de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel

Class PolynomialKernelFunction

java.lang.Object

de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractPrimitiveSimilarityFunction<NumberVector>

de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractVectorSimilarityFunction

de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.PolynomialKernelFunction

All Implemented Interfaces:

DistanceFunction<NumberVector>, PrimitiveDistanceFunction<NumberVector>, PrimitiveSimilarityFunction<NumberVector>, SimilarityFunction<NumberVector>

Direct Known Subclasses:

LinearKernelFunction

public class PolynomialKernelFunction
extends AbstractVectorSimilarityFunction
implements PrimitiveDistanceFunction<NumberVector>

Polynomial Kernel function that computes a similarity between the two feature vectors V1 and V2 defined by (V1^T*V2)^degree.

Author:

Simon Paradies

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	PolynomialKernelFunction.Parameterizer Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private double	bias Bias of the similarity function.
static int	DEFAULT_DEGREE The default degree.
private int	degree Degree of the polynomial kernel function.

Constructor Summary

Constructors

Constructor and Description
PolynomialKernelFunction(int degree) Constructor.
PolynomialKernelFunction(int degree, double bias) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
double	distance(NumberVector fv1, NumberVector fv2) Computes the distance between two given DatabaseObjects according to this distance function.
<T extends NumberVector> DistanceSimilarityQuery<T>	instantiate(Relation<T> database) Instantiate with a representation to get the actual similarity query.
boolean	isMetric() Is this distance function metric (in particular, does it satisfy the triangle equation?)
double	similarity(NumberVector o1, NumberVector o2) Computes the similarity between two given DatabaseObjects according to this similarity function.

Methods inherited from class de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractVectorSimilarityFunction

getInputTypeRestriction

Methods inherited from class de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractPrimitiveSimilarityFunction

isSymmetric

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction

getInputTypeRestriction

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction

isSymmetric

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction

isSymmetric

Field Detail

DEFAULT_DEGREE

public static final int DEFAULT_DEGREE

The default degree.

See Also:

Constant Field Values

degree

private final int degree

Degree of the polynomial kernel function.

bias

private final double bias

Bias of the similarity function.

Constructor Detail

PolynomialKernelFunction

public PolynomialKernelFunction(int degree,
                        double bias)

Constructor.

Parameters:

degree - Kernel degree

bias - Bias offset

PolynomialKernelFunction

public PolynomialKernelFunction(int degree)

Constructor.

Parameters:

degree - Kernel degree

Method Detail

similarity

public double similarity(NumberVector o1,
                NumberVector o2)

Description copied from interface: PrimitiveSimilarityFunction

Computes the similarity between two given DatabaseObjects according to this similarity function.

Specified by:

similarity in interface PrimitiveSimilarityFunction<NumberVector>

Specified by:

similarity in class AbstractPrimitiveSimilarityFunction<NumberVector>

Parameters:

o1 - first DatabaseObject

o2 - second DatabaseObject

Returns:

the similarity between two given DatabaseObjects according to this similarity function

isMetric

public boolean isMetric()

Description copied from interface: DistanceFunction

Is this distance function metric (in particular, does it satisfy the triangle equation?)

Specified by:

isMetric in interface DistanceFunction<NumberVector>

Returns:

true when metric.

distance

public double distance(NumberVector fv1,
              NumberVector fv2)

Description copied from interface: PrimitiveDistanceFunction

Computes the distance between two given DatabaseObjects according to this distance function.

Specified by:

distance in interface PrimitiveDistanceFunction<NumberVector>

Parameters:

fv1 - first DatabaseObject

fv2 - second DatabaseObject

Returns:

the distance between two given DatabaseObjects according to this distance function

instantiate

public <T extends NumberVector> DistanceSimilarityQuery<T> instantiate(Relation<T> database)

Description copied from interface: SimilarityFunction

Instantiate with a representation to get the actual similarity query.

Specified by:

instantiate in interface DistanceFunction<NumberVector>

Specified by:

instantiate in interface SimilarityFunction<NumberVector>

Overrides:

instantiate in class AbstractPrimitiveSimilarityFunction<NumberVector>

Parameters:

database - Representation to use

Returns:

Actual distance query.