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.  val DEBUG: Boolean

   Attributes

   protected

5.  val MAX_ITER: Int

   Attributes

   protected

6.  var _k: Int

   Attributes

   protected

7.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

8.  val cent: MatrixD

   Attributes

   protected

9.  def centroids(): MatrixD

   Return the centroids.

   Return the centroids. Should only be called after cluster ().

   Definition Classes

   KMeansPPClusterer → Clusterer

10.  def classify(y: VectorD): Int

    Given a new point/vector y, determine which cluster it belongs to.

    Given a new point/vector y, determine which cluster it belongs to.

    y

    the vector to classify

    Definition Classes

    KMeansPPClusterer → Clusterer

11.  def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

12.  def cluster(): Array[Int]

    Given a set of points/vectors, put them in clusters, returning the cluster assignment vector.

    Given a set of points/vectors, put them in clusters, returning the cluster assignment vector. A basic goal is to minimize the sum of the distances between points within each cluster.

    Definition Classes

    KMeansPPClusterer → Clusterer

13.  def clusterHartigan(): Array[Int]

    Cluster the points using a version of the Hartigan-Wong algorithm.

    Cluster the points using a version of the Hartigan-Wong algorithm.

    See also

    www.tqmp.org/RegularArticles/vol09-1/p015/p015.pdf

14.  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

15.  val clustr: Array[Int]

    Attributes

    protected

16.  def csize(): VectorI

    Return the sizes of the centroids.

    Return the sizes of the centroids. Should only be called after cluster ().

    Definition Classes

    KMeansPPClusterer → Clusterer

17.  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

18.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

19.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

20.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

21.  final def flaw(method: String, message: String): Unit

    Definition Classes

    Error

22.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

23.  def getName(i: Int): String

    Get the name of the i-th cluster.

    Get the name of the i-th cluster.

    Definition Classes

    Clusterer

24.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

25.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

26.  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

27.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

28.  final def notify(): Unit

    Definition Classes

    AnyRef

29.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

30.  val pdf: VectorD

    Attributes

    protected

31.  val raniv: PermutedVecI

    Attributes

    protected

32.  val sizes: VectorI

    Attributes

    protected

33.  def sse(): Double

    Compute the sum of squared errors (distance sqaured from centroid for all points)

34.  def sse(c: Int): Double

    Compute the sum of squared errors (distance squared) from all points in cluster 'c' to the cluster's centroid.

    Compute the sum of squared errors (distance squared) from all points in cluster 'c' to the cluster's centroid.

    c

    the current cluster

35.  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

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

    Definition Classes

    AnyRef

37.  def toString(): String

    Definition Classes

    AnyRef → Any

38.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

40.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )