Package org.deidentifier.arx.metric.v2

Klasse AbstractMetricMultiDimensional

java.lang.Object

org.deidentifier.arx.metric.Metric<AbstractILMultiDimensional>

org.deidentifier.arx.metric.v2.AbstractMetricMultiDimensional

Alle implementierten Schnittstellen:

Serializable

Bekannte direkte Unterklassen:

AbstractMetricMultiDimensionalPotentiallyPrecomputed, MetricMDHeight, MetricMDNMLoss, MetricMDNMPrecision, MetricMDNUEntropyPrecomputed, MetricMDStatic

public abstract class AbstractMetricMultiDimensional
extends Metric<AbstractILMultiDimensional>

This class provides an abstract skeleton for the implementation of multi-dimensional metrics.

Siehe auch:

• Serialisierte Form

Verschachtelte Klassen - Übersicht

Von Klasse geerbte verschachtelte Klassen/Schnittstellen org.deidentifier.arx.metric.Metric

Metric.AggregateFunction

Feldübersicht

Felder

Modifizierer und Typ	Feld	Beschreibung
protected int	k	Minimal size of equivalence classes enforced by the differential privacy model

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
protected AbstractILMultiDimensional	createInformationLoss(double[] values)	Helper method for creating information loss.
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.
protected DistributionAggregateFunction[]	getAggregationFunctionsGeneralized()	Returns relevant aggregation functions
protected DistributionAggregateFunction[]	getAggregationFunctionsNonGeneralized()	Returns relevant aggregation functions
protected int[]	getAggregationIndicesGeneralized()	Returns the indicies of aggregated variables
protected int[]	getAggregationIndicesNonGeneralized()	Returns the indicies of aggregated variables
protected DataAggregationInformation	getAggregationInformation()	Needed for microaggregation
protected int	getDimensions()	Returns the number of dimensions.
protected int	getDimensionsAggregated()	Returns the number of dimensions.
protected int	getDimensionsGeneralized()	Returns the number of dimensions.
protected void	initialize(int dimensions)	For backwards compatibility only.
protected void	initializeInternal(DataManager manager, DataDefinition definition, Data input, GeneralizationHierarchy[] hierarchies, ARXConfiguration config)	Implement this to initialize the metric.
protected void	setMax(double[] max)	Sets the maximal information loss.
protected void	setMin(double[] min)	Sets the minimal information loss.

Von Klasse geerbte Methoden org.deidentifier.arx.metric.Metric

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, getDescription, getGeneralizationFactor, getGeneralizationSuppressionFactor, getInformationLoss, getInformationLoss, getInformationLossInternal, getInformationLossInternal, getLowerBound, getLowerBound, getLowerBoundInternal, getLowerBoundInternal, getName, getNumRecords, getScore, getSubset, getSuppressionFactor, initialize, isAbleToHandleClusteredMicroaggregation, isAbleToHandleMicroaggregation, isGSFactorSupported, isIndependent, isMonotonic, isMonotonicWithGeneralization, isMonotonicWithSuppression, isMultiDimensional, isPrecomputed, isScoreFunctionSupported, isWeighted, list, render, round, toString

Von Klasse geerbte Methoden java.lang.Object

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

Felddetails

k

protected int k

Minimal size of equivalence classes enforced by the differential privacy model

Methodendetails

createMaxInformationLoss

public InformationLoss<?> createMaxInformationLoss()

Beschreibung aus Klasse kopiert: Metric

Returns an instance of the maximal value.

Angegeben von:

createMaxInformationLoss in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

createMinInformationLoss

public InformationLoss<?> createMinInformationLoss()

Beschreibung aus Klasse kopiert: Metric

Returns an instance of the minimal value.

Angegeben von:

createMinInformationLoss in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

getAggregateFunction

public Metric.AggregateFunction getAggregateFunction()

Beschreibung aus Klasse kopiert: Metric

Returns the aggregate function of a multi-dimensional metric, null otherwise.

Setzt außer Kraft:

getAggregateFunction in Klasse Metric<AbstractILMultiDimensional>

Gibt zurück:

createInformationLoss

protected AbstractILMultiDimensional createInformationLoss(double[] values)

Helper method for creating information loss.

Parameter:

values -

Gibt zurück:

getAggregationFunctionsGeneralized

protected DistributionAggregateFunction[] getAggregationFunctionsGeneralized()

Returns relevant aggregation functions

Gibt zurück:

getAggregationFunctionsNonGeneralized

protected DistributionAggregateFunction[] getAggregationFunctionsNonGeneralized()

Returns relevant aggregation functions

Gibt zurück:

getAggregationIndicesGeneralized

protected int[] getAggregationIndicesGeneralized()

Returns the indicies of aggregated variables

Gibt zurück:

getAggregationIndicesNonGeneralized

protected int[] getAggregationIndicesNonGeneralized()

Returns the indicies of aggregated variables

Gibt zurück:

getAggregationInformation

protected DataAggregationInformation getAggregationInformation()

Needed for microaggregation

Gibt zurück:

getDimensions

protected int getDimensions()

Returns the number of dimensions.

Gibt zurück:

getDimensionsAggregated

protected int getDimensionsAggregated()

Returns the number of dimensions.

Gibt zurück:

getDimensionsGeneralized

protected int getDimensionsGeneralized()

Returns the number of dimensions.

Gibt zurück:

initialize

protected void initialize(int dimensions)

For backwards compatibility only.

Parameter:

dimensions -

initializeInternal

protected void initializeInternal(DataManager manager,
 DataDefinition definition,
 Data input,
 GeneralizationHierarchy[] hierarchies,
 ARXConfiguration config)

Beschreibung aus Klasse kopiert: Metric

Implement this to initialize the metric.

Angegeben von:

initializeInternal in Klasse Metric<AbstractILMultiDimensional>

Parameter:

manager -

definition -

input -

hierarchies -

config -

setMax

protected void setMax(double[] max)

Sets the maximal information loss.

Parameter:

max -

setMin

protected void setMin(double[] min)

Sets the minimal information loss.

Parameter:

min -