PprNegativeExampleSelector

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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6. final def eq(arg0: AnyRef): Boolean

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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9. def findPotentialPredictions(source: Int, range: Set[Int], knownPositives: Dataset[NodePairInstance]): Set[Int]

   

   Like the above, but just for one source node at a time.

10. def findPotentialPredictions(domain: Set[Int], range: Set[Int], knownPositives: Dataset[NodePairInstance]): Dataset[NodePairInstance]

    

    This one is similar to selectNegativeExamples, but instead of looking specifically for _training_ examples that are close to the given positive examples, we look across the whole domain and range of a relation to find things to score.

    This one is similar to selectNegativeExamples, but instead of looking specifically for _training_ examples that are close to the given positive examples, we look across the whole domain and range of a relation to find things to score. The point of this is to actually perform KB completion, instead of just training a model or doing cross validation. So this method is used to generate possible predictions for NELL's ongoing run, for instance.

11. implicit val formats: DefaultFormats.type

    

12. final def getClass(): Class[_]

    

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13. val graph: Graph

    

14. def hashCode(): Int

    

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15. final def isInstanceOf[T0]: Boolean

    

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16. val maxAttempts: Int

    

17. val maxPotentialPredictions: Int

    

18. final def ne(arg0: AnyRef): Boolean

    

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19. val negativesPerPositive: Int

    

20. final def notify(): Unit

    

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21. final def notifyAll(): Unit

    

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22. val paramKeys: Seq[String]

    

23. def pickPredictionsByPpr(pprValues: Map[Int, Map[Int, Int]], knownPositives: Dataset[NodePairInstance]): Seq[(Int, Int)]

    

24. val pprComputer: PprComputer

    

25. def sampleByPrr(data: Dataset[NodePairInstance], otherPositiveInstances: Seq[NodePairInstance], pprValues: Map[Int, Map[Int, Int]]): Seq[(Int, Int)]

    

26. def selectNegativeExamples(data: Dataset[NodePairInstance], otherPositiveInstances: Seq[NodePairInstance], allowedSources: Option[Set[Int]], allowedTargets: Option[Set[Int]]): Dataset[NodePairInstance]

    

    Returns a collection of negative instances sampled according to PPR from the positive examples in the input data.

    Returns a collection of negative instances sampled according to PPR from the positive examples in the input data. The does _NOT_ merge the newly created negative instances with the original dataset. The caller must do that, if they wish.

27. final def synchronized[T0](arg0: ⇒ T0): T0

    

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28. def toString(): String

    

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29. final def wait(): Unit

    

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30. final def wait(arg0: Long, arg1: Int): Unit

    

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31. final def wait(arg0: Long): Unit

    

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32. def weightedSample(weight_list: Array[(Int, Int)], total_weight: Double, default: Int): Int