Class

ml.combust.mleap.core.feature

MinMaxScalerModel

Related Doc: package feature

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case class MinMaxScalerModel(originalMin: Vector, originalMax: Vector) extends Serializable with Product

Class for MinMax Scaler Transformer

MinMax Scaler will use the Min/Max values to scale input features.

originalMin

minimum values from training features

originalMax

maximum values from training features

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@SparkCode()
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Product, Equals, Serializable, Serializable, AnyRef, Any
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Instance Constructors

  1. new MinMaxScalerModel(originalMin: Vector, originalMax: Vector)

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    originalMin

    minimum values from training features

    originalMax

    maximum values from training features

Value Members

  1. final def !=(arg0: Any): Boolean

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    AnyRef → Any

  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def apply(vector: Vector): Vector

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    Scale a feature vector using the min/max

    Scale a feature vector using the min/max

    vector

    feature vector

    returns

    scaled feature fector

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

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

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  8. def finalize(): Unit

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

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

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  10. final def isInstanceOf[T0]: Boolean

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    Definition Classes
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  11. val minArray: Array[Double]

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

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    Definition Classes
    AnyRef

  13. final def notify(): Unit

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    Definition Classes
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  14. final def notifyAll(): Unit

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  15. val originalMax: Vector

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    maximum values from training features

  16. val originalMin: Vector

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    minimum values from training features

  17. val originalRange: Array[Double]

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

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  19. final def wait(): Unit

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    @throws( ... )

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

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

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Inherited from Product

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Inherited from Serializable

Inherited from Serializable

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