## Uses of Class
org.deidentifier.arx.metric.Metric

Packages that use Metric
org.deidentifier.arx: This package provides the public API for the ARX anonymization framework.
org.deidentifier.arx.framework.check: This package implements the core functionality of the ARX framework, i.e., a highly efficient check for k-anonymity
org.deidentifier.arx.gui.model
org.deidentifier.arx.metric: Package providing access to quality models
org.deidentifier.arx.metric.v2: Main package implementing quality models
org.deidentifier.arx.test

### Uses of Metric in org.deidentifier.arx

Methods in org.deidentifier.arx that return Metric
`Metric<?>` ARXConfiguration.`getQualityModel()` Returns the quality model to be used for optimizing output data.
`Metric<?>` ARXConfiguration.ARXConfigurationInternal.`getQualityModel()` Returns the quality model to be used for optimizing output data.

Methods in org.deidentifier.arx with parameters of type Metric
`static ARXConfiguration` ARXConfiguration.`create(double suppressionLimit, Metric<?> metric)` Creates a new configuration that allows the given percentage of outliers and thus implements tuple suppression.
`static ARXConfiguration` ARXConfiguration.`create(Metric<?> metric)` Creates a new configuration that allows to define the metric for measuring information loss.
`void` ARXLattice.Access.`setQualityModel(Metric<?> model)` Accessor method
`void` ARXConfiguration.`setQualityModel(Metric<?> model)` Sets the quality model to be used for optimizing output data.

Constructors in org.deidentifier.arx with parameters of type Metric
Constructor and Description
`ARXResult(DataHandle handle, DataDefinition definition, ARXLattice lattice, int historySize, double snapshotSizeSnapshot, double snapshotSizeDataset, Metric<?> metric, ARXConfiguration config, ARXLattice.ARXNode optimum, long time, SolutionSpace<?> solutionSpace, ARXProcessStatistics statistics)` Internal constructor for deserialization.

### Uses of Metric in org.deidentifier.arx.framework.check

Methods in org.deidentifier.arx.framework.check that return Metric
`Metric<?>` TransformationChecker.`getMetric()` Returns the utility measure

Constructors in org.deidentifier.arx.framework.check with parameters of type Metric
Constructor and Description
`TransformationApplicator(DataManager manager, DataMatrix buffer, Metric<?> metric, ARXConfiguration.ARXConfigurationInternal config)` Creates a new transformation applicator instance.
`TransformationChecker(DataManager manager, Metric<?> metric, ARXConfiguration.ARXConfigurationInternal config, int historyMaxSize, double snapshotSizeDataset, double snapshotSizeSnapshot, SolutionSpace<?> solutionSpace)` Creates a new transformation checker.

### Uses of Metric in org.deidentifier.arx.gui.model

Methods in org.deidentifier.arx.gui.model that return Metric
`Metric<?>` ModelConfiguration.`getMetric()` Delegates to an instance of ARXConfiguration.

Methods in org.deidentifier.arx.gui.model with parameters of type Metric
`void` ModelConfiguration.`setMetric(Metric<?> metric)` Delegates to an instance of ARXConfiguration.

### Uses of Metric in org.deidentifier.arx.metric

Subclasses of Metric in org.deidentifier.arx.metric
`class` `MetricAECS` This class provides an implementation of the (normalized) average equivalence class size metric.
`class` `MetricDefault` This class provides an abstract skeleton for the implementation of metrics.
`class` `MetricDM` This class provides an implementation of the DM metric (non-monotonic).
`class` `MetricDMStar` This class provides an implementation of the DM* metric (monotonic variant of the Discernability Metric).
`class` `MetricEntropy` This class provides an efficient implementation of the non-uniform entropy metric.
`class` `MetricHeight` This class provides an implementation of the Height metric.
`class` `MetricNMEntropy` This class provides an efficient implementation of a non-monotonic and non-uniform entropy metric.
`class` `MetricNMPrecision` This class provides an implementation of a weighted precision metric as proposed in: Sweeney, L. (2002).
`class` `MetricPrecision` This class provides an implementation of a monotonic weighted precision metric.
`class` `MetricStatic` This class provides an implementation of a static metric in which information loss is user-defined per generalization level.
`class` `MetricWeighted<T extends InformationLoss<?>>` This class provides an abstract skeleton for the implementation of weighted metrics.

