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

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

Xiangrui Meng created SPARK-7753:
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             Summary: 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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