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https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14547149#comment-14547149
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Hae Joon Lee commented on FLINK-1731:
-------------------------------------
Hi, I am testing K-mean right now.
I faced an error "could not find implicit value for parameter"
I solved a lot of things except for this one.
* Error:(142, 53) could not find implicit value for parameter op:
breeze.linalg.operators.OpDiv.Impl2[breeze.linalg.Vector[Double],Long,That].map(x
=> LabeledVector(x._1, x._2.asBreeze / 1L)).withForwardedFields("_1->id")
{code:title=KMeans.scala|borderStyle=solid}
val finalCentroids = centroids.iterate(numIterations) { currentCentroids =>
val newCentroids: DataSet[LabeledVector] = input
.map(new SelectNearestCenterMapper).withBroadcastSet(currentCentroids,
CENTROIDS)
.map(x => (x.label, x.vector, 1L)).withForwardedFields("_1; _2")
.groupBy(x => x._1)
.reduce((p1, p2) => (p1._1, (p1._2.asBreeze + p2._2.asBreeze).fromBreeze,
p1._3 + p2._3)).withForwardedFields("_1")
.map(x => LabeledVector(x._1, x._2.asBreeze :/
x._3)).withForwardedFields("_1->id")
newCentroids
}
{code}
As far as I know, the error "could not find implicit value for parameter" can
be solved by putting exact import class.
I put 'import breeze.linalg.operators._' on import line as well.
but it does not work.
Have you ever seen this kind of error before?
> Add kMeans clustering algorithm to machine learning library
> -----------------------------------------------------------
>
> Key: FLINK-1731
> URL: https://issues.apache.org/jira/browse/FLINK-1731
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Peter Schrott
> Labels: ML
>
> The Flink repository already contains a kMeans implementation but it is not
> yet ported to the machine learning library. I assume that only the used data
> types have to be adapted and then it can be more or less directly moved to
> flink-ml.
> The kMeans++ [1] and the kMeans|| [2] algorithm constitute a better
> implementation because the improve the initial seeding phase to achieve near
> optimal clustering. It might be worthwhile to implement kMeans||.
> Resources:
> [1] http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf
> [2] http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
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