Package org.deidentifier.arx.metric.v2

Klasse AbstractMetricSingleDimensional

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional

Alle implementierten Schnittstellen:

Serializable

Bekannte direkte Unterklassen:

MetricSDAECS, MetricSDClassification, MetricSDNMAmbiguity, MetricSDNMDiscernability, MetricSDNMEntropyBasedInformationLoss, MetricSDNMKLDivergence, MetricSDNMPublisherPayout

public abstract class AbstractMetricSingleDimensional
extends Metric<ILSingleDimensional>

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

Siehe auch:

• Serialisierte Form

Verschachtelte Klassen - Übersicht

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

Metric.AggregateFunction

Konstruktorübersicht

Konstruktoren

Modifizierer	Konstruktor	Beschreibung
protected	AbstractMetricSingleDimensional(boolean monotonicWithGeneralization, boolean monotonicWithSuppression, boolean independent)	Creates a new instance.
protected	AbstractMetricSingleDimensional(boolean monotonicWithGeneralization, boolean monotonicWithSuppression, boolean independent, double gsFactor)	Creates a new instance.

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
ILSingleDimensionalWithBound	createInformationLoss(double loss)	Create a loss object
ILSingleDimensionalWithBound	createInformationLoss(double loss, double bound)	Create a loss object
InformationLoss<?>	createMaxInformationLoss()	Returns an instance of the maximal value.
InformationLoss<?>	createMinInformationLoss()	Returns an instance of the minimal value.
protected org.deidentifier.arx.framework.check.distribution.DistributionAggregateFunction[]	getAggregationFunctionsGeneralized()	Returns relevant aggregation functions
protected org.deidentifier.arx.framework.check.distribution.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 org.deidentifier.arx.framework.data.DataAggregationInformation	getAggregationInformation()	Needed for microaggregation
protected Double	getNumTuples()	Returns the number of rows in the dataset or subset.
protected void	initializeInternal(org.deidentifier.arx.framework.data.DataManager manager, DataDefinition definition, org.deidentifier.arx.framework.data.Data input, org.deidentifier.arx.framework.data.GeneralizationHierarchy[] hierarchies, ARXConfiguration config)	Implement this to initialize the metric.
protected void	setNumTuples(Double tuples)	Returns the number of rows in the dataset or subset.

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, getAggregateFunction, 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

Konstruktordetails

AbstractMetricSingleDimensional

protected AbstractMetricSingleDimensional(boolean monotonicWithGeneralization,
 boolean monotonicWithSuppression,
 boolean independent)

Creates a new instance.

Parameter:

monotonicWithGeneralization -

monotonicWithSuppression -

independent -

AbstractMetricSingleDimensional

protected AbstractMetricSingleDimensional(boolean monotonicWithGeneralization,
 boolean monotonicWithSuppression,
 boolean independent,
 double gsFactor)

Creates a new instance.

Parameter:

monotonicWithGeneralization -

monotonicWithSuppression -

independent -

gsFactor -

Methodendetails

createInformationLoss

public ILSingleDimensionalWithBound createInformationLoss(double loss)

Create a loss object

Parameter:

loss -

Gibt zurück:

createInformationLoss

public ILSingleDimensionalWithBound createInformationLoss(double loss,
 double bound)

Create a loss object

Parameter:

loss -

bound -

Gibt zurück:

createMaxInformationLoss

public InformationLoss<?> createMaxInformationLoss()

Beschreibung aus Klasse kopiert: Metric

Returns an instance of the maximal value.

Angegeben von:

createMaxInformationLoss in Klasse Metric<ILSingleDimensional>

Gibt zurück:

createMinInformationLoss

public InformationLoss<?> createMinInformationLoss()

Beschreibung aus Klasse kopiert: Metric

Returns an instance of the minimal value.

Angegeben von:

createMinInformationLoss in Klasse Metric<ILSingleDimensional>

Gibt zurück:

getAggregationFunctionsGeneralized

protected org.deidentifier.arx.framework.check.distribution.DistributionAggregateFunction[] getAggregationFunctionsGeneralized()

Returns relevant aggregation functions

Gibt zurück:

getAggregationFunctionsNonGeneralized

protected org.deidentifier.arx.framework.check.distribution.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 org.deidentifier.arx.framework.data.DataAggregationInformation getAggregationInformation()

Needed for microaggregation

Gibt zurück:

getNumTuples

protected Double getNumTuples()

Returns the number of rows in the dataset or subset.

Gibt zurück:

initializeInternal

protected void initializeInternal(org.deidentifier.arx.framework.data.DataManager manager,
 DataDefinition definition,
 org.deidentifier.arx.framework.data.Data input,
 org.deidentifier.arx.framework.data.GeneralizationHierarchy[] hierarchies,
 ARXConfiguration config)

Beschreibung aus Klasse kopiert: Metric

Implement this to initialize the metric.

Angegeben von:

initializeInternal in Klasse Metric<ILSingleDimensional>

Parameter:

manager -

definition -

input -

hierarchies -

config -

setNumTuples

protected void setNumTuples(Double tuples)

Returns the number of rows in the dataset or subset.

Parameter:

tuples -