Hi Gilles and Randy,
that sounds really interesting I'll try this out, both the sigmodial and
the scaling stuff!
I'm relative new to all this least squares things and I'm not a
mathematician so I need to learn a lot, so don't hesitate to point out
things that might seem obvious to the
Hi Gilles,
I may be wrong in understanding your issue, but in general, I believe you
should scale your features.
https://towardsdatascience.com/gradient-descent-the-learning-rate-and-the-importance-of-feature-scaling-6c0b416596e1
Thank you very much Henri.
I am watching now
https://issues.apache.org/jira/browse/JEXL-351
and also considering the suggested workaround.
Regards.
On 2021/06/08 09:38:35, Henri Biestro wrote:
> Hi Francesco;
> I was able to reproduce your problem and found the source of the regression
>
Hello.
Le mar. 8 juin 2021 à 08:14, Christoph Läubrich
a écrit :
>
> Hi Gilles,
>
> I have used the the INFINITY approach for a while now and it works quite
> good. I just recently found a problem where I got very bad fits after a
> handful of iterations using the LevenbergMarquardtOptimizer.
>
Hi Francesco;
I was able to reproduce your problem and found the source of the regression
that I logged as JEXL-351.
The culprit is an unfortunate implementation change in 3.2 that break templates
used with strict sandboxing.
On your side, if you want to go further with JEXL 3.2, you may want
Hi Henri,
thanks for your support.
In order to replicate the failure, just do as follows, from root directory:
mvn -T 1C -PskipTests,all
cd core/provisioning-api
mvn clean test -Dtest=MailTemplateTest
As you might have noticed, Java 11 is required (but works any version above).
Regards.
On
Hi Gilles,
I have used the the INFINITY approach for a while now and it works quite
good. I just recently found a problem where I got very bad fits after a
handful of iterations using the LevenbergMarquardtOptimizer.
The problem arises whenever there is a relative small range of valid
