diff options
| author | Xiangrui Meng <meng@databricks.com> | 2014-08-18 18:20:54 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2014-08-18 18:20:54 -0700 |
| commit | 217b5e915e2f21f047dfc4be680cd20d58baf9f8 (patch) | |
| tree | da708d2bd7290989bcdd5c884086be2bf7c3096f /examples/src | |
| parent | c8b16ca0d86cc60fb960eebf0cb383f159a88b03 (diff) | |
| download | spark-217b5e915e2f21f047dfc4be680cd20d58baf9f8.tar.gz spark-217b5e915e2f21f047dfc4be680cd20d58baf9f8.tar.bz2 spark-217b5e915e2f21f047dfc4be680cd20d58baf9f8.zip | |
[SPARK-3108][MLLIB] add predictOnValues to StreamingLR and fix predictOn
It is useful in streaming to allow users to carry extra data with the prediction, for monitoring the prediction error for example. freeman-lab
Author: Xiangrui Meng <meng@databricks.com>
Closes #2023 from mengxr/predict-on-values and squashes the following commits:
cac47b8 [Xiangrui Meng] add classtag
2821b3b [Xiangrui Meng] use mapValues
0925efa [Xiangrui Meng] add predictOnValues to StreamingLR and fix predictOn
Diffstat (limited to 'examples/src')
| -rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala index 0e992fa996..c5bd5b0b17 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala @@ -59,10 +59,10 @@ object StreamingLinearRegression { val testData = ssc.textFileStream(args(1)).map(LabeledPoint.parse) val model = new StreamingLinearRegressionWithSGD() - .setInitialWeights(Vectors.dense(Array.fill[Double](args(3).toInt)(0))) + .setInitialWeights(Vectors.zeros(args(3).toInt)) model.trainOn(trainingData) - model.predictOn(testData).print() + model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print() ssc.start() ssc.awaitTermination() |