scala

Type Members

1. final case class KCenters[V <: GVector[V], D[X <: GVector[X]] <: Distance[X]](k: Int, metric: D[V], minShift: Double, maxIterations: Int, customCenters: HashMap[Int, V] = immutable.HashMap.empty[Int, V]) extends KCentersAncestor[V, D[V], KCentersModel[V, D]] with Product with Serializable

   

2. trait KCentersAncestor[V <: GVector[V], D <: Distance[V], CM <: KCentersModelAncestor[V, D]] extends KCommons[V, D] with ClusteringAlgorithmLocal[V, CM]

   

   The famous K-Centers using a user-defined dissmilarity measure.

3. final case class KCentersModel[V <: GVector[V], D[X <: GVector[X]] <: Distance[X]](k: Int, metric: D[V], minShift: Double, maxIterations: Int, centers: HashMap[Int, V] = immutable.HashMap.empty[Int, V]) extends KCentersModelAncestor[V, D[V]] with KnnModelModel[V, D[V]] with Product with Serializable

   

   Generic KCenters model

4. trait KCentersModelAncestor[V <: GVector[V], D <: Distance[V]] extends KCentersModelCommons[V, D] with ClusteringModelLocal[V] with CenterModelLocalCz[V, D]

   

5. trait KCentersModelCommons[V <: GVector[V], D <: Distance[V]] extends CenterModel[V, D] with KCommonsArgs[V, D]

   
   D

   Trait regrouping commons elements between KCenters models descendant as well for scala than spark

6. trait KCommons[V <: GVector[V], D <: Distance[V]] extends KCommonsArgs[V, D] with ClusteringSharedTypes

   

7. trait KCommonsArgs[V <: GVector[V], D <: Distance[V]] extends MinShiftArgs with MaxIterationsArgs with KArgs with MetricArgs[V, D]

   

8. final case class KMeans[D <: ContinuousDistance](k: Int, metric: D, minShift: Double, maxIterations: Int, customCenters: HashMap[Int, ScalarVector] = ...) extends KCentersAncestor[ScalarVector, D, KMeansModel[D]] with ClusteringAlgorithmLocalScalar[KMeansModel[D]] with Product with Serializable

   

   The famous K-Means using a user-defined dissmilarity measure.

   The famous K-Means using a user-defined dissmilarity measure.

   k

   number of clusters

   metric

   a defined continuous dissimilarity measure on a GVector descendant

   minShift

   The stopping criteria, ie the distance under which centers are mooving from their previous position

   maxIterations

   maximal number of iteration

9. final case class KMeansModel[D <: ContinuousDistance](k: Int, metric: D, minShift: Double, maxIterations: Int, centers: HashMap[Int, ScalarVector] = ...) extends KCentersModelAncestor[ScalarVector, D] with ClusteringModelLocalScalar with CenterModelLocalReal[D] with KnnModelModelScalar[D] with Product with Serializable

   

   KMeans model

10. final case class KModes[D <: BinaryDistance](k: Int, metric: D, minShift: Double, maxIterations: Int, customCenters: HashMap[Int, BinaryVector] = ...) extends KCentersAncestor[BinaryVector, D, KModesModel[D]] with ClusteringAlgorithmLocalBinary[KModesModel[D]] with Product with Serializable

    

    The famous K-Means using a user-defined dissmilarity measure.

    The famous K-Means using a user-defined dissmilarity measure.

    k

    number of clusters seeked

    metric

    a defined binary dissimilarity measure on a GVector descendant

    minShift

    The stopping criteria, ie the distance under which centers are mooving from their previous position

    maxIterations

    maximal number of iteration

11. final case class KModesModel[D <: BinaryDistance](k: Int, metric: D, minShift: Double, maxIterations: Int, centers: HashMap[Int, BinaryVector] = ...) extends KCentersModelAncestor[BinaryVector, D] with ClusteringModelLocalBinary with CenterModelLocalBinary[D] with KnnModelModelBinary[D] with Product with Serializable

    

    KModes model

12. final case class KPrototypes[D <: MixedDistance](k: Int, metric: D, minShift: Double, maxIterations: Int, customCenters: HashMap[Int, MixedVector] = ...) extends KCentersAncestor[MixedVector, D, KPrototypesModels[D]] with Product with Serializable

    

    The famous K-Prototypes using a user-defined dissmilarity measure.

    The famous K-Prototypes using a user-defined dissmilarity measure.

    k

    number of clusters seeked

    metric

    a defined dissimilarity measure

    minShift

    The stopping criteria, ie the distance under which centers are mooving from their previous position

    maxIterations

    maximal number of iteration

13. final case class KPrototypesModels[D <: MixedDistance](k: Int, metric: D, minShift: Double, maxIterations: Int, centers: HashMap[Int, MixedVector] = ...) extends KCentersModelAncestor[MixedVector, D] with CenterModelMixedLocal[D] with KnnModelModelMixed[D] with Product with Serializable

    

    KPrototypes model