Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/653#discussion_r12315475
--- Diff: docs/mllib-naive-bayes.md ---
@@ -58,29 +67,36 @@ optionally smoothing parameter `lambda` as input, and
output a
can be used for evaluation and prediction.
{% highlight java %}
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.classification.NaiveBayes;
+import org.apache.spark.mllib.classification.NaiveBayesModel;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import scala.Tuple2;
JavaRDD<LabeledPoint> training = ... // training set
JavaRDD<LabeledPoint> test = ... // test set
-NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
+final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
-JavaRDD<Double> prediction = model.predict(test.map(new
Function<LabeledPoint, Vector>() {
--- End diff --
Aha, can you help me with a hint? I didn't realize that an `RDD[Double]`
isn't magically an `RDD<Double>` in Java. The Java signature of the `predict()`
method already ends up at `RDD<Object>`, and Java casting doesn't help. I don't
believe I want to change the method. (Which incidentally just does the same
operation I 'inlined' into this example, which works. Hm.)
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