PseudoRefPrecision (LIMES Core Library 1.8.2 API)

java.lang.Object
- org.aksw.limes.core.evaluation.qualititativeMeasures.APRF
- - org.aksw.limes.core.evaluation.qualititativeMeasures.APseudoPRF
  - - org.aksw.limes.core.evaluation.qualititativeMeasures.PseudoPrecision
    - - org.aksw.limes.core.evaluation.qualititativeMeasures.PseudoRefPrecision

All Implemented Interfaces:

IQualitativeMeasure
```
public class PseudoRefPrecision
extends PseudoPrecision
```
Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference Precision.
Thereby, not relying on any gold standard. The basic idea is to measure the quality of the given Mapping by calculating how close it is to an assumed 1-to-1 Mapping between source and target.

Since:

1.0

Version:

1.0

Author:

Klaus Lyko (lyko@informatik.uni-leipzig.de), Axel-C. Ngonga Ngomo (ngonga@informatik.uni-leipzig.de), Mofeed Hassan (mounir@informatik.uni-leipzig.de)

Field Summary
- Fields inherited from class org.aksw.limes.core.evaluation.qualititativeMeasures.APseudoPRF
  symmetricPrecision

Constructor Summary

Constructors
Constructor Description

PseudoRefPrecision()

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`double`	`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 .

Methods inherited from class org.aksw.limes.core.evaluation.qualititativeMeasures.APseudoPRF
getUse1To1Mapping, isSymmetricPrecision, isUse1To1Mapping, setSymmetricPrecision, setUse1To1Mapping

Methods inherited from class org.aksw.limes.core.evaluation.qualititativeMeasures.APRF
falseNegative, trueFalsePositive, trueNegative

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - PseudoRefPrecision
```
public PseudoRefPrecision()
```
- Method Detail
  - calculate
```
public double 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 .
    
    Specified by:
    
    calculate in interface IQualitativeMeasure
    
    Overrides:
    
    calculate in class PseudoPrecision
    
    Parameters:
    
    predictions - The predictions provided by a machine learning algorithm.
    
    goldStandard - It contains the gold standard (reference mapping) combined with the source and target URIs.
    
    Returns:
    
    double - This returns the calculated pseudo reference Precision.