dk.bayes.math.covfunc

CovMatern52

Related Doc: package covfunc

case class CovMatern52(sf: Double, ell: Double) extends CovFunc with Product with Serializable

Matern covariance with v=5/2

Implementation based 'http://www.gaussianprocess.org/gpml/code/matlab/doc/index.html'

sf

- log of signal standard deviation

ell

- log of length scale standard deviation

Linear Supertypes
Serializable, Serializable, Product, Equals, CovFunc, AnyRef, Any
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  1. CovMatern52
  2. Serializable
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Instance Constructors

  1. new CovMatern52(sf: Double, ell: Double)

    sf

    - log of signal standard deviation

    ell

    - log of length scale standard deviation

Value Members

  1. final def !=(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  5. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. def cov(x1: Array[Double], x2: Array[Double]): Double

    Returns similarity between two vectors.

    Returns similarity between two vectors.

    x1

    [Dx1] vector

    x2

    [Dx1] vector

    Definition Classes
    CovMatern52CovFunc
  7. def cov(x1: Double, x2: Double): Double

    Definition Classes
    CovFunc
  8. def cov(x: Array[Double]): DenseMatrix[Double]

    Definition Classes
    CovFunc
  9. def cov(x: DenseMatrix[Double]): DenseMatrix[Double]

    x

    [N x D] vector, N - number of random variables, D - dimensionality of random variable

    returns

    [N x N] covariance matrix

    Definition Classes
    CovFunc
  10. def covNM(x: DenseMatrix[Double], z: DenseMatrix[Double]): DenseMatrix[Double]

    x

    [N x D] vector, N - number of random variables, D - dimensionality of random variable

    z

    [M x D] vector, N - number of random variables, D - dimensionality of random variable

    returns

    [N x M] covariance matrix

    Definition Classes
    CovFunc
  11. def df_dEll(x1: DenseVector[Double], x2: DenseVector[Double]): Double

    Returns derivative of similarity between two vectors with respect to ell.

    Returns derivative of similarity between two vectors with respect to ell.

    x1

    [Dx1] vector

    x2

    [Dx1] vector

  12. def df_dSf(x1: Array[Double], x2: Array[Double]): Double

    Returns derivative of similarity between two vectors with respect to sf.

    Returns derivative of similarity between two vectors with respect to sf.

    x1

    [Dx1] vector

    x2

    [Dx1] vector

  13. val ell: Double

    - log of length scale standard deviation

  14. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  15. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  18. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  19. final def notify(): Unit

    Definition Classes
    AnyRef
  20. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  21. val sf: Double

    - log of signal standard deviation

  22. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  23. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  24. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  25. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from CovFunc

Inherited from AnyRef

Inherited from Any

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