org.apache.spark.mllib.feature

StandardScalerModel

class StandardScalerModel extends VectorTransformer

:: Experimental :: Represents a StandardScaler model that can transform vectors.

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@Experimental()
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VectorTransformer, Serializable, Serializable, AnyRef, Any
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  1. final def !=(arg0: AnyRef): Boolean

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  6. final def asInstanceOf[T0]: T0

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  13. final def isInstanceOf[T0]: Boolean

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  14. val mean: Vector

    column mean values

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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

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  19. def toString(): String

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  20. def transform(vector: Vector): Vector

    Applies standardization transformation on a vector.

    Applies standardization transformation on a vector.

    vector

    Vector to be standardized.

    returns

    Standardized vector. If the variance of a column is zero, it will return default 0.0 for the column with zero variance.

    Definition Classes
    StandardScalerModel → VectorTransformer

  21. def transform(data: RDD[Vector]): RDD[Vector]

    Applies transformation on an RDD[Vector].

    Applies transformation on an RDD[Vector].

    data

    RDD[Vector] to be transformed.

    returns

    transformed RDD[Vector].

    Definition Classes
    VectorTransformer

  22. val variance: Vector

    column variance values

  23. final def wait(): Unit

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

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

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  26. val withMean: Boolean

    whether to center the data before scaling

  27. val withStd: Boolean

    whether to scale the data to have unit standard deviation

Inherited from VectorTransformer

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