de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel
Class LinearKernelFunction<O extends NumberVector<?,?>>

java.lang.Object
  de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractPrimitiveSimilarityFunction<O,DoubleDistance>
      de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.LinearKernelFunction<O>

Type Parameters:

O - vector type

All Implemented Interfaces:

DistanceFunction<O,DoubleDistance>, PrimitiveDistanceFunction<O,DoubleDistance>, PrimitiveSimilarityFunction<O,DoubleDistance>, SimilarityFunction<O,DoubleDistance>, InspectionUtilFrequentlyScanned, Parameterizable

public class LinearKernelFunction<O extends NumberVector<?,?>>
extends AbstractPrimitiveSimilarityFunction<O,DoubleDistance>
implements PrimitiveDistanceFunction<O,DoubleDistance>

Provides a linear Kernel function that computes a similarity between the two feature vectors V1 and V2 defined by V1^T*V2.

Constructor Summary

LinearKernelFunction()
Provides a linear Kernel function that computes a similarity between the two vectors V1 and V2 defined by V1^T*V2.

Method Summary

DoubleDistance	distance(O fv1, O fv2) Computes the distance between two given DatabaseObjects according to this distance function.
DoubleDistance	getDistanceFactory() Method to get the distance functions factory.
VectorFieldTypeInformation<? super O>	getInputTypeRestriction() Get the input data type of the function.
<T extends O> DistanceSimilarityQuery<T,DoubleDistance>	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?)
DoubleDistance	similarity(O o1, O o2) Provides a linear Kernel function that computes a similarity between the two feature vectors V1 and V2 definded by V1^T*V2

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.DistanceFunction

isSymmetric

Constructor Detail

LinearKernelFunction

public LinearKernelFunction()

Provides a linear Kernel function that computes a similarity between the two vectors V1 and V2 defined by V1^T*V2.

Method Detail

similarity

public DoubleDistance similarity(O o1,
                                 O o2)

Provides a linear Kernel function that computes a similarity between the two feature vectors V1 and V2 definded by V1^T*V2

Specified by:

similarity in interface PrimitiveSimilarityFunction<O extends NumberVector<?,?>,DoubleDistance>

Specified by:

similarity in class AbstractPrimitiveSimilarityFunction<O extends NumberVector<?,?>,DoubleDistance>

Parameters:

o1 - first feature vector

o2 - second feature vector

Returns:

the linear kernel similarity between the given two vectors as an instance of DoubleDistance.

distance

public DoubleDistance distance(O fv1,
                               O fv2)

Description copied from interface: PrimitiveDistanceFunction

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

Specified by:

distance in interface PrimitiveDistanceFunction<O extends NumberVector<?,?>,DoubleDistance>

Parameters:

fv1 - first DatabaseObject

fv2 - second DatabaseObject

Returns:

the distance between two given DatabaseObjects according to this distance function

getInputTypeRestriction

public VectorFieldTypeInformation<? super O> getInputTypeRestriction()

Description copied from interface: SimilarityFunction

Get the input data type of the function.

Specified by:

getInputTypeRestriction in interface DistanceFunction<O extends NumberVector<?,?>,DoubleDistance>

Specified by:

getInputTypeRestriction in interface PrimitiveDistanceFunction<O extends NumberVector<?,?>,DoubleDistance>

Specified by:

getInputTypeRestriction in interface PrimitiveSimilarityFunction<O extends NumberVector<?,?>,DoubleDistance>

Specified by:

getInputTypeRestriction in interface SimilarityFunction<O extends NumberVector<?,?>,DoubleDistance>

Specified by:

getInputTypeRestriction in class AbstractPrimitiveSimilarityFunction<O extends NumberVector<?,?>,DoubleDistance>

Returns:

Type restriction

getDistanceFactory

public DoubleDistance getDistanceFactory()

Description copied from interface: DistanceFunction

Method to get the distance functions factory.

Specified by:

getDistanceFactory in interface DistanceFunction<O extends NumberVector<?,?>,DoubleDistance>

Specified by:

getDistanceFactory in interface SimilarityFunction<O extends NumberVector<?,?>,DoubleDistance>

Returns:

Factory for distance objects

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<O extends NumberVector<?,?>,DoubleDistance>

Returns:

true when metric.

instantiate

public <T extends O> DistanceSimilarityQuery<T,DoubleDistance> 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<O extends NumberVector<?,?>,DoubleDistance>

Specified by:

instantiate in interface SimilarityFunction<O extends NumberVector<?,?>,DoubleDistance>

Overrides:

instantiate in class AbstractPrimitiveSimilarityFunction<O extends NumberVector<?,?>,DoubleDistance>

Parameters:

database - Representation to use

Returns:

Actual distance query.