; }

    RealVector parameterVector() const {
        return m_base->parameterVector();
    }
    void setParameterVector(RealVector const& newParameters) {
        m_base->setParameterVector(newParameters);
    }

    std::size_t numberOfParameters() const {
        return m_base->numberOfParameters();
    }
    
    ///\brief creates the internal state of the kernel
    boost::shared_ptr<State> createState()const{
        return m_base->createState();
    }
    
    const double factor() {
        return m_factor;
    }
    void setFactor( double f ) {
        RANGE_CHECK( f > 0 );
        m_factor = f;
    }
    
    const base_type* base() const {
        return m_base;
    }

    double eval(ConstInputReference x1, ConstInputReference x2) const {
        SIZE_CHECK(x1.size() == x2.size());
        return m_factor * m_base->eval(x1, x2);
    }
    
    void eval(ConstBatchInputReference x1, ConstBatchInputReference x2, RealMatrix& result) const{
        m_base->eval(x1, x2,result);
        result *= m_factor;
    }

    void eval(ConstBatchInputReference x1, ConstBatchInputReference x2, RealMatrix& result, State& state) const{
        m_base->eval(x1, x2,result,state);
        result *= m_factor;
    }
    
    /// calculates the weighted derivate w.r.t. the parameters of the base kernel
    void weightedParameterDerivative(
        ConstBatchInputReference batchX1, 
        ConstBatchInputReference batchX2, 
        RealMatrix const& coefficients,
        State const& state, 
        RealVector& gradient
    ) const{
        m_base->weightedParameterDerivative( batchX1, batchX2, coefficients, state, gradient );
        gradient *= m_factor;
    }
    /// calculates the weighted derivate w.r.t. argument \f$ x_1 \f$
    void weightedInputDerivative( 
        ConstBatchInputReference batchX1, 
        ConstBatchInputReference batchX2, 
        RealMatrix const& coefficientsX2,
        State const& state, 
        BatchInputType& gradient
    ) const{
        SIZE_CHECK(coefficientsX2.size1() == batchSize(batchX1));
        SIZE_CHECK(coefficientsX2.size2() == batchSize(batchX2));
        m_base->weightedInputDerivative( batchX1, batchX2, coefficientsX2, state, gradient );
        gradient *= m_factor;
    }
    
    void read(InArchive& ar){
        ar >> m_factor;
        ar >> *m_base;
    }

    /// \brief The kernel does not serialize anything
    void write(OutArchive& ar) const{
        ar << m_factor;
        //const cast needed to prevent warning
        ar << const_cast<AbstractKernelFunction<InputType> const&>(*m_base);
    }

protected:
    AbstractKernelFunction<InputType>* m_base; ///< kernel to scale
    double m_factor; ///< scaling factor
};

typedef ScaledKernel<> DenseScaledKernel;
typedef ScaledKernel<CompressedRealVector> CompressedScaledKernel;

}
#endif