const exprCwise = ndarray_cwise({
args: ['array', 'array'],
body: function (r, x) { r = (x * x + 2 * x + 1); }
});movaps %xmm1,%xmm3
mulsd %xmm1,%xmm3
addsd %xmm2,%xmm3
lea (%r11,%rax,1),%rdx
addsd %xmm0,%xmm3
npm or are you trying to clone and install locally?
make install, encountering this error should simply stop the add-on compilation process and the rest of the local install should succeed.
HEAD is likely to have some missing headers, etc.
Hey @conartist6 ! Thanks for stopping by. 👋
And thanks for the link to iter-tools. We’ve been following your work for a while now and certainly appreciate the work you’ve been putting into making iterators and iterables more useful.
In terms of our work, we’ve mainly focused on prng, math, and stats iterators. And in terms of collaboration, we typically try to keep everything in-house.
Certainly happy to hop on a call if you’d like to know more. 🙂
I've been working with a few others on a simple estimation-style programming language that treats probability distributions as a first class citizen. It's meant for quick estimation by non-programmers, using uncertainty. Older version here: https://www.lesswrong.com/posts/i5BWqSzuLbpTSoTc4/squiggle-an-overview
I've long been frustrated by the lack of good JS numerics tooling. I just recently found stdlib, will definitely be looking to port some functionality over to using it. Really happy to see this.
No rush at all (and feel free to ignore if annoying), but I have some questions:
- Is funding still useful, on your side? Is it a bottleneck for any work?
- Have you considered implementing a Metalog distribution? https://en.wikipedia.org/wiki/Metalog_distribution
- Have you considered having types/abstractions for generic distribution shapes? For example, converting a set of xy-coordinates into a pdf, then getting statistics and being able to do fast manipulations on that. This is a big deal for us, I'm sure we're making mistakes. Similarly, curious if stdlib could do convolution in order to combine arbitrary probability densities.
Overall, I'm really interested in work around probability distributions and forecasting using javascript/Rescript. There's a fair amount of interest in this area now, and the tooling is pretty awful.
Hey @OAGr! Thanks for reaching out! Sorry for the delay.
Re: your work. Looks neat! Thanks for the links!
Re: 1. Yes, funding is always useful, as depending on the size, we can allocate developer resources accordingly. Also helps with focusing work if there are particular initiatives which are desired.
Re: 2. We have primarily stuck to the most common distributions and those distributions which are in SciPy. Not opposed to adding Metalog; however, given the nature of the distribution, need to figure out what an implementation would look like.
Re: 3. We haven’t invested much in terms of empircal distribution functionality. @Planeshifter has built most of the stats functionality. More recently, we’ve focused on lower level numerics and tooling. Am certainly open to exploring functionality. If you have specific APIs you have in mind from, say, R/Python/MATLAB/Julia, would help make things a bit more concrete.
Re: tooling. Agreed. We’re hoping to change that. Just a matter of getting enough person hours to drive things forward! 😅
I am bioinformatician/data scientist and would like to use stdlib in my new project.
I spent some time on docs, browsed this room. There is documentation on installation, individual package usage, but I need a kickstart.
I think there are some tutorials/videos/courses etc. but I was not able to find any. Can you please share some?
Thank you!
(see: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/kstest and the example code) can be used.