Uniform whitespace across scala examples

examples/src/main/scala/spark/examples/LocalKMeans.scala

@@ -10,73 +10,73 @@ import scala.collection.mutable.HashSet
  * K-means clustering.
  */
 object LocalKMeans {
-	val N = 1000
-	val R = 1000   	// Scaling factor
-	val D = 10
-	val K = 10
-	val convergeDist = 0.001
-	val rand = new Random(42)
-  	
-	def generateData = {
-	    def generatePoint(i: Int) = {
-	      Vector(D, _ => rand.nextDouble * R)
-	    }
-	    Array.tabulate(N)(generatePoint)
-	  }
-	
-	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]) {
-	  val data = generateData
-		var points = new HashSet[Vector]
-		var kPoints = new HashMap[Int, Vector]
-		var tempDist = 1.0
-		
-		while (points.size < K) {
-			points.add(data(rand.nextInt(N)))
-		}
-		
-		val iter = points.iterator
-		for (i <- 1 to points.size) {
-			kPoints.put(i, iter.next())
-		}
-
-		println("Initial centers: " + kPoints)
-
-		while(tempDist > convergeDist) {
-			var closest = data.map (p => (closestPoint(p, kPoints), (p, 1)))
-			
-			var mappings = closest.groupBy[Int] (x => x._1)
-			
-			var pointStats = mappings.map(pair => pair._2.reduceLeft [(Int, (Vector, Int))] {case ((id1, (x1, y1)), (id2, (x2, y2))) => (id1, (x1 + x2, y1+y2))})
-			
-			var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1/mapping._2._2)}
-			
-			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)
-	}
+  val N = 1000
+  val R = 1000    // Scaling factor
+  val D = 10
+  val K = 10
+  val convergeDist = 0.001
+  val rand = new Random(42)
+
+  def generateData = {
+    def generatePoint(i: Int) = {
+      Vector(D, _ => rand.nextDouble * R)
+    }
+    Array.tabulate(N)(generatePoint)
+  }
+
+  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]) {
+    val data = generateData
+    var points = new HashSet[Vector]
+    var kPoints = new HashMap[Int, Vector]
+    var tempDist = 1.0
+
+    while (points.size < K) {
+      points.add(data(rand.nextInt(N)))
+    }
+
+    val iter = points.iterator
+    for (i <- 1 to points.size) {
+      kPoints.put(i, iter.next())
+    }
+
+    println("Initial centers: " + kPoints)
+
+    while(tempDist > convergeDist) {
+      var closest = data.map (p => (closestPoint(p, kPoints), (p, 1)))
+
+      var mappings = closest.groupBy[Int] (x => x._1)
+
+      var pointStats = mappings.map(pair => pair._2.reduceLeft [(Int, (Vector, Int))] {case ((id1, (x1, y1)), (id2, (x2, y2))) => (id1, (x1 + x2, y1+y2))})
+
+      var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1/mapping._2._2)}
+
+      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)
+  }
 }

examples/src/main/scala/spark/examples/MultiBroadcastTest.scala

@@ -8,7 +8,7 @@ object MultiBroadcastTest {
       System.err.println("Usage: BroadcastTest <master> [<slices>] [numElem]")
       System.exit(1)
     }
-    
+
     val sc = new SparkContext(args(0), "Broadcast Test",
       System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
@@ -19,7 +19,7 @@ object MultiBroadcastTest {
     for (i <- 0 until arr1.length) {
       arr1(i) = i
     }
-    
+
     var arr2 = new Array[Int](num)
     for (i <- 0 until arr2.length) {
       arr2(i) = i
@@ -30,7 +30,7 @@ object MultiBroadcastTest {
     sc.parallelize(1 to 10, slices).foreach {
       i => println(barr1.value.size + barr2.value.size)
     }
-    
+
     System.exit(0)
   }
 }

examples/src/main/scala/spark/examples/SimpleSkewedGroupByTest.scala

@@ -11,7 +11,7 @@ object SimpleSkewedGroupByTest {
         "[numMappers] [numKVPairs] [valSize] [numReducers] [ratio]")
       System.exit(1)
     }  
-    
+
     var numMappers = if (args.length > 1) args(1).toInt else 2
     var numKVPairs = if (args.length > 2) args(2).toInt else 1000
     var valSize = if (args.length > 3) args(3).toInt else 1000
@@ -20,7 +20,7 @@ object SimpleSkewedGroupByTest {
 
     val sc = new SparkContext(args(0), "GroupBy Test",
       System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
-    
+
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
       var result = new Array[(Int, Array[Byte])](numKVPairs)

examples/src/main/scala/spark/examples/SkewedGroupByTest.scala

@@ -10,7 +10,7 @@ object SkewedGroupByTest {
       System.err.println("Usage: GroupByTest <master> [numMappers] [numKVPairs] [KeySize] [numReducers]")
       System.exit(1)
     }  
-    
+
     var numMappers = if (args.length > 1) args(1).toInt else 2
     var numKVPairs = if (args.length > 2) args(2).toInt else 1000
     var valSize = if (args.length > 3) args(3).toInt else 1000
@@ -18,7 +18,7 @@ object SkewedGroupByTest {
 
     val sc = new SparkContext(args(0), "GroupBy Test",
       System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
-    
+
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random