[SPARK-7940] Enforce whitespace checking for DO, TRY, CATCH, FINALLY, MATCH, LARROW, RARROW in style checker.

…

Author: Reynold Xin <rxin@databricks.com>

Closes #6491 from rxin/more-whitespace and squashes the following commits:

f6e63dc [Reynold Xin] [SPARK-7940] Enforce whitespace checking for DO, TRY, CATCH, FINALLY, MATCH, LARROW, RARROW in style checker.

2 files changed, 3 insertions, 3 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index ee710fc1ed..a6d1398fc2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -83,7 +83,7 @@ class Node (
   def predict(features: Vector) : Double = {
     if (isLeaf) {
       predict.predict
-    } else{
+    } else {
       if (split.get.featureType == Continuous) {
         if (features(split.get.feature) <= split.get.threshold) {
           leftNode.get.predict(features)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 966811a5a3..b1014ab7c6 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -119,7 +119,7 @@ object LogisticRegressionSuite {
       }
       // Preventing the overflow when we compute the probability
       val maxMargin = margins.max
-      if (maxMargin > 0) for (i <-0 until nClasses) margins(i) -= maxMargin
+      if (maxMargin > 0) for (i <- 0 until nClasses) margins(i) -= maxMargin
 
       // Computing the probabilities for each class from the margins.
       val norm = {
@@ -130,7 +130,7 @@ object LogisticRegressionSuite {
         }
         temp
       }
-      for (i <-0 until nClasses) probs(i) /= norm
+      for (i <- 0 until nClasses) probs(i) /= norm
 
       // Compute the cumulative probability so we can generate a random number and assign a label.
       for (i <- 1 until nClasses) probs(i) += probs(i - 1)