/usr/include/openturns/SVMKernelRegressionGradient.hxx is in libopenturns-dev 0.15-2.
This file is owned by root:root, with mode 0o644.
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/**
* @file SVMKernelRegressionGradient.hxx
* @brief Implementation of SVM regression gradient
*
* (C) Copyright 2005-2011 EDF-EADS-Phimeca
*
* This library 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 2.1 of the License.
*
* This library 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 this library; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
* @author: $LastChangedBy: schueller $
* @date: $LastChangedDate: 2008-05-05 08:50:15 +0200 (lun, 05 mai 2008) $
* Id: $Id: SVMKernelRegressionGradient.hxx 805 2008-05-05 06:50:15Z schueller $
*/
#ifndef OPENTURNS_SVMKERNELREGRESSIONGRADIENT_HXX
#define OPENTURNS_SVMKERNELREGRESSIONGRADIENT_HXX
#include "SVMKernel.hxx"
#include "NumericalMathGradientImplementation.hxx"
#include "SVMKernelRegressionEvaluation.hxx"
#include "NumericalSample.hxx"
namespace OpenTURNS
{
namespace Uncertainty
{
namespace Algorithm
{
class SVMKernelRegressionEvaluation;
/**
* @class SVMKernelRegressionGradient
*
* Implementation of SVM regression gradient
*/
class SVMKernelRegressionGradient
: public Base::Func::NumericalMathGradientImplementation
{
CLASSNAME;
public:
typedef NumericalMathGradientImplementation::Description Description;
typedef NumericalMathGradientImplementation::NumericalPoint NumericalPoint;
typedef Base::Stat::NumericalSample NumericalSample;
typedef NumericalMathGradientImplementation::InvalidArgumentException InvalidArgumentException;
typedef NumericalMathGradientImplementation::InternalException InternalException;
typedef Uncertainty::Algorithm::SVMKernel SVMKernel;
typedef NumericalMathGradientImplementation::Matrix Matrix;
typedef Uncertainty::Algorithm::SVMKernelRegressionEvaluation SVMKernelRegressionEvaluation;
typedef Pointer<SVMKernelRegressionEvaluation> SVMEvaluation;
/** Default constructor */
SVMKernelRegressionGradient();
/** Constructor with parameters */
SVMKernelRegressionGradient(const SVMKernel & kernel,
const NumericalPoint & lagrangeMultiplier,
const NumericalSample & dataIn,
const NumericalScalar constant);
/** Constructor from SVMKernelRegressionEvaluation */
SVMKernelRegressionGradient(const SVMEvaluation & p_svmEvaluation);
/** Virtual constructor */
virtual SVMKernelRegressionGradient * clone() const;
/** Comparison operator */
Bool operator ==(const SVMKernelRegressionGradient & other) const;
/** String converter */
virtual String __repr__() const;
/** Test for actual implementation */
virtual Bool isActualImplementation() const;
/** Gradient method */
virtual Matrix gradient(const NumericalPoint & inP) const
/* throw(InvalidArgumentException, InternalException) */;
/** Accessor for input point dimension */
virtual UnsignedLong getInputDimension() const
/* throw(InternalException) */;
/** Accessor for output point dimension */
virtual UnsignedLong getOutputDimension() const
/* throw(InternalException) */;
private:
protected:
SVMKernel kernel_;
NumericalPoint lagrangeMultiplier_;
NumericalSample dataIn_;
NumericalScalar constant_;
SVMEvaluation p_svmEvaluation_;
}; /* class SVMKernelRegressionGradient */
} /* namespace Func */
} /* namespace Base */
} /* namespace OpenTURNS */
#endif /* OPENTURNS_SVMKERNELREGRESSIONGRADIENT_HXX */
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