Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6499#discussion_r32784720
--- Diff: docs/mllib-clustering.md ---
@@ -593,6 +593,50 @@ ssc.start()
ssc.awaitTermination()
{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+First we import the neccessary classes.
+
+{% highlight python %}
+from pyspark.mllib.linalg import Vectors
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.clustering import StreamingKMeans
+{% endhighlight %}
+
+Then we make an input stream of vectors for training, as well as a stream
of labeled data
+points for testing. We assume a StreamingContext `ssc` has been created,
see
+[Spark Streaming Programming
Guide](streaming-programming-guide.html#initializing) for more info.
+
+{% highlight python %}
+def parse(lp):
+ label = float(lp[lp.find('(') + 1: lp.find(',')])
+ vec = Vectors.dense(lp[lp.find('[') + 1: lp.find(']')].split(','))
+ return LabeledPoint(label, vec)
+
+trainingData = ssc.textFileStream("/training/data/dir").map(Vectors.parse)
+testData = ssc.textFileStream("/testing/data/dir").map(parse)
+{% endhighlight %}
+
+We create a model with random clusters and specify the number of clusters
to find
+
+{% highlight python %}
+model = StreamingKMeans(k=2, decayFactor=1.0).setRandomCenters(3, 1.0, 0)
+{% endhighlight %}
+
+Now register the streams for training and testing and start the job,
printing
+the predicted cluster assignments on new data points as they arrive.
+
+{% highlight python %}
+model.trainOn(trainingData)
+model.predictOnValues(testData.map(lambda lp: (lp.label, lp.features)))
--- End diff --
There is no output from this example. Call `print`.
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