[SPARK-15363][ML][EXAMPLE] Example code shouldn't use VectorImplicits._, asML/fromML

## What changes were proposed in this pull request?

(Please fill in changes proposed in this fix)
In this DataFrame example, we use VectorImplicits._, which is private API.

Since Vectors object has public API, we use Vectors.fromML instead of implicts.

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)

Manually run the example.

Author: [email protected] <[email protected]>

Closes apache#13213 from wangmiao1981/ml.

Author: wangmiao1981

examples/src/main/scala/org/apache/spark/examples/ml/DataFrameExample.scala

@@ -25,7 +25,7 @@ import scopt.OptionParser
 
 import org.apache.spark.examples.mllib.AbstractParams
 import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.mllib.linalg.VectorImplicits._
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.sql.{DataFrame, Row, SparkSession}
 
@@ -81,7 +81,7 @@ object DataFrameExample {
     // Convert features column to an RDD of vectors.
     val features = df.select("features").rdd.map { case Row(v: Vector) => v }
     val featureSummary = features.aggregate(new MultivariateOnlineSummarizer())(
-      (summary, feat) => summary.add(feat),
+      (summary, feat) => summary.add(Vectors.fromML(feat)),
       (sum1, sum2) => sum1.merge(sum2))
     println(s"Selected features column with average values:\n ${featureSummary.mean.toString}")