Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/653#discussion_r12303197
--- 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 --
I was not able to get `NaiveBayes.predict()`'s return type to work here,
even after using `.rdd()` and `toJavaRDD()` and even `new JavaRDD(...)`. The
generic type `Double` always seemed to be lost, due to `ClassTag` voodoo I
imagine. Hence this way. Maybe there was a way to make it work, but this works
fine for the tiny example.
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