formatting

Author: manishamde

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala

@@ -36,7 +36,7 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                                1, 2, ... , k-1. It's important to note that features are
  *                                zero-indexed.
  * @param maxMemoryInMB maximum memory in MB allocated to histogram aggregation. Default value is
- *                  128 MB.
+ *                      128 MB.
  *
  */
 @Experimental

mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala

@@ -407,7 +407,7 @@ class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
 
     val leftFilter = Filter(new Split(0, 400, FeatureType.Continuous, List()), -1)
     val rightFilter = Filter(new Split(0, 400, FeatureType.Continuous, List()) ,1)
-    val filters = Array[List[Filter]](List(),List(leftFilter), List(rightFilter))
+    val filters = Array[List[Filter]](List(), List(leftFilter), List(rightFilter))
     val parentImpurities = Array(0.5, 0.5, 0.5)
 
     // Single group second level tree construction.
@@ -463,7 +463,7 @@ object DecisionTreeSuite {
   def generateOrderedLabeledPoints(): Array[LabeledPoint] = {
     val arr = new Array[LabeledPoint](1000)
     for (i <- 0 until 1000) {
-      if (i < 600){
+      if (i < 600) {
         val lp = new LabeledPoint(0.0, Vectors.dense(i.toDouble, 1000.0 - i))
         arr(i) = lp
       } else {
@@ -477,7 +477,7 @@ object DecisionTreeSuite {
   def generateCategoricalDataPoints(): Array[LabeledPoint] = {
     val arr = new Array[LabeledPoint](1000)
     for (i <- 0 until 1000) {
-      if (i < 600){
+      if (i < 600) {
         arr(i) = new LabeledPoint(1.0, Vectors.dense(0.0, 1.0))
       } else {
         arr(i) = new LabeledPoint(0.0, Vectors.dense(1.0, 0.0))