Scaler

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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

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9. def equals(arg0: Any): Boolean

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10. def finalize(): Unit

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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 scale[M <: LinearModel[M]](estimator: SummarizableEstimator[M], scaler: ScalerEstimator[M] = new ScalerEstimator[M]())(implicit m: Manifest[M]): UnwrappedStage[M, Unscaler[M]]

    Given a linear model estimator first scale the data based on mean/variance, then train the model and unscale its weights.

    Given a linear model estimator first scale the data based on mean/variance, then train the model and unscale its weights.

    estimator

    Nested linear model estimator.

    scaler

    Pre configured scaler (by default with mean and sd).

    returns

    Linear model as produced by the nested estimator with unscaled weights.

18. def scaleComposite[M <: LinearModel[M], C <: CombinedModel[M, C]](estimator: SummarizableEstimator[C], scaler: ScalerEstimator[C] = new ScalerEstimator[C]()): UnwrappedStage[C, Unscaler[C]]

    Extention for scaling composite models assuming their are composites of linear models.

    Extention for scaling composite models assuming their are composites of linear models.

    estimator

    Nested composite estimator.

    scaler

    Scaler with te settings.

    returns

    Composite model as produced by the nested estimator with all parts unscaled.

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

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20. def toString(): String

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21. final def wait(): Unit

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

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

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