[SPARK-4710] [mllib] Eliminate MLlib compilation warnings

Renamed StreamingKMeans to StreamingKMeansExample to avoid warning about name conflict with StreamingKMeans class.

Added import to DecisionTreeRunner to eliminate warning.

CC: mengxr

Author: Joseph K. Bradley <[email protected]>

Closes #3568 from jkbradley/ml-compilation-warnings and squashes the following commits:

64d6bc4 [Joseph K. Bradley] Updated DecisionTreeRunner.scala and StreamingKMeans.scala to eliminate compilation warnings, including renaming StreamingKMeans to StreamingKMeansExample.

(cherry picked from commit 4ac2151)
Signed-off-by: Xiangrui Meng <[email protected]>

Author: jkbradley

examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala

@@ -17,6 +17,8 @@
 
 package org.apache.spark.examples.mllib
 
+import scala.language.reflectiveCalls
+
 import scopt.OptionParser
 
 import org.apache.spark.{SparkConf, SparkContext}

examples/src/main/scala/org/apache/spark/examples/mllib/StreamingKMeans.scala renamed to examples/src/main/scala/org/apache/spark/examples/mllib/StreamingKMeansExample.scala

@@ -17,10 +17,10 @@
 
 package org.apache.spark.examples.mllib
 
+import org.apache.spark.SparkConf
+import org.apache.spark.mllib.clustering.StreamingKMeans
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.clustering.StreamingKMeans
-import org.apache.spark.SparkConf
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 
 /**
@@ -36,28 +36,28 @@ import org.apache.spark.streaming.{Seconds, StreamingContext}
  * `(y,[x1,x2,x3,...,xn])`
  * Where y is some identifier. n must be the same for train and test.
  *
- * Usage: StreamingKmeans <trainingDir> <testDir> <batchDuration> <numClusters> <numDimensions>
+ * Usage:
+ *   StreamingKMeansExample <trainingDir> <testDir> <batchDuration> <numClusters> <numDimensions>
  *
  * To run on your local machine using the two directories `trainingDir` and `testDir`,
  * with updates every 5 seconds, 2 dimensions per data point, and 3 clusters, call:
- *    $ bin/run-example \
- *        org.apache.spark.examples.mllib.StreamingKMeans trainingDir testDir 5 3 2
+ *    $ bin/run-example mllib.StreamingKMeansExample trainingDir testDir 5 3 2
  *
  * As you add text files to `trainingDir` the clusters will continuously update.
  * Anytime you add text files to `testDir`, you'll see predicted labels using the current model.
  *
  */
-object StreamingKMeans {
+object StreamingKMeansExample {
 
   def main(args: Array[String]) {
     if (args.length != 5) {
       System.err.println(
-        "Usage: StreamingKMeans " +
+        "Usage: StreamingKMeansExample " +
           "<trainingDir> <testDir> <batchDuration> <numClusters> <numDimensions>")
       System.exit(1)
     }
 
-    val conf = new SparkConf().setMaster("local").setAppName("StreamingLinearRegression")
+    val conf = new SparkConf().setMaster("local").setAppName("StreamingKMeansExample")
     val ssc = new StreamingContext(conf, Seconds(args(2).toLong))
 
     val trainingData = ssc.textFileStream(args(0)).map(Vectors.parse)