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com.twitter.scalding.examples

KMeans

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object KMeans

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Type Members

  1. type LabeledVector = (Int, Vector[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. def apply(k: Int, points: TypedPipe[Vector[Double]]): Execution[(Int, ValuePipe[List[LabeledVector]], TypedPipe[LabeledVector])]

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  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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

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  11. def hashCode(): Int

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  12. def initializeClusters(k: Int, points: TypedPipe[Vector[Double]]): (ValuePipe[List[LabeledVector]], TypedPipe[LabeledVector])

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  13. final def isInstanceOf[T0]: Boolean

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  14. def kmeans(k: Int, clusters: ValuePipe[List[LabeledVector]], points: TypedPipe[LabeledVector]): Execution[(Int, ValuePipe[List[LabeledVector]], TypedPipe[LabeledVector])]

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  15. def kmeansStep(k: Int, s: Stat, clusters: ValuePipe[List[LabeledVector]], points: TypedPipe[LabeledVector]): Execution[(ValuePipe[List[LabeledVector]], TypedPipe[LabeledVector])]

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    This runs one step in a kmeans algorithm It returns the number of vectors that changed clusters, the new clusters and the new list of labeled vectors

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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

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  20. def toString(): String

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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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