org.deidentifier.arx.metric.v2

Class AbstractMetricSingleDimensional

java.lang.Object

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

org.deidentifier.arx.metric.v2.AbstractMetricSingleDimensional

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

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.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class org.deidentifier.arx.metric.Metric

Metric.AggregateFunction

Constructor Summary

Constructors

Modifier	Constructor and Description
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.

Method Summary

Modifier and Type	Method and Description
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 java.lang.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(java.lang.Double tuples) Returns the number of rows in the dataset or subset.

Methods inherited from class 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

Methods inherited from class java.lang.Object

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

Constructor Detail

AbstractMetricSingleDimensional

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

Creates a new instance.

Parameters:

monotonicWithGeneralization -

monotonicWithSuppression -

independent -

AbstractMetricSingleDimensional

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

Creates a new instance.

Parameters:

monotonicWithGeneralization -

monotonicWithSuppression -

independent -

gsFactor -

Method Detail

createInformationLoss

public ILSingleDimensionalWithBound createInformationLoss(double loss)

Create a loss object

Parameters:

loss -

Returns:

createInformationLoss

public ILSingleDimensionalWithBound createInformationLoss(double loss,
                                                          double bound)

Create a loss object

Parameters:

loss -

bound -

Returns:

createMaxInformationLoss

public InformationLoss<?> createMaxInformationLoss()

Description copied from class: Metric

Returns an instance of the maximal value.

Specified by:

createMaxInformationLoss in class Metric<ILSingleDimensional>

Returns:

createMinInformationLoss

public InformationLoss<?> createMinInformationLoss()

Description copied from class: Metric

Returns an instance of the minimal value.

Specified by:

createMinInformationLoss in class Metric<ILSingleDimensional>

Returns:

getAggregationFunctionsGeneralized

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

Returns relevant aggregation functions

Returns:

getAggregationFunctionsNonGeneralized

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

Returns relevant aggregation functions

Returns:

getAggregationIndicesGeneralized

protected int[] getAggregationIndicesGeneralized()

Returns the indicies of aggregated variables

Returns:

getAggregationIndicesNonGeneralized

protected int[] getAggregationIndicesNonGeneralized()

Returns the indicies of aggregated variables

Returns:

getAggregationInformation

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

Needed for microaggregation

Returns:

getNumTuples

protected java.lang.Double getNumTuples()

Returns the number of rows in the dataset or subset.

Returns:

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)

Description copied from class: Metric

Implement this to initialize the metric.

Specified by:

initializeInternal in class Metric<ILSingleDimensional>

setNumTuples

protected void setNumTuples(java.lang.Double tuples)

Returns the number of rows in the dataset or subset.

Parameters:

tuples -