mulan.evaluation.measure
Class LossBasedRankingMeasureBase

java.lang.Object
  mulan.evaluation.measure.MeasureBase
      mulan.evaluation.measure.RankingMeasureBase
          mulan.evaluation.measure.LossBasedRankingMeasureBase

All Implemented Interfaces:

Serializable, Measure

Direct Known Subclasses:

ErrorSetSize, IsError, OneError, RankingLoss

public abstract class LossBasedRankingMeasureBase
extends RankingMeasureBase

Version:

2010.11.10

Author:

Grigorios Tsoumakas

See Also:

Serialized Form

Field Summary

Fields inherited from class mulan.evaluation.measure.RankingMeasureBase

count, sum

Constructor Summary

LossBasedRankingMeasureBase(RankingLossFunction aLoss)
Creates a loss-based ranking measure

Method Summary

double	getIdealValue() Gets an 'ideal' value of a measure.
String	getName() Gets the name of a measure.
protected void	updateRanking(int[] ranking, boolean[] truth) Updates the measure based on an example

Methods inherited from class mulan.evaluation.measure.RankingMeasureBase

getValue, reset, updateInternal

Methods inherited from class mulan.evaluation.measure.MeasureBase

makeCopy, toString, update

Methods inherited from class java.lang.Object

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

Constructor Detail

LossBasedRankingMeasureBase

public LossBasedRankingMeasureBase(RankingLossFunction aLoss)

Creates a loss-based ranking measure

Parameters:

aLoss - a ranking loss function

Method Detail

updateRanking

protected void updateRanking(int[] ranking,
                             boolean[] truth)

Description copied from class: RankingMeasureBase

Updates the measure based on an example

Specified by:

updateRanking in class RankingMeasureBase

Parameters:

ranking - the predicted ranking

truth - the ground truth

getName

public String getName()

Description copied from interface: Measure

Gets the name of a measure.

Returns:

the name of a measure.

getIdealValue

public double getIdealValue()

Description copied from interface: Measure

Gets an 'ideal' value of a measure. The 'ideal' means, that the value represents the best achievable performance of a learner for an prediction of a multi-label task and associated true labels.

Returns:

the ideal value