[phobos] fastDice

[Andrei Alexandrescu]

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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>
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