Class/Object

org.apache.flink.ml.preprocessing

MinMaxScaler

Related Docs: object MinMaxScaler | package preprocessing

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class MinMaxScaler extends Transformer[MinMaxScaler]

Scales observations, so that all features are in a user-specified range. By default for MinMaxScaler transformer range = [0,1].

This transformer takes a subtype of Vector of values and maps it to a scaled subtype of Vector such that each feature lies between a user-specified range.

This transformer can be prepended to all Transformer and org.apache.flink.ml.pipeline.Predictor implementations which expect as input a subtype of Vector or a LabeledVector.

Example: 

  1. val trainingDS: DataSet[Vector] = env.fromCollection(data)
val transformer = MinMaxScaler().setMin(-1.0)
transformer.fit(trainingDS)
val transformedDS = transformer.transform(trainingDS)

    Parameters

    - Min: The minimum value of the range of the transformed data set; by default equal to 0 - Max: The maximum value of the range of the transformed data set; by default equal to 1

Linear Supertypes
Transformer[MinMaxScaler], Serializable, Serializable, Estimator[MinMaxScaler], WithParameters, AnyRef, Any
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  1. MinMaxScaler
  2. Transformer
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  4. Serializable
  5. Estimator
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Instance Constructors

  1. new MinMaxScaler()

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  5. def chainPredictor[P <: Predictor[P]](predictor: P): ChainedPredictor[MinMaxScaler, P]

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    Chains a Transformer with a Predictor to form a ChainedPredictor.

    Chains a Transformer with a Predictor to form a ChainedPredictor.

    P

    Type of the Predictor

    predictor

    Trailing Predictor of the resulting pipeline

    Definition Classes
    Transformer

  6. def chainTransformer[T <: Transformer[T]](transformer: T): ChainedTransformer[MinMaxScaler, T]

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    Chains two Transformer to form a ChainedTransformer.

    Chains two Transformer to form a ChainedTransformer.

    T

    Type of the Transformer

    transformer

    Right side transformer of the resulting pipeline

    Definition Classes
    Transformer

  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

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    AnyRef → Any

  10. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  11. def fit[Training](training: DataSet[Training], fitParameters: ParameterMap = ParameterMap.Empty)(implicit fitOperation: FitOperation[MinMaxScaler, Training]): Unit

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    Fits the estimator to the given input data.

    Fits the estimator to the given input data. The fitting logic is contained in the FitOperation. The computed state will be stored in the implementing class.

    Training

    Type of the training data

    training

    Training data

    fitParameters

    Additional parameters for the FitOperation

    fitOperation

    FitOperation which encapsulates the algorithm logic

    Definition Classes
    Estimator

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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

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    AnyRef

  16. final def notify(): Unit

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    Definition Classes
    AnyRef

  17. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  18. val parameters: ParameterMap

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    Definition Classes
    WithParameters

  19. def setMax(max: Double): MinMaxScaler

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    Sets the maximum for the range of the transformed data

    Sets the maximum for the range of the transformed data

    max

    the user-specified maximum value.

    returns

    the MinMaxScaler instance with its minimum value set to the user-specified value.

  20. def setMin(min: Double): MinMaxScaler

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    Sets the minimum for the range of the transformed data

    Sets the minimum for the range of the transformed data

    min

    the user-specified minimum value.

    returns

    the MinMaxScaler instance with its minimum value set to the user-specified value.

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

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    Definition Classes
    AnyRef

  22. def toString(): String

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    Definition Classes
    AnyRef → Any

  23. def transform[Input, Output](input: DataSet[Input], transformParameters: ParameterMap = ParameterMap.Empty)(implicit transformOperation: TransformDataSetOperation[MinMaxScaler, Input, Output]): DataSet[Output]

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    Transform operation which transforms an input DataSet of type I into an output DataSet of type O.

    Transform operation which transforms an input DataSet of type I into an output DataSet of type O. The actual transform operation is implemented within the TransformDataSetOperation.

    Input

    Input data type

    Output

    Output data type

    input

    Input DataSet of type I

    transformParameters

    Additional parameters for the TransformDataSetOperation

    transformOperation

    TransformDataSetOperation which encapsulates the algorithm's logic

    Definition Classes
    Transformer

  24. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    @throws( ... )

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

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    Definition Classes
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    Annotations
    @throws( ... )

Inherited from Transformer[MinMaxScaler]

Inherited from Serializable

Inherited from Serializable

Inherited from Estimator[MinMaxScaler]

Inherited from WithParameters

Inherited from AnyRef

Inherited from Any

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