srowen commented on a change in pull request #27758: [SPARK-31007][ML][WIP]
KMeans optimization based on triangle-inequality
URL: https://github.com/apache/spark/pull/27758#discussion_r408157550
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File path:
mllib/src/main/scala/org/apache/spark/mllib/clustering/DistanceMeasure.scala
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@@ -17,23 +17,124 @@
package org.apache.spark.mllib.clustering
+import org.apache.spark.SparkContext
import org.apache.spark.annotation.Since
+import org.apache.spark.broadcast.Broadcast
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.BLAS.{axpy, dot, scal}
import org.apache.spark.mllib.util.MLUtils
private[spark] abstract class DistanceMeasure extends Serializable {
+ /**
+ * Statistics used in triangle inequality to obtain useful bounds to find
closest centers.
+ * @param distance distance between two centers
+ */
+ def computeStatistics(distance: Double): Double
+
+ /**
+ * Statistics used in triangle inequality to obtain useful bounds to find
closest centers.
+ *
+ * @return A symmetric matrix containing statistics, matrix(i)(j) represents:
+ * 1, a lower bound r of the center i, if i==j. If distance between
point x and center i
+ * is less than f(r), then center i is the closest center to point x.
+ * 2, a lower bound r=matrix(i)(j) to help avoiding unnecessary
distance computation.
+ * Given point x, let i be current closest center, and d be current
best distance,
+ * if d < f(r), then we no longer need to compute the distance to
center j.
+ */
+ def computeStatistics(centers: Array[VectorWithNorm]): Array[Array[Double]]
= {
+ val k = centers.length
+ if (k == 1) return Array(Array(Double.NaN))
+
+ val stats = Array.ofDim[Double](k, k)
+ var i = 0
+ while (i < k) {
+ stats(i)(i) = Double.PositiveInfinity
+ i += 1
+ }
+ i = 0
+ while (i < k) {
+ var j = i + 1
+ while (j < k) {
+ val d = distance(centers(i), centers(j))
+ val s = computeStatistics(d)
+ stats(i)(j) = s
Review comment:
If you're micro-optimizing, I suppose you can lift stats(i) out of the loop,
but it may not o anything
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