Packages

c

scalation.analytics.fda

KMeansClustering_F

class KMeansClustering_F extends Clusterer

The KMeansClustering_F class provides a simple form of k-means clustering that simply smoothes the data and then appliers KMeansClustering.

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Clusterer, AnyRef, Any
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Instance Constructors

  1. new KMeansClustering_F(x: MatrixD, t: VectorD, τ: VectorD, k: Int, s: Int = 0)

    x

    the vectors/points to be clustered stored as rows of a matrix

    t

    the time points

    τ

    the time points for knots

    k

    the number of clusters to make

    s

    the random number stream (to vary the clusters made)

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def centroids(): MatrixD

    As seen from class KMeansClustering_F, the missing signatures are as follows.

    As seen from class KMeansClustering_F, the missing signatures are as follows. For convenience, these are usable as stub implementations.

    Definition Classes
    KMeansClustering_FClusterer
  6. def classify(y: VectorD): Int

    Given a new point/vector 'y', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.

    Given a new point/vector 'y', determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.

    y

    the vector to classify

    Definition Classes
    KMeansClustering_FClusterer
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def cluster(): Array[Int]

    Create 'k' clusters consisting of points/rows that are closest to each other.

    Create 'k' clusters consisting of points/rows that are closest to each other.

    Definition Classes
    KMeansClustering_FClusterer
  9. val clustered: Boolean

    Flag indicating whether the points have already been clusterer

    Flag indicating whether the points have already been clusterer

    Attributes
    protected
    Definition Classes
    Clusterer
  10. def csize(): VectorI

    Return the sizes (number of points within) of the clusters.

    Return the sizes (number of points within) of the clusters. Should only be called after 'cluster ()'.

    Definition Classes
    KMeansClustering_FClusterer
  11. def distance(u: VectorD, v: VectorD): Double

    Compute a distance metric (e.g., distance squared) between vectors/points 'u' and 'v'.

    Compute a distance metric (e.g., distance squared) between vectors/points 'u' and 'v'. Override this methods to use a different metric, e.g., 'norm' - the Euclidean distance, 2-norm 'norm1' - the Manhattan distance, 1-norm

    u

    the first vector/point

    v

    the second vector/point

    Definition Classes
    Clusterer
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  15. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  16. def getName(i: Int): String

    Get the name of the i-th cluster.

    Get the name of the i-th cluster.

    Definition Classes
    Clusterer
  17. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. def name_(n: Array[String]): Unit

    Set the names for the clusters.

    Set the names for the clusters.

    n

    the array of names

    Definition Classes
    Clusterer
  20. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. final def notify(): Unit
    Definition Classes
    AnyRef
  22. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  23. def sse(x: MatrixD): Double

    Compute the sum of squared errors within the clusters, where error is indicated by e.g., the distance from a point to its centroid.

    Compute the sum of squared errors within the clusters, where error is indicated by e.g., the distance from a point to its centroid.

    Definition Classes
    Clusterer
  24. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  25. def toString(): String
    Definition Classes
    AnyRef → Any
  26. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  27. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Clusterer

Inherited from AnyRef

Inherited from Any

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