scala

Type Members

1. final case class KCenters[V <: GVector[V], D[X <: GVector[X]] <: Distance[X]](k: Int, metric: D[V], epsilon: 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], epsilon: 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]

   

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

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

   

7. final case class KMeans[V <: Seq[Double], D[X <: Seq[Double]] <: ContinuousDistance[X]](k: Int, metric: D[V], epsilon: Double, maxIterations: Int, customCenters: HashMap[Int, ScalarVector[V]] = ...) extends KCentersAncestor[ScalarVector[V], D[V], KMeansModel[V, D]] with ClusteringAlgorithmLocalScalar[V, KMeansModel[V, 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

   epsilon

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

   maxIterations

   maximal number of iteration

8. final case class KMeansModel[V <: Seq[Double], D[X <: Seq[Double]] <: ContinuousDistance[X]](k: Int, metric: D[V], epsilon: Double, maxIterations: Int, centers: HashMap[Int, ScalarVector[V]] = ...) extends ClusteringModelLocalScalar[V] with KCentersModelAncestor[ScalarVector[V], D[V]] with CenterModelLocalReal[V, D[V]] with KnnModelModelReal[V, D[V]] with Product with Serializable

   

   KMeans model

9. final case class KModes[V <: Seq[Int], D[X <: Seq[Int]] <: BinaryDistance[X]](k: Int, metric: D[V], epsilon: Double, maxIterations: Int, customCenters: HashMap[Int, BinaryVector[V]] = ...) extends KCentersAncestor[BinaryVector[V], D[V], KModesModel[V, D]] with ClusteringAlgorithmLocalBinary[V, KModesModel[V, 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

   epsilon

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

   maxIterations

   maximal number of iteration

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

    

    KModes model

11. final case class KPrototypes[Vb <: Seq[Int], Vs <: Seq[Double], D[X <: Seq[Int], Y <: Seq[Double]] <: MixedDistance[X, Y]](k: Int, metric: D[Vb, Vs], epsilon: Double, maxIterations: Int, customCenters: HashMap[Int, MixedVector[Vb, Vs]] = ...) extends KCentersAncestor[MixedVector[Vb, Vs], D[Vb, Vs], KPrototypesModels[Vb, Vs, 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

    epsilon

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

    maxIterations

    maximal number of iteration

12. final case class KPrototypesModels[Vb <: Seq[Int], Vs <: Seq[Double], D[X <: Seq[Int], Y <: Seq[Double]] <: MixedDistance[X, Y]](k: Int, metric: D[Vb, Vs], epsilon: Double, maxIterations: Int, centers: HashMap[Int, MixedVector[Vb, Vs]] = ...) extends KCentersModelAncestor[MixedVector[Vb, Vs], D[Vb, Vs]] with CenterModelMixedLocal[Vb, Vs, D[Vb, Vs]] with KnnModelModelMixed[Vb, Vs, D[Vb, Vs]] with Product with Serializable

    

    KPrototypes model