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author | Sean Owen <sowen@cloudera.com> | 2015-03-11 13:15:19 +0000 |
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committer | Sean Owen <sowen@cloudera.com> | 2015-03-11 13:15:19 +0000 |
commit | 6e94c4eadf443ac3d34eaae4c334c8386fdec960 (patch) | |
tree | f55b474a450b1c3cd085b63dd3cd9291d812fa0f /mllib | |
parent | ec30c17822329e6d2b8c85625b31ba8bd8679fcf (diff) | |
download | spark-6e94c4eadf443ac3d34eaae4c334c8386fdec960.tar.gz spark-6e94c4eadf443ac3d34eaae4c334c8386fdec960.tar.bz2 spark-6e94c4eadf443ac3d34eaae4c334c8386fdec960.zip |
SPARK-6225 [CORE] [SQL] [STREAMING] Resolve most build warnings, 1.3.0 edition
Resolve javac, scalac warnings of various types -- deprecations, Scala lang, unchecked cast, etc.
Author: Sean Owen <sowen@cloudera.com>
Closes #4950 from srowen/SPARK-6225 and squashes the following commits:
3080972 [Sean Owen] Ordered imports: Java, Scala, 3rd party, Spark
c67985b [Sean Owen] Resolve javac, scalac warnings of various types -- deprecations, Scala lang, unchecked cast, etc.
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala index c399496568..5f5a996a87 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -199,12 +199,12 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { assert(formatVersion == thisFormatVersion) val rank = (metadata \ "rank").extract[Int] val userFeatures = sqlContext.parquetFile(userPath(path)) - .map { case Row(id: Int, features: Seq[Double]) => - (id, features.toArray) + .map { case Row(id: Int, features: Seq[_]) => + (id, features.asInstanceOf[Seq[Double]].toArray) } val productFeatures = sqlContext.parquetFile(productPath(path)) - .map { case Row(id: Int, features: Seq[Double]) => - (id, features.toArray) + .map { case Row(id: Int, features: Seq[_]) => + (id, features.asInstanceOf[Seq[Double]].toArray) } new MatrixFactorizationModel(rank, userFeatures, productFeatures) } |