Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/2942#discussion_r19490338
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/StreamingKMeans.scala
---
@@ -0,0 +1,75 @@
+/*
+ * 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.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 text files must be vector data in the form
+ * `[x1,x2,x3,...,xn]`
+ * Where n is the number of dimensions. 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(Vectors.parse)
+
+ val model = new StreamingKMeans()
+ .setK(args(3).toInt)
+ .setDecayFactor(1.0)
+ .setRandomCenters(args(4).toInt)
+
+ model.trainOn(trainingData)
+ model.predictOn(testData).print()
--- End diff --
ditto: use `predictOnValues`
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