Hi all,
please, check out the repo: github.com/akopich/spark-gp/. I've
implemented the regressor.
Simon, have you still got smth to try it out on?
Best,
Valeriy.
On 02/15/2018 05:16 PM, Аванесов Валерий wrote:
Hi all,
I've created a new JIRA.
https://issues.apache.org/jira/browse/SPARK-23437
All concerned are welcome to discuss.
Best,
Valeriy.
On Sat, Feb 3, 2018 at 9:24 PM, Valeriy Avanesov <acop...@gmail.com
<mailto:acop...@gmail.com>> wrote:
Hi,
no, I don't thing we should actually compute the n \times n
matrix. Leave alone inverting it. However, variational inference
is only one of the many sparse GP approaches. Another option could
be Bayesian Committee.
Best,
Valeriy.
On 02/02/2018 09:43 PM, Simon Dirmeier wrote:
Hey,
I wanted to see that for a long time, too. :) If you'd plan on
implementing this, I could contribute.
However, I am not too familiar with variational inference for
the GPs which is what you would need I guess.
Or do you think it is feasible to compute the full kernel for
the GP?
Cheers,
S
Am 01.02.18 um 20:01 schrieb Valeriy Avanesov:
Hi all,
it came to my surprise that there is no implementation of
Gaussian Process in Spark MLlib. The approach is widely
known, employed and scalable (its sparse versions). Is
there a good reason for that? Has it been discussed before?
If there is a need in this approach being a part of MLlib
I am eager to contribute.
Best,
Valeriy.
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