mulan.evaluation.measure
Class ExampleBasedRecall

java.lang.Object
  mulan.evaluation.measure.MeasureBase
      mulan.evaluation.measure.BipartitionMeasureBase
          mulan.evaluation.measure.ExampleBasedBipartitionMeasureBase
              mulan.evaluation.measure.ExampleBasedRecall

All Implemented Interfaces:

Serializable, Measure

public class ExampleBasedRecall
extends ExampleBasedBipartitionMeasureBase

Implementation of the example-based recall measure.

Version:

2012.05.29

Author:

Grigorios Tsoumakas

See Also:

Serialized Form

Field Summary

Fields inherited from class mulan.evaluation.measure.ExampleBasedBipartitionMeasureBase

count, sum

Constructor Summary

ExampleBasedRecall()

Method Summary

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

Methods inherited from class mulan.evaluation.measure.ExampleBasedBipartitionMeasureBase

getValue, reset

Methods inherited from class mulan.evaluation.measure.BipartitionMeasureBase

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

ExampleBasedRecall

public ExampleBasedRecall()

Method Detail

getName

public String getName()

Description copied from interface: Measure

Gets the name of a measure.

Returns:

the name of a measure.

updateBipartition

protected void updateBipartition(boolean[] bipartition,
                                 boolean[] truth)

Description copied from class: BipartitionMeasureBase

Updates the measure based on an example

Specified by:

updateBipartition in class BipartitionMeasureBase

Parameters:

bipartition - the predicted bipartition

truth - the ground truth

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