Class/Object

org.clustering4ever.clustering.scala.meanshift

GradientAscentScalar

Related Docs: object GradientAscentScalar | package meanshift

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final case class GradientAscentScalar[D <: ContinuousDistance, KArgs <: EstimatorArgs, K[X <: GVector[X], Y <: EstimatorArgs] <: Estimator[X, Y]](minShift: Double, maxIterations: Int, kernel: K[ScalarVector, KArgs], metric: D, alternativeVectorID: Int) extends GradientAscentAncestor[ScalarVector, D, KArgs, K[ScalarVector, KArgs]] with Product with Serializable

Mean Shift gradient ascent

kernel

defines the nature of kernel and its parameters used in the gradient ascent

Linear Supertypes
Product, Equals, GradientAscentAncestor[ScalarVector, D, KArgs, K[ScalarVector, KArgs]], GradientAscentArgs[ScalarVector, D], MetricArgs[ScalarVector, D], MaxIterationsArgs, MinShiftArgs, AlgorithmsArguments, Serializable, Serializable, AnyRef, Any
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Inherited
  1. GradientAscentScalar
  2. Product
  3. Equals
  4. GradientAscentAncestor
  5. GradientAscentArgs
  6. MetricArgs
  7. MaxIterationsArgs
  8. MinShiftArgs
  9. AlgorithmsArguments
  10. Serializable
  11. Serializable
  12. AnyRef
  13. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new GradientAscentScalar(minShift: Double, maxIterations: Int, kernel: K[ScalarVector, KArgs], metric: D, alternativeVectorID: Int)

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    kernel

    defines the nature of kernel and its parameters used in the gradient ascent

Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. final val alternativeVectorID: Int

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    The ID where the mode is stored

    The ID where the mode is stored

    Definition Classes
    GradientAscentScalar → GradientAscentAncestor

  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  6. def clone(): AnyRef

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

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

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    Definition Classes
    AnyRef

  8. final def fit[O, Pz[Y, Z <: GVector[Z]] <: Preprocessable[Y, Z, Pz], GS[X] <: GenSeq[X]](data: GS[Pz[O, ScalarVector]]): GS[(Pz[O, ScalarVector], Double)]

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    O

    the raw object nature

    Pz

    a descendant of preprocessable

    GS

    the nature of the collection The main method to run the gradient ascent

    Definition Classes
    GradientAscentAncestor

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

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    Definition Classes
    AnyRef → Any
    Annotations
    @HotSpotIntrinsicCandidate()

  10. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  11. final val kernel: K[ScalarVector, KArgs]

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    defines the nature of kernel and its parameters used in the gradient ascent

    defines the nature of kernel and its parameters used in the gradient ascent

    Definition Classes
    GradientAscentScalar → GradientAscentAncestor

  12. final val maxIterations: Int

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    The maximum iterations number allowed for this algorithm

    The maximum iterations number allowed for this algorithm

    Definition Classes
    GradientAscentScalar → GradientAscentAncestor → MaxIterationsArgs

  13. final val metric: D

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    The metric used to measure the shift distance of the mode at each iteration

    The metric used to measure the shift distance of the mode at each iteration

    Definition Classes
    GradientAscentScalar → GradientAscentAncestor → MetricArgs

  14. final val minShift: Double

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    The threshold under which points are considered stationary

    The threshold under which points are considered stationary

    Definition Classes
    GradientAscentScalar → GradientAscentAncestor → MinShiftArgs

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

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    Definition Classes
    AnyRef

  16. final def notify(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @HotSpotIntrinsicCandidate()

  17. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @HotSpotIntrinsicCandidate()

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

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    Definition Classes
    AnyRef

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  21. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit

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

    (Since version ) see corresponding Javadoc for more information.

Inherited from Product

Inherited from Equals

Inherited from GradientAscentAncestor[ScalarVector, D, KArgs, K[ScalarVector, KArgs]]

Inherited from GradientAscentArgs[ScalarVector, D]

Inherited from MetricArgs[ScalarVector, D]

Inherited from MaxIterationsArgs

Inherited from MinShiftArgs

Inherited from AlgorithmsArguments

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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