Packages

  • package root
    Definition Classes
    root
  • package breeze
    Definition Classes
    root
  • package optimize

    Definition Classes
    breeze
  • object AdaptiveGradientDescent

    Implements the L2^2 and L1 updates from Duchi et al 2010 Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.

    Implements the L2^2 and L1 updates from Duchi et al 2010 Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.

    Basically, we use "forward regularization" and an adaptive step size based on the previous gradients.

    Definition Classes
    optimize
  • class L2Regularization [T] extends StochasticGradientDescent[T]

    Implements the L2 regularization update.

    Implements the L2 regularization update.

    Each step is:

    x_{t+1}i = (s_{ti} * x_{ti} - \eta * g_ti) / (eta * regularization + delta + s_ti)

    where g_ti is the gradient and s_ti = \sqrt(\sum_t'{t} g_ti2)

    Definition Classes
    AdaptiveGradientDescent
  • History

case class History(sumOfSquaredGradients: T) extends Product with Serializable

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  1. new History(sumOfSquaredGradients: T)

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  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
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    Definition Classes
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  5. def clone(): AnyRef
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  9. final def isInstanceOf[T0]: Boolean
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  10. final def ne(arg0: AnyRef): Boolean
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  11. final def notify(): Unit
    Definition Classes
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  12. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  13. val sumOfSquaredGradients: T
  14. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  15. final def wait(): Unit
    Definition Classes
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  16. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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  17. final def wait(arg0: Long): Unit
    Definition Classes
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