Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/653#discussion_r12314729
--- 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 --
Yeah, being Java, there is not the usual Vector import issue that you see
in Scala, if that's what you mean. It wouldn't begin to compile without that
import. Here's the touched-up version, that I couldn't make work:
```
JavaRDD<Double> prediction = model.predict(test.map(new
Function<LabeledPoint, Vector>() {
public Vector call(LabeledPoint p) {
return p.features();
}
}).rdd()).toJavaRDD();
```
The returned `RDD` and `JavaRDD` both have generic type `Object` instead of
`Double`, and I could not see why, given that the `predict()` method plainly
returns `RDD[Double]`. Maybe you see something I am missing.
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