cc.factorie.optimize

ExponentiatedGradient

class ExponentiatedGradient extends GradientOptimizer

This implements the Exponentiated Gradient algorithm of Kivinen and Warmuth - also known as Entropic Mirror Descent (Beck and Teboulle)

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Instance Constructors

  1. new ExponentiatedGradient(rate: Double = 1.0)

    rate

    The base learning rate

Value Members

  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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  6. final def asInstanceOf[T0]: T0

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

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  10. def finalize(): Unit

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  11. def finalizeWeights(weights: WeightsSet): Unit

    Once learning is done, the weights should be copied back into normal tensors.

    Once learning is done, the weights should be copied back into normal tensors.

    weights

    The weights

    Definition Classes
    ExponentiatedGradientGradientOptimizer
  12. final def getClass(): Class[_]

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  13. def hashCode(): Int

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  14. def initializeWeights(weights: WeightsSet): Unit

    Some optimizers swap out weights with special purpose tensors for e.

    Some optimizers swap out weights with special purpose tensors for e.g. efficient scoring while learning.

    weights

    The weights

    Definition Classes
    ExponentiatedGradientGradientOptimizer
  15. def isConverged: Boolean

    Whether the optimizer has converged yet.

    Whether the optimizer has converged yet.

    Definition Classes
    ExponentiatedGradientGradientOptimizer
  16. final def isInstanceOf[T0]: Boolean

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

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. def reset(): Unit

    Reset the optimizers internal state (such as Hessian approximation, etc.

    Reset the optimizers internal state (such as Hessian approximation, etc.)

    Definition Classes
    ExponentiatedGradientGradientOptimizer
  21. def step(weights: WeightsSet, gradient: WeightsMap, value: Double): Unit

    Updates the weights according to the gradient.

    Updates the weights according to the gradient.

    weights

    The weights

    gradient

    The gradient

    value

    The value

    Definition Classes
    ExponentiatedGradientGradientOptimizer
  22. final def synchronized[T0](arg0: ⇒ T0): T0

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  23. def toString(): String

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  24. final def wait(): Unit

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

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

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Inherited from GradientOptimizer

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