Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4401#discussion_r24216733
--- Diff: docs/mllib-clustering.md ---
@@ -168,6 +187,112 @@ print("Within Set Sum of Squared Error = " +
str(WSSSE))
</div>
+#### GaussianMixture
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+In the following example after loading and parsing data, we use a
+[`GaussianMixture`](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture)
+object to cluster the data into two clusters. The number of desired
clusters is passed
+to the algorithm. We then output the parameters of the mixture model.
+
+{% highlight scala %}
+import org.apache.spark.mllib.clustering.GaussianMixture
+import org.apache.spark.mllib.linalg.Vectors
+
+// Load and parse the data
+val data = sc.textFile("data/mllib/gmm_data.txt")
+val parsedData = data.map(s => Vectors.dense(s.trim.split('
').map(_.toDouble))).cache()
+
+// Cluster the data into two classes using GaussianMixture
+val gmm = new GaussianMixture().setK(2).run(parsedData)
+
+// output parameters of max-likelihood model
+for (i <- 0 until gmm.k) {
+ println("weight=%f\nmu=%s\nsigma=\n%s\n" format
+ (gmm.weights(i), gmm.gaussians(i).mu, gmm.gaussians(i).sigma))
+}
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+All of MLlib's methods use Java-friendly types, so you can import and call
them there the same
+way you do in Scala. The only caveat is that the methods take Scala RDD
objects, while the
+Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD
to a Scala one by
+calling `.rdd()` on your `JavaRDD` object. A self-contained application
example
+that is equivalent to the provided example in Scala is given below:
+
+{% highlight java %}
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.clustering.GaussianMixture;
+import org.apache.spark.mllib.clustering.GaussianMixtureModel;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.SparkConf;
+
+public class GaussianMixtureExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("GaussianMixture Example");
+ JavaSparkContext sc = new JavaSparkContext(conf);
+
+ // Load and parse data
+ String path = "data/mllib/gmm_data.txt";
+ JavaRDD<String> data = sc.textFile(path);
+ JavaRDD<Vector> parsedData = data.map(
+ new Function<String, Vector>() {
+ public Vector call(String s) {
+ String[] sarray = s.trim().split(" ");
+ double[] values = new double[sarray.length];
+ for (int i = 0; i < sarray.length; i++)
+ values[i] = Double.parseDouble(sarray[i]);
+ return Vectors.dense(values);
+ }
+ }
+ );
+ parsedData.cache();
+
+ // Cluster the data into two classes using GaussianMixture
+ GaussianMixtureModel gmm = new
GaussianMixture().setK(2).run(parsedData.rdd());
+
+ // Output the parameters of the mixture model
+ for(int j=0; j<gmm.k(); j++) {
+ System.out.println("weight=%f\nmu=%s\nsigma=\n%s\n",
+ gmm.weights()[j], gmm.gaussians()[j].mu(),
gmm.gaussians()[j].sigma());
+ }
+ }
+}
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+In the following example after loading and parsing data, we use a
+[`GaussianMixture`](api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture)
--- End diff --
This should be linked to the Python doc, e.g.,
`[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html)`.
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