ASF GitHub Bot commented on FLINK-5588:

Github user tillrohrmann commented on the issue:

    Hi @skonto, if you have activated the Travis integration for your own repo, 
then you can restart the testing job there. For the apache account it is not 
possible. However, I've seen that only the last build ran out of time. We're 
currently at the very limit in terms of runtime what Travis allows. That is a 
known issue and we try to address it soon.

> Add a unit scaler based on different norms
> ------------------------------------------
>                 Key: FLINK-5588
>                 URL: https://issues.apache.org/jira/browse/FLINK-5588
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Stavros Kontopoulos
>            Assignee: Stavros Kontopoulos
>            Priority: Minor
> So far ML has two scalers: min-max and the standard scaler.
> A third one frequently used, is the scaler to unit.
> We could implement a transformer for this type of scaling for different norms 
> available to the user.
> I will make a separate class for the Normalization per sample procedure by 
> using the Transformer API because it is easy to add
> it, fit method does nothing in this case.
> Scikit-learn has also some calls available outside the Transform API, we 
> might want add that in the future.
> These calls work on any axis but they are not re-usable in a pipeline [4]
> Right now the existing scalers in Flink ML support per feature normalization 
> by using the Transformer API. 
> Resources
> [1] https://en.wikipedia.org/wiki/Feature_scaling
> [2] 
> http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html
> [3] https://spark.apache.org/docs/2.1.0/mllib-feature-extraction.html
> [4] http://scikit-learn.org/stable/modules/preprocessing.html

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