Methods in org.deidentifier.arx.metric that return Metric
`static Metric<ILSingleDimensional>` Metric.`createAECSMetric()` Creates a new instance of the AECS metric.
`static Metric<ILSingleDimensional>` Metric.`createAECSMetric(double gsFactor)` Creates a new instance of the AECS metric.
`static Metric<ILSingleDimensional>` Metric.`createAmbiguityMetric()` Creates an instance of the ambiguity metric.
`static Metric<ILSingleDimensional>` Metric.`createClassificationMetric()` Creates an instance of the classification metric.
`static Metric<ILSingleDimensional>` Metric.`createClassificationMetric(double gsFactor)` Creates an instance of the classification metric.
`static Metric<ILSingleDimensional>` Metric.`createDiscernabilityMetric()` Creates an instance of the discernability metric.
`static Metric<ILSingleDimensional>` Metric.`createDiscernabilityMetric(boolean monotonic)` Creates an instance of the discernability metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createEntropyMetric()` Creates an instance of the non-monotonic non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createEntropyMetric(boolean monotonic)` Creates an instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createEntropyMetric(boolean monotonic, double gsFactor)` Creates an instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createEntropyMetric(boolean monotonic, double gsFactor, Metric.AggregateFunction function)` Creates an instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createEntropyMetric(boolean monotonic, Metric.AggregateFunction function)` Creates an instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createEntropyMetric(double gsFactor)` Creates an instance of the non-monotonic non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createHeightMetric()` Creates an instance of the height metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createHeightMetric(Metric.AggregateFunction function)` Creates an instance of the height metric.
`abstract Metric<?>` MetricDescription.`createInstance(MetricConfiguration config)` Creates an instance with the given configuration options.
`static Metric<ILSingleDimensional>` Metric.`createKLDivergenceMetric()` Creates an instance of the KL Divergence metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createLossMetric()` Creates an instance of the loss metric which treats generalization and suppression equally.
`static Metric<AbstractILMultiDimensional>` Metric.`createLossMetric(double gsFactor)` Creates an instance of the loss metric with factors for weighting generalization and suppression.
`static Metric<AbstractILMultiDimensional>` Metric.`createLossMetric(double gsFactor, Metric.AggregateFunction function)` Creates an instance of the loss metric with factors for weighting generalization and suppression.
`static Metric<AbstractILMultiDimensional>` Metric.`createLossMetric(Metric.AggregateFunction function)` Creates an instance of the loss metric which treats generalization and suppression equally.
`static Metric<?>` Metric.`createMetric(Metric<?> metric, int minLevel, int maxLevel)` This method supports backwards compatibility.
`static Metric<AbstractILMultiDimensional>` Metric.`createNormalizedEntropyMetric()` Creates an instance of the normalized entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createNormalizedEntropyMetric(Metric.AggregateFunction function)` Creates an instance of the normalized entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecisionMetric()` Creates an instance of the non-monotonic precision metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecisionMetric(boolean monotonic)` Creates an instance of the precision metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecisionMetric(boolean monotonic, double gsFactor)` Creates an instance of the precision metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecisionMetric(boolean monotonic, double gsFactor, Metric.AggregateFunction function)` Creates an instance of the precision metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecisionMetric(boolean monotonic, Metric.AggregateFunction function)` Creates an instance of the precision metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecisionMetric(double gsFactor)` Creates an instance of the non-monotonic precision metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecisionMetric(double gsFactor, Metric.AggregateFunction function)` Creates an instance of the non-monotonic precision metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecisionMetric(Metric.AggregateFunction function)` Creates an instance of the non-monotonic precision metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedEntropyMetric(double threshold)` Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedEntropyMetric(double threshold, boolean monotonic)` Creates a potentially precomputed instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedEntropyMetric(double threshold, boolean monotonic, double gsFactor)` Creates a potentially precomputed instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedEntropyMetric(double threshold, boolean monotonic, double gsFactor, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedEntropyMetric(double threshold, boolean monotonic, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedEntropyMetric(double threshold, double gsFactor)` Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedLossMetric(double threshold)` Creates a potentially precomputed instance of the loss metric which treats generalization and suppression equally.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedLossMetric(double threshold, double gsFactor)` Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedLossMetric(double threshold, double gsFactor, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedLossMetric(double threshold, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the loss metric which treats generalization and suppression equally.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedNormalizedEntropyMetric(double threshold)` Creates a potentially precomputed instance of the normalized entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createPrecomputedNormalizedEntropyMetric(double threshold, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the normalized entropy metric.
`static Metric<AbstractILMultiDimensional>` Metric.`createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss)` Creates an instance of a metric with statically defined information loss.
`static Metric<AbstractILMultiDimensional>` Metric.`createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss, Metric.AggregateFunction function)` Creates an instance of a metric with statically defined information loss.

Methods in org.deidentifier.arx.metric with parameters of type Metric
`static InformationLoss<?>` InformationLoss.`createInformationLoss(InformationLoss<?> loss, Metric<?> metric, int minLevel, int maxLevel)` Converter method, converting information loss from version 1 to information loss from version 2, if necessary.
`static Metric<?>` Metric.`createMetric(Metric<?> metric, int minLevel, int maxLevel)` This method supports backwards compatibility.
`abstract boolean` MetricDescription.`isInstance(Metric<?> metric)` Returns whether the given metric is an instance of this description.

