jsat.distributions.kernels

Class PukKernel

java.lang.Object

jsat.distributions.kernels.BaseL2Kernel

jsat.distributions.kernels.PukKernel

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, KernelTrick, Parameterized

public class PukKernel
extends BaseL2Kernel
implements Parameterized

The PUK kernel is an alternative to the RBF Kernel. By altering the omega parameter the behavior of the PUK kernel can be controlled. The sigma parameter works in the same way as the RBF Kernel.

See: Üstün, B., Melssen, W. J.,&Buydens, L. M. C. (2006). Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems, 81(1), 29–40. doi:10.1016/j.chemolab.2005.09.003

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
PukKernel(double sigma, double omega) Creates a new PUK Kernel

Method Summary

Modifier and Type	Method and Description
PukKernel	clone()
double	eval(int a, int b, java.util.List<? extends Vec> trainingSet, java.util.List<java.lang.Double> cache) Produces the correct kernel evaluation given the training set and the cache generated by KernelTrick.getAccelerationCache(List).
double	eval(int a, Vec b, java.util.List<java.lang.Double> qi, java.util.List<? extends Vec> vecs, java.util.List<java.lang.Double> cache) Computes the kernel product between one vector in the original list of vectors with that of another vector not from the original list, but had information generated by KernelTrick.getQueryInfo(jsat.linear.Vec).
double	eval(Vec a, Vec b) Evaluate this kernel function for the two given vectors.
double	getOmega()
double	getSigma()
static Distribution	guessOmega(DataSet d) Guesses the distribution to use for the ω parameter
static Distribution	guessSigma(DataSet d) Guesses the distribution to use for the λ parameter
boolean	normalized() This method indicates if a kernel is a normalized kernel or not.
void	setOmega(double omega) Sets the omega parameter value, which controls the shape of the kernel
void	setSigma(double sigma) Sets the sigma parameter value, which controls the width of the kernel

Methods inherited from class jsat.distributions.kernels.BaseL2Kernel

addToCache, evalSum, evalSum, getAccelerationCache, getQueryInfo, supportsAcceleration

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Methods inherited from interface jsat.distributions.kernels.KernelTrick

toString

Constructor Detail

PukKernel

public PukKernel(double sigma,
                 double omega)

Creates a new PUK Kernel

Parameters:

sigma - the width parameter of the kernel

omega - the shape parameter of the kernel

Method Detail

setOmega

public void setOmega(double omega)

Sets the omega parameter value, which controls the shape of the kernel

Parameters:

omega - the positive parameter value

getOmega

public double getOmega()

setSigma

public void setSigma(double sigma)

Sets the sigma parameter value, which controls the width of the kernel

Parameters:

sigma - the positive parameter value

getSigma

public double getSigma()

eval

public double eval(Vec a,
                   Vec b)

Description copied from interface: KernelTrick

Evaluate this kernel function for the two given vectors.

Specified by:

eval in interface KernelTrick

Specified by:

eval in class BaseL2Kernel

Parameters:

a - the first vector

b - the first vector

Returns:

the evaluation

clone

public PukKernel clone()

Specified by:

clone in interface KernelTrick

Specified by:

clone in class BaseL2Kernel

eval

public double eval(int a,
                   Vec b,
                   java.util.List<java.lang.Double> qi,
                   java.util.List<? extends Vec> vecs,
                   java.util.List<java.lang.Double> cache)

Description copied from interface: KernelTrick

Computes the kernel product between one vector in the original list of vectors with that of another vector not from the original list, but had information generated by KernelTrick.getQueryInfo(jsat.linear.Vec).
If the cache input is null, then KernelTrick.eval(jsat.linear.Vec, jsat.linear.Vec) will be called directly.

Specified by:

eval in interface KernelTrick

Specified by:

eval in class BaseL2Kernel

Parameters:

a - the index of the vector in the cache

b - the other vector

qi - the query information about b

vecs - the list of vectors used to build the cache

cache - the cache associated with the given list of vectors

Returns:

the kernel product of the two vectors

eval

public double eval(int a,
                   int b,
                   java.util.List<? extends Vec> trainingSet,
                   java.util.List<java.lang.Double> cache)

Description copied from interface: KernelTrick

Produces the correct kernel evaluation given the training set and the cache generated by KernelTrick.getAccelerationCache(List). The training vectors should be in the same order.

Specified by:

eval in interface KernelTrick

Specified by:

eval in class BaseL2Kernel

Parameters:

a - the index of the first training vector

b - the index of the second training vector

trainingSet - the list of training set vectors

cache - the double list of cache values generated by this kernel for the given training set

Returns:

the same kernel evaluation result as KernelTrick.eval(jsat.linear.Vec, jsat.linear.Vec)

guessOmega

public static Distribution guessOmega(DataSet d)

Guesses the distribution to use for the ω parameter

Parameters:

d - the dataset to get the guess for

Returns:

the guess for the ω parameter

See Also:

setOmega(double)

guessSigma

public static Distribution guessSigma(DataSet d)

Guesses the distribution to use for the λ parameter

Parameters:

d - the dataset to get the guess for

Returns:

the guess for the λ parameter

See Also:

setSigma(double)

normalized

public boolean normalized()

Description copied from interface: KernelTrick

This method indicates if a kernel is a normalized kernel or not. A normalized kernel is one in which k(x,x) = 1 for the same object, and no value greater than 1 can be returned.

Specified by:

normalized in interface KernelTrick

Returns:

true if this is a normalized kernel. false otherwise.