Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/11119#discussion_r78689714
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
---
@@ -303,6 +322,29 @@ class KMeans @Since("1.5.0") (
@Since("1.5.0")
def setSeed(value: Long): this.type = set(seed, value)
+ /** @group setParam */
+ @Since("2.1.0")
+ def setInitialModel(value: KMeansModel): this.type = set(initialModel,
value)
+
+ /** @group setParam */
+ @Since("2.1.0")
+ def setInitialModel(value: Model[_]): this.type = {
--- End diff --
As a follow on, we could eliminate the setter `def setInitialModel(value:
Model[_])`. To have better documentation, we could leave the param as abstract
in the `HasInitialModel` trait:
````scala
def hasInitialModel: Param[T]
````
Then, when we add this to new models, we implement the param there. So, in
KMeansParams:
````scala
/**
* Param for KMeansModel to use for warm start".
* @group param
*/
final val hasInitialModel: Param[KMeansModel] = new
Param[KMeansModel](this, "initialModel",
"A KMeansModel to use for warm start")
````
That way the params are explicit in what type of model is used for initial
model and the documentation is more clear.
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