Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11132#discussion_r53558806
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/SVDExample.scala ---
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext
+// $example on$
+import org.apache.spark.mllib.linalg.Matrix
+import org.apache.spark.mllib.linalg.SingularValueDecomposition
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+// $example off$
+
+object SVDExample {
+
+ def main(args: Array[String]): Unit = {
+
+ val conf = new SparkConf().setAppName("SVDExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ val data = Array(
+ Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
+ Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
+ Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0))
+
+ val dataRDD = sc.parallelize(data, 2)
+
+ val mat: RowMatrix = new RowMatrix(dataRDD)
+
+ // Compute the top 5 singular values and corresponding singular
vectors.
+ val svd: SingularValueDecomposition[RowMatrix, Matrix] =
mat.computeSVD(5, computeU = true)
+ val U: RowMatrix = svd.U // The U factor is a RowMatrix.
+ val s: Vector = svd.s // The singular values are stored in a local
dense vector.
+ val V: Matrix = svd.V // The V factor is a local dense matrix.
+ // $example off$
+ val collect = U.rows.collect()
+ println("U factor is:")
+ collect.foreach { vector => println(vector) }
+ println(s"Singular values are: $s")
+ println(s"V factor is: $V")
--- End diff --
`s"V factor is\n: $V"`
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