Class

net.sansa_stack.ml.spark.mining.amieSpark.MineRules

Algorithm

Related Doc: package MineRules

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class Algorithm extends Serializable

Algorithm that mines the Rules.

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Instance Constructors

  1. new Algorithm(k: KB, mHC: Double, mL: Int, mCon: Double, hdfsP: String)

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Value Members

  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. def acceptedForOutput(outMap: Map[String, ArrayBuffer[(ArrayBuffer[RDFTriple], RuleContainer)]], r: RuleContainer, minConf: Double, k: KB, sc: SparkContext, sqlContext: SQLContext): Boolean

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    checks if rule is a useful output

    checks if rule is a useful output

    r

    rule

    minConf

    min. confidence

  5. final def asInstanceOf[T0]: T0

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  6. def calcName(whole: ArrayBuffer[RDFTriple]): String

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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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    @throws( classOf[java.lang.Throwable] )

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

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  12. def hashCode(): Int

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  13. val hdfsPath: String

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  14. final def isInstanceOf[T0]: Boolean

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  15. val kb: KB

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  16. val maxLen: Int

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  17. val minConf: Double

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  18. val minHC: Double

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

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  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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  22. def refine(c: Int, id: Int, r: RuleContainer, dataFrameRuleParts: RDD[(RDFTriple, Int, Int)], sc: SparkContext, sqlContext: SQLContext): RDD[(RDFTriple, Int, Int)]

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    exploring the search space by iteratively extending rules using a set of mining operators: - add dangling atom - add instantiated atom - add closing atom

  23. def ruleMining(sc: SparkContext, sqlContext: SQLContext): ArrayBuffer[RuleContainer]

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  24. def sort(tp: ArrayBuffer[RDFTriple]): ArrayBuffer[RDFTriple]

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

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  26. def toString(): String

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  27. final def wait(): Unit

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

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

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