I just realized, that I did not check what Guile implements as non-SRFIs. I found: https://www.gnu.org/software/guile/manual/html_node/Random.html which has `random:normal`! I should have checked that first. Still good to know, what a can of worms normal distribution implementation can be.
On 30.05.20 22:21, Zelphir Kaltstahl wrote: > Hi Guile Users! > > I recently wrote a little program involving lots of uniformly > distributed random integers. For that I used SRFI-27 and it works fine. > > Then I thought: How would I get normal distributed random numbers? I > don't have a project or program in mind for this, but it struck me, that > I do not know, how to get a normal distribution from a uniform > distribution. So I dug into the matter … > > Turns out the math is not really my friend: > > * https://stackoverflow.com/a/3265174 – OK, if that's true, then don't > use Box-Muller-Transform > * https://stackoverflow.com/a/86885 – The what? I need to somehow > inverse the Gaussian distribution to get a function to calculate normal > distributed values from uniformly distributed values? Something like > that. Safe to say it is above my current math skills. > * The wiki page also does not help me much: > https://en.wikipedia.org/wiki/Inverse_transform_sampling Seems too > complicated. > > So I thought: "OK, maybe I can simply copy, how other languages > implement it!" The wiki page mentions, that R actually makes use of the > inverse thingy. So I set out to look at R source code: > > * https://github.com/wch/r-source/blob/master/src/nmath/rnorm.c – OK, > looks simple enough … Lets see what `norm_rand` is … > * https://github.com/wch/r-source/blob/master/src/nmath/snorm.c#L62 – > yeah … well … I'm not gonna implement _that_ pile of … Just look at the > lines > https://github.com/wch/r-source/blob/master/src/nmath/snorm.c#L135-L196 > what a mess! Not a single comment to help understanding in it. Such a > disappointment. > * Python also seems to only use an approximation with magic constants: > https://github.com/python/cpython/blob/3.8/Lib/random.py#L443 > > So it seems, that there is no easy way to implement it properly with > correct tails to the left and right side of the distribution, something > clean and not made with mathematical traps built-in. Or is there? > > I found a post about using 2 normal distributions to do > Box-Muller-transform: > https://www.alanzucconi.com/2015/09/16/how-to-sample-from-a-gaussian-distribution/ > > However, it seems to require a uniform float not integer and it is the > Box-Muller-transform, which is said to clamp between -6 and 6 according > to the people writing the answers on stackoverflow. > > So my question is: Is there a good implementation in the Guile universe > already? (Or a simple way to implement it?) I don't really need it right > now, but I think this thing could be an obstacle for many people without > serious math knowledge and it would be good to know, where to find it, > should one have need for normal distributed random numbers. > > Regards, > Zelphir > >
