Merge pull request #557 from ScrapCodes/style. Closes #557.

SPARK-1058, Fix Style Errors and Add Scala Style to Spark Build.

Author: Patrick Wendell <pwendell@gmail.com>
Author: Prashant Sharma <scrapcodes@gmail.com>

== Merge branch commits ==

commit 1a8bd1c059b842cb95cc246aaea74a79fec684f4
Author: Prashant Sharma <scrapcodes@gmail.com>
Date:   Sun Feb 9 17:39:07 2014 +0530

    scala style fixes

commit f91709887a8e0b608c5c2b282db19b8a44d53a43
Author: Patrick Wendell <pwendell@gmail.com>
Date:   Fri Jan 24 11:22:53 2014 -0800

    Adding scalastyle snapshot

4 files changed, 18 insertions, 15 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index efc0eb9353..efe99a31be 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -106,7 +106,8 @@ class PythonMLLibAPI extends Serializable {
     bytes
   }
 
-  private def trainRegressionModel(trainFunc: (RDD[LabeledPoint], Array[Double]) => GeneralizedLinearModel,
+  private def trainRegressionModel(
+      trainFunc: (RDD[LabeledPoint], Array[Double]) => GeneralizedLinearModel,
       dataBytesJRDD: JavaRDD[Array[Byte]], initialWeightsBA: Array[Byte]):
       java.util.LinkedList[java.lang.Object] = {
     val data = dataBytesJRDD.rdd.map(xBytes => {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala
index e476b53450..8803c4c1a0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala
@@ -105,7 +105,7 @@ object SVD {
       cols.flatMap{ case (colind1, mval1) =>
                     cols.map{ case (colind2, mval2) =>
                             ((colind1, colind2), mval1*mval2) } }
-    }.reduceByKey(_+_)
+    }.reduceByKey(_ + _)
 
     // Construct jblas A^T A locally
     val ata = DoubleMatrix.zeros(n, n)
@@ -145,10 +145,10 @@ object SVD {
     // Multiply A by VS^-1
     val aCols = data.map(entry => (entry.j, (entry.i, entry.mval)))
     val bRows = vsirdd.map(entry => (entry._1._1, (entry._1._2, entry._2)))
-    val retUdata = aCols.join(bRows).map( {case (key, ( (rowInd, rowVal), (colInd, colVal)) )
-        => ((rowInd, colInd), rowVal*colVal)}).reduceByKey(_+_)
+    val retUdata = aCols.join(bRows).map( {case (key, ( (rowInd, rowVal), (colInd, colVal)))
+        => ((rowInd, colInd), rowVal*colVal)}).reduceByKey(_ + _)
           .map{ case ((row, col), mval) => MatrixEntry(row, col, mval)}
-    val retU = SparseMatrix(retUdata, m, sigma.length) 
+    val retU = SparseMatrix(retUdata, m, sigma.length)
    
     MatrixSVD(retU, retS, retV)  
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index b77364e08d..cd80134737 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -142,7 +142,7 @@ object GradientDescent extends Logging {
     var regVal = 0.0
 
     for (i <- 1 to numIterations) {
-      val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42+i).map {
+      val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42 + i).map {
         case (y, features) =>
           val featuresCol = new DoubleMatrix(features.length, 1, features:_*)
           val (grad, loss) = gradient.compute(featuresCol, y, weights)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index c5f64b1350..a990e0fb01 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -84,8 +84,9 @@ case class Rating(val user: Int, val product: Int, val rating: Double)
  * [[http://research.yahoo.com/pub/2433]], adapted for the blocked approach used here.
  *
  * Essentially instead of finding the low-rank approximations to the rating matrix `R`,
- * this finds the approximations for a preference matrix `P` where the elements of `P` are 1 if r > 0
- * and 0 if r = 0. The ratings then act as 'confidence' values related to strength of indicated user
+ * this finds the approximations for a preference matrix `P` where the elements of `P` are 1 if
+ * r > 0 and 0 if r = 0. The ratings then act as 'confidence' values related to strength of
+ * indicated user
  * preferences rather than explicit ratings given to items.
  */
 class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var lambda: Double,
@@ -152,8 +153,8 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
     val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock)
     val (productInLinks, productOutLinks) = makeLinkRDDs(numBlocks, ratingsByProductBlock)
 
-    // Initialize user and product factors randomly, but use a deterministic seed for each partition
-    // so that fault recovery works
+    // Initialize user and product factors randomly, but use a deterministic seed for each
+    // partition so that fault recovery works
     val seedGen = new Random()
     val seed1 = seedGen.nextInt()
     val seed2 = seedGen.nextInt()
@@ -268,7 +269,8 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
       val groupedRatings = blockRatings(productBlock).groupBy(_.product).toArray
       // Sort them by product ID
       val ordering = new Ordering[(Int, ArrayBuffer[Rating])] {
-        def compare(a: (Int, ArrayBuffer[Rating]), b: (Int, ArrayBuffer[Rating])): Int = a._1 - b._1
+        def compare(a: (Int, ArrayBuffer[Rating]), b: (Int, ArrayBuffer[Rating])): Int =
+            a._1 - b._1
       }
       Sorting.quickSort(groupedRatings)(ordering)
       // Translate the user IDs to indices based on userIdToPos
@@ -369,7 +371,8 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
     val tempXtX = DoubleMatrix.zeros(triangleSize)
     val fullXtX = DoubleMatrix.zeros(rank, rank)
 
-    // Compute the XtX and Xy values for each user by adding products it rated in each product block
+    // Compute the XtX and Xy values for each user by adding products it rated in each product
+    // block
     for (productBlock <- 0 until numBlocks) {
       for (p <- 0 until blockFactors(productBlock).length) {
         val x = new DoubleMatrix(blockFactors(productBlock)(p))
@@ -544,9 +547,8 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    */
-  def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double)
-  : MatrixFactorizationModel =
-  {
+  def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double,
+      alpha: Double): MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, lambda, -1, alpha)
   }