BregmanPoint, BregmanCenter, BregmanPointOps

For efficient repeated computation of distance between a fixed set of points and varying cluster centers, is it convenient to pre-compute certain information and associate that information with the point or the cluster center. The class that represent an enriched point is BregmanPoint. The class that represent the enriched cluster center is BregmanCenter. Users of this package do not construct instances of these objects directly.

package com.massivedatascience.divergence

trait BregmanPoint

trait BregmanCenter

We enrich a Bregman divergence with a set of commonly used operations, including factory methods toPoint and toCenter to construct instances of the aforementioned BregmanPoint and BregmanCenter.

The enriched trait is the BregmanPointOps.

package com.massivedatascience.clusterer

trait BregmanPointOps  {
  type P = BregmanPoint
  type C = BregmanCenter

  val divergence: BregmanDivergence

  def toPoint(v: WeightedVector): P

  def toCenter(v: WeightedVector): C

  def centerMoved(v: P, w: C): Boolean

  def findClosest(centers: IndexedSeq[C], point: P): (Int, Double)

  def findClosestCluster(centers: IndexedSeq[C], point: P): Int

  def distortion(data: RDD[P], centers: IndexedSeq[C])

  def pointCost(centers: IndexedSeq[C], point: P): Double

  def distance(p: BregmanPoint, c: BregmanCenter): Double
}