Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1311#discussion_r14862917
--- Diff: docs/mllib-dimensionality-reduction.md ---
@@ -57,10 +57,57 @@ 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.
{% endhighlight %}
+
+Same code applies to `IndexedRowMatrix`.
+The only difference that the `U` matrix becomes an `IndexedRowMatrix`.
</div>
+<div data-lang="java" markdown="1">
+In order to run the following standalone application using Spark framework
make
+sure that you follow the instructions provided at section [Standalone
+Applications](quick-start.html) of the quick-start guide. What is more, you
+should include to your build file *spark-mllib* as a dependency.
+
+{% highlight java %}
+import java.util.LinkedList;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.linalg.Matrix;
+import org.apache.spark.mllib.linalg.distributed.RowMatrix;
+import org.apache.spark.mllib.linalg.SingularValueDecomposition;
+
+public class SVD {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("SVD Example");
+ SparkContext sc = new SparkContext(conf);
+
+ double[][] array = ...
+ LinkedList<Vector> rowsList = new LinkedList<Vector>();
+ for (int i = 0; i < array.length; i++) {
+ Vector currentRow = Vectors.dense(array[i]);
+ rowsList.add(currentRow);
+ }
+ JavaRDD<Vector> rows =
JavaSparkContext.fromSparkContext(sc).parallelize(rowsList);
+
+ // Create a RowMatrix from JavaRDD<Vector>.
+ RowMatrix mat = new RowMatrix(JavaRDD.toRDD(rows));
--- End diff --
ditto: `.rdd()`
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