[jira] [Resolved] (SPARK-7753) Improve kernel density API

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-7753?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiangrui Meng resolved SPARK-7753.
----------------------------------
       Resolution: Fixed
    Fix Version/s: 1.4.0

Issue resolved by pull request 6279
[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
>             Fix For: 1.4.0
>
>
> 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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