breeze.optimize

CachedBatchDiffFunction

class CachedBatchDiffFunction[T] extends BatchDiffFunction[T]

Linear Supertypes
BatchDiffFunction[T], (T, IndexedSeq[Int]) ⇒ Double, DiffFunction[T], StochasticDiffFunction[T], (T) ⇒ Double, AnyRef, Any
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Inherited
  1. CachedBatchDiffFunction
  2. BatchDiffFunction
  3. Function2
  4. DiffFunction
  5. StochasticDiffFunction
  6. Function1
  7. AnyRef
  8. Any
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Visibility
  1. Public
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Instance Constructors

  1. new CachedBatchDiffFunction(obj: BatchDiffFunction[T])(implicit arg0: CanCopy[T])

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

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

    Definition Classes
    Function1
    Annotations
    @unspecialized()

  7. def apply(x: T, batch: IndexedSeq[Int]): Double

    Definition Classes
    BatchDiffFunction → Function2

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

    Definition Classes
    StochasticDiffFunction → Function1

  9. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

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

    Definition Classes
    BatchDiffFunction → DiffFunction

  11. def calculate(x: T, range: IndexedSeq[Int]): (Double, T)

    Calculates both the value and the gradient at a point

    Calculates both the value and the gradient at a point

    Definition Classes
    CachedBatchDiffFunction → BatchDiffFunction

  12. 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
    BatchDiffFunction → StochasticDiffFunction

  13. def clone(): AnyRef

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

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

    Definition Classes
    Function1
    Annotations
    @unspecialized()

  15. def curried: (T) ⇒ (IndexedSeq[Int]) ⇒ Double

    Definition Classes
    Function2
    Annotations
    @unspecialized()

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

    Definition Classes
    AnyRef

  17. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  18. def finalize(): Unit

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

  19. def fullRange: IndexedSeq[Int]

    The full size of the data

    The full size of the data

    Definition Classes
    CachedBatchDiffFunction → BatchDiffFunction

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

    Definition Classes
    AnyRef → Any

  21. def gradientAt(x: T, range: IndexedSeq[Int]): T

    calculates the gradient at a point

    calculates the gradient at a point

    Definition Classes
    CachedBatchDiffFunction → BatchDiffFunction

  22. def gradientAt(x: T): T

    calculates the gradient at a point

    calculates the gradient at a point

    Definition Classes
    BatchDiffFunction → StochasticDiffFunction

  23. def groupItems(groupSize: Int): BatchDiffFunction[T]

    Definition Classes
    BatchDiffFunction

  24. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  25. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  27. final def notify(): Unit

    Definition Classes
    AnyRef

  28. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  30. 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
    StochasticDiffFunction

  31. def toString(): String

    Definition Classes
    Function2 → AnyRef → Any

  32. def tupled: ((T, IndexedSeq[Int])) ⇒ Double

    Definition Classes
    Function2
    Annotations
    @unspecialized()

  33. def valueAt(x: T, range: IndexedSeq[Int]): Double

    calculates the value at a point

    calculates the value at a point

    Definition Classes
    CachedBatchDiffFunction → BatchDiffFunction

  34. def valueAt(x: T): Double

    calculates the value at a point

    calculates the value at a point

    Definition Classes
    BatchDiffFunction → StochasticDiffFunction

  35. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  38. def withRandomBatches(size: Int): StochasticDiffFunction[T]

    Definition Classes
    BatchDiffFunction

  39. def withScanningBatches(size: Int): StochasticDiffFunction[T]

    Definition Classes
    BatchDiffFunction

Inherited from BatchDiffFunction[T]

Inherited from (T, IndexedSeq[Int]) ⇒ Double

Inherited from DiffFunction[T]

Inherited from StochasticDiffFunction[T]

Inherited from (T) ⇒ Double

Inherited from AnyRef

Inherited from Any

Ungrouped