Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1311#discussion_r14862918
--- Diff: docs/mllib-dimensionality-reduction.md ---
@@ -91,4 +138,51 @@ val pc: Matrix = mat.computePrincipalComponents(10) //
Principal components are
val projected: RowMatrix = mat.multiply(pc)
{% endhighlight %}
</div>
+
+<div data-lang="java" markdown="1">
+
+The following code demonstrates how to compute principal components on a
tall-and-skinny `RowMatrix`
+and use them to project the vectors into a low-dimensional space.
+The number of columns should be small, e.g, less than 1000.
+
+{% 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;
+
+public class PCA {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("PCA 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.
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