### Uses of Metric in org.deidentifier.arx.metric.v2

Subclasses of Metric in org.deidentifier.arx.metric.v2
`class` `AbstractMetricMultiDimensional` This class provides an abstract skeleton for the implementation of multi-dimensional metrics.
`class` `AbstractMetricMultiDimensionalPotentiallyPrecomputed` This class provides an abstract skeleton for the implementation of metrics that can either be precomputed or not.
`class` `AbstractMetricSingleDimensional` This class provides an abstract skeleton for the implementation of single-dimensional metrics.
`class` `MetricMDHeight` This class provides an implementation of the Height metric.
`class` `MetricMDNMLoss` This class implements a variant of the Loss metric.
`class` `MetricMDNMLossPotentiallyPrecomputed` This class implements a variant of the Loss metric.
`class` `MetricMDNMLossPrecomputed` This class implements a variant of the Loss metric.
`class` `MetricMDNMPrecision` This class provides an implementation of a weighted precision metric as proposed in: Sweeney, L. (2002).
`class` `MetricMDNUEntropy` This class provides an implementation of the non-uniform entropy metric.
`class` `MetricMDNUEntropyPotentiallyPrecomputed` This class provides an implementation of the non-uniform entropy metric.
`class` `MetricMDNUEntropyPrecomputed` This class provides an efficient implementation of the non-uniform entropy metric.
`class` `MetricMDNUNMEntropy` This class provides an implementation of the non-uniform entropy metric.
`class` `MetricMDNUNMEntropyPotentiallyPrecomputed` This class provides an implementation of the non-uniform entropy metric.
`class` `MetricMDNUNMEntropyPrecomputed` This class provides an implementation of the non-uniform entropy metric.
`class` `MetricMDNUNMNormalizedEntropy` This class provides an implementation of normalized non-uniform entropy.
`class` `MetricMDNUNMNormalizedEntropyPotentiallyPrecomputed` This class provides an implementation of normalized non-uniform entropy.
`class` `MetricMDNUNMNormalizedEntropyPrecomputed` This class provides an efficient implementation of normalized non-uniform entropy.
`class` `MetricMDPrecision` This class provides an implementation of a weighted precision metric as proposed in: Sweeney, L. (2002).
`class` `MetricMDStatic` This class provides an implementation of a static metric in which information loss is user-defined per generalization level.
`class` `MetricSDAECS` This class provides an implementation of the (normalized) average equivalence class size metric.
`class` `MetricSDClassification` This class provides an implementation of the classification metric.
`class` `MetricSDDiscernability` This class provides an implementation of the monotonic DM* metric.
`class` `MetricSDNMAmbiguity` This class implements a variant of the Ambiguity metric.
`class` `MetricSDNMDiscernability` This class provides an implementation of the non-monotonic DM metric.
`class` `MetricSDNMEntropyBasedInformationLoss` This class implements a the entropy-based information loss model proposed in: A Game Theoretic Framework for Analyzing Re-Identification Risk.
`class` `MetricSDNMKLDivergence` This class implements the KL Divergence metric.
`class` `MetricSDNMPublisherPayout` This class implements a model which maximizes publisher benefit according to the model proposed in: A Game Theoretic Framework for Analyzing Re-Identification Risk.

