Renamed SparkLocalKMeans to SparkKMeans
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@ -1,67 +1,73 @@
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package spark.examples
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import java.util.Random
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import Vector._
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import spark.SparkContext
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import spark.SparkContext._
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import spark.examples.Vector._
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import scala.collection.mutable.HashMap
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import scala.collection.mutable.HashSet
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object SparkKMeans {
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def parseVector(line: String): Vector = {
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return new Vector(line.split(' ').map(_.toDouble))
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}
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val R = 1000 // Scaling factor
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val rand = new Random(42)
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def parseVector(line: String): Vector = {
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return new Vector(line.split(' ').map(_.toDouble))
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}
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def closestPoint(p: Vector, centers: HashMap[Int, Vector]): Int = {
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var index = 0
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var bestIndex = 0
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var closest = Double.PositiveInfinity
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for (i <- 1 to centers.size) {
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val vCurr = centers.get(i).get
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val tempDist = p.squaredDist(vCurr)
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if (tempDist < closest) {
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closest = tempDist
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bestIndex = i
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}
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}
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return bestIndex
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}
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def closestCenter(p: Vector, centers: Array[Vector]): Int = {
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var bestIndex = 0
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var bestDist = p.squaredDist(centers(0))
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for (i <- 1 until centers.length) {
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val dist = p.squaredDist(centers(i))
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if (dist < bestDist) {
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bestDist = dist
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bestIndex = i
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}
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}
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return bestIndex
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}
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def main(args: Array[String]) {
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if (args.length < 4) {
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System.err.println("Usage: SparkLocalKMeans <master> <file> <k> <convergeDist>")
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System.exit(1)
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}
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val sc = new SparkContext(args(0), "SparkLocalKMeans")
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val lines = sc.textFile(args(1))
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val data = lines.map(parseVector _).cache()
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val K = args(2).toInt
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val convergeDist = args(3).toDouble
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var points = data.takeSample(false, K, 42)
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var kPoints = new HashMap[Int, Vector]
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var tempDist = 1.0
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for (i <- 1 to points.size) {
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kPoints.put(i, points(i-1))
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}
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def main(args: Array[String]) {
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if (args.length < 3) {
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System.err.println("Usage: SparkKMeans <master> <file> <dimensions> <k> <iters>")
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System.exit(1)
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}
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val sc = new SparkContext(args(0), "SparkKMeans")
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val lines = sc.textFile(args(1))
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val points = lines.map(parseVector _).cache()
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val dimensions = args(2).toInt
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val k = args(3).toInt
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val iterations = args(4).toInt
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while(tempDist > convergeDist) {
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var closest = data.map (p => (closestPoint(p, kPoints), (p, 1)))
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var pointStats = closest.reduceByKey {case ((x1, y1), (x2, y2)) => (x1 + x2, y1+y2)}
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var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1/mapping._2._2)}.collect()
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tempDist = 0.0
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for (mapping <- newPoints) {
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tempDist += kPoints.get(mapping._1).get.squaredDist(mapping._2)
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}
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for (newP <- newPoints) {
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kPoints.put(newP._1, newP._2)
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}
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}
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// Initialize cluster centers randomly
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val rand = new Random(42)
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var centers = new Array[Vector](k)
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for (i <- 0 until k)
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centers(i) = Vector(dimensions, _ => 2 * rand.nextDouble - 1)
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println("Initial centers: " + centers.mkString(", "))
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for (i <- 1 to iterations) {
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println("On iteration " + i)
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// Map each point to the index of its closest center and a (point, 1) pair
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// that we will use to compute an average later
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val mappedPoints = points.map { p => (closestCenter(p, centers), (p, 1)) }
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// Compute the new centers by summing the (point, 1) pairs and taking an average
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val newCenters = mappedPoints.reduceByKey {
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case ((sum1, count1), (sum2, count2)) => (sum1 + sum2, count1 + count2)
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}.map {
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case (id, (sum, count)) => (id, sum / count)
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}.collect
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// Update the centers array with the new centers we collected
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for ((id, value) <- newCenters) {
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centers(id) = value
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}
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}
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println("Final centers: " + centers.mkString(", "))
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}
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println("Final centers: " + kPoints)
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}
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}
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