Github user Ishiihara commented on a diff in the pull request:
https://github.com/apache/spark/pull/2494#discussion_r17886499
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala ---
@@ -30,9 +30,20 @@ import org.apache.spark.rdd.RDD
* Inverse document frequency (IDF).
* The standard formulation is used: `idf = log((m + 1) / (d(t) + 1))`,
where `m` is the total
* number of documents and `d(t)` is the number of documents that contain
term `t`.
+ *
+ * This implementation supports filtering out terms which do not appear in
a minimum number
+ * of documents (controlled by the variable minimumOccurence). For terms
that are not in
+ * at least `minimumOccurence` documents, the IDF is found as 0, resulting
in TF-IDFs of 0.
+ *
+ * @param minimumOccurence minimum of documents in which a term
+ * should appear for filtering
+ *
+ *
*/
@Experimental
-class IDF {
+class IDF(minimumOccurence: Long) {
--- End diff --
You can add a val before minimumOccurence. Alternatively, if you want to
set set minimumOccurence after new IDF(), you can define a private field and
use a setter to set the value.
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