A checklist is forming...

1) speed (that probably means C or Cython)
2) correctness (demonstrate correct randomness according to the distribution
AND on differerent platforms)
3) documentation (references, etc)

(any other ideas, feel free to add to this thread ;-)


On Sun, Sep 5, 2010 at 5:08 PM, David Kirkby <david.kir...@onetel.net>wrote:

> On 5 September 2010 05:28, William Stein <wst...@gmail.com> wrote:
> > scipy.stats has random number generators for about a 100 different
> > families of distributions.   The last time I tried them, they were of
> > variable quality, in that some of them were (very, very) slow.  But
> > the range of distributions was really impressive.      You should look
> > at the official book about numpy (yes, numpy), which Travis Oliphant
> > wrote.  It has a pretty good list of the distributions in numpy.stats
> > = scipy.stats.
>
>
> > Whatever you do, I hope you'll benchmark carefully as you go.  A
> > package that generates random numbers 1000 times slower than MATLAB is
> > going to be very annoying to use.  Non-cryptography people usually
> > generate random numbers because they want a lot of them.
>
> If they are of variable quality, I would suggest that not only
> benchmarking them is important, but testing them for randomness is
> too. I know that some implementations that work on one word-size
> computer perform really poor if the word size is increased. So an
> algorithm only tested on a 32-bit system might perform very badly in a
> 64-bit one.
>
> Most make use of a CPUs register overflowing at some point. If the
> word size changes, so the overflow point changes, so the algorithm has
> changes.
>
> Dave
>
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