[Beowulf] OT: public random numbers?

Robert G. Brown rgb at phy.duke.edu
Thu Aug 25 23:07:17 PDT 2011


On Fri, 26 Aug 2011, Vincent Diepeveen wrote:

> If we assume that reality of life represents randomness, which is another
> rather good question in how far that theory is plausible, then using that
> assumption i'm very sure that the RNG's i investigated so far
> have a distribution which is too perfect, more perfect than i have seen
> in any reality.

That's because you live in a different reality than everybody else,
Vincent.

> In fact most RNG's fill all slots faster than O ( n log n ), yet it's O ( n 
> log n )
> that they follow.

In fact, they don't.

> This is RNG's that have come through all tests as being a good and
> very acceptabe RNG to be used.

No, it's not.

> Realize i'm no RNG expert, so all the names of all those tests.
>
> For me it's just push button technology. I just designed a test
> and found it very odd that all RNG's have such perfect distributions
> that they don't even miss a single slot.

It's odd because your test is broken.

>
> I'd argue the only test that would be interesting to me to see how it
> might be in reality is the lottery machine test - yet with really a lot
> of balls. I'd prefer 10k balls over a 1000 in fact - yet for practical
> reasons i would agree with a number of above a 1000.
>
> Paper fiddling is really not interesting to me there to prove anything,
> as what i've seen in reality in randomness is total different from how
> RNG's model that.

Let's try a bit of "paper fiddling".  The expected number of filled slots
is (this is actual code, not pseudocode, for n slots):

  nlogn = log10(n)*n;
  expected = (n - n*pow(1.0-1.0/(1.0*n),nlogn));

The reasoning is enormously simple.  The probability of a slot being
empty after one pull is (1 - 1/n). After nlogn pulls, it is p_e = (1 -
1/n)^nlogn.  The probability of a slot being filled is thus 1 - p_e, and
given n slots n - n*(1-1/n)^nlogn of them "should" be filled, within
random noise, n*(1-1/n)^nlogn of them "should" be empty.

Well, I've got a random number generator tester harness, so I hacked
your test into it. One major bug in your code, BTW, is using a modulus
to generate your random numbers -- dunno what that's about, but if your
rng returned numbers between (say) 0 and 7 and you use it to generate
numbers in the range 0 to 5 by means of r%5 then you'll get (for the
sequence of numbers) 0 1 2 3 4 0 1 2.  Note well that you get twice as
many 0's, 1's and 2's as 3's and 4's assuming a random draw on 0 to 7.
So you aren't even testing a uniformly distributed sequence of integers.

Fixing this relatively minor bug, removing your breakout and actually
counting up filledn for the full nlogn samples, and applying the test to
mt19937, we get:

rgb at lilith|B:1009>./dieharder -r 6 -n 10000000 -p 1 -t 1 | head
n = 10000000
nlogn = 70000000
table not all filled: filledn = 9990811, expected = 9990881

We run it again:
rgb at lilith|B:1010>./dieharder -r 6 -n 10000000 -p 1 -t 1 | head
n = 10000000
nlogn = 70000000
table not all filled: filledn = 9990802, expected = 9990881

We run it for R250 -- a well-known not-good generator:
rgb at lilith|B:1012>./dieharder -g 16 -r 6 -n 10000000 -p 1 -t 1 | head
n = 10000000
nlogn = 70000000
table not all filled: filledn = 9990794, expected = 9990881

We run it on the literally infamous randu:
n = 10000000
nlogn = 70000000
table not all filled: filledn = 9999482, expected = 9990881

Note, Vincent, that the last two examples of correctly computed results
from known-terrible generators are much farther from the expected mean
than mt19937, a well-known damn good one.  This suggests that your test
(perhaps unsurprisingly) has some sensitivity, not because some slots
are or aren't empty, but because the NUMBER of slots that are or aren't
empty isn't quite correct.  Note also that in the "paper fiddling"
analysis above, the use of nlogn is quite unimportant -- we could make
this an independent variable and evaluate the table filling for any
value m of pulls, as long as our expected value is n - n*(1 - 1/n)^m.

If I have the energy, I'll see if the distribution of filledn around
expected is e.g. Gaussian -- it seems pretty reasonable that it would be
-- with some expected or empirically computable variance.  If it is,
then this can be fairly easily turned into an actual test that returns a
p-value that humans can use to make rational judgements, or rational
humans can use to make judgements or something like that.  I doubt that
the test will have MUCH sensitivity -- modern generators are way too
good to have their flaws picked out quite this simply, although
Marsaglia's "monkey tests" do something very similar although a lot more
sophisticated mathematically (and arguably more sensitive) and do
suffice to nail randu (anything nails randu) and semi-weak tests like
R250.

