Package jgpml.covariancefunctions

Class CovLINone

java.lang.Object

jgpml.covariancefunctions.CovLINone

All Implemented Interfaces:

CovarianceFunction

public class CovLINone
extends Object
implements CovarianceFunction

Linear covariance function with a single hyperparameter. The covariance function is parameterized as:

k(x^p,x^q) = x^p'*inv(P)*x^q + 1./t2;

where the P matrix is t2 times the unit matrix. The second term plays the role of the bias. The hyperparameter is:

[ log(sqrt(t2)) ]

Constructor Summary

Constructors

Constructor	Description
CovLINone()

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
static void	main(String[] args)
int	numParameters()	Returns the number of hyperparameters of thisCovarianceFunction

Methods inherited from class java.lang.Object

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

Constructor Details

CovLINone

public CovLINone()

Method Details

main

public static void main(String[] args)

numParameters

public int numParameters()

Returns the number of hyperparameters of thisCovarianceFunction

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