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