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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.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import org.apache.spark.SparkConf
// $example on$
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
// $example off$
import org.apache.spark.streaming._
object StreamingLinearRegressionExample {
def main(args: Array[String]): Unit = {
if (args.length != 2) {
System.err.println("Usage: StreamingLinearRegressionExample <trainingDir> <testDir>")
System.exit(1)
}
val conf = new SparkConf().setAppName("StreamingLinearRegressionExample")
val ssc = new StreamingContext(conf, Seconds(1))
// $example on$
val trainingData = ssc.textFileStream(args(0)).map(LabeledPoint.parse).cache()
val testData = ssc.textFileStream(args(1)).map(LabeledPoint.parse)
val numFeatures = 3
val model = new StreamingLinearRegressionWithSGD()
.setInitialWeights(Vectors.zeros(numFeatures))
model.trainOn(trainingData)
model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()
ssc.start()
ssc.awaitTermination()
// $example off$
ssc.stop()
}
}
// scalastyle:on println
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