[Tutor] Generating random numbers

[Venkatesh Babu]

Hi,

Thanks very much!!! I went thru the file random.py and
those links. My doubt is cleared.

-Venkatesh

--- Tim Peters <tim.one at comcast.net> wrote:
> [Venkatesh Babu]
> > Just wanted to clarify my point once again.
> >
> > So, do u mean to say that in a single thread, the
> > following sequence of calls are OK:
> >
> > random.gauss(50, 5)
> > random.gauss(50, 5)
> > ... Some code....
> > random.gauss(25, 4)
> > random.gauss(25, 4)
> > ... Some more code....
> > random.gauss(50, 5)
> > random.gauss(50, 5)
> 
> Yes, that's what I thought you meant, and it's fine.
> 
> > Because till now I was thinking that generation of
> > random numbers following a probability
> distribution
> > should maintain some history information.
> 
> Virtually all methods of generating random variates
> following a particular
> distribution work by applying transformations to a
> uniform random float in
> 0.0 to 1.0.  For example, to generate a random float
> uniformly from 1 to 5,
> take a random float uniformly from 0 to 1, multiply
> it by 4, then add 1.  It
> doesn't need any history, it only needs to be given
> a uniform random float
> in 0 to 1 to work with.
> 
> You can read the code in random.py to see that
> almost all the methods work
> "like that".  Some are very subtle, and require
> difficult correctness proofs
> if you *really* want to know how they work.
> 
> The gauss() method used by Python is briefly but
> nicely described here:
> 
>    
> http://en.wikipedia.org/wiki/Box-Muller_transform
> 
> That page shows how to generate random *unit* normal
> variates given a source
> of random uniform variates in 0 to 1.  Dealing with
> arbitrary mu and sigma
> is a trivial transformation beyond that (after you
> get it, multiply the
> random unit normal variate by sigma, then add mu). 
> More detail can be found
> here:
> 
>     http://www.taygeta.com/random/gaussian.html
> 
> As that page says <wink>,
> 
>     Finding transformations like the Box-Muller is a
> tedious process,
>     and in the case of empirical distributions it is
> not possible.
> 


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