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frameless.ml.classification

TypedKMeans

Related Docs: object TypedKMeans | package classification

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class TypedKMeans[Inputs] extends TypedEstimator[Inputs, Output, KMeansModel]

K-means clustering with support for k-means|| initialization proposed by Bahmani et al.

See also

Bahmani et al., Scalable k-means++.

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TypedEstimator[Inputs, Output, KMeansModel], AnyRef, Any
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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. val estimator: KMeans

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    TypedKMeans → TypedEstimator

  9. def finalize(): Unit

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

  10. def fit[T, F[_]](ds: TypedDataset[T])(implicit smartProject: SmartProject[T, Inputs], F: SparkDelay[F]): F[AppendTransformer[Inputs, Output, KMeansModel]]

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    Definition Classes
    TypedEstimator

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. def setInitMode(value: KMeansInitMode): TypedKMeans[Inputs]

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  18. def setInitSteps(value: Int): TypedKMeans[Inputs]

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  19. def setK(value: Int): TypedKMeans[Inputs]

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  20. def setMaxIter(value: Int): TypedKMeans[Inputs]

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  21. def setSeed(value: Long): TypedKMeans[Inputs]

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  22. def setTol(value: Double): TypedKMeans[Inputs]

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

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  24. def toString(): String

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  25. final def wait(): Unit

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

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

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Inherited from TypedEstimator[Inputs, Output, KMeansModel]

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