Addressed style comments from Ryan LeCompte

3 files changed, 13 insertions, 13 deletions

diff --git a/mllib/src/main/scala/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/spark/ml/clustering/KMeans.scala
index 6d78f926c2..b0e141ff32 100644
--- a/mllib/src/main/scala/spark/ml/clustering/KMeans.scala
+++ b/mllib/src/main/scala/spark/ml/clustering/KMeans.scala
@@ -100,7 +100,7 @@ class KMeans private (
 
     val sc = data.sparkContext
 
-    var centers = if (initializationMode == KMeans.RANDOM) {
+    val centers = if (initializationMode == KMeans.RANDOM) {
       initRandom(data)
     } else {
       initKMeansParallel(data)
@@ -131,13 +131,11 @@ class KMeans private (
         val sums = Array.fill(runs, k)(new DoubleMatrix(dims))
         val counts = Array.fill(runs, k)(0L)
 
-        for (point <- points) {
-          for ((centers, runIndex) <- activeCenters.zipWithIndex) {
-            val (bestCenter, cost) = KMeans.findClosest(centers, point)
-            costAccums(runIndex) += cost
-            sums(runIndex)(bestCenter).addi(new DoubleMatrix(point))
-            counts(runIndex)(bestCenter) += 1
-          }
+        for (point <- points; (centers, runIndex) <- activeCenters.zipWithIndex) {
+          val (bestCenter, cost) = KMeans.findClosest(centers, point)
+          costAccums(runIndex) += cost
+          sums(runIndex)(bestCenter).addi(new DoubleMatrix(point))
+          counts(runIndex)(bestCenter) += 1
         }
 
         val contribs = for (i <- 0 until runs; j <- 0 until k) yield {
diff --git a/mllib/src/main/scala/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/spark/ml/recommendation/ALS.scala
index 0c6fa6f741..6c9fb2359c 100644
--- a/mllib/src/main/scala/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/spark/ml/recommendation/ALS.scala
@@ -168,7 +168,9 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
     val grouped = ratings.partitionBy(new HashPartitioner(numBlocks))
     val links = grouped.mapPartitionsWithIndex((blockId, elements) => {
       val ratings = elements.map(_._2).toArray
-      Iterator((blockId, (makeInLinkBlock(numBlocks, ratings), makeOutLinkBlock(numBlocks, ratings))))
+      val inLinkBlock = makeInLinkBlock(numBlocks, ratings)
+      val outLinkBlock = makeOutLinkBlock(numBlocks, ratings)
+      Iterator.single((blockId, (inLinkBlock, outLinkBlock)))
     }, true)
     links.persist(StorageLevel.MEMORY_AND_DISK)
     (links.mapValues(_._1), links.mapValues(_._2))
diff --git a/mllib/src/main/scala/spark/ml/regression/RidgeRegression.scala b/mllib/src/main/scala/spark/ml/regression/RidgeRegression.scala
index 2d07c77141..a6ececbeb6 100644
--- a/mllib/src/main/scala/spark/ml/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/spark/ml/regression/RidgeRegression.scala
@@ -140,8 +140,8 @@ class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double)
 object RidgeRegression {
 
   /**
-   * Train a ridge regression model given an RDD of (response, features) pairs. 
-   * We use the closed form solution to compute the cross-validation score for 
+   * Train a ridge regression model given an RDD of (response, features) pairs.
+   * We use the closed form solution to compute the cross-validation score for
    * a given lambda. The optimal lambda is computed by performing binary search
    * between the provided bounds of lambda.
    *
@@ -159,8 +159,8 @@ object RidgeRegression {
   }
 
   /**
-   * Train a ridge regression model given an RDD of (response, features) pairs. 
-   * We use the closed form solution to compute the cross-validation score for 
+   * Train a ridge regression model given an RDD of (response, features) pairs.
+   * We use the closed form solution to compute the cross-validation score for
    * a given lambda. The optimal lambda is computed by performing binary search
    * between lambda values of 0 and 100.
    *