Verwendungen von Klasse
org.deidentifier.arx.metric.Metric

Packages, die Metric verwenden

Package	Beschreibung
org.deidentifier.arx	This package provides the public API for the ARX anonymization framework.
org.deidentifier.arx.metric	Package providing access to quality models
org.deidentifier.arx.metric.v2	Main package implementing quality models

Verwendungen von Metric in org.deidentifier.arx

Methoden in org.deidentifier.arx, die Metric zurückgeben

Modifizierer und Typ	Methode	Beschreibung
Metric<?>	ARXConfiguration.ARXConfigurationInternal.getQualityModel()	Returns the quality model to be used for optimizing output data.
Metric<?>	ARXConfiguration.getQualityModel()	Returns the quality model to be used for optimizing output data.

Methoden in org.deidentifier.arx mit Parametern vom Typ Metric

Modifizierer und Typ	Methode	Beschreibung
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	ARXConfiguration.setQualityModel(Metric<?> model)	Sets the quality model to be used for optimizing output data.
void	ARXLattice.Access.setQualityModel(Metric<?> model)	Accessor method

Konstruktoren in org.deidentifier.arx mit Parametern vom Typ Metric

Modifizierer	Konstruktor	Beschreibung
	ARXResult(DataHandle handle, DataDefinition definition, ARXLattice lattice, int historySize, double snapshotSizeSnapshot, double snapshotSizeDataset, Metric<?> metric, ARXConfiguration config, ARXLattice.ARXNode optimum, long time, org.deidentifier.arx.framework.lattice.SolutionSpace<?> solutionSpace, ARXProcessStatistics statistics)	Internal constructor for deserialization.

Verwendungen von Metric in org.deidentifier.arx.metric

Unterklassen von Metric in org.deidentifier.arx.metric

Modifizierer und Typ	Klasse	Beschreibung
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.

Methoden in org.deidentifier.arx.metric, die Metric zurückgeben

Modifizierer und Typ	Methode	Beschreibung
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(Map<String,List<Double>> loss)	Creates an instance of a metric with statically defined information loss.
static Metric<AbstractILMultiDimensional>	Metric.createStaticMetric(Map<String,List<Double>> loss, Metric.AggregateFunction function)	Creates an instance of a metric with statically defined information loss.

Methoden in org.deidentifier.arx.metric mit Parametern vom Typ Metric

Modifizierer und Typ	Methode	Beschreibung
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.

Verwendungen von Metric in org.deidentifier.arx.metric.v2

Unterklassen von Metric in org.deidentifier.arx.metric.v2

Modifizierer und Typ	Klasse	Beschreibung
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.

Methoden in org.deidentifier.arx.metric.v2, die Metric zurückgeben

Modifizierer und Typ	Methode	Beschreibung
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(Map<String,List<Double>> loss)	Creates an instance of a metric with statically defined information loss.
static Metric<AbstractILMultiDimensional>	__MetricV2.createStaticMetric(Map<String,List<Double>> loss, Metric.AggregateFunction function)	Creates an instance of a metric with statically defined information loss.