breeze.optimize.StochasticAveragedGradient

History

case class History(stepSize: Double, range: IndexedSeq[Int], currentSum: T, previousGradients: IndexedSeq[T], nextPos: Int) extends Product with Serializable

Any history the derived minimization function needs to do its updates. typically an approximation to the second derivative/hessian matrix.

Definition Classes
StochasticAveragedGradientFirstOrderMinimizer
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Instance Constructors

  1. new History(stepSize: Double, range: IndexedSeq[Int], currentSum: T, previousGradients: IndexedSeq[T], nextPos: Int)

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    Annotations
    @throws( ... )
  8. val currentSum: T

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

    Definition Classes
    AnyRef
  10. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

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    AnyRef → Any
  12. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  13. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  14. val nextPos: Int

  15. final def notify(): Unit

    Definition Classes
    AnyRef
  16. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  17. val previousGradients: IndexedSeq[T]

  18. val range: IndexedSeq[Int]

  19. val stepSize: Double

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

    Definition Classes
    AnyRef
  21. final def wait(): Unit

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    @throws( ... )
  22. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  23. final def wait(arg0: Long): Unit

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    @throws( ... )

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