| Re: random musings [message #53121] |
Tue, 20 March 2007 01:53  |
Paolo Grigis
Messages: 171
Registered: December 2003
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Senior Member |
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Christopher Thom wrote:
> Quoth Wayne Landsman:
>
>> On Mar 19, 4:30 pm, Christopher Thom <c...@oddjob.uchicago.edu> wrote:
>>> Hi all,
>>>
>>> this morning I started writing some monte carlo code, in which I'm
>>> generating random arrays, and scaling/shifting them to fit the (gaussian)
>>> distribution parameters I need. One thing is not clear in the online help:
>>> Which random function is "better"? Should I be using randomn() or
>>> randomu()?
>> I believe these functions are identical. In the ancient days, IDL had
>> two types of randomn number generators: RANDOMU, for a uniform
>> distribution, and RANDOMN for a normal distribution. When addition
>> distributions (e.g. binomial, Poisson, Gamma) were added (in V5.0 I
>> believe), RSI chose against adding additional RANDOM* functions, and
>> instead made all the distributions available from either RANDOMU or
>> RANDOMN.
>
> Hmmmm. If this is true, then the documentation is definitely in need of an
> update. Randomn() supposedly uses the "box muller method", while randomu()
> uses one from an ACM paper that is cited, with a "bays durham shuffle
> added", giving something "similar to ran1()" from NR in C section 7.1.
> If they are the same thing, it is certainly not clear from the docs. :-(
In most implementations, uniformly distributed random numbers are generated
first, and then some trick is used to transform these into the distribution
you want (e.g. normal in this case). Therefore, "ran1()" (or whatever flavor
thereof they use internally) produces the uniform samples, an then the box
muller transformation is applied to these values to get the normally
distributed samples.
Ciao,
Paolo
>
> OTOH, the interfaces seem very similar...they all provide the same flags
> (/gamma, /normal etc), which certainly points towards a common algorithm.
>
> Oh. I just did the obvious thing -- tried both with the same seed:
>
> IDL> x=randomn(180479L, 5,/normal)
> IDL> y=randomu(180479L, 5,/normal)
> IDL> print,x,y
> 2.01146 -1.55186 -0.0893551 -0.627972 -0.780082
> 2.01146 -1.55186 -0.0893551 -0.627972 -0.780082
>
> Surely different algorithms would return different randomness for the same
> seed.
>
> Problem solved...
>
> cheers
> chris
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