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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.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.clustering.StreamingKMeans
+import org.apache.spark.SparkConf
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+
+/**
+ * Estimate clusters on one stream of data and make predictions
+ * on another stream, where the data streams arrive as text files
+ * into two different directories.
+ *
+ * The rows of the training text files must be vector data in the form
+ * `[x1,x2,x3,...,xn]`
+ * Where n is the number of dimensions.
+ *
+ * The rows of the test text files must be labeled data in the form
+ * `(y,[x1,x2,x3,...,xn])`
+ * Where y is some identifier. n must be the same for train and test.
+ *
+ * Usage: StreamingKmeans <trainingDir> <testDir> <batchDuration> <numClusters> <numDimensions>
+ *
+ * To run on your local machine using the two directories `trainingDir` and `testDir`,
+ * with updates every 5 seconds, 2 dimensions per data point, and 3 clusters, call:
+ *    $ bin/run-example \
+ *        org.apache.spark.examples.mllib.StreamingKMeans trainingDir testDir 5 3 2
+ *
+ * As you add text files to `trainingDir` the clusters will continuously update.
+ * Anytime you add text files to `testDir`, you'll see predicted labels using the current model.
+ *
+ */
+object StreamingKMeans {
+
+  def main(args: Array[String]) {
+    if (args.length != 5) {
+      System.err.println(
+        "Usage: StreamingKMeans " +
+          "<trainingDir> <testDir> <batchDuration> <numClusters> <numDimensions>")
+      System.exit(1)
+    }
+
+    val conf = new SparkConf().setMaster("local").setAppName("StreamingLinearRegression")
+    val ssc = new StreamingContext(conf, Seconds(args(2).toLong))
+
+    val trainingData = ssc.textFileStream(args(0)).map(Vectors.parse)
+    val testData = ssc.textFileStream(args(1)).map(LabeledPoint.parse)
+
+    val model = new StreamingKMeans()
+      .setK(args(3).toInt)
+      .setDecayFactor(1.0)
+      .setRandomCenters(args(4).toInt, 0.0)
+
+    model.trainOn(trainingData)
+    model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()
+
+    ssc.start()
+    ssc.awaitTermination()
+  }
+}