Class

org.clustering4ever.spark.clustering.clusterwise

ClusterwiseModel

Related Doc: package clusterwise

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final case class ClusterwiseModel(xyTrain: GenSeq[(Int, (Array[Double], Array[Double], Int))], interceptXYcoefPredByClass: Map[Int, (Array[Double], DenseMatrix[Double], Array[(Int, Array[Double])])], standardizationParameters: Option[(ArrayBuffer[Double], ArrayBuffer[Double], ArrayBuffer[Double], ArrayBuffer[Double])] = None, metric: ContinuousDistance = new Euclidean) extends Product with Serializable

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  1. ClusterwiseModel
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Instance Constructors

  1. new ClusterwiseModel(xyTrain: GenSeq[(Int, (Array[Double], Array[Double], Int))], interceptXYcoefPredByClass: Map[Int, (Array[Double], DenseMatrix[Double], Array[(Int, Array[Double])])], standardizationParameters: Option[(ArrayBuffer[Double], ArrayBuffer[Double], ArrayBuffer[Double], ArrayBuffer[Double])] = None, metric: ContinuousDistance = new Euclidean)

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Type Members

  1. final type IDXYtest = ParSeq[(Int, (Xvector, Yvector))]

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  2. final type IDXtest = Seq[(Long, Xvector)]

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  3. final type Xvector = Array[Double]

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  4. final type Yvector = Array[Double]

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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. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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    @throws( ... )

  6. def cwPredictionKNNdistributed(xyTest: RDD[(Int, (Xvector, Yvector))], k: Int, g: Int): RDD[(Int, (Int, DenseVector[Double]))]

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

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  8. def finalize(): Unit

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

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

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  10. val interceptXYcoefPredByClass: Map[Int, (Array[Double], DenseMatrix[Double], Array[(Int, Array[Double])])]

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  11. final def isInstanceOf[T0]: Boolean

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  12. val metric: ContinuousDistance

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. def predictClusterViaKNNLocal(xyTest: ParSeq[(Int, (Xvector, Yvector))], k: Int, g: Int, withY: Boolean = true): ParSeq[(Int, (Int, DenseVector[Double]))]

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  17. def predictKNN(toPredict: RDD[(Long, Xvector)], k: Int, g: Int): RDD[(Long, (Int, Array[Double]))]

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  18. val standardizationParameters: Option[(ArrayBuffer[Double], ArrayBuffer[Double], ArrayBuffer[Double], ArrayBuffer[Double])]

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  19. def standardizeData(toStandardize: RDD[(Long, Xvector)]): RDD[(Long, Array[Double])]

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

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  21. final def wait(): Unit

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

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

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  24. val xyTrain: GenSeq[(Int, (Array[Double], Array[Double], Int))]

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