breeze.optimize

ApproximateGradientFunction

class ApproximateGradientFunction[K, T] extends DiffFunction[T]

Approximates a gradient by finite differences.

Linear Supertypes
DiffFunction[T], StochasticDiffFunction[T], (T) ⇒ Double, AnyRef, Any
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Inherited
  1. ApproximateGradientFunction
  2. DiffFunction
  3. StochasticDiffFunction
  4. Function1
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new ApproximateGradientFunction(f: (T) ⇒ Double, epsilon: Double = 1E-5)(implicit zeros: CanCreateZerosLike[T, T], view: <:<[T, Tensor[K, Double]], copy: CanCopy[T])

Value Members

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

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef → Any

  4. def andThen[A](g: (Double) ⇒ A): (T) ⇒ A

    Definition Classes
    Function1
    Annotations
    @unspecialized()

  5. final def apply(x: T): Double

    Definition Classes
    StochasticDiffFunction → Function1

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def cached(implicit copy: CanCopy[T]): DiffFunction[T]

    Definition Classes
    DiffFunction

  8. def calculate(x: T): (Double, T)

    Calculates both the value and the gradient at a point

    Calculates both the value and the gradient at a point

    Definition Classes
    ApproximateGradientFunction → StochasticDiffFunction

  9. def calculateAndPrint(x: T, trueGrad: T): (Double, T)

  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  11. def compose[A](g: (A) ⇒ T): (A) ⇒ Double

    Definition Classes
    Function1
    Annotations
    @unspecialized()

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

    Definition Classes
    AnyRef

  13. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  14. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  16. def gradientAt(x: T): T

    calculates the gradient at a point

    calculates the gradient at a point

    Definition Classes
    StochasticDiffFunction

  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  20. final def notify(): Unit

    Definition Classes
    AnyRef

  21. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  23. def throughLens[U](implicit l: Isomorphism[T, U]): DiffFunction[U]

    Lenses provide a way of mapping between two types, which we typically use to convert something to a DenseVector or other Tensor for optimization purposes.

    Lenses provide a way of mapping between two types, which we typically use to convert something to a DenseVector or other Tensor for optimization purposes.

    Definition Classes
    DiffFunction → StochasticDiffFunction

  24. def toString(): String

    Definition Classes
    Function1 → AnyRef → Any

  25. def valueAt(x: T): Double

    calculates the value at a point

    calculates the value at a point

    Definition Classes
    ApproximateGradientFunction → StochasticDiffFunction

  26. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from DiffFunction[T]

Inherited from StochasticDiffFunction[T]

Inherited from (T) ⇒ Double

Inherited from AnyRef

Inherited from Any

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