Package jgpml.covariancefunctions

Class CovNoise

java.lang.Object

jgpml.covariancefunctions.CovNoise

All Implemented Interfaces:

CovarianceFunction

public class CovNoise
extends Object
implements CovarianceFunction

Independent covariance function, ie "white noise", with specified variance. The covariance function is specified as:

k(x^p,x^q) = s2 * \delta(p,q)

where s2 is the noise variance and \delta(p,q) is a Kronecker delta function which is 1 iff p=q and zero otherwise. The hyperparameter is

[ log(sqrt(s2)) ]

Constructor Summary

Constructors

Constructor	Description
CovNoise()	Creates a new CovNoise CovarianceFunction

Method Summary

Modifier and Type	Method	Description
Jama.Matrix	compute(Jama.Matrix loghyper, Jama.Matrix X)	Compute covariance matrix of a dataset X
Jama.Matrix[]	compute(Jama.Matrix loghyper, Jama.Matrix X, Jama.Matrix Xstar)	Compute compute test set covariances
Jama.Matrix	computeDerivatives(Jama.Matrix loghyper, Jama.Matrix X, int index)	Coompute the derivatives of this CovarianceFunction with respect to the hyperparameter with index idx
int	numParameters()	Returns the number of hyperparameters of CovSEard

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

CovNoise

public CovNoise()

Creates a new CovNoise CovarianceFunction

Method Details

numParameters

public int numParameters()

Returns the number of hyperparameters of CovSEard

Specified by:

numParameters in interface CovarianceFunction

Returns:

number of hyperparameters

compute

public Jama.Matrix compute(Jama.Matrix loghyper,
 Jama.Matrix X)

Compute covariance matrix of a dataset X

Specified by:

compute in interface CovarianceFunction

Parameters:

loghyper - column Matrix of hyperparameters

X - input dataset

Returns:

K covariance Matrix

compute

public Jama.Matrix[] compute(Jama.Matrix loghyper,
 Jama.Matrix X,
 Jama.Matrix Xstar)

Compute compute test set covariances

Specified by:

compute in interface CovarianceFunction

Parameters:

loghyper - column Matrix of hyperparameters

X - input dataset

Xstar - test set

Returns:

[K(Xstar, Xstar) K(X,Xstar)]

computeDerivatives

public Jama.Matrix computeDerivatives(Jama.Matrix loghyper,
 Jama.Matrix X,
 int index)

Coompute the derivatives of this CovarianceFunction with respect to the hyperparameter with index idx

Specified by:

computeDerivatives in interface CovarianceFunction

Parameters:

loghyper - hyperparameters

X - input dataset

index - hyperparameter index

Returns:

Matrix of derivatives