fr.lip6.kernel
Class AdaptativeGaussL2<T extends MLVector<?>>

java.lang.Object
  fr.lip6.kernel.Kernel<T>
      fr.lip6.kernel.AdaptativeGaussL2<T>

Type Parameters:

T - type of vectors in input space

All Implemented Interfaces:

java.io.Serializable

public class AdaptativeGaussL2<T extends MLVector<?>>
extends Kernel<T>

Gaussian kernel using L2 distance :
k(x,y) = exp(d(x,y)), with d L2 distance.

Author:

dpicard

See Also:

Serialized Form

Constructor Summary

AdaptativeGaussL2(T[] set, int[] clas)

Method Summary

double	getDefaultSigma()
void	setSigma(double sigma)
double	valueOf(T t1) kernel similarity to zero
double	valueOf(T t1, T t2) compute the kernel similarity between two element of input space

Methods inherited from class fr.lip6.kernel.Kernel

NormalizedValueOf

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AdaptativeGaussL2

public AdaptativeGaussL2(T[] set,
                         int[] clas)

Method Detail

valueOf

public double valueOf(T t1)

Description copied from class: Kernel

kernel similarity to zero

Specified by:

valueOf in class Kernel<T extends MLVector<?>>

Parameters:

t1 - the element to compute the similarity to zero

valueOf

public double valueOf(T t1,
                      T t2)

Description copied from class: Kernel

compute the kernel similarity between two element of input space

Specified by:

valueOf in class Kernel<T extends MLVector<?>>

Parameters:

t1 - first element

t2 - second element

Returns:

the kernel value

getDefaultSigma

public double getDefaultSigma()

Returns:

the sigma

setSigma

public void setSigma(double sigma)

Parameters:

sigma - the sigma to set