KCentersModelAncestor

abstract val algorithmID: ClusteringAlgorithmNature

abstract val centers: HashMap[ClusterID, V]

abstract val epsilon: Double

The stopping threshold for iterations

abstract val k: Int

The number of initial seeked clusters, the real number of cluster can be smaller, cf centers.size

abstract val maxIterations: Int

The maximum number of authorized iterations

abstract val metric: D

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final def centerPredict[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz], GS[X] <: GenSeq[X]](data: GS[Cz[O, V]])(implicit d: DummyImplicit): GS[Cz[O, V]]

final def centerPredict[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz]](cz: Cz[O, V]): ClusterID

final def centerPredict[GS[X] <: GenSeq[X]](data: GS[V]): GS[(ClusterID, V)]

final def centerPredict(v: V): ClusterID

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 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 prototypesDistancePerPoint[O, Cz[Y, Z <: GVector[Z]] <: Clusterizable[Y, Z, Cz], GS[X] <: GenSeq[X]](data: GS[Cz[O, V]]): GS[(Cz[O, V], HashMap[ClusterID, Double])]

Compute the distance between every points and all centers

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