EpsilonProximityModelAncestor

abstract val algorithmID: ClusteringAlgorithmNature

abstract val datapointWithClusterIDSortedByPointID: ArrayBuffer[(Long, (V, ClusterID))]

abstract val epsilon: Double

The value epsilon given/determined by the epsilon proximity algorithm

abstract val inputDataHashCode: Int

abstract val metric: D

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

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

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

final def knnPredict(v: V, k: Int): ClusterID

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

def knnPredict(v: V, k: Int, trainDS: Seq[(ClusterID, V)]): ClusterID

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

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

def knnPredictWithNN(v: V, k: Int, trainDS: Seq[(ClusterID, V)]): (ClusterID, Seq[(ClusterID, V)])

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

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

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

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit