public abstract class AbstractMetricMultiDimensionalPotentiallyPrecomputed extends AbstractMetricMultiDimensional
Metric.AggregateFunction
Modifier and Type | Method and Description |
---|---|
InformationLoss<?> |
createMaxInformationLoss()
Returns an instance of the maximal value.
|
InformationLoss<?> |
createMinInformationLoss()
Returns an instance of the minimal value.
|
Metric.AggregateFunction |
getAggregateFunction()
Returns the aggregate function of a multi-dimensional metric, null otherwise.
|
double |
getGeneralizationFactor()
Returns the factor used weight generalized values.
|
double |
getGeneralizationSuppressionFactor()
Returns the factor weighting generalization and suppression.
|
ILScore |
getScore(Transformation<?> node,
HashGroupify groupify)
Calculates the score.
|
double |
getSuppressionFactor()
Returns the factor used to weight suppressed values.
|
boolean |
isIndependent()
Returns whether this metric requires the transformed data or groups to
determine information loss.
|
boolean |
isPrecomputed()
Returns whether the metric is precomputed
|
boolean |
isScoreFunctionSupported()
Returns whether the metric provides a score function
|
createAECSMetric, createAECSMetric, createAmbiguityMetric, createClassificationMetric, createClassificationMetric, createDiscernabilityMetric, createDiscernabilityMetric, createEntropyBasedInformationLossMetric, createEntropyBasedInformationLossMetric, createEntropyMetric, createEntropyMetric, createEntropyMetric, createEntropyMetric, createEntropyMetric, createEntropyMetric, createHeightMetric, createHeightMetric, createInstanceOfHighestScore, createInstanceOfLowestScore, createKLDivergenceMetric, createLossMetric, createLossMetric, createLossMetric, createLossMetric, createMetric, createNormalizedEntropyMetric, createNormalizedEntropyMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecisionMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedEntropyMetric, createPrecomputedLossMetric, createPrecomputedLossMetric, createPrecomputedLossMetric, createPrecomputedLossMetric, createPrecomputedNormalizedEntropyMetric, createPrecomputedNormalizedEntropyMetric, createPublisherPayoutMetric, createPublisherPayoutMetric, createStaticMetric, createStaticMetric, getConfiguration, getDescription, getInformationLoss, getInformationLoss, getLowerBound, getLowerBound, getName, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isGSFactorSupported, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isWeighted, list, render, toString
public InformationLoss<?> createMaxInformationLoss()
Metric
createMaxInformationLoss
in class AbstractMetricMultiDimensional
public InformationLoss<?> createMinInformationLoss()
Metric
createMinInformationLoss
in class AbstractMetricMultiDimensional
public Metric.AggregateFunction getAggregateFunction()
Metric
getAggregateFunction
in class AbstractMetricMultiDimensional
public double getGeneralizationFactor()
Metric
getGeneralizationFactor
in class Metric<AbstractILMultiDimensional>
public double getGeneralizationSuppressionFactor()
Metric
getGeneralizationSuppressionFactor
in class Metric<AbstractILMultiDimensional>
public ILScore getScore(Transformation<?> node, HashGroupify groupify)
Metric
getScore
in class Metric<AbstractILMultiDimensional>
public double getSuppressionFactor()
Metric
getSuppressionFactor
in class Metric<AbstractILMultiDimensional>
public boolean isIndependent()
Metric
isIndependent
in class Metric<AbstractILMultiDimensional>
public boolean isPrecomputed()
Metric
isPrecomputed
in class Metric<AbstractILMultiDimensional>
public boolean isScoreFunctionSupported()
Metric
isScoreFunctionSupported
in class Metric<AbstractILMultiDimensional>