added links to spark documentation, and moved import for style reasons

src/main/scala/com/cloudera/sparkts/TimeSeriesRDD.scala

@@ -28,6 +28,7 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.{Partition, Partitioner, SparkContext, TaskContext}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.StatCounter
 
@@ -281,7 +282,9 @@ class TimeSeriesRDD(val index: DateTimeIndex, parent: RDD[(String, Vector[Double
   /**
    * Converts a TimeSeriesRDD into a distributed IndexedRowMatrix, useful to take advantage
    * of Spark MLlib's statistic functions on matrices in a distributed fashion. This is only
-   * supported for cases with a uniform time series index
+   * supported for cases with a uniform time series index. See
+   * [[http://spark.apache.org/docs/latest/mllib-data-types.html]] for more information on the
+   * matrix data structure
    * @param nPartitions number of partitions, default to -1, which represents the same number
    *                    as currently used for the TimeSeriesRDD
    * @return an equivalent IndexedRowMatrix
@@ -306,9 +309,11 @@ class TimeSeriesRDD(val index: DateTimeIndex, parent: RDD[(String, Vector[Double
   /**
    * Converts a TimeSeriesRDD into a distributed RowMatrix, note that indices in
    * a RowMatrix are not significant, and thus this is a valid operation regardless
-   * of the type of time index
+   * of the type of time index.  See
+   * [[http://spark.apache.org/docs/latest/mllib-data-types.html]] for more information on the
+   * matrix data structure
    * @param nPartitions
-   * @return
+   * @return an equivalent RowMatrix
    */
   def toRowMatrix(nPartitions: Int = -1): RowMatrix = {
     val instants = this.toInstants(nPartitions)