[jira] [Assigned] (SPARK-17141) MinMaxScaler behaves weird when min and max have the same value and some values are NaN

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17141?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-17141:
------------------------------------

    Assignee: Apache Spark

> MinMaxScaler behaves weird when min and max have the same value and some values are NaN
> ---------------------------------------------------------------------------------------
>
>                 Key: SPARK-17141
>                 URL: https://issues.apache.org/jira/browse/SPARK-17141
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 1.6.2, 2.0.0
>         Environment: Databrick's Community, Spark 2.0 + Scala 2.10
>            Reporter: Alberto Bonsanto
>            Assignee: Apache Spark
>            Priority: Minor
>
> When you have a {{DataFrame}} with a column named {{features}}, which is a {{DenseVector}} and the *maximum* and *minimum* and some values are {{Double.NaN}} they get replaced by 0.5, and they should remain with the same value, I believe.
> I know how to fix it, but I haven't ever made a pull request. You can check the bug in this [notebook|https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/2485090270202665/3126465289264547/8589256059752547/latest.html]



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