Class

org.clustering4ever.clustering.epsilonproximity.scala

EpsilonProximityModelBinary

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final case class EpsilonProximityModelBinary[V <: Seq[Int], D[X <: Seq[Int]] <: BinaryDistance[X]](datapointWithClusterIDSortedByPointID: ArrayBuffer[(Long, (BinaryVector[V], Int))], epsilon: Double, metric: D[V], inputDataHashCode: Int) extends EpsilonProximityModelAncestor[BinaryVector[V], D[V]] with Product with Serializable

Linear Supertypes
Product, Equals, EpsilonProximityModelAncestor[BinaryVector[V], D[V]], DataBasedModel[BinaryVector[V], D[V]], KnnModelModelCz[BinaryVector[V], D[V]], KnnModelModel[BinaryVector[V], D[V]], MetricModel[BinaryVector[V], D[V]], ClusteringModelLocal[BinaryVector[V]], ClusteringModel, ClusteringSharedTypes, Serializable, Serializable, AnyRef, Any
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Inherited
  1. EpsilonProximityModelBinary
  2. Product
  3. Equals
  4. EpsilonProximityModelAncestor
  5. DataBasedModel
  6. KnnModelModelCz
  7. KnnModelModel
  8. MetricModel
  9. ClusteringModelLocal
  10. ClusteringModel
  11. ClusteringSharedTypes
  12. Serializable
  13. Serializable
  14. AnyRef
  15. Any
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  1. Public
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Instance Constructors

  1. new EpsilonProximityModelBinary(datapointWithClusterIDSortedByPointID: ArrayBuffer[(Long, (BinaryVector[V], Int))], epsilon: Double, metric: D[V], 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.EpsilonProximityBinary.type

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    Definition Classes
    EpsilonProximityModelBinary → 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 datapointWithClusterIDSortedByPointID: ArrayBuffer[(Long, (BinaryVector[V], Int))]

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    Definition Classes
    EpsilonProximityModelBinary → DataBasedModel

  8. final val epsilon: Double

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    The value epsilon given/determined by the epsilon proximity algorithm

    The value epsilon given/determined by the epsilon proximity algorithm

    Definition Classes
    EpsilonProximityModelBinary → 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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    Attributes
    protected
    Definition Classes
    EpsilonProximityModelBinary → EpsilonProximityModelAncestor

  13. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  14. final def knnPredict[GS[X] <: GenSeq[X]](gs: GS[BinaryVector[V]], k: Int): GS[(ClusterID, BinaryVector[V])]

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    Definition Classes
    DataBasedModel

  15. final def knnPredict[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz], GS[X] <: GenSeq[X]](gs: GS[Cz[O, BinaryVector[V]]], k: Int)(implicit d: DummyImplicit): GS[Cz[O, BinaryVector[V]]]

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    Definition Classes
    DataBasedModel

  16. final def knnPredict(v: BinaryVector[V], k: Int): ClusterID

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    Definition Classes
    DataBasedModel

  17. final def knnPredict[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz]](cz: Cz[O, BinaryVector[V]], k: Int, trainDS: Seq[Cz[O, BinaryVector[V]]], clusteringNumber: Int): ClusterID

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    Definition Classes
    KnnModelModelCz

  18. final def knnPredict(v: BinaryVector[V], k: Int, trainDS: Seq[(ClusterID, BinaryVector[V])]): ClusterID

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    Definition Classes
    KnnModelModel

  19. final def knnPredictWithNN(v: BinaryVector[V], k: Int): (ClusterID, Seq[(Long, BinaryVector[V])])

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    Definition Classes
    DataBasedModel

  20. final def knnPredictWithNN[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz]](cz: Cz[O, BinaryVector[V]], k: Int, trainDS: Seq[Cz[O, BinaryVector[V]]], clusteringNumber: Int): (ClusterID, Seq[Cz[O, BinaryVector[V]]])

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    Definition Classes
    KnnModelModelCz

  21. final def knnPredictWithNN(v: BinaryVector[V], k: Int, trainDS: Seq[(ClusterID, BinaryVector[V])]): (ClusterID, Seq[(ClusterID, BinaryVector[V])])

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    Definition Classes
    KnnModelModel

  22. final val metric: D[V]

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    Definition Classes
    EpsilonProximityModelBinary → DataBasedModel → MetricModel

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

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    Definition Classes
    AnyRef

  24. final def notify(): Unit

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    Definition Classes
    AnyRef

  25. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  26. final def obtainClustering[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz], GS[X] <: GenSeq[X]](data: GS[Cz[O, BinaryVector[V]]]): GS[Cz[O, BinaryVector[V]]]

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    Attributes
    protected[org.clustering4ever.clustering]
    Definition Classes
    EpsilonProximityModelAncestor → ClusteringModelLocal

  27. final def obtainClusteringIDs[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz], GS[X] <: GenSeq[X]](data: GS[Cz[O, BinaryVector[V]]]): GS[ClusterID]

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

  28. final def obtainInputDataClustering[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz], GS[X] <: GenSeq[X]](data: GS[Cz[O, BinaryVector[V]]], isDatasetSortedByID: Boolean = false): GS[Cz[O, BinaryVector[V]]]

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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

    Definition Classes
    EpsilonProximityModelAncestor

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

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    Definition Classes
    AnyRef

  30. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

Inherited from EpsilonProximityModelAncestor[BinaryVector[V], D[V]]

Inherited from DataBasedModel[BinaryVector[V], D[V]]

Inherited from KnnModelModelCz[BinaryVector[V], D[V]]

Inherited from KnnModelModel[BinaryVector[V], D[V]]

Inherited from MetricModel[BinaryVector[V], D[V]]

Inherited from ClusteringModelLocal[BinaryVector[V]]

Inherited from ClusteringModel

Inherited from ClusteringSharedTypes

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped