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https://issues.apache.org/jira/browse/FLINK-2312?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14635095#comment-14635095
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Sachin Goel edited comment on FLINK-2312 at 7/21/15 1:21 PM:
-------------------------------------------------------------
While trying to generalize to any type, I encounter this error: {{Could not
find implicit value for evidence parameter of type
typeinfo.TypeInformation[(T,Int)]}}. This occurs while performing a Map
operation on DataSet[T], which results in a DataSet[(T,Int)].
Here is the relevant code:
{{val randomData = data.map(new RichMapFunction[T,(T,Int)] {...}}}
was (Author: sachingoel0101):
While trying to generalize to any type, I encounter this error: {{Could not
find implicit value for evidence parameter of type
typeinfo.TypeInformation[(T,Int)]}}. This occurs while performing a Map
operation on DataSet[T], which results in a DataSet[(T,Int)].
Here is the relevant code:
{{
def randomSplit[T](data: DataSet[T],ratios: Array[Double]): Array[DataSet[T]] =
{
...
val randomData = data.map(new RichMapFunction[T,(T,Int)] {...}
...
}
}}
> Random Splits
> -------------
>
> Key: FLINK-2312
> URL: https://issues.apache.org/jira/browse/FLINK-2312
> Project: Flink
> Issue Type: Wish
> Components: Machine Learning Library
> Reporter: Maximilian Alber
> Assignee: pietro pinoli
> Priority: Minor
>
> In machine learning applications it is common to split data sets into f.e.
> training and testing set.
> To the best of my knowledge there is at the moment no nice way in Flink to
> split a data set randomly into several partitions according to some ratio.
> The wished semantic would be the same as of Sparks RDD randomSplit.
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