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diff --git a/examples/src/main/python/mllib/latent_dirichlet_allocation_example.py b/examples/src/main/python/mllib/latent_dirichlet_allocation_example.py
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+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+from pyspark import SparkContext
+# $example on$
+from pyspark.mllib.clustering import LDA, LDAModel
+from pyspark.mllib.linalg import Vectors
+# $example off$
+
+if __name__ == "__main__":
+    sc = SparkContext(appName="LatentDirichletAllocationExample")  # SparkContext
+
+    # $example on$
+    # Load and parse the data
+    data = sc.textFile("data/mllib/sample_lda_data.txt")
+    parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
+    # Index documents with unique IDs
+    corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
+
+    # Cluster the documents into three topics using LDA
+    ldaModel = LDA.train(corpus, k=3)
+
+    # Output topics. Each is a distribution over words (matching word count vectors)
+    print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize())
+          + " words):")
+    topics = ldaModel.topicsMatrix()
+    for topic in range(3):
+        print("Topic " + str(topic) + ":")
+        for word in range(0, ldaModel.vocabSize()):
+            print(" " + str(topics[word][topic]))
+
+    # Save and load model
+    ldaModel.save(sc, "target/org/apache/spark/PythonLatentDirichletAllocationExample/LDAModel")
+    sameModel = LDAModel\
+        .load(sc, "target/org/apache/spark/PythonLatentDirichletAllocationExample/LDAModel")
+    # $example off$
+
+    sc.stop()