Github user keypointt commented on a diff in the pull request:
https://github.com/apache/spark/pull/11108#discussion_r55274411
--- Diff: docs/mllib-statistics.md ---
@@ -584,24 +299,24 @@ distribution `N(0, 1)`, and then map it to `N(1, 4)`.
Refer to the [`RandomRDDs` Java
docs](api/java/org/apache/spark/mllib/random/RandomRDDs) for details on the API.
-{% highlight java %}
-import org.apache.spark.SparkContext;
-import org.apache.spark.api.JavaDoubleRDD;
-import static org.apache.spark.mllib.random.RandomRDDs.*;
-
-JavaSparkContext jsc = ...
-
-// Generate a random double RDD that contains 1 million i.i.d. values
drawn from the
-// standard normal distribution `N(0, 1)`, evenly distributed in 10
partitions.
-JavaDoubleRDD u = normalJavaRDD(jsc, 1000000L, 10);
-// Apply a transform to get a random double RDD following `N(1, 4)`.
-JavaDoubleRDD v = u.map(
- new Function<Double, Double>() {
- public Double call(Double x) {
- return 1.0 + 2.0 * x;
- }
- });
-{% endhighlight %}
+-{% highlight java %}
+ -import org.apache.spark.SparkContext;
+ -import org.apache.spark.api.JavaDoubleRDD;
+ -import static org.apache.spark.mllib.random.RandomRDDs.*;
+ -
+ -JavaSparkContext jsc = ...
+ -
+ -// Generate a random double RDD that contains 1 million i.i.d. values
drawn from the
+ -// standard normal distribution `N(0, 1)`, evenly distributed in 10
partitions.
+ -JavaDoubleRDD u = normalJavaRDD(jsc, 1000000L, 10);
+ -// Apply a transform to get a random double RDD following `N(1, 4)`.
+ -JavaDoubleRDD v = u.map(
+ - new Function<Double, Double>() {
+ - public Double call(Double x) {
+ - return 1.0 + 2.0 * x;
+ - }
+ - });
+ -{% endhighlight %}
--- End diff --
got it, fixing them now :p
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