Object

org.emmalanguage.lib.ml.optimization.error

mse

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object mse extends Error

Mean Squared Error.

This is similar to the SSE with normalization according to dataset-size

loss: E(w) = 1/2m sum{ (wTx - y)**2 } gradient: dE(w) = 1/m sum{ (wTx - y) *x }

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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[ID](weights: DVector, instances: DataBag[LDPoint[ID, Double]]): Double

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    mse → Error

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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

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  11. def gradient[ID](weights: DVector, instances: DataBag[LDPoint[ID, Double]]): DVector

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    mse → Error

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

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  18. def toString(): String

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  19. final def wait(): Unit

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

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

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