Github user sethah commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11119#discussion_r79957341
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala 
---
    @@ -81,11 +81,23 @@ private[clustering] trait KMeansParams extends Params 
with HasMaxIter with HasFe
       def getInitSteps: Int = $(initSteps)
     
       /**
    +   * Param for KMeansModel to use for warm start.
    +   * @group param
    +   */
    +  final val initialModel: Param[KMeansModel] =
    +    new Param[KMeansModel](this, "initialModel", "A KMeansModel for warm 
start.")
    +
    +  /**
        * Validates and transforms the input schema.
        * @param schema input schema
        * @return output schema
        */
       protected def validateAndTransformSchema(schema: StructType): StructType 
= {
    +    if (isDefined(initialModel)) {
    +      val kOfInitialModel = 
$(initialModel).parentModel.clusterCenters.length
    +      require(kOfInitialModel == $(k),
    +        s"mismatched cluster count, ${$(k)} cluster centers required but 
$kOfInitialModel found.")
    +    }
    --- End diff --
    
    This method is called at the start of training, and right now this is the 
only place we check `k`. TBH, I think it would be cleaner to implement a 
isInitialModelValid method in the companion object which checks the k and the 
feature dimensions all in one place. Checking the k parameter here is a bit odd 
since it's not exactly schema related. Seems cleaner to me to check everything 
initialModel related in one place. 


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