covariance_functions.cpp

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/*
    SPDX-FileCopyrightText: 2014-2017 Max Planck Society.
    All rights reserved.
 
    SPDX-License-Identifier: BSD-3-Clause
*/
 
/**
 * @file
 * @date      2014-2017
 * @copyright Max Planck Society
 *
 * @author    Edgar D. Klenske <[email protected]>
 * @author    Stephan Wenninger <[email protected]>
 * @author    Raffi Enficiaud <[email protected]>
 *
 * @brief     The file holds the covariance functions that can be used with the GP class.
 */
 
#include "covariance_functions.h"
#include "math_tools.h"
 
namespace covariance_functions
{
 
/* PeriodicSquareExponential */
PeriodicSquareExponential::PeriodicSquareExponential() :
    hyperParameters(Eigen::VectorXd::Zero(4)), extraParameters(Eigen::VectorXd::Ones(1) * std::numeric_limits<double>::max()) { }
 
PeriodicSquareExponential::PeriodicSquareExponential(const Eigen::VectorXd &hyperParameters_) :
    hyperParameters(hyperParameters_), extraParameters(Eigen::VectorXd::Ones(1) * std::numeric_limits<double>::max()) { }
 
Eigen::MatrixXd PeriodicSquareExponential::evaluate(const Eigen::VectorXd &x, const Eigen::VectorXd &y)
{
 
    double lsSE0 = exp(hyperParameters(0));
    double svSE0 = exp(2 * hyperParameters(1));
    double lsP  = exp(hyperParameters(2));
    double svP  = exp(2 * hyperParameters(3));
 
    double plP  = exp(extraParameters(0));
 
    // Work with arrays internally, convert to matrix for return value.
    // This is because all the operations act elementwise, and Eigen::Arrays
    // do so, too.
 
    // Compute Distances
    Eigen::ArrayXXd squareDistanceXY = math_tools::squareDistance( x.transpose(), y.transpose());
 
    // fast version
    return svSE0 * ((-0.5 / std::pow(lsSE0, 2)) * squareDistanceXY).exp()
           + svP * (-2 * (((M_PI / plP) * squareDistanceXY.sqrt()).sin() / lsP).square()).exp();
 
    /* // verbose version
    // Square Exponential Kernel
    Eigen::ArrayXXd K0 = squareDistanceXY / std::pow(lsSE0, 2);
    K0 = svSE0 * (-0.5 * K0).exp();
 
    // Periodic Kernel
    Eigen::ArrayXXd K1 = (M_PI * squareDistanceXY.sqrt() / plP);
    K1 = K1.sin() / lsP;
    K1 = K1.square();
    K1 = svP * (-2 * K1).exp();
 
    // Combined Kernel
    return K0 + K1;
    */
}
 
void PeriodicSquareExponential::setParameters(const Eigen::VectorXd &params)
{
    this->hyperParameters = params;
}
 
void PeriodicSquareExponential::setExtraParameters(const Eigen::VectorXd &params)
{
    this->extraParameters = params;
}
 
const Eigen::VectorXd &PeriodicSquareExponential::getParameters() const
{
    return this->hyperParameters;
}
 
const Eigen::VectorXd &PeriodicSquareExponential::getExtraParameters() const
{
    return this->extraParameters;
}
 
int PeriodicSquareExponential::getParameterCount() const
{
    return 4;
}
 
int PeriodicSquareExponential::getExtraParameterCount() const
{
    return 1;
}
 
 
/* PeriodicSquareExponential2 */
PeriodicSquareExponential2::PeriodicSquareExponential2() :
    hyperParameters(Eigen::VectorXd::Zero(6)), extraParameters(Eigen::VectorXd::Ones(1) * std::numeric_limits<double>::max()) { }
 
PeriodicSquareExponential2::PeriodicSquareExponential2(const Eigen::VectorXd &hyperParameters_) :
    hyperParameters(hyperParameters_), extraParameters(Eigen::VectorXd::Ones(1) * std::numeric_limits<double>::max()) { }
 
Eigen::MatrixXd PeriodicSquareExponential2::evaluate(const Eigen::VectorXd &x, const Eigen::VectorXd &y)
{
 
    double lsSE0 = exp(hyperParameters(0));
    double svSE0 = exp(2 * hyperParameters(1));
    double lsP  = exp(hyperParameters(2));
    double svP  = exp(2 * hyperParameters(3));
    double lsSE1 = exp(hyperParameters(4));
    double svSE1 = exp(2 * hyperParameters(5));
 
    double plP  = exp(extraParameters(0));
 
    // Work with arrays internally, convert to matrix for return value.
    // This is because all the operations act elementwise, and Eigen::Arrays
    // do so, too.
 
    // Compute Distances
    Eigen::ArrayXXd squareDistanceXY = math_tools::squareDistance( x.transpose(), y.transpose());
 
    // fast version
    return svSE0 * ((-0.5 / std::pow(lsSE0, 2)) * squareDistanceXY).exp()
           + svP * (-2 * (((M_PI / plP) * squareDistanceXY.sqrt()).sin() / lsP).square()).exp()
           + svSE1 * ((-0.5 / std::pow(lsSE1, 2)) * squareDistanceXY).exp();
 
    /* // verbose version
    // Square Exponential Kernel
    Eigen::ArrayXXd K0 = squareDistanceXY / pow(lsSE0, 2);
    K0 = svSE0 * (-0.5 * K0).exp();
 
    // Periodic Kernel
    Eigen::ArrayXXd K1 = (M_PI * squareDistanceXY.sqrt() / plP);
    K1 = K1.sin() / lsP;
    K1 = K1.square();
    K1 = svP * (-2 * K1).exp();
 
    // Square Exponential Kernel
    Eigen::ArrayXXd K2 = squareDistanceXY / pow(lsSE1, 2);
    K2 = svSE1 * (-0.5 * K2).exp();
 
    // Combined Kernel
    return K0 + K1 + K2;
    */
}
 
void PeriodicSquareExponential2::setParameters(const Eigen::VectorXd &params)
{
    this->hyperParameters = params;
}
 
void PeriodicSquareExponential2::setExtraParameters(const Eigen::VectorXd &params)
{
    this->extraParameters = params;
}
 
const Eigen::VectorXd &PeriodicSquareExponential2::getParameters() const
{
    return this->hyperParameters;
}
 
const Eigen::VectorXd &PeriodicSquareExponential2::getExtraParameters() const
{
    return this->extraParameters;
}
 
int PeriodicSquareExponential2::getParameterCount() const
{
    return 6;
}
 
int PeriodicSquareExponential2::getExtraParameterCount() const
{
    return 1;
}
 
}  // namespace covariance_functions