Github user derrickburns commented on a diff in the pull request:
https://github.com/apache/spark/pull/2419#discussion_r17639919
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansPlusPlus.scala ---
@@ -0,0 +1,198 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.clustering
+
+import org.apache.spark.mllib.base.{PointOps, FP, Infinity, One, Zero}
+import org.apache.spark.util.random.XORShiftRandom
+import org.apache.spark.{Logging, SparkContext}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+
+/**
+ *
+ * The KMeans++ initialization algorithm
+ *
+ * @param pointOps distance function
+ * @tparam P point type
+ * @tparam C center type
+ */
+private[mllib] class KMeansPlusPlus[P <: FP : ClassTag, C <: FP :
ClassTag](
+ pointOps: PointOps[P, C]) extends Serializable with Logging {
+
+ /**
+ * We will maintain for each point the distance to its closest cluster
center.
+ * Since only one center is added on each iteration, recomputing the
closest cluster center
+ * only requires computing the distance to the new cluster center if
+ * that distance is less than the closest cluster center.
+ */
+ case class FatPoint(location: P, index: Int, weight: Double, distance:
Double)
+
+ /**
+ * K-means++ on the weighted point set `points`. This first does the
K-means++
+ * initialization procedure and then rounds of Lloyd's algorithm.
+ */
+
+ def cluster(
+ sc: SparkContext,
+ seed: Int,
+ points: Array[C],
+ weights: Array[Double],
+ k: Int,
+ maxIterations: Int,
+ numPartitions: Int): Array[C] = {
+ val centers: Array[C] = getCenters(sc, seed, points, weights, k,
numPartitions, 1)
+ val pts = sc.parallelize(points.map(pointOps.centerToPoint))
+ new MultiKMeans(pointOps, maxIterations).cluster(pts,
Array(centers))._2.centers
+ }
+
+ /**
+ * Select centers in rounds. On each round, select 'perRound' centers,
with probability of
+ * selection equal to the product of the given weights and distance to
the closest cluster center
+ * of the previous round.
+ *
+ * @param sc the Spark context
+ * @param seed a random number seed
+ * @param points the candidate centers
+ * @param weights the weights on the candidate centers
+ * @param k the total number of centers to select
+ * @param numPartitions the number of data partitions to use
+ * @param perRound the number of centers to add per round
+ * @return an array of at most k cluster centers
+ */
+ def getCenters(sc: SparkContext, seed: Int, points: Array[C], weights:
Array[Double], k: Int,
+ numPartitions: Int, perRound: Int): Array[C] = {
+ assert(points.length > 0)
+ assert(k > 0)
+ assert(numPartitions > 0)
+ assert(perRound > 0)
+
+ if (points.length < k) log.warn("number of clusters requested {}
exceeds number of points {}",
+ k, points.length)
+ val centers = new ArrayBuffer[C](k)
+ val rand = new XORShiftRandom(seed)
+ centers += points(pickWeighted(rand, weights))
+ log.info("starting kMeansPlusPlus initialization on {} points",
points.length)
+
+ var more = true
+ var fatPoints = initialFatPoints(points, weights)
+ fatPoints = updateDistances(fatPoints, centers.view.take(1))
+
+ while (centers.length < k && more) {
+ val chosen = choose(fatPoints, seed ^ (centers.length << 24), rand,
perRound)
+ val newCenters = chosen.map(points(_))
+ fatPoints = updateDistances(fatPoints, newCenters)
+ log.info("chose {} points", chosen.length)
+ for (index <- chosen) {
+ log.info(" center({}) = points({})", centers.length, index)
+ centers += points(index)
+ }
+ more = chosen.nonEmpty
+ }
+ val result = centers.take(k)
+ log.info("completed kMeansPlusPlus initialization with {} centers of
{} requested",
+ result.length, k)
+ result.toArray
+ }
+
+ /**
+ * Choose points
+ *
+ * @param fatPoints points to choose from
+ * @param seed random number seed
+ * @param rand random number generator
+ * @param count number of points to choose
+ * @return indices of chosen points
+ */
+ def choose(fatPoints: Array[FatPoint], seed: Int, rand: XORShiftRandom,
count: Int) =
+ (0 until count).flatMap { x => pickCenter(rand,
fatPoints.iterator)}.map { _.index}
+
+ /**
+ * Create initial fat points with weights given and infinite distance to
closest cluster center.
+ * @param points points
+ * @param weights weights of points
+ * @return fat points with given weighs and infinite distance to closest
cluster center
+ */
+ def initialFatPoints(points: Array[C], weights: Array[Double]):
Array[FatPoint] =
+ (0 until points.length).map{ i => FatPoint(
pointOps.centerToPoint(points(i)), i, weights(i),
+ Infinity)}.toArray
+
+ /**
+ * Update the distance of each point to its closest cluster center,
given only the given cluster
+ * centers that were modified.
+ *
+ * @param points set of candidate initial cluster centers
+ * @param center new cluster center
+ * @return points with their distance to closest to cluster center
updated
+ */
+
+ def updateDistances(points: Array[FatPoint], center: Seq[C]):
Array[FatPoint] =
+ points.map { p =>
+ var i = 0
+ val to = center.length
+ var dist = p.distance
+ val point = p.location
+ while (i < to) {
+ dist = pointOps.distance(point, center(i), dist)
+ i = i + 1
+ }
+ p.copy(distance=dist)
+ }
+
+ /**
+ * Pick a point at random, weighing the choices by the given weight
vector.
+ * Return -1 if all weights are 0.0
+ *
+ * @param rand random number generator
+ * @param weights the weights of the points
+ * @return the index of the point chosen
+ */
+ def pickWeighted(rand: XORShiftRandom, weights: Array[Double]): Int = {
+ val r = rand.nextDouble() * weights.sum
+ var i = 0
+ var curWeight = 0.0
+ while (i < weights.length && curWeight < r) {
+ assert(weights(i) >= 0.0)
+ curWeight += weights(i)
+ i += 1
+ }
+ if (i == 0) throw new IllegalArgumentException("all weights are zero")
--- End diff --
Checks for illegal weight vector and throws exception instead of returning
-1
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