ModelPredictionPipe

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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. def apply[T, Q, R, M <: Model[T, Q, R]](featuresSc: ReversibleScaler[Q], outputSc: ReversibleScaler[R], m: M): ModelPredictionPipe[T, Q, Q, R, R, M]

   

   Create a ModelPredictionPipe instance given scaling relationships for features and outputs, along with a DynaML Model

5. def apply[T, Q, R, M <: Model[T, Q, R]](m: M): ModelPredictionPipe[T, Q, Q, R, R, M]

   

   Create a ModelPredictionPipe instance (having no pre or post processing steps) given a DynaML Model

6. def apply[T, P, Q, R, S, M <: Model[T, Q, R]](pre: DataPipe[P, Q], m: M, po: DataPipe[R, S]): ModelPredictionPipe[T, P, Q, R, S, M]

   

   Create a ModelPredictionPipe instance given a pre-processing flow, a DynaML Model and a post-processing flow respectively.

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. 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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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. final def wait(): Unit

    

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

    

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

    

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