Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/15999#discussion_r89456806
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala ---
@@ -804,13 +804,13 @@ object DistributedLDAModel extends
MLReadable[DistributedLDAModel] {
*
* Input data (featuresCol):
* LDA is given a collection of documents as input data, via the
featuresCol parameter.
- * Each document is specified as a [[Vector]] of length vocabSize, where
each entry is the
+ * Each document is specified as a `Vector` of length vocabSize, where
each entry is the
* count for the corresponding term (word) in the document. Feature
transformers such as
* [[org.apache.spark.ml.feature.Tokenizer]] and
[[org.apache.spark.ml.feature.CountVectorizer]]
* can be useful for converting text to word count vectors.
*
- * @see [[http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation Latent
Dirichlet allocation
- * (Wikipedia)]]
+ * @see <a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation";>
+ * Latent Dirichlet allocation (Wikipedia)</a>
--- End diff --
I observed that there are three cases of the indentations for `@see`.
```
@see ...
...
```
```
@see ...
...
```
```
@see ...
...
```
I just tried to match those up to `@note` in the way I did before which is
the first case.
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