TaylorGaussian Structure

Taylor approximation for the explicit Gaussian kernel.

Namespace: Accord.Statistics.Kernels
Assembly: Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0

Syntax

[SerializableAttribute]
public struct TaylorGaussian : ITransform, 
	IKernel, IKernel<double[]>, ICloneable, ITransform<double[]>, 
	ILinear, ILinear<double[]>, IReverseDistance, IDistance, 
	IDistance<double[]>, IDistance<double[], double[]>

<SerializableAttribute>
Public Structure TaylorGaussian
	Implements ITransform, IKernel, IKernel(Of Double()), 
	ICloneable, ITransform(Of Double()), ILinear, ILinear(Of Double()), 
	IReverseDistance, IDistance, IDistance(Of Double()), IDistance(Of Double(), Double())

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The TaylorGaussian type exposes the following members.

Constructors

	Name	Description
	TaylorGaussian(Double, Int32)	Constructs a new TaylorGaussian kernel with the given sigma.
	TaylorGaussian(Gaussian, Int32)	Constructs a new TaylorGaussian kernel with the given sigma.

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Properties

	Name	Description
	Degree	Gets or sets the approximation degree for this kernel. Default is 1024.
	Gaussian	Gets or sets the Gaussian kernel being approximated by this Taylor expansion.

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Methods

	Name	Description
	Add	Elementwise addition of a and b, storing in result.
	Clone	Creates a new object that is a copy of the current instance.
	Compress	Compress a set of support vectors and weights into a single parameter vector.
	CreateVector	Creates an input vector from the given double values.
	Distance	Computes the distance d(x,y) between points x and y.
	Equals	Indicates whether this instance and a specified object are equal. (Inherited from ValueType.)
	Function	Gaussian Kernel function.
	GetHashCode	Returns the hash code for this instance. (Inherited from ValueType.)
	GetLength	Gets the number of parameters in the input vectors.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Product(Double, Double, Double)	Elementwise multiplication of scalar a and vector b, storing in result.
	Product(Double, Double, Double)	Elementwise multiplication of vector a and vector b, accumulating in result.
	ReverseDistance	Computes the squared distance in input space between two points given in feature space.
	ToDouble	Converts the input vectors to a double-precision representation.
	ToString	Returns the fully qualified type name of this instance. (Inherited from ValueType.)
	Transform(Double)	Projects an input point into feature space.
	Transform(Double)	Projects a set of input points into feature space.

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Extension Methods

	Name	Description
	Distance	Computes the kernel distance for a kernel function even if it doesn't implement the IDistance interface. Can be used to check the proper implementation of the distance function. (Defined by Tools.)
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Remarks

References:

Lin, Keng-Pei, and Ming-Syan Chen. "Efficient kernel approximation for large-scale support vector machine classification." Proceedings of the Eleventh SIAM International Conference on Data Mining. 2011. Available on: http://epubs.siam.org/doi/pdf/10.1137/1.9781611972818.19

Reference

Accord.Statistics.Kernels Namespace