Hi Yu Ishikawa,
I'm sorry but I can't share my code via github at the moment. Hopefully
in some months I can.
I don't want to change the type of the label but that would be also a
very nice improvement.
Making LabeledPoint abstract is exactly what I need. That enables me to
create a class like
LabeledPointTuplePair(label: Double, features: Vector, tuplePair: (Tuple,
Tuple)) extends LabeledPoint
and use it in combination of the existing ML algorithms.
In my limited understanding of the MLlib I agree with your proposal of
the LabeledPoint interface
abstract class LabeledPoint[T](label: T, features: Vector)
In my opinion making LabeledPoint abstract is necessary and introducing
a generic label would be nice to have.
Just to clarify my priorities.
Kind Regards,
Niklas Wilcke
On 25.09.2014 16:02, Yu Ishikawa wrote:
> Hi Niklas Wilcke,
>
> As you said, it is difficult to extend LabeledPoint class in
> mllib.regression.
> Do you want to extend LabeledPoint class in order to use any other type
> exclude Double type?
> If you have your code on Github, could you show us it? I want to know what
> you want to do.
>
>> Community
> By the way, I think LabeledPoint class is very useful exclude
> mllib.regression package.
> Especially, some estimation algorithms should use a type for the labels
> exclude Double type,
> such as String type. The common generics labeled-point class would be useful
> in MLlib.
> I'd like to get your thoughts on it.
>
> For example,
> ```
> abstract class LabeledPoint[T](label: T, features: Vector)
> ```
>
> thanks
>
>
>
>
>
>
> -----
> -- Yu Ishikawa
> --
> View this message in context:
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> Sent from the Apache Spark Developers List mailing list archive at Nabble.com.
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