cmp::covariance::WhiteNoise Class Reference

White noise covariance function. More...

#include <covariance.h>

Inheritance diagram for cmp::covariance::WhiteNoise:

Collaboration diagram for cmp::covariance::WhiteNoise:

Public Member Functions
	WhiteNoise (const WhiteNoise &)=default
	WhiteNoise (WhiteNoise &&)=default
WhiteNoise &	operator= (const WhiteNoise &)=default
WhiteNoise &	operator= (WhiteNoise &&)=default
	WhiteNoise (const size_t &indexX=-1, double tol=1e-10)
double	eval (const Eigen::VectorXd &x1, const Eigen::VectorXd &x2, const Eigen::VectorXd &par) const
double	evalGradient (const Eigen::VectorXd &x1, const Eigen::VectorXd &x2, const Eigen::VectorXd &par, const size_t &i) const
double	evalHessian (const Eigen::VectorXd &x1, const Eigen::VectorXd &x2, const Eigen::VectorXd &par, const size_t &i, const size_t &j) const
Public Member Functions inherited from cmp::covariance::Covariance
virtual	~Covariance ()=default

Static Public Member Functions
static std::shared_ptr< Covariance >	make (const int &i=-1, double tol=1e-10)

Private Attributes
int	indexX_ {-1}
	Dimension index to evaluate, or -1 for the full isotropic kernel.
double	tol_ {1e-10}
	Distance tolerance threshold.

Detailed Description

White noise covariance function.

Mathematical Formulation Evaluates the Kronecker delta kernel between two inputs \(\mathbf{x}_1, \mathbf{x}_2\):

\[ k(\mathbf{x}_1, \mathbf{x}_2) = \begin{cases} 1.0 & \text{if } d < \text{tol} \\ 0.0 & \text{otherwise} \end{cases} \]

where \(d\) is the distance between the two points.

Implementation Algorithm Computes distance \(d\) and compares it to a small tolerance threshold tol_.

Constructor & Destructor Documentation

WhiteNoise() [1/3]

cmp::covariance::WhiteNoise::WhiteNoise ( const WhiteNoise &  )

default

WhiteNoise() [2/3]

cmp::covariance::WhiteNoise::WhiteNoise ( WhiteNoise &&  )

default

WhiteNoise() [3/3]

cmp::covariance::WhiteNoise::WhiteNoise ( const size_t &  indexX = -1, double  tol = 1e-10  )

inline

Member Function Documentation

eval()

double cmp::covariance::WhiteNoise::eval ( const Eigen::VectorXd &  x1, const Eigen::VectorXd &  x2, const Eigen::VectorXd &  par  ) const

inlinevirtual

Implements cmp::covariance::Covariance.

evalGradient()

double cmp::covariance::WhiteNoise::evalGradient ( const Eigen::VectorXd &  x1, const Eigen::VectorXd &  x2, const Eigen::VectorXd &  par, const size_t &  i  ) const

inlinevirtual

Implements cmp::covariance::Covariance.

evalHessian()

double cmp::covariance::WhiteNoise::evalHessian ( const Eigen::VectorXd &  x1, const Eigen::VectorXd &  x2, const Eigen::VectorXd &  par, const size_t &  i, const size_t &  j  ) const

inlinevirtual

Implements cmp::covariance::Covariance.

make()

static std::shared_ptr< Covariance > cmp::covariance::WhiteNoise::make ( const int &  i = -1, double  tol = 1e-10  )

inlinestatic

operator=() [1/2]

WhiteNoise & cmp::covariance::WhiteNoise::operator= ( const WhiteNoise &  )

default

operator=() [2/2]

WhiteNoise & cmp::covariance::WhiteNoise::operator= ( WhiteNoise &&  )

default

Member Data Documentation

indexX_

int cmp::covariance::WhiteNoise::indexX_ {-1}

private

Dimension index to evaluate, or -1 for the full isotropic kernel.

tol_

double cmp::covariance::WhiteNoise::tol_ {1e-10}

private

Distance tolerance threshold.

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The documentation for this class was generated from the following file:

• include/covariance.h