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-
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 wrote:
> Hi,
>
> no, I don't thing we should actually compute the n \times n matrix. Leave
> alone invertin
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 w
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,
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 MLl
