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

Наследование: KernelBase, IKernel, IDistance, IReverseDistance

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Открытые методы

Метод	Описание
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

Описание методов

Distance() публичный Метод

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.
Результат	double

Estimate() публичный статический Метод

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.
Результат	SparseGaussian

Function() публичный Метод

Gaussian Kernel function.

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

ReverseDistance() публичный Метод

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.
Результат	double

SparseGaussian() публичный Метод

Constructs a new Sparse Gaussian Kernel

public SparseGaussian ( ) : System
Результат	System

SparseGaussian() публичный Метод

Constructs a new Sparse Gaussian Kernel

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