[Haskell-cafe] Comment on "The Historical Futurism of Haskell" by Andrew Boardman
dominik.schrempf at gmail.com
Tue Sep 14 08:27:34 UTC 2021
Dominic Steinitz <dominic at steinitz.org> writes:
> On 12 Sep 2021, at 13:00, haskell-cafe-request at haskell.org wrote:
> In particular, I am a mathematician/statistician working in evolutionary
> biology. I work with multivariate distributions (hardly any of those are readily
> available on Hackage), I work with a lot of random numbers (the support for
> random sampling is mediocre, at best; 'splitmix' is standard by now but not
> supported by the most important statistics library of Haskell), I work with
> numerical optimization (I envy Pythonians for their libraries, although I still
> prefer Haskell because what I achieve, at least I get right), I work with Markov
> chains (yes, I had to write my own MCMC library in order to run proper Markov
> chains), I need to plot my data (there is no superb standard plotting library
> available in Haskell). By now, I do maintain library packages providing answers
> to some of these problems, but it was (and is) a lot of work.
> I have to take issue with your statement about random sampling. I think we have a really good story with random numbers now. They are of high quality and fast. R and possibly Python and Julia by comparison still use Mersenne Twister, of lower
> quality, slower and without a good story for generating independent sequences for parallel computations. I maintain random-fu (sampling from distributions) and using the new random number generator it is now several times (x4?) faster than it
> was. Conceivably it could be made even faster.
Thank you for mentioning 'random-fu'. It makes me feel like wanting to change
from using 'statistics' to 'random-fu'. I started using 'statistics' because I
liked (depended on?) the notion of a 'Distribution' which can be instance of
many classes (but I just saw that this is also the case for 'random-fu', maybe I
overlooked it). I liked that there is a distinction between discrete and
continuous distributions, and that there are more statistical functions
available such as quantiles, and so on. The package 'statistics' only supports
random number generation using the Mersenne Twister. It also does not support
multivariate distributions. Right now, I am considering changing to 'random-fu'.
What also kept me from using 'random-fu' is the following sentence in the
description of the package:
"Quality is prioritized over speed, but performance is an important goal too."
This sounds to me like 'random-fu' focuses on the generation of
cryptographically secure random numbers which is not what I need.
> Please give details on where you think we can improve and better still contribute your own improvements :-)
In my opinion it would be great to:
- separate continuous from discrete distributions
- have one set of type classes used by 'random-fu' and 'statistics' (and all
other packages working with distributions)
- implement more and multivariate distributions (I implemented the 'dirichlet'
distribution for 'statistics'; it is available on Hackage but it is not
completely finished, and I don't consider myself able enough to contribute to
core libraries yet; there is also 'random-fu-multivariate' but it only has the
multivariate normal distribution)
> In terms of MCMC, I think Jared Tobin wrote some libraries but I don’t think they are maintained. I maintain an SMC library but I don’t know how much use it gets. Tom Nielsen, Henrik Nilsson and I wrote Haskell “bindings” for Stan:
> https://nottingham-repository.worktribe.com/output/1151875/getting-more-out-of-stan-some-ideas-from-the-haskell-bindings. It would be a lot of work to e.g. re-create Stan in Haskell natively.
I am aware of Jared Tobins packages! They are great entry points but not
flexible enough for what I am doing. If you are interested, have a look at the
'mcmc' package, which I am developing.
Thank you, I didn't know about the Stan Haskell bindings and will have a look.
> I agree about plotting but inline-r makes it possible to use ggplot in R via Haskell which makes things like drawing maps with reasonable projections relatively straightforward.
> More generally, I think we have a good set of bindings for the ODE solver library SUNDIALS and also for other numeric libraries (e.g. LAPACK and BLAS). The problem we have is not enough hands working on such things.
> I now sadly return to programming in Julia.
Thanks for you input!
> PS - there is probably more I could say on numerical stuff in Haskell but the above already looks like “stream of consciousness”.
> Dominic Steinitz
> dominic at steinitz.org
> Twitter: @idontgetoutmuch
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