MinMaxScaler

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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. object Max extends Parameter[Double] with Product with Serializable

   

5. object Min extends Parameter[Double] with Product with Serializable

   

6. def apply(): MinMaxScaler

   

7. final def asInstanceOf[T0]: T0

   

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8. def clone(): AnyRef

   

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

   

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10. def equals(arg0: Any): Boolean

    

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11. def finalize(): Unit

    

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12. implicit val fitLabeledVectorMinMaxScaler: FitOperation[MinMaxScaler, LabeledVector]

    

    Trains the MinMaxScaler by learning the minimum and maximum of the features of the training data which is of type LabeledVector.

    Trains the MinMaxScaler by learning the minimum and maximum of the features of the training data which is of type LabeledVector. The minimum and maximum are used to transform the given input data.

13. implicit def fitVectorMinMaxScaler[T <: Vector]: FitOperation[MinMaxScaler, T]

    

    Trains the MinMaxScaler by learning the minimum and maximum of each feature of the training data.

    Trains the MinMaxScaler by learning the minimum and maximum of each feature of the training data. These values are used in the transform step to transform the given input data.

    T

    Input data type which is a subtype of Vector

    returns

    FitOperation training the MinMaxScaler on subtypes of Vector

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

    

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15. def hashCode(): Int

    

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16. final def isInstanceOf[T0]: Boolean

    

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

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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

    

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21. def toString(): String

    

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22. implicit val transformLabeledVectors: TransformDataSetOperation[MinMaxScaler, LabeledVector, LabeledVector]

    

23. implicit def transformVectors[T <: Vector](implicit arg0: BreezeVectorConverter[T], arg1: TypeInformation[T], arg2: ClassTag[T]): TransformDataSetOperation[MinMaxScaler, T, T]

    

    TransformDataSetOperation which scales input data of subtype of Vector with respect to the calculated minimum and maximum of the training data.

    TransformDataSetOperation which scales input data of subtype of Vector with respect to the calculated minimum and maximum of the training data. The minimum and maximum values of the resulting data is configurable.

    T

    Type of the input and output data which has to be a subtype of Vector

    returns

    TransformDataSetOperation scaling subtypes of Vector such that the feature values are in the configured range

24. final def wait(): Unit

    

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

    

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

    

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