StationaryKernel

Instance Constructors

new StationaryKernel(amplitude: Double = 1.0, noise: Double = 1e-4, lengthScale: DenseVector[Double] = DenseVector(1.0))

amplitude
the covariance amplitude
noise
the observation noise
lengthScale
the length scale of the kernel. This controls the complexity of the kernel, or the degree to which it can vary within a given region of the function's domain. Higher values allow less variation, and lower values allow more.

Abstract Value Members

abstract def fromPairwiseDistances(dists: DenseMatrix[Double]): DenseMatrix[Double]

Computes the kernel function from the pairwise distances between points.
Computes the kernel function from the pairwise distances between points. Implementing classes should override this to provide the specific kernel computation.
dists
the m x p matrix of pairwise distances between m and p points
returns
the m x p covariance matrix

Attributes
protected[com.linkedin.photon.ml.hyperparameter.estimators.kernels]
abstract def getInitialKernel(x: DenseMatrix[Double], y: DenseVector[Double]): Kernel

Builds a kernel with initial settings, based on the observations
Builds a kernel with initial settings, based on the observations
x
the observed features
y
the observed labels
returns
the initial kernel

Definition Classes
Kernel
abstract def withParams(theta: DenseVector[Double]): Kernel

Creates a new kernel function of the same type, with the given parameters
Creates a new kernel function of the same type, with the given parameters
theta
the parameter vector for the new kernel function
returns
the new kernel function

Definition Classes
Kernel

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
val amplitudeScale: Double
def apply(x1: DenseMatrix[Double], x2: DenseMatrix[Double]): DenseMatrix[Double]

Applies the kernel functions to the two sets of points
Applies the kernel functions to the two sets of points
x1
the matrix containing the first set of points, where each of the m rows is a point in the space
x2
the matrix containing the second set of points, where each of the p rows is a point in the space
returns
the m x p covariance matrix

Definition Classes
StationaryKernel → Kernel
def apply(x: DenseMatrix[Double]): DenseMatrix[Double]

Applies the kernel function to the given points
Applies the kernel function to the given points
x
the matrix of points, where each of the m rows is a point in the space
returns
the m x m covariance matrix

Definition Classes
StationaryKernel → Kernel
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def expandDimensions(param: DenseVector[Double], dim: Int): DenseVector[Double]

If only one parameter value has been specified, builds a new vector with the single value repeated to fill all dimensions
If only one parameter value has been specified, builds a new vector with the single value repeated to fill all dimensions
param
the initial parameters
dim
the dimensions of the final vector
returns
the vector with all dimensions specified

Definition Classes
Kernel
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 getParams: DenseVector[Double]

Returns the kernel parameters as a vector
Returns the kernel parameters as a vector
returns
the kernel parameters

Definition Classes
StationaryKernel → Kernel
def hashCode(): Int

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

Definition Classes
Any
val lengthScaleMax: Double
def logLikelihood(x: DenseMatrix[Double], y: DenseVector[Double]): Double

Computes the log likelihood of the kernel parameters
Computes the log likelihood of the kernel parameters
x
the observed features
y
the observed labels
returns
the log likelihood

Definition Classes
StationaryKernel → Kernel
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val noiseScale: Double
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def pairwiseDistances(x1: DenseMatrix[Double], x2: DenseMatrix[Double]): DenseMatrix[Double]

Computes the pairwise squared distance between the points in two sets
Computes the pairwise squared distance between the points in two sets
x1
the matrix containing the first set of points, where each of the m rows is a point in the space
x2
the matrix containing the second set of points, where each of the p rows is a point in the space
returns
the m x p matrix of distances

Attributes
protected[com.linkedin.photon.ml.hyperparameter.estimators.kernels]
def pairwiseDistances(x: DenseMatrix[Double]): DenseMatrix[Double]

Computes the pairwise squared distances between all points
Computes the pairwise squared distances between all points
x
the matrix of points, where each of the m rows is a point in the space
returns
the m x m matrix of distances

Attributes
protected[com.linkedin.photon.ml.hyperparameter.estimators.kernels]
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 extends Kernel

Instance Constructors

new StationaryKernel(amplitude: Double = 1.0, noise: Double = 1e-4, lengthScale: DenseVector[Double] = DenseVector(1.0))

Abstract Value Members

abstract def fromPairwiseDistances(dists: DenseMatrix[Double]): DenseMatrix[Double]

abstract def getInitialKernel(x: DenseMatrix[Double], y: DenseVector[Double]): Kernel

abstract def withParams(theta: DenseVector[Double]): Kernel

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val amplitudeScale: Double

def apply(x1: DenseMatrix[Double], x2: DenseMatrix[Double]): DenseMatrix[Double]

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

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def expandDimensions(param: DenseVector[Double], dim: Int): DenseVector[Double]

def finalize(): Unit

final def getClass(): Class[_]

def getParams: DenseVector[Double]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val lengthScaleMax: Double

def logLikelihood(x: DenseMatrix[Double], y: DenseVector[Double]): Double

final def ne(arg0: AnyRef): Boolean

val noiseScale: Double

final def notify(): Unit

final def notifyAll(): Unit

def pairwiseDistances(x1: DenseMatrix[Double], x2: DenseMatrix[Double]): DenseMatrix[Double]

def pairwiseDistances(x: DenseMatrix[Double]): DenseMatrix[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 Kernel

Inherited from AnyRef

Inherited from Any

Ungrouped