use API instead of implicits

Author: yinxusen

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

@@ -26,8 +26,7 @@ import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.feature.{CountVectorizer, CountVectorizerModel, RegexTokenizer, StopWordsRemover}
 import org.apache.spark.ml.linalg.{Vector => MLVector}
 import org.apache.spark.mllib.clustering.{DistributedLDAModel, EMLDAOptimizer, LDA, OnlineLDAOptimizer}
-import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.mllib.linalg.VectorImplicits
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Row, SparkSession}
 
@@ -227,7 +226,7 @@ object LDAExample {
     val documents = model.transform(df)
       .select("features")
       .rdd
-      .map { case Row(features: MLVector) => VectorImplicits.mlVectorToMLlibVector(features) }
+      .map { case Row(features: MLVector) => Vectors.fromML(features) }
       .zipWithIndex()
       .map(_.swap)