Class

org.clustering4ever.clustering.epsilonproximity.rdd

EpsilonProximityModelScalar

Related Doc: package rdd

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final case class EpsilonProximityModelScalar[D <: ContinuousDistance, Hash <: HashingScalar](clusterizedVectorsSortedByID: RDD[(Long, (ScalarVector, Int))], metric: D, clustersNumber: Int, inputDataHashCode: Int) extends EpsilonProximityModelAncestor[ScalarVector, D, Hash] with Product with Serializable

D

the distance

Hash

the hashing function

Linear Supertypes
Product, Equals, EpsilonProximityModelAncestor[ScalarVector, D, Hash], ClusteringModelDistributed[ScalarVector], ClusteringModel, ClusteringSharedTypes, Serializable, Serializable, AnyRef, Any
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Inherited
  1. EpsilonProximityModelScalar
  2. Product
  3. Equals
  4. EpsilonProximityModelAncestor
  5. ClusteringModelDistributed
  6. ClusteringModel
  7. ClusteringSharedTypes
  8. Serializable
  9. Serializable
  10. AnyRef
  11. Any
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Visibility
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Instance Constructors

  1. new EpsilonProximityModelScalar(clusterizedVectorsSortedByID: RDD[(Long, (ScalarVector, Int))], metric: D, clustersNumber: Int, inputDataHashCode: Int)

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Type Members

  1. final type ClusterID = Int

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    Definition Classes
    ClusteringSharedTypes

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 algorithmID: extensibleAlgorithmNature.EpsilonProximityScalar.type

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    Definition Classes
    EpsilonProximityModelScalar → ClusteringModel

  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
    @throws( ... )

  7. final val clusterizedVectorsSortedByID: RDD[(Long, (ScalarVector, Int))]

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    The clusterized dataset in its basic form RDD[ID, (Vector, ClusterID)]

    The clusterized dataset in its basic form RDD[ID, (Vector, ClusterID)]

    Definition Classes
    EpsilonProximityModelScalar → EpsilonProximityModelAncestor

  8. final val clustersNumber: Int

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    The number of generated clusters

    The number of generated clusters

    Definition Classes
    EpsilonProximityModelScalar → EpsilonProximityModelAncestor

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

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    Definition Classes
    AnyRef

  10. def finalize(): Unit

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

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

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    Definition Classes
    AnyRef → Any

  12. final val inputDataHashCode: Int

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    The hashcode of input dataset to prevent missuse of some methods

    The hashcode of input dataset to prevent missuse of some methods

    Attributes
    protected
    Definition Classes
    EpsilonProximityModelScalar → EpsilonProximityModelAncestor

  13. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  14. final val metric: D

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    The metric used in this algorithm/model

    The metric used in this algorithm/model

    Definition Classes
    EpsilonProximityModelScalar → EpsilonProximityModelAncestor

  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

  17. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  18. final def obtainClustering[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, ScalarVector]])(implicit ct: ClassTag[Cz[O, ScalarVector]]): RDD[Cz[O, ScalarVector]]

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    General methods to obtain a clustering from the model in order to measure performances scores

    General methods to obtain a clustering from the model in order to measure performances scores

    Attributes
    protected[org.clustering4ever.clustering]
    Definition Classes
    EpsilonProximityModelScalar → ClusteringModelDistributed

  19. final def obtainClusteringIDs[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, ScalarVector]])(implicit ct: ClassTag[Cz[O, ScalarVector]]): RDD[ClusterID]

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    returns

    a RDD of clusterIDs

    Attributes
    protected[org.clustering4ever.clustering]
    Definition Classes
    ClusteringModelDistributed

  20. final def obtainInputDataClustering[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz]](data: RDD[Cz[O, ScalarVector]])(implicit ct: ClassTag[Cz[O, ScalarVector]]): RDD[Cz[O, ScalarVector]]

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    This method work only with input dataset which generate this model, please use others method for new set of points

    This method work only with input dataset which generate this model, please use others method for new set of points

    returns

    the clusterized dataset

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

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    Definition Classes
    AnyRef

  22. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

Inherited from EpsilonProximityModelAncestor[ScalarVector, D, Hash]

Inherited from ClusteringModelDistributed[ScalarVector]

Inherited from ClusteringModel

Inherited from ClusteringSharedTypes

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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