CovSEARDIso

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

Squared Exponential covariance function with isotropic distance measure. The covariance function is parameterized as:

k(x^p,xq) = sf^{2 * exp(-(x}p - x^{q)'*inv(P)*(x}p - x^q)/2)

where the P matrix is ell^{2 times the unit matrix and sf}2 is the signal variance.

sf: - log of signal standard deviation
ell: - vector of log of length scale standard deviation

Linear Supertypes

Serializable, Serializable, Product, Equals, CovFunc, AnyRef, Any

Instance Constructors

new CovSEARDIso(sf: Double, ell: Array[Double])

sf
- log of signal standard deviation
ell
- vector of log of length scale standard deviation

Value Members

final def !=(arg0: Any): Boolean

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

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

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

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
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
CovSEARDIso → CovFunc
def cov(x1: DenseVector[Double], x2: DenseVector[Double]): Double
def cov(x1: Double, x2: Double): Double

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

Definition Classes
CovFunc
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
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
def covWithD(x: DenseMatrix[Double], computeDfDSf: Boolean = true, computeDfDell: Boolean = true): (DenseMatrix[Double], Option[DenseMatrix[Double]], Option[Array[DenseMatrix[Double]]])

x
[N x D] vector, N - number of random variables, D - dimensionality of random variable
returns
([N x N] covariance matrix, [N x N] partial derivatives matrix with respect to sf parameter, array of [NxN] partial derivative matrix with respect to ell parameters)
val ell: Array[Double]

- vector of log of length scale standard deviation
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

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

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

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val sf: Double

- log of signal standard deviation
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package covfunc

case class CovSEARDIso(sf: Double, ell: Array[Double]) extends CovFunc with Product with Serializable

Instance Constructors

new CovSEARDIso(sf: Double, ell: Array[Double])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def cov(x1: Array[Double], x2: Array[Double]): Double

def cov(x1: DenseVector[Double], x2: DenseVector[Double]): Double

def cov(x1: Double, x2: Double): Double

def cov(x: Array[Double]): DenseMatrix[Double]

def cov(x: DenseMatrix[Double]): DenseMatrix[Double]

def covNM(x: DenseMatrix[Double], z: DenseMatrix[Double]): DenseMatrix[Double]

def covWithD(x: DenseMatrix[Double], computeDfDSf: Boolean = true, computeDfDell: Boolean = true): (DenseMatrix[Double], Option[DenseMatrix[Double]], Option[Array[DenseMatrix[Double]]])

val ell: Array[Double]

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val sf: Double

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

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from CovFunc

Inherited from AnyRef

Inherited from Any

Ungrouped