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

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable
          de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction<O,D>
              de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractDistanceFunction<O,D>
                  de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.AbstractKernelFunction<O,DoubleDistance>
                      de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.AbstractDoubleKernelFunction<O>
                          de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.LinearKernelFunction<O>

Type Parameters:

O - vector type

All Implemented Interfaces:

DistanceFunction<O,DoubleDistance>, MeasurementFunction<O,DoubleDistance>, KernelFunction<O,DoubleDistance>, SimilarityFunction<O,DoubleDistance>, Parameterizable

public class LinearKernelFunction<O extends FeatureVector<O,?>>
extends AbstractDoubleKernelFunction<O>

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

Author:

Simon Paradies

Field Summary

Fields inherited from class de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction

INFINITY_PATTERN

Fields inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable

optionHandler

Fields inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debug, logger

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

String	shortDescription() Returns the required input pattern.
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.kernel.AbstractDoubleKernelFunction

distance, infiniteDistance, nullDistance, undefinedDistance, valueOf

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

similarity, similarity

Methods inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractDistanceFunction

distance, distance, isInfiniteDistance, isNullDistance, isUndefinedDistance

Methods inherited from class de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction

getDatabase, matches, requiredInputPattern, setDatabase, setRequiredInputPattern

Methods inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable

addOption, addParameterizable, addParameterizable, checkGlobalParameterConstraints, collectOptions, getAttributeSettings, getParameters, rememberParametersExcept, removeOption, removeParameterizable, setParameters

Methods inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debugFine, debugFiner, debugFinest, exception, progress, verbose, warning

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

distance, distance

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.MeasurementFunction

isInfiniteDistance, isNullDistance, isUndefinedDistance, requiredInputPattern, setDatabase

Methods inherited from interface de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable

checkGlobalParameterConstraints, collectOptions, getParameters, setParameters

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

Parameters:

o1 - first feature vector

o2 - second feature vector

Returns:

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

shortDescription

public String shortDescription()

Description copied from class: AbstractMeasurementFunction

Returns the required input pattern.

Specified by:

shortDescription in interface Parameterizable

Overrides:

shortDescription in class AbstractMeasurementFunction<O extends FeatureVector<O,?>,DoubleDistance>

Returns:

Description of the class