Github user avulanov commented on a diff in the pull request:
https://github.com/apache/spark/pull/10806#discussion_r54820556
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala ---
@@ -224,142 +261,133 @@ class KMeans private (
/**
* Implementation of K-Means algorithm.
*/
- private def runAlgorithm(data: RDD[VectorWithNorm]): KMeansModel = {
+ private def runAlgorithm(
+ data: RDD[(DenseMatrix, DenseMatrix)],
+ centers: Array[VectorWithNorm]): KMeansModel = {
val sc = data.sparkContext
- val initStartTime = System.nanoTime()
-
- // Only one run is allowed when initialModel is given
- val numRuns = if (initialModel.nonEmpty) {
- if (runs > 1) logWarning("Ignoring runs; one run is allowed when
initialModel is given.")
- 1
- } else {
- runs
- }
-
- val centers = initialModel match {
- case Some(kMeansCenters) => {
- Array(kMeansCenters.clusterCenters.map(s => new VectorWithNorm(s)))
- }
- case None => {
- if (initializationMode == KMeans.RANDOM) {
- initRandom(data)
- } else {
- initKMeansParallel(data)
- }
- }
- }
- val initTimeInSeconds = (System.nanoTime() - initStartTime) / 1e9
- logInfo(s"Initialization with $initializationMode took " +
"%.3f".format(initTimeInSeconds) +
- " seconds.")
-
- val active = Array.fill(numRuns)(true)
- val costs = Array.fill(numRuns)(0.0)
-
- var activeRuns = new ArrayBuffer[Int] ++ (0 until numRuns)
+ var done = false
+ var costs = 0.0
var iteration = 0
-
val iterationStartTime = System.nanoTime()
- // Execute iterations of Lloyd's algorithm until all runs have
converged
- while (iteration < maxIterations && !activeRuns.isEmpty) {
+ // Execute Lloyd's algorithm until converged or reached the max number
of iterations
--- End diff --
Are there differences in implementation between Lloyd's algorithm and
KMeans? It would be useful to comment it here.
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