[Beowulf] OT: public random numbers?

Robert G. Brown rgb at phy.duke.edu
Fri Aug 26 05:57:55 PDT 2011

On Fri, 26 Aug 2011, Vincent Diepeveen wrote:


Bullshit.  "Every programmer" isn't dumb as a post.  Or wasn't my
argument clear enough?  Do you need me to actually post the code for how
the GSL -- written by at least some of these programmers -- do this?
Here, I'll try again.  This time I'll use smaller numbers and make an
actual table of the outcomes:  Imagine only two lousy random bits,
enough to make 00, 01, 10, 11 (or 0,1,2,3).  Here is the probability

r =   0    1    2    3
p = 0.25 0.25 0.25 0.25

Let us generate N samples from this distribution.  Our expected
frequency of the occurrence of all of these numbers is:

r =     0      1      2      3
Np = 0.25*N 0.25*N 0.25*N 0.25*N

Is this all clear?  If I generate 100 random numbers, the expected
number of 3's is 0.25*100 = 25.  Now apply mod 3  the outcomes are now:

r =     0      1      2      3
r%3 =   0      1      2      0
Np = 0.25*N 0.25*N 0.25*N 0.25*N

You now sum the number of outcomes for each element in the mod 3 table,
since we have two values of r that make one value of r%3 and frequency
clearly aggregates as the outcomes are independent.

r%3 =   0      1      2
Np = 0.50*N 0.25*N 0.25*N 0.25*N

It is therefore twice as likely that two random bits, modulus 3, will
produce a zero.

> Apologies for the caps. I hope how important this is. You're claiming all 
> programmers
> use random numbers in a faulty manner?

They don't.  Only you do.  Everybody else takes a uniform deviate and
scales it by the number of desired integer outcomes, checking to make
sure that they don't go out of bounds and thereby e.g. get an incorrect
endpoint frequency.  The gsl code is open source and it takes two
minutes to download it and check (I just timed it).  Go on, look.  the
file is rng/rng.c in the gsl distro directory, the function name is
gsl_rng_uniform_int.  No modulus.

The exception is (obviously) when the range is a power of 2.  In that
case ONLY, r%n where r is a binary uint and n is a power of 2 will
(obviously) equally balance the table above.  Personally I'd use >> and
shift the bits because it is faster than mod, but suit yourself, after
you've learned what you are doing.

> This is important enough to further discuss about it.
> As nearly always you need random numbers from within a given domain say 0.. 
> n-1
> So projecting a RNG onto that domain is pretty crucial. How would you want to 
> do that in a correct manner?
> In the slot test in fact a simple AND is enough.

No, as I've just proven algebraically.  The correct manner for general n
is the gsl code, but in rough terms it is n*r/r_max (with care used to
avoid roundoff errors at the ends as noted).  If you've been using
modulus, all your results are crap.

Look, the reason God invented the GSL and made it open source is so
numb-nuts and smart people alike wouldn't have to constantly reinvent
the wheel, badly.  Use it.  Don't question it -- you obviously aren't
competent to.  Just use it.  If you want a random integer from 0 to n,
use gsl_rn_uniform_int.  If you want this for e.g. mt19937 don't write
the latter, set up the gsl to use it to generate your ints.  Learn to
use it carefully, use it correctly, but use it.

> It's not interesting to discuss - but yes this strategy makes money in 
> casino's,
> you just get thrown out of the casino and end up at the blacklist if you do.

You are clearly too stupid to be allowed out of the house without a
caretaker.  I'm not going to walk you through the proof that this isn't
so as it is openly published and I've already referenced a step my step
analysis that you can't be bothered, apparently, to actually read.  I'll
just reiterate the previous offer -- I, too, am happy to buy a roulette
wheel and you can come over and bet Martingale against me all day.  Just
one 0, no limits and no quitting, infinite credit on both sides, we play
until it is obvious to you that you are losing, have lost, will always
lose, and the longer you play the more that you will lose.  Loser buys
the winner a case of truly excellent beer.

Look, why don't you fix your random number code and try again, since
your simulations are obviously trash.  It isn't difficult to show this
with simulations, once you actually code them correctly, but I have to
go and don't have time to do it for you.


Robert G. Brown	                       http://www.phy.duke.edu/~rgb/
Duke University Dept. of Physics, Box 90305
Durham, N.C. 27708-0305
Phone: 1-919-660-2567  Fax: 919-660-2525     email:rgb at phy.duke.edu

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