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Posted to issues@spark.apache.org by "Mike Dusenberry (JIRA)" <ji...@apache.org> on 2015/07/14 23:31:05 UTC

[jira] [Updated] (SPARK-7883) Fixing broken trainImplicit example in MLlib Collaborative Filtering documentation.

     [ https://issues.apache.org/jira/browse/SPARK-7883?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Mike Dusenberry updated SPARK-7883:
-----------------------------------
    Target Version/s: 1.4.0, 1.0.3, 1.1.2, 1.2.3, 1.3.2  (was: 1.0.3, 1.1.2, 1.2.3, 1.3.2, 1.4.0)
              Labels: spark.tc  (was: )

> Fixing broken trainImplicit example in MLlib Collaborative Filtering documentation.
> -----------------------------------------------------------------------------------
>
>                 Key: SPARK-7883
>                 URL: https://issues.apache.org/jira/browse/SPARK-7883
>             Project: Spark
>          Issue Type: Bug
>          Components: Documentation, MLlib
>    Affects Versions: 1.0.2, 1.1.1, 1.2.2, 1.3.1, 1.4.0
>            Reporter: Mike Dusenberry
>            Assignee: Mike Dusenberry
>            Priority: Trivial
>              Labels: spark.tc
>             Fix For: 1.0.3, 1.1.2, 1.2.3, 1.3.2, 1.4.0
>
>
> The trainImplicit Scala example near the end of the MLlib Collaborative Filtering documentation refers to an ALS.trainImplicit function signature that does not exist.  Rather than add an extra function, let's just fix the example.
> Currently, the example refers to a function that would have the following signature: 
> def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, alpha: Double) : MatrixFactorizationModel
> Instead, let's change the example to refer to this function, which does exist (notice the addition of the lambda parameter):
> def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double) : MatrixFactorizationModel



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