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

Inheritance: KernelBase, IKernel, IDistance, IReverseDistance

Show file Open project: accord-net/framework Class Usage Examples

Public Methods

Method	Description
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

Method Details

Distance() public method

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.
return	double

Estimate() public static method

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.
return	SparseGaussian

Function() public method

Gaussian Kernel function.

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

ReverseDistance() public method

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.
return	double

SparseGaussian() public method

Constructs a new Sparse Gaussian Kernel

public SparseGaussian ( ) : System
return	System

SparseGaussian() public method

Constructs a new Sparse Gaussian Kernel

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