C# 클래스 Accord.Statistics.Kernels.Sparse.SparseGaussian

상속: KernelBase, IKernel, IDistance, IReverseDistance

파일 보기 프로젝트 열기: accord-net/framework 1 사용 예제들

공개 메소드들

메소드	설명
Distance ( double x, double y ) : double	Computes the distance in input space between two points given in feature space.
Estimate ( double inputs, int samples, DoubleRange &range ) : SparseGaussian	Estimate appropriate values for sigma given a data set. This method uses a simple heuristic to obtain appropriate values for sigma in a radial basis function kernel. The heuristic is shown by Caputo, Sim, Furesjo and Smola, "Appearance-based object recognition using SVMs: which kernel should I use?", 2002.
Function ( double x, double y ) : double	Gaussian Kernel function.
ReverseDistance ( double x, double y ) : double	Computes the squared distance in input space between two points given in feature space.
SparseGaussian ( ) : System	Constructs a new Sparse Gaussian Kernel
SparseGaussian ( double sigma ) : System	Constructs a new Sparse Gaussian Kernel

메소드 상세

Distance() 공개 메소드

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

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

Estimate() 공개 정적인 메소드

Estimate appropriate values for sigma given a data set.

This method uses a simple heuristic to obtain appropriate values for sigma in a radial basis function kernel. The heuristic is shown by Caputo, Sim, Furesjo and Smola, "Appearance-based object recognition using SVMs: which kernel should I use?", 2002.

public static Estimate ( double inputs, int samples, DoubleRange &range ) : SparseGaussian
inputs	double	The data set.
samples	int	The number of random samples to analyze.
range	AForge.DoubleRange	The range of suitable values for sigma.
리턴	SparseGaussian

Function() 공개 메소드

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

ReverseDistance() 공개 메소드

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

SparseGaussian() 공개 메소드

Constructs a new Sparse Gaussian Kernel

public SparseGaussian ( ) : System
리턴	System

SparseGaussian() 공개 메소드

Constructs a new Sparse Gaussian Kernel

public SparseGaussian ( double sigma ) : System
sigma	double	The standard deviation for the Gaussian distribution. Default is 1.
리턴	System