Package jgpml.covariancefunctions

Class CovLINard

java.lang.Object
jgpml.covariancefunctions.CovLINard
All Implemented Interfaces:
CovarianceFunction
public class CovLINard extends Object implements CovarianceFunction
Linear covariance function with Automatic Relevance Determination (ARD). The covariance function is parameterized as:

k(x^p,x^q) = x^p'*inv(P)*x^q

where the P matrix is diagonal with ARD parameters ell_1^2,...,ell_D^2, where D is the dimension of the input space. The hyperparameters are:

[ log(ell_1)
log(ell_2)
.
log(ell_D) ]

Note that there is no bias term; use covConst to add a bias.

  • Constructor Summary

    Constructors
    Constructor
    Description
    CovLINard(int inputDimension)
    Creates a new CovSEard 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
    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

    • CovLINard

      public CovLINard(int inputDimension)
      Creates a new CovSEard CovarianceFunction
      Parameters:
      inputDimension - muber of dimension of the input

  • 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