On 5/27/26 7:19 AM, λx.x wrote:
this is somewhat tangential; i am not necessarily talking of hashing, but more
cryptographic functions shipped with Guile as a whole.
if Guile is going to ship with a cryptographic hash function, what about other
cryptographic functions? Are we satisfied with the RNG interfaces provided in
core and SRFI 27? whether they RNGs provided are cryptographically secure
does not seem to be documented in the manual, at least not explicitly.
What I found lacking are functions normal/Gaussian distributed random number
generation. Last time I checked there was no such thing. Generating a normal
distribution from uniform distributions can be mathematically quite challenging.
So many algorithms for approximation, and many of them requiring deep
mathematical understanding to understand when one such algorithm would yield
good enough results and when not, and what parameters to tweak to make it
suitable and so on and on. Or blindly copying without understanding and just
praying. Not very keen on having to improvise something like that, with limited
mathematical understanding and Wikipedia being an impenetrable wall of math for
such algorithms.
I went as far as checking NumPy, how normal distributed random numbers are
generated there, but the code sucks so hard, it is also impenetrable for someone
not knowing the mathematical formula it tried to express and understanding that
in turn. One letter variables or abbreviations everywhere, that no one other
than mathematicians will be able to interpret, with no regard for readability at
all. Just like what one would expect a mathematician without any software
development experience to write : )
Having a good (and readable! perhaps with references!) algorithm for that in the
standard library, or in a supported SRFI, or as an extension to a supported
SRFI, would be great. And the docs should of course state what purposes it is
useful for and whether it is cryptographically secure or not.
Not that Python necessarily is the yardstick for all the things, but here is its
standard library: https://docs.python.org/3/library/random.html#random.gauss.
Best regards,
Zelphir
--
repositories: https://codeberg.org/ZelphirKaltstahl
