[SciPy-Dev] Optimization to stats.dlaplace.rvs

[Evgeni Burovski]

Yes, an efficient implementation of dlaplace._rvs would be in scope, I'd
think.

сб, 16 нояб. 2019 г., 0:01 Andrew Reed <reed at cs.unc.edu>:

> All,
>
> Some of you may have seen a message I sent to the NumPy mailing list about
> adding a two-sided geometric distribution and/or the comments to my PR on
> Github:
>
> https://mail.python.org/pipermail/numpy-discussion/2019-November/080223.html
> https://github.com/numpy/numpy/pull/14890
>
> Bottom line, rather than add it as a distribution to NumPy, it was
> suggested that I look into adding it to stats.dlaplace.rvs (which currently
> uses the inverted CDF) and I was provided with some code to get me started.
>
> I have been able to add the suggested code, with only minor tweaks, to
> SciPy.  A few tests with timeit seem to confirm that the new code provides
> a speedup of about 250% on my machine.  Furthermore, the default rvs
> function would get killed when I tried to generate 100 million samples,
> whereas this new code can generate at least 100 million samples (I get a
> MemoryError on my VM when I try to go any higher).
>
> I think I'm at the point now where I need to start working through some
> broadcasting errors, but before I do, I wanted to gauge the potential
> interest in these improvements.
>
> I imagine that implementing a new method for rvs will probability break
> the repeatability of previous versions of SciPy, and I'm not sure if this
> is a distribution that warrants optimization.
>
> Thanks,
> Andrew
>
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