StationaryKernel

Stationary Covariance Functions

C(x,y) = C_s(||x-y||)

An abstract representation of stationary kernel functions, this requires an implicit variable which represents how addition, subtraction etc are carried out for the input domain T

Linear Supertypes

CovarianceFunction[T, V, M], Kernel[T, V], Serializable, Serializable, AnyRef, Any

Known Subclasses

CauchyKernel, CubicSplineARDKernel, CubicSplineKernel, GaussianSpectralKernel, GenericMaternARDKernel, GenericMaternKernel, GenericRBFKernel, LaplacianKernel, RBFKernel, RationalQuadraticKernel, SEKernel, TStudentKernel, WaveletKernel

Instance Constructors

new StationaryKernel()(implicit ev: Field[T])

Abstract Value Members

abstract def buildCrossKernelMatrix[S <: Seq[T]](dataset1: S, dataset2: S): M

Definition Classes
CovarianceFunction
abstract def buildKernelMatrix[S <: Seq[T]](mappedData: S, length: Int): KernelMatrix[M]

Definition Classes
CovarianceFunction
abstract def evalAt(config: Map[String, Double])(x: T): V
abstract def gradientAt(config: Map[String, Double])(x: T, y: T): Map[String, V]

Definition Classes
CovarianceFunction
abstract val hyper_parameters: List[String]

Definition Classes
CovarianceFunction

Concrete 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 block(h: String*): Unit

Definition Classes
CovarianceFunction
def block_all_hyper_parameters: Unit

Definition Classes
CovarianceFunction
var blocked_hyper_parameters: List[String]

Definition Classes
CovarianceFunction
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def effective_hyper_parameters: List[String]

Definition Classes
CovarianceFunction
def effective_state: Map[String, Double]

Definition Classes
CovarianceFunction
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def eval(x: T): V
def evaluate(x: T, y: T): V

Definition Classes
CovarianceFunction → Kernel
def evaluateAt(config: Map[String, Double])(x: T, y: T): V

Definition Classes
StationaryKernel → CovarianceFunction
def finalize(): Unit

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

Definition Classes
AnyRef → Any
def gradient(x: T, y: T): Map[String, V]

Definition Classes
CovarianceFunction
def hashCode(): Int

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
def setHyperParameters(h: Map[String, Double]): StationaryKernel.this.type

Definition Classes
CovarianceFunction
var state: Map[String, Double]

Definition Classes
CovarianceFunction
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
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 kernels

abstract class StationaryKernel[T, V, M] extends CovarianceFunction[T, V, M]

Stationary Covariance Functions

Instance Constructors

new StationaryKernel()(implicit ev: Field[T])

Abstract Value Members

abstract def buildCrossKernelMatrix[S <: Seq[T]](dataset1: S, dataset2: S): M

abstract def buildKernelMatrix[S <: Seq[T]](mappedData: S, length: Int): KernelMatrix[M]

abstract def evalAt(config: Map[String, Double])(x: T): V

abstract def gradientAt(config: Map[String, Double])(x: T, y: T): Map[String, V]

abstract val hyper_parameters: List[String]

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def block(h: String*): Unit

def block_all_hyper_parameters: Unit

var blocked_hyper_parameters: List[String]

def clone(): AnyRef

def effective_hyper_parameters: List[String]

def effective_state: Map[String, Double]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def eval(x: T): V

def evaluate(x: T, y: T): V

def evaluateAt(config: Map[String, Double])(x: T, y: T): V

def finalize(): Unit

final def getClass(): Class[_]

def gradient(x: T, y: T): Map[String, V]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def setHyperParameters(h: Map[String, Double]): StationaryKernel.this.type

var state: Map[String, Double]

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from CovarianceFunction[T, V, M]

Inherited from Kernel[T, V]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped