Packages

o

scalation.analytics

Initializer

object Initializer

The Initializer object provides functions to initialize the parameters/weights of Neural Networks. Supports Uniform, Normal and Nguyen & Widrow methods.

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. Initializer
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  9. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  10. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  11. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  13. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  14. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  15. def toString(): String
    Definition Classes
    AnyRef → Any
  16. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  17. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  18. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  19. def weightMat(rows: Int, cols: Int, stream: Int = 0, limit: Double = -1.0): MatriD

    Generate a random weight/parameter matrix with elements values in (0, limit).

    Generate a random weight/parameter matrix with elements values in (0, limit).

    rows

    the number of rows

    cols

    the number of columns

    stream

    the random number stream to use

    limit

    the maximum value for any weight

  20. def weightMat2(rows: Int, cols: Int, stream: Int = 0): MatriD

    Generate a random weight/parameter matrix with elements values from the Standard Normal distribution.

    Generate a random weight/parameter matrix with elements values from the Standard Normal distribution.

    rows

    the number of rows

    cols

    the number of columns

    stream

    the random number stream to use

  21. def weightMat3(rows: Int, cols: Int, stream: Int = 0): MatriD

    Generate a random weight/parameter matrix with elements values from the Nguyen & Widrow method.

    Generate a random weight/parameter matrix with elements values from the Nguyen & Widrow method.

    rows

    the number of rows

    cols

    the number of columns

    stream

    the random number stream to use

    See also

    ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6945481

  22. def weightVec(rows: Int, stream: Int = 0, limit: Double = -1.0): VectoD

    Generate a random weight/parameter matrix with elements values in (0, limit).

    Generate a random weight/parameter matrix with elements values in (0, limit).

    rows

    the number of rows

    stream

    the random number stream to use

    limit

    the maximum value for any weight

  23. def weightVec2(rows: Int, stream: Int = 0): VectoD

    Generate a random weight/parameter matrix with elements values from the Standard Normal distribution.

    Generate a random weight/parameter matrix with elements values from the Standard Normal distribution.

    rows

    the number of rows

    stream

    the random number stream to use

  24. def weightVec3(rows: Int, stream: Int = 0): VectoD

    Generate a random weight/parameter matrix with elements values from the Nguyen & Widrow Method.

    Generate a random weight/parameter matrix with elements values from the Nguyen & Widrow Method.

    rows

    the number of rows

    stream

    the random number stream to use

    See also

    ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6945481

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from AnyRef

Inherited from Any

Ungrouped