org.kramerlab.autoencoder.math.optimization

CG_Rasmussen3

class CG_Rasmussen3 extends Minimizer

Rasmussen's fmincg.m reimplementation

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Instance Constructors

  1. new CG_Rasmussen3()

Value Members

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

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

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  6. val EXT: Double

  7. val INT: Double

  8. val MAX: Int

  9. val RATIO: Int

  10. val RHO: Double

  11. val SIG: Double

  12. final def asInstanceOf[T0]: T0

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  13. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  14. final def eq(arg0: AnyRef): Boolean

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  15. def equals(arg0: Any): Boolean

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  16. def finalize(): Unit

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

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  18. def hashCode(): Int

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  19. final def isInstanceOf[T0]: Boolean

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  20. val length: Int

  21. def manimize[V <: VectorSpace[V]](f: DifferentiableFunction[V], start: V): V

    Definition Classes
    Minimizer
  22. def minimize[V <: VectorSpace[V]](f: DifferentiableFunction[V], startPoint: V, progressObservers: List[Observer[V]]): V

    Finds a local minimum for a differentiable function f using it's values and gradient.

    Finds a local minimum for a differentiable function f using it's values and gradient.

    Definition Classes
    CG_Rasmussen3Minimizer
  23. def minimize[V <: VectorSpace[V], Fitness](f: DifferentiableFunction[V], start: V, terminationCriterion: TerminationCriterion[V, (Int, Int)], resultSelector: ResultSelector[V, Fitness], progressObservers: List[Observer[V]])(implicit arg0: Ordering[Fitness]): V

    If implemented, this minimization method can be used in order to use the minimization process as a source of possible candidate solutions, where the actual solution is not the one that minimizes f, but some other point, that maximizes some other fitness function.

    If implemented, this minimization method can be used in order to use the minimization process as a source of possible candidate solutions, where the actual solution is not the one that minimizes f, but some other point, that maximizes some other fitness function. This can be very useful for learning algorithms where the candidate solutions are tested on a separate validation set in order to avoid overfitting. In general, this method just throws UnsupportedOperationException.

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

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  25. final def notify(): Unit

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  26. final def notifyAll(): Unit

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  27. val red: Int

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

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  29. def toString(): String

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  30. final def wait(): Unit

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

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

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