Class ARD

Automatic Relevance Determination Kernel

Inheritance

Object

KernelFunction

ARD

Implements

IKernelFunctionWithParams

IKernelFunction

Inherited Members

KernelFunction.thetaNames

KernelFunction.thetaValues

KernelFunction.thetaName2Index

KernelFunction.HyperNames

KernelFunction.EvaluateX1X2(Vector, Vector)

KernelFunction.EvaluateX1X2(Vector, Vector, Vector)

KernelFunction.EvaluateX(Vector)

KernelFunction.EvaluateX(Vector, Vector)

KernelFunction.Item[String]

KernelFunction.NameToIndex(String)

KernelFunction.IndexToName(Int32)

KernelFunction.ThetaCount

KernelFunction.Write(StreamWriter)

KernelFunction.Cholesky(IKernelFunction, Dictionary<Int32, Vector>)

Object.Equals(Object)

Object.Equals(Object, Object)

Object.GetHashCode()

Object.GetType()

Object.MemberwiseClone()

Object.ReferenceEquals(Object, Object)

Object.ToString()

Namespace: Microsoft.ML.Probabilistic.Distributions.Kernels

Assembly: Microsoft.ML.Probabilistic.dll

Syntax

[Serializable]
public class ARD : KernelFunction, IKernelFunctionWithParams, IKernelFunction

Constructors

ARD()

Default constructor

Declaration

public ARD()

ARD(Double[], Double)

Constructs an ARD kernel from a vector of log lengths, and a log signal variance

Declaration

[Construction(new string[]{"LogLengths", "LogSigVar"})]
public ARD(double[] logLengths, double logSigVar)

Parameters

Type	Name	Description
Double[]	logLengths
Double	logSigVar

Properties

Item[Int32]

Sets or gets a log hyper-parameter by index

Declaration

public override double this[int index] { get; set; }

Parameters

Type	Name	Description
Int32	index

Property Value

Type	Description
Double

Overrides

KernelFunction.Item[Int32]

LogLengths

Declaration

public double[] LogLengths { get; }

Property Value

Type	Description
Double[]

LogSigVar

Declaration

public double LogSigVar { get; }

Property Value

Type	Description
Double

TypeVersion

The static version for the derived class

Declaration

public override int TypeVersion { get; }

Property Value

Type	Description
Int32

Overrides

KernelFunction.TypeVersion

Methods

EvaluateX(Vector, ref Vector, ref Vector)

Evaluates the kernel for a single vector (which is used for both slots)

Declaration

public override double EvaluateX(Vector x, ref Vector xDeriv, ref Vector logThetaDeriv)

Parameters

Type	Name	Description
Vector	x	Vector
Vector	xDeriv	Derivative of the kernel value with respect to x
Vector	logThetaDeriv	Derivative of the kernel value with respect to the log hyper-parameters

Returns

Type	Description
Double

Overrides

KernelFunction.EvaluateX(Vector, ref Vector, ref Vector)

EvaluateX1X2(Vector, Vector, ref Vector, ref Vector)

Evaluates the kernel for a pair of vectors

Declaration

public override double EvaluateX1X2(Vector x1, Vector x2, ref Vector x1Deriv, ref Vector logThetaDeriv)

Parameters

Type	Name	Description
Vector	x1	First vector
Vector	x2	Second vector
Vector	x1Deriv	Derivative of the kernel value with respect to x1 input vector
Vector	logThetaDeriv	Derivative of the kernel value with respect to the log hyper-parameters

Returns

Type	Description
Double

Overrides

KernelFunction.EvaluateX1X2(Vector, Vector, ref Vector, ref Vector)

InitialiseFromData(IList<Vector>, Vector)

Initialises the parameters from data

Declaration

public void InitialiseFromData(IList<Vector> X, Vector y)

Parameters

Type	Name	Description
IList<Vector>	X	X data - initialises lengths
Vector	y	y data - initialises signal standard deviation

Read(StreamReader)

Reads the parameters in from a stream

Declaration

public override void Read(StreamReader sr)

Parameters

Type	Name	Description
StreamReader	sr	Stream reader

Overrides

KernelFunction.Read(StreamReader)

SetupParams(Double[], Double)

Sets up names and values of parameters

Declaration

public void SetupParams(double[] logLengths, double logSigVar)

Parameters

Type	Name	Description
Double[]	logLengths	Log of the lengths
Double	logSigVar	Log of the signal variance

Implements

IKernelFunctionWithParams

IKernelFunction