PprNegativeExampleSelector

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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

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9. def equals(arg0: Any): Boolean

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10. def finalize(): Unit

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

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

12. 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.

13. implicit val formats: DefaultFormats.type

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

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15. val graph: Graph

16. def hashCode(): Int

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17. final def isInstanceOf[T0]: Boolean

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18. val maxAttempts: Int

19. val maxPotentialPredictions: Int

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

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21. val negativesPerPositive: Int

22. final def notify(): Unit

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23. final def notifyAll(): Unit

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24. val paramKeys: Seq[String]

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

26. val pprComputer: PprComputer

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

28. def selectNegativeExamples(data: Dataset[NodePairInstance], allowedSources: Set[Int], allowedTargets: Set[Int]): Dataset[NodePairInstance]

    Returns a new Dataset that includes the input data and negative instances sampled according to PPR from the positive examples in the input data.

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

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30. def toString(): String

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31. final def wait(): Unit

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

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

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