Hi Jinhong,
Based on the error message, your second collection of vectors has a
dimension of 804202, while the dimension of your training vectors
was 144109. So please make sure your test dataset are of the same dimension
as the training data.
>From the test dataset you posted, the vector dimens
Welcome to try and contribute to our BigDL:
https://github.com/intel-analytics/BigDL
It's native on Spark and fast by leveraging Intel MKL.
2017-02-23 4:51 GMT-08:00 Joeri Hermans :
> Hi Nikita,
>
> We are actively working on this: https://github.com/cerndb/dist-keras
> This will allow you to ru
Hi Zak,
Indeed the function is missing in DataFrame-based API. I can probably
provide some quick prototype to see if it we can merge the function into
next release. I would send update here and feel free to give it a try.
Regards,
Yuhao
2016-11-01 10:00 GMT-07:00 Zak H :
> Hi,
>
> I'm using the
You may also find VectorSlicer and SQLTransformer useful in your case. Just
out of curiosity, how would you typically handles categorical features,
except for OneHotEncoder.
Regards,
Yuhao
2016-07-01 4:00 GMT-07:00 Yanbo Liang :
> You can combine the columns which are need to be normalized into
Congratulations Yanbo
2016-06-04 23:43 GMT-07:00 Hyukjin Kwon :
> Congratulations!
>
> 2016-06-04 11:48 GMT+09:00 Matei Zaharia :
>
>> Hi all,
>>
>> The PMC recently voted to add Yanbo Liang as a committer. Yanbo has been
>> a super active contributor in many areas of MLlib. Please join me in
>>
Hi all,
I got a simple processing job for 2 accounts on 8 partitions. It's
roughly 2500 accounts on each partition. Each account will take about 1s to
complete the computation. That means each partition will take about 2500
seconds to finish the batch.
My question is how can I get the detaile