Revert de01b6deaaee1b43321e0aac330f4a98c0ea61c6^..HEAD

1 files changed, 0 insertions, 73 deletions

diff --git a/examples/src/main/scala/spark/examples/SparkLocalKMeans.scala b/examples/src/main/scala/spark/examples/SparkLocalKMeans.scala
deleted file mode 100644
index 8d9527b7c1..0000000000
--- a/examples/src/main/scala/spark/examples/SparkLocalKMeans.scala
+++ /dev/null
@@ -1,73 +0,0 @@
-package spark.examples
-
-import java.util.Random
-import Vector._
-import spark.SparkContext
-import spark.SparkContext._
-import scala.collection.mutable.HashMap
-import scala.collection.mutable.HashSet
-
-object SparkLocalKMeans {
-	val R = 1000   	// Scaling factor
-	val rand = new Random(42)
-  	
-	def parseVector(line: String): Vector = {
-	    return new Vector(line.split(' ').map(_.toDouble))
-	}
-	
-	def closestPoint(p: Vector, centers: HashMap[Int, Vector]): Int = {
-		var index = 0
-		var bestIndex = 0
-		var closest = Double.PositiveInfinity
-	
-		for (i <- 1 to centers.size) {
-			val vCurr = centers.get(i).get
-			val tempDist = p.squaredDist(vCurr)
-			if (tempDist < closest) {
-				closest = tempDist
-				bestIndex = i
-			}
-		}
-	
-		return bestIndex
-	}
-
-	def main(args: Array[String]) {
-		if (args.length < 4) {
-	      System.err.println("Usage: SparkLocalKMeans <master> <file> <k> <convergeDist>")
-	      System.exit(1)
-	    }
-	    val sc = new SparkContext(args(0), "SparkLocalKMeans")
-	    val lines = sc.textFile(args(1))
-	    val data = lines.map(parseVector _).cache()
-	   	val K = args(2).toInt
-	    val convergeDist = args(3).toDouble
-	
-		var points = data.sample(false, (K+1)/data.count().toDouble, 42).collect
-		var kPoints = new HashMap[Int, Vector]
-		var tempDist = 1.0
-		
-		for (i <- 1 to points.size) {
-			kPoints.put(i, points(i-1))
-		}
-
-		while(tempDist > convergeDist) {
-			var closest = data.map (p => (closestPoint(p, kPoints), (p, 1)))
-			
-			var pointStats = closest.reduceByKey {case ((x1, y1), (x2, y2)) => (x1 + x2, y1+y2)}
-			
-			var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1/mapping._2._2)}.collect()
-			
-			tempDist = 0.0
-			for (mapping <- newPoints) {
-				tempDist += kPoints.get(mapping._1).get.squaredDist(mapping._2)
-			}
-			
-			for (newP <- newPoints) {
-				kPoints.put(newP._1, newP._2)
-			}
-		}
-
-		println("Final centers: " + kPoints)
-	}
-}