Uses of Class
org.aksw.limes.core.datastrutures.GoldStandard

Packages that use GoldStandard

Package	Description
org.aksw.limes.core.datastrutures
org.aksw.limes.core.evaluation.qualititativeMeasures

Uses of GoldStandard in org.aksw.limes.core.datastrutures

Fields in org.aksw.limes.core.datastrutures declared as GoldStandard

Modifier and Type	Field and Description
GoldStandard	TaskData.goldStandard The Gold Standard used to evaluate the machine learning algorithm. It combines the reference mapping and the source and target datasets URIs

Constructors in org.aksw.limes.core.datastrutures with parameters of type GoldStandard

Constructor and Description
TaskData(AMapping mapping, GoldStandard goldStandard)
TaskData(GoldStandard goldStandard, ACache source, ACache target)
TaskData(GoldStandard goldStandard, ACache source, ACache target, EvaluationData evalData)
TaskData(GoldStandard goldStandard, AMapping mapping, ACache source, ACache target)

Uses of GoldStandard in org.aksw.limes.core.evaluation.qualititativeMeasures

Methods in org.aksw.limes.core.evaluation.qualititativeMeasures with parameters of type GoldStandard

Modifier and Type	Method and Description
double	PseudoPrecision.calculate(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo precision of the machine learning predictions compared to a gold standard , which is basically how well the mapping maps one single s to one single t.
double	AUC.calculate(AMapping predictions, GoldStandard goldStandard)
double	IQualitativeMeasure.calculate(AMapping predictions, GoldStandard goldStandard) The method to be implemented for calculating the accuracy of the machine learning predictions compared to a gold standard
double	PseudoFMeasure.calculate(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo F-Measure of the machine learning predictions compared to a gold standard for beta = 1 .
double	PseudoRefPrecision.calculate(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo reference Precision of the machine learning predictions compared to a gold standard for beta = 1 .
double	PseudoRefRecall.calculate(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo reference Recall of the machine learning predictions compared to a gold standard for beta = 1 .
abstract double	APseudoPRF.calculate(AMapping predictions, GoldStandard goldStandard) The Abstract method to be implemented for calculating the accuracy of the machine learning predictions compared to a gold standard
double	Accuracy.calculate(AMapping predictions, GoldStandard goldStandard) The method calculates the accuracy of the machine learning predictions compared to a gold standard
abstract double	APRF.calculate(AMapping predictions, GoldStandard goldStandard) The Abstract method to be implemented for calculating the accuracy of the machine learning predictions compared to a gold standard
double	PseudoRecall.calculate(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo recall of the machine learning predictions compared to a gold standard , which is how many of the s and t were mapped.
double	FMeasure.calculate(AMapping predictions, GoldStandard goldStandard) The method calculates the F-Measure of the machine learning predictions compared to a gold standard
double	Precision.calculate(AMapping predictions, GoldStandard goldStandard) The method calculates the precision of the machine learning predictions compared to a gold standard
double	Recall.calculate(AMapping predictions, GoldStandard goldStandard) The method calculates the recall of the machine learning predictions compared to a gold standard
double	PseudoFMeasure.calculate(AMapping predictions, GoldStandard goldStandard, double beta) The method calculates the pseudo F-Measure of the machine learning predictions compared to a gold standard for different beta values
double	FMeasure.calculate(AMapping predictions, GoldStandard goldStandard, double beta) The method calculates the F-Measure of the machine learning predictions compared to a gold standard
Map<EvaluatorType,Double>	QualitativeMeasuresEvaluator.evaluate(AMapping predictions, GoldStandard goldStandard, Set<EvaluatorType> evaluationMeasures)
double	PseudoRefFMeasure.precision(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo reference precision of the machine learning predictions compared to a gold standard
double	PseudoFMeasure.precision(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo precision of the machine learning predictions compared to a gold standard
double	FMeasure.precision(AMapping predictions, GoldStandard goldStandard) The method calculates the precision of the machine learning predictions compared to a gold standard
double	PseudoRefFMeasure.recall(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo reference recall of the machine learning predictions compared to a gold standard
double	PseudoFMeasure.recall(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo recall of the machine learning predictions compared to a gold standard
double	FMeasure.recall(AMapping predictions, GoldStandard goldStandard) The method calculates the recall of the machine learning predictions compared to a gold standard
static double	APRF.trueNegative(AMapping predictions, GoldStandard goldStandard)