[ 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org