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1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

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6.  def cumDistance(x: MatrixD, cl: Clusterer, clustr: Array[Int], k: Int): VectorD

   Compute a sum of pairwise distances between points in each cluster (in one direction).

   Compute a sum of pairwise distances between points in each cluster (in one direction).

   x

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

   cl

   the Clusterer use to compute the distance metric

   clustr

   the cluster assignments

   k

   the number of clusters

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

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8.  def equals(arg0: Any): Boolean

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9.  def finalize(): Unit

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10.  final def getClass(): Class[_]

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11.  def hashCode(): Int

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12.  final def isInstanceOf[T0]: Boolean

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13.  def kMeansPP(x: MatrixD, kMax: Int, algo: Algorithm = HARTIGAN, b: Int = 1, useSVD: Boolean = true, plot: Boolean = false): (KMeansPPClusterer, Array[Int], Int)

    Return a KMeansPPClusterer clustering on the given points with an optimal number of clusters k chosen using the Gap statistic.

    Return a KMeansPPClusterer clustering on the given points with an optimal number of clusters k chosen using the Gap statistic.

    x

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

    kMax

    the upper bound on the number of clusters

    algo

    the reassignment aslgorithm used by KMeansPlusPlusClusterer

    b

    the number of reference distributions to create (default = 1)

    useSVD

    use SVD to account for the shape of the points (default = true)

    plot

    whether or not to plot the logs of the within-SSEs (default = false)

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

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15.  final def notify(): Unit

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16.  final def notifyAll(): Unit

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17.  def reference(x: MatrixD, useSVD: Boolean = true, stream: Int = 0): MatrixD

    Compute a reference distribution based on a set of points.

    Compute a reference distribution based on a set of points.

    x

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

    useSVD

    use SVD to account for the shape of the points (default = true)

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

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19.  def toString(): String

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20.  final def wait(): Unit

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

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

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23.  def withinSSE(x: MatrixD, cl: Clusterer, clustr: Array[Int], k: Int): Double

    Compute the within sum of squared errors in terms of distances between between points within a cluster (in one direction).

    Compute the within sum of squared errors in terms of distances between between points within a cluster (in one direction).

    x

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

    cl

    the Clusterer use to compute the distance metric

    clustr

    the cluster assignments

    k

    the number of clusters