KMediansClustering

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

4. object CoreSet

   

   Definition Classes

   KMediansCoreSetClustering

5. object CoreSetTree extends Serializable

   

   Definition Classes

   KMediansCoreSetClustering

6. def alignCustom(t1: ClusteringLibModule.Table, t2: ClusteringLibModule.Table): M[(ClusteringLibModule.Table, ClusteringLib.Morph1Apply)]

   

7. lazy val alignment: Custom

   

   This specifies how to align the 2 arguments as they are inputted.

   This specifies how to align the 2 arguments as they are inputted. For instance, if we use MorphismAlignment.Cross, then the 2 tables will be crossed, then passed to morph1.

   Definition Classes

   KMediansClustering → Morphism2

8. def approxKMedian(points: Array[Array[Double]], weights: Array[Long], k: Int): (Double, Array[Array[Double]], Array[Boolean])

   

   Returns a clustering that is within 2 times the cost of the optimal k-medians clustering.

   Returns a clustering that is within 2 times the cost of the optimal k-medians clustering.

   The algorithm is fairly simple. It starts with a ranomd seed cluster. It then adds a new cluster by finding the point that is farthest away from its nearest cluster. This point is the seed for a new cluster. We repeat until we have k clusters.

   Definition Classes

   KMediansCoreSetClustering

   Note

   Clustering to Minimize the Maximum Intercluster Distance, Gonzalez 1984

9. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

10. def assign(points: Array[Array[Double]], clustering: Array[Array[Double]]): (Array[Double], Array[Int])

    

    This returns a 2-tuple of an array of distances of each point to their nearest center and an array of cluster indexes each point belongs to.

    This returns a 2-tuple of an array of distances of each point to their nearest center and an array of cluster indexes each point belongs to.

    Definition Classes

    KMediansCoreSetClustering

11. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

12. def dist(x: Array[Double], y: Array[Double]): Double

    

    Definition Classes

    KMediansCoreSetClustering

13. def distMin(x: Array[Double], y: Array[Double]): Double

    

    Definition Classes

    KMediansCoreSetClustering

14. def distSq(x: Array[Double], y: Array[Double]): Double

    

    Definition Classes

    KMediansCoreSetClustering

15. val epsilon: Double

    

    Definition Classes

    KMediansClustering → KMediansCoreSetClustering

16. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

17. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

18. def extract(coreSetTree: CoreSetTree, k: Int, jtype: JType, modelId: Int): ClusteringLibModule.Table

    

19. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

20. lazy val fqn: String

    

    Definition Classes

    FunctionLike

21. final def getClass(): Class[_]

    

    Definition Classes

    AnyRef → Any

22. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

23. val idPolicy: IdentityPolicy

    

    Definition Classes

    Morphism2Like

24. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

25. def kMediansCost(points: Array[Array[Double]], weights: Array[Long], centers: Array[Array[Double]], threshold: Double): Double

    

    This returns the cost of the k-medians clustering given by centers.

    This returns the cost of the k-medians clustering given by centers. The points must also be associated with a set of weights.

    Definition Classes

    KMediansCoreSetClustering

26. implicit def monoidKS: Monoid[KS]

    

27. def morph1Apply(ks: List[Int]): ClusteringLib.Morph1Apply

    

28. val multivariate: Boolean

    

    Definition Classes

    Morphism2

29. val name: String

    

    Definition Classes

    Morphism2 → FunctionLike

30. val namespace: Vector[String]

    

    Definition Classes

    Morphism2 → FunctionLike

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

    

    Definition Classes

    AnyRef

32. final def notify(): Unit

    

    Definition Classes

    AnyRef

33. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

34. val opcode: Int

    

    Definition Classes

    Morphism2 → FunctionLike

35. def reducerFeatures(k: Int): CReducer[CoreSetTree]

    

36. def reducerKS: CReducer[KS]

    

37. val rowLevel: Boolean

    

    Definition Classes

    Morphism2 → FunctionLike

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

    

    Definition Classes

    AnyRef

39. def toString(): String

    

    Definition Classes

    FunctionLike → AnyRef → Any

40. val tpe: BinaryOperationType

    

    Definition Classes

    KMediansClustering → FunctionLike

41. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

44. def weightArray(xs: Array[Double], ws: Array[Long]): Unit

    

    Definition Classes

    KMediansCoreSetClustering