protected def efficientNeighbors()

in cassovary-core/src/main/scala/com/twitter/cassovary/algorithms/linkanalysis/LinkAnalysis.scala [54:79]

• 8 lines of code
• 5 McCabe index (conditional complexity)

  protected def efficientNeighbors(node: Node): CSeq[Int] = if (isInStored) node.inboundNodes() else node.outboundNodes()
  protected def efficientNeighborCount(node: Node): Int  = efficientNeighbors(node).length

  /**
   * Run a single iteration through our algorithm.
   * @param currState The starting iteration that our algorithm will be applied to.
   * @return A new iteration.
   */
  def iterate(currState: T): T

  /**
   * Provides default initial start values for our algorithms.
   * @return An default starting iteration.
   */
  protected def defaultInitialState: T

  /**
   * Calculate the error between two arrays using either the T1 error or the T2 error.  This is a
   * convenience method.
   * @param t1 Flag `true` to calculate the T1 error (the sum of absolute differences between two arrays).  Flag
   *           `false` to calculate the T2 error (the sum of the squared differences between two arrays).
   */
  protected def deltaOfArrays(a: Array[Double], b: Array[Double], t1: Boolean = true): Double = {
    val difference = (a zip b).map { case(v1, v2) => if (t1) Math.abs(v1 - v2) else Math.pow(v1 - v2, 2) }.sum
    if (t1) difference else Math.sqrt(difference)
  }