Good idea. Didn't think of Boost, and obviously licensing issues won't
be problematic there. Will do, since it will probably just be a trivial
translation job.
On 1/2/2011 6:36 PM, Andrei Alexandrescu wrote:
Also, Boost has great random numbers. For now, could you be the leader
on putting Gaussians into Phobos?
Andrei
On 1/2/11 5:23 PM, David Simcha wrote:
A decent Gaussian is trivial:
http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
Also see my dstats.random lib. Most of this stuff requires binary
attribution because it was translated from the Numpy code, but the
Box-Muller Gaussian is arguably not original enough to be copyrightable,
i.e. you can't copyright algorithms, Box-Muller is a very well-known
algorithm, and there's a very limited number of ways to express the
algorithm in code once it's specified.
Poisson is a PITA IIRC. (I have code to do it, but again it requires
binary attribution.)
Where is Zipf used? I left it out of my Numpy port that I did awhile
back. IIRC it was because Wikipedia didn't provide enough information on
it to make my code reasonably testable.
On 1/2/2011 5:59 PM, Andrei Alexandrescu wrote:
On 9/11/10 3:29 PM, David Simcha wrote:
I've written a few functions that generate random numbers from an
arbitrary discrete distribution in O(log N) time, where N is the
number
of possible values, using SortedRange.lowerBound(). It's similar to
dice() except that in exchange for O(N) auxiliary space and upfront
initialization cost you get O(log N) generation. I've attached the
code,
which is fairly simple. Should this go in std.random, or is needing
this
O(log N) performance on dice() niche enough that this belongs in my
dstats lib instead?
I'm not sure. You may want to ask the question on the newsgroup.
BTW, I think std.random is in dire need of a few classic distribution
(Gaussian, Poisson, Zipf come to mind). Anyone inclined?
Andrei
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