Made GaussianMixtureModel class serializable

Modified sum function for better performance

Author: Travis Galoppo

mllib/src/main/scala/org/apache/spark/mllib/clustering/GMMExpectationMaximization.scala

@@ -201,7 +201,7 @@ class GMMExpectationMaximization private (
   /** Sum the values in array of doubles */
   private def sum(x : Array[Double]) : Double = {
     var s : Double = 0.0
-    x.foreach(u => s += u)
+    (0 until x.length).foreach(j => s += x(j))
     s
   }
 

mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala

@@ -25,7 +25,10 @@ import org.apache.spark.mllib.linalg.Vector
  * from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are the respective 
  * mean and covariance for each Gaussian distribution i=1..k. 
  */
-class GaussianMixtureModel(val w: Array[Double], val mu: Array[Vector], val sigma: Array[Matrix]) {
+class GaussianMixtureModel(
+  val w: Array[Double], 
+  val mu: Array[Vector], 
+  val sigma: Array[Matrix]) extends Serializable {
 
   /** Number of gaussians in mixture */
   def k: Int = w.length;