Github user MLnick commented on the issue:
https://github.com/apache/spark/pull/11119
Isn't the point of an initial model mainly the cluster centers? If you
override k what happens to the cluster centers? Discard them? Why not then
just start again rather than have an initial model
On Sat, 8 Oct 2016 at 09:04, DB Tsai <notificati...@github.com> wrote:
> *@dbtsai* commented on this pull request.
> ------------------------------
>
> In mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
> <https://github.com/apache/spark/pull/11119>:
>
> > + assert(wrongDimModelThrown.getMessage.contains("mismatched
dimension"))
> + }
> +
> + test("Infer K from an initial model if K is unset") {
> + val kmeans = new KMeans()
> + val testNewK = 10
> + val randomModel = KMeansSuite.generateRandomKMeansModel(dim,
testNewK)
> + assert(kmeans.setInitialModel(randomModel).getK === testNewK)
> + }
> +
> + test("Initialize using a model with wrong K if K is set") {
> + val kmeans = new KMeans().setK(k).setSeed(1).setMaxIter(1)
> +
> + val wrongKModel = KMeansSuite.generateRandomKMeansModel(3, k + 1)
> + val wrongKModelThrown = intercept[IllegalArgumentException] {
> + kmeans.setInitialModel(wrongKModel).fit(dataset)
>
> I think the behavior could be that setInitialModel should overwrite the
> configuration including # of iterations and k, etc. What do you think?
>
> â
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