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https://issues.apache.org/jira/browse/SPARK-7753?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14551996#comment-14551996
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Apache Spark commented on SPARK-7753:
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User 'mengxr' has created a pull request for this issue:
https://github.com/apache/spark/pull/6279
> Improve kernel density API
> --------------------------
>
> Key: SPARK-7753
> URL: https://issues.apache.org/jira/browse/SPARK-7753
> Project: Spark
> Issue Type: Sub-task
> Components: MLlib
> Affects Versions: 1.4.0
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
>
> Kernel density estimation is provided in many statistics libraries:
> http://en.wikipedia.org/wiki/Kernel_density_estimation#Statistical_implementation.
> We should make sure that we implement a similar API. The two most important
> parameters of kernel density estimation are kernel type and bandwidth.
> Besides density estimation, it is also used for smoothing. The current API is
> designed only for Gaussian kernel and density estimation:
> {code}
> def kernelDensity(samples: RDD[Double], standardDeviation: Double,
> evaluationPoints: Iterable[Double]): Array[Double]
> {code}
> It would be nice if we can come up with an extensible API.
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