NoRegularization

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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. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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

    

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13. final def notify(): Unit

    

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14. final def notifyAll(): Unit

    

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15. def regLoss(oldLoss: Double, weightVector: Vector, regularizationParameter: Double): Double

    

    Returns the unmodified loss value

    Returns the unmodified loss value

    The updated loss is oldLoss

    oldLoss

    The loss to be updated

    weightVector

    The weights used to update the loss

    regularizationParameter

    The regularization parameter which is ignored

    returns

    Updated loss

    Definition Classes

    NoRegularization → RegularizationPenalty

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

    

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17. def takeStep(weightVector: Vector, gradient: Vector, regularizationConstant: Double, learningRate: Double): Vector

    

    Calculates the new weights based on the gradient

    Calculates the new weights based on the gradient

    The updated weight is w - learningRate *gradient where w is the weight vector

    weightVector

    The weights to be updated

    gradient

    The gradient according to which we will update the weights

    regularizationConstant

    The regularization parameter which is ignored

    learningRate

    The effective step size for this iteration

    returns

    Updated weights

    Definition Classes

    NoRegularization → RegularizationPenalty

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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