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twiecki on pre-commit-config-update-0

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Osvaldo Martin
@aloctavodia
@junpenglao I think the lack of response for the last two days is a very clear answer ;-)
Great! I see you have already done a PR
Junpeng Lao
@junpenglao
The gitter channel will remain open for light conversation ;-)
Does anyone have an example of multidimensional input in Gaussian process regression? So x shape = (num records, p) and y shape = (num records) where p > 1. Thanks
Bill Engels
@bwengals
Set the input dimension arg: cov = pm.gp.cov.Matern52(p, lengthscales)
Thanks Bill. I did that, I thought I had everything setup correctly but when I pass in x as observed I get an error. A shape mismatch error, input[0].shape[1] = Num records, but input[1].shape[1] = p. That's why I was looking for an example to study... if I have the model setup correctly I should be able to pass the 2d ndarray directly to observed right? I was thinking maybe it was a theano issue that.
My example works for the 1d case but fails for p>1. So I'm wondering if I need to wrap the 2d x in a theano object maybe?
Bill Engels
@bwengals
Oh i see, sorry about that. the gp library is will be changing a fair bit soon, so these things should be smoothed out in the future
try this:
gp = pm.gp.GP("gp", cov_func=cov, X=X, sigma=sn, observed=y)
setting X as an argument
also, were trying to move questions like this over to https://discourse.pymc.io/ . You'll probably get a quicker response there next time :)
I actually just joined discourse. I'll get a concrete example and post there. Thanks very much Bill, much appreciated.
Bill Engels
@bwengals
thanks! see u over there
Dani Arribas-Bel
@darribas
hello, I have a hierarchical model which was working fine in PyMC 3.0 and, when I upgraded to 3.1 today, I get the FloatingPointError: NaN occurred in optimization. error on the first ADVI iteration. All the parameters I set for the model (priors, etc.) have not changed, I've only changed the API to access ADVI to pm.fit. Am I missing something obvious with the upgrade?
Short answer is there might be some hidden problem in your initial model, if the above posts did not solve your problem, please open a discussion on discourse with your code and (simulated) data
Dani Arribas-Bel
@darribas
:+1: Excellent! thank you very much @junpenglao !
dlovell
@tsdlovell
is there a Contributor License Agreement for pymc3?
Majid alDosari
@majidaldo
to revive the tensorflow discussion, the main advantage to using tensorflow is proper multiple gpu support
oh is discourse being used now?
Thomas Wiecki
@twiecki
yes, this channel isn't really watched anymore, everyone move to discourse
Enrique Eduardo Löser
@hanzy1110
hey! Has anyone here done any model calibration using pymc? I've been struggling for a while with some really simple eqns but I can't get any of the samplers to get me reasonable results. Any guidance would be appreciated
Osvaldo Martin
@aloctavodia
hi @hanzy1110 please use https://discourse.pymc.io/ to ask this type of questions as that is the official channel, we no longer use gitter.
matrixbot
@matrixbot
Dominik Stańczak Hey everyone, just wanted to say thank you for all your work on this awesome library 🙂 I made a little write-up of a practice project I've been working on recently, I used pymc3 and StarCraft replays to find out how much my performance fluctuates 😁 https://stanczakdominik.github.io/posts/bayes-sc2-part-1 if anyone's interested 🙂
Peng Yu
@yupbank
hello, which sampler is good at sampling from multi-modal distribution ? i have defined a potential,but the MCMC seems to stuck at one local point
matrixbot
@matrixbot
Dominik Stańczak As far as I know, none of them... :(
Dominik Stańczak Multimodals are this huge unsolved problem
Dominik Stańczak I may be wrong though!
Osvaldo Martin
@aloctavodia
@yupbank Sorry for the late response SMC can handle multi-modal distributions. But notice that SMC (with a metropolis kernel as implemented in PyMC3) can have problems for high-dimensional posteriors
Osvaldo Martin
@aloctavodia
@matrixbot @yupbank Notice that it is better to post comments and questions on https://discourse.pymc.io/