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Edited git docs to highlight code. #5878

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33 changes: 17 additions & 16 deletions docs/mllib-clustering.md
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Original file line number Diff line number Diff line change
Expand Up @@ -204,19 +204,20 @@ 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);

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);
Expand All @@ -232,20 +233,20 @@ public class GaussianMixtureExample {
}
);
parsedData.cache();

// Cluster the data into two classes using GaussianMixture
GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd());

// Save and load GaussianMixtureModel
gmm.save(sc, "myGMMModel")
GaussianMixtureModel sameModel = GaussianMixtureModel.load(sc, "myGMMModel")
// 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>

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