Methods in org.deidentifier.arx.metric.v2 that return Metric
`static Metric<ILSingleDimensional>` __MetricV2.`createAECSMetric()` Creates a new instance of the AECS metric.
`static Metric<ILSingleDimensional>` __MetricV2.`createAECSMetric(double gsFactor)` Creates a new instance of the AECS metric.
`static Metric<ILSingleDimensional>` __MetricV2.`createAECSMetric(int rowCount)` Creates a new instance of the AECS metric.
`static Metric<ILSingleDimensional>` __MetricV2.`createAmbiguityMetric()` Creates an instance of the ambiguity metric.
`static Metric<ILSingleDimensional>` __MetricV2.`createClassificationMetric()` Creates an instance of the classification metric.
`static Metric<ILSingleDimensional>` __MetricV2.`createClassificationMetric(double gsFactor)` Creates an instance of the classification metric.
`static Metric<ILSingleDimensional>` __MetricV2.`createDiscernabilityMetric()` Creates an instance of the discernability metric.
`static Metric<ILSingleDimensional>` __MetricV2.`createDiscernabilityMetric(boolean monotonic)` Creates an instance of the discernability metric.
`static Metric<ILSingleDimensional>` __MetricV2.`createDiscernabilityMetric(boolean monotonic, double numTuples)` Creates an instance of the discernability metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createEntropyMetric()` Creates an instance of the non-monotonic non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createEntropyMetric(boolean monotonic)` Creates an instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createEntropyMetric(boolean monotonic, double gsFactor)` Creates an instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createEntropyMetric(boolean monotonic, double[][] cache, int[][][] cardinalities, int[][][] hierarchies)` Creates an instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createEntropyMetric(boolean monotonic, double gsFactor, Metric.AggregateFunction function)` Creates an instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createEntropyMetric(boolean monotonic, Metric.AggregateFunction function)` Creates an instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createEntropyMetric(double gsFactor)` Creates an instance of the non-monotonic non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createHeightMetric()` Creates an instance of the height metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createHeightMetric(int minHeight, int maxHeight)` Creates an instance of the height metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createHeightMetric(Metric.AggregateFunction function)` Creates an instance of the height metric.
`static Metric<ILSingleDimensional>` __MetricV2.`createKLDivergenceMetric()` Creates an instance of the KL Divergence metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createLossMetric()` Creates an instance of the loss metric which treats generalization and suppression equally.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createLossMetric(double gsFactor)` Creates an instance of the loss metric with factors for weighting generalization and suppression.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createLossMetric(double gsFactor, Metric.AggregateFunction function)` Creates an instance of the loss metric with factors for weighting generalization and suppression.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createLossMetric(Metric.AggregateFunction function)` Creates an instance of the loss metric which treats generalization and suppression equally.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createNormalizedEntropyMetric()` Creates an instance of the normalized entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createNormalizedEntropyMetric(Metric.AggregateFunction function)` Creates an instance of the normalized entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecisionMetric()` Creates an instance of the non-monotonic precision metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecisionMetric(boolean monotonic)` Creates an instance of the precision metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecisionMetric(boolean monotonic, double gsFactor)` Creates an instance of the precision metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecisionMetric(boolean monotonic, double gsFactor, Metric.AggregateFunction function)` Creates an instance of the precision metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecisionMetric(boolean monotonic, int[] heights, double cells)` Creates an instance of the precision metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecisionMetric(boolean monotonic, Metric.AggregateFunction function)` Creates an instance of the precision metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecisionMetric(double gsFactor)` Creates an instance of the non-monotonic precision metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecisionMetric(double gsFactor, Metric.AggregateFunction function)` Creates an instance of the non-monotonic precision metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecisionMetric(Metric.AggregateFunction function)` Creates an instance of the non-monotonic precision metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedEntropyMetric(double threshold)` Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedEntropyMetric(double threshold, boolean monotonic)` Creates a potentially precomputed instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedEntropyMetric(double threshold, boolean monotonic, double gsFactor)` Creates a potentially precomputed instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedEntropyMetric(double threshold, boolean monotonic, double gsFactor, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedEntropyMetric(double threshold, boolean monotonic, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedEntropyMetric(double threshold, double gsFactor)` Creates a potentially precomputed instance of the non-monotonic non-uniform entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedLossMetric(double threshold)` Creates a potentially precomputed instance of the loss metric which treats generalization and suppression equally.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedLossMetric(double threshold, double gsFactor)` Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedLossMetric(double threshold, double gsFactor, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the loss metric with factors for weighting generalization and suppression.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedLossMetric(double threshold, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the loss metric which treats generalization and suppression equally.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedNormalizedEntropyMetric(double threshold)` Creates a potentially precomputed instance of the normalized entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createPrecomputedNormalizedEntropyMetric(double threshold, Metric.AggregateFunction function)` Creates a potentially precomputed instance of the normalized entropy metric.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss)` Creates an instance of a metric with statically defined information loss.
`static Metric<AbstractILMultiDimensional>` __MetricV2.`createStaticMetric(java.util.Map<java.lang.String,java.util.List<java.lang.Double>> loss, Metric.AggregateFunction function)` Creates an instance of a metric with statically defined information loss.

### Uses of Metric in org.deidentifier.arx.test

Fields in org.deidentifier.arx.test declared as Metric
`Metric<?>` AbstractTestUtilityMetricsPrecomputation.ARXUtilityMetricsTestCase.`m1` First model
`Metric<?>` AbstractTestUtilityMetricsPrecomputation.ARXUtilityMetricsTestCase.`m2` Second model

Constructors in org.deidentifier.arx.test with parameters of type Metric
Constructor and Description
`ARXUtilityMetricsTestCase(ARXConfiguration config, java.lang.String sensitiveAttribute, java.lang.String dataset, Metric<?> m1, Metric<?> m2)` Creates a new instance.
`ARXUtilityMetricsTestCase(ARXConfiguration config, java.lang.String sensitiveAttribute, java.lang.String dataset, Metric<?> m1, Metric<?> m2)` Creates a new instance.