use treeAggregate instead of aggregate

Author: Liquan Pei

mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala

@@ -30,6 +30,7 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.HashPartitioner
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.mllib.rdd.RDDFunctions._
 /**
  *  Entry in vocabulary 
  */
@@ -111,9 +112,9 @@ class Word2Vec(
   }
 
   private def learnVocabPerPartition(words:RDD[String]) {
-    
+
   }
-  
+
   private def createExpTable(): Array[Double] = {
     val expTable = new Array[Double](EXP_TABLE_SIZE)
     var i = 0
@@ -254,7 +255,7 @@ class Word2Vec(
       val (aggSyn0, aggSyn1, _, _) =
         // TODO: broadcast temp instead of serializing it directly 
         // or initialize the model in each executor
-        newSentences.aggregate((syn0Global.clone(), syn1Global.clone(), 0, 0))(
+        newSentences.treeAggregate((syn0Global.clone(), syn1Global.clone(), 0, 0))(
           seqOp = (c, v) => (c, v) match { 
           case ((syn0, syn1, lastWordCount, wordCount), sentence) =>
             var lwc = lastWordCount