C# 클래스 Accord.Statistics.Kernels.TaylorGaussian

상속: Accord.Statistics.Kernels.Gaussian, ITransform

공개 메소드들

메소드	설명
Add ( double a, double b, double result ) : double[]	Elementwise addition of a and b, storing in result.
Clone ( ) : object	Creates a new object that is a copy of the current instance.
Compress ( double weights, double supportVectors, double &c ) : double[]	Compress a set of support vectors and weights into a single parameter vector.
Distance ( double x, double y ) : double	Computes the distance `d(x,y)` between points x and y.
Function ( double x, double y ) : double	Gaussian Kernel function.
Product ( double a, double b, double result ) : void	Elementwise multiplication of scalar a and vector b, storing in result.
ReverseDistance ( double x, double y ) : double	Computes the squared distance in input space between two points given in feature space.
TaylorGaussian ( Accord.Statistics.Kernels.Gaussian gaussian, int degree = 1024 ) : System	Constructs a new TaylorGaussian kernel with the given sigma.
TaylorGaussian ( double sigma, int degree = 1024 ) : System	Constructs a new TaylorGaussian kernel with the given sigma.
Transform ( double input ) : double[]	Projects an input point into feature space.

메소드	설명
createCoefficients ( int degree ) : void

Elementwise addition of a and b, storing in result.

public Add ( double a, double b, double result ) : double[]
a	double	The first vector to add.
b	double	The second vector to add.
result	double	An array to store the result.
리턴	double[]

Creates a new object that is a copy of the current instance.

public Clone ( ) : object
리턴	object

Compress a set of support vectors and weights into a single parameter vector.

public Compress ( double weights, double supportVectors, double &c ) : double[]
weights	double	The weights associated with each support vector.
supportVectors	double	The support vectors.
c	double	The constant (bias) value.
리턴	double[]

Computes the distance d(x,y) between points x and y.

public Distance ( double x, double y ) : double
x	double	The first point x.
y	double	The second point y.
리턴	double

Gaussian Kernel function.

public Function ( double x, double y ) : double
x	double	Vector x in input space.
y	double	Vector y in input space.
리턴	double

Elementwise multiplication of scalar a and vector b, storing in result.

public Product ( double a, double b, double result ) : void
a	double	The scalar to be multiplied.
b	double	The vector to be multiplied.
result	double	An array to store the result.
리턴	void

Computes the squared distance in input space between two points given in feature space.

public ReverseDistance ( double x, double y ) : double
x	double	Vector x in feature (kernel) space.
y	double	Vector y in feature (kernel) space.
리턴	double

Constructs a new TaylorGaussian kernel with the given sigma.

public TaylorGaussian ( Accord.Statistics.Kernels.Gaussian gaussian, int degree = 1024 ) : System
gaussian	Accord.Statistics.Kernels.Gaussian	The original Gaussian kernel to be approximated.
degree	int	The Gaussian approximation degree. Default is 1024.
리턴	System

Constructs a new TaylorGaussian kernel with the given sigma.

public TaylorGaussian ( double sigma, int degree = 1024 ) : System
sigma	double	The kernel's sigma parameter.
degree	int	The Gaussian approximation degree. Default is 1024.
리턴	System

Projects an input point into feature space.

public Transform ( double input ) : double[]
input	double	The input point to be projected into feature space.
리턴	double[]