Now, let's see what we've learned from this fiddling.  One is that
without it, you just waste a lot of people's time making egregious and
false claims that belittle the tremendously sophisticated and difficult
work a whole lot of "fiddlers" have put into inventing, writing, and
testing modern RNGs.  The truth is that >>all<< RNGs in dieharder "pass"
your test (if the test is "producing at least one zero") once your test
isn't broken.  We've learned that in fact, the best of the modern RNGs
are damn good, and that you could work for five years trying to invent a
test that is good enough to fail any of them and still not succeed.
Finally, we've learned that you should not, not, not take your
Martingale to a casino and try the doubling strategy out to make money,
or if you do put a firm upper bound -- something like 63 Euro -- on what
you're willing to lose with your base stake of 1 Euro.  That way you
have maybe a 40% chance of doubling your 63 Euro before you go broke.
Really, you should read the Wikipedia article I linked, in spite of the
fact that it presents more "paper fiddling".

   Sincerely,

       rgb

(See P.S. comments below...)

>>> n = 2^30; // 2 to the power 30
>>> 
>>> Function TestNumbersForRandomness(RNG,n) {
>>> declare array hashtable[size n];
>>> 
>>> guessednlogn = 2 * (log n / log 2) * n;

Why guess nlogn?  nlog is n*log10(n).  Why nlogn anyway?  Call it m and
make it a parameter.

>>> for( i = 0 ; i < n ; i++ )
>>>  hashtable[i] = FALSE;
>>> 
>>> ndraws = filledn = 0;
>>> while( ndraws  < guessednlogn ) {
>>>   randomnumber = RNG();
>>>   r = randomnumber % n; //     randomnumber =  r  (mod n)

no, r = n*RNG_UNIFORM(); where RNG_UNIFORM() is e.g. RNG/UINT_MAX.  Yes
there are roundoff errors, but they are uniform and consistent and as
you can see, don't affect this problem.  What you have isn't even close
to uniform -- it is badly nonrandom.

>>>   if( hashtable[r] == FALSE ) {
>>>      hashtable[r] = TRUE;
>>>      filledn++;

>>>      if( filledn >= n )
>>>        break;

Don't break.  Just count up filledn.  It will never be more than n now
anyway, for any n, or any reasonable m. There probably is some number of
pulls that will raise "expected" to n, but it is pretty big compared to
n, way bigger than nlogn.

>>>
>>>  }
>>>  ndraws++;
>>> }
>>> 
>>> if( filledn >= n )
>>>   print "With high degree of certainty data generated by a RNG\n");
>>> else
>>>   print "Not so sure it's a RNG\n";
>>> 
>>> }

I'm guessing the correct statistic here is something like |expected -
filledn|/expected, but as I said, I haven't really worked at it.  I
haven't decided whether or not it is worth adding this to dieharder --
without a formal derivation of the expected statistic it would be yet
another empirical test, which means you're really comparing one RNG to
another presumed better one, which I don't like.  And do I have time to
do the "fiddling" needed to do a proper derivation?  Aye, that's the
rub...;-)

    rgb

>>> 
>>> 
>>> 
>>> 
>>> 
>>> Regards,
>>> Vincent
>>> 
>>> 
>>> 
>>> 
>>>> -- both unpredictable and
>>>> flat/decorrelated at all orders, and even though there aren't really
>>>> enough of them for my purposes, I've used them as one of the (small)
>>>> "gold standard" sources for testing dieharder even as I test them.  For
>>>> all practical purposes threefish or aes are truly random as well and
>>>> they are a lot faster and easier to use as gold standard generators,
>>>> though.
>>>> I don't quite understand why the single site restriction is important --
>>>> this site has been up for years and I don't expect it to go away soon;
>>>> it is quite reliable.  I don't think there is anything secret about how
>>>> the numbers are generated, and I'll certify that the numbers it produces
>>>> don't make dieharder unhappy.  So 1 is fixable with a bit of effort on
>>>> your part; 6 I don't really understand but the guy who runs the site is
>>>> clearly willing to construct a custom feed for cash customers, if there
>>>> is enough value in whatever it is you are trying to do to pay for
>>>> access.  If it's just a lottery, well, lord, I can think of a dozen ways
>>>> to make numbers so random that they'd be unimpeachable for any sort of
>>>> lottery, both unpredictable and uncorrelated, and they don't any of them
>>>> require any significant amount of entropy to get started.
>>>> I will add one warning -- "randomness" is a rather stringent
>>>> mathematical criterion, and is generally tested against the null
>>>> hypothesis.  Amateurs who want to make random number generators out of
>>>> supposedly "random" data streams or fancy algorithms almost invariably
>>>> fail, sometimes spectacularly so.  There are a half dozen or more
>>>> really, really good pseudorandom number generators out there and it is
>>>> easy to hotwire them together into an xor-based high entropy stream that
>>>> basically never repeats (feeding it a bit of real entropy now and then
>>>> as it operates).  I would strongly counsel you against trying to take
>>>> e.g. weather data and make something "random" out of it.  Unless you
>>>> really know what you are doing, you will probably make something that
>>>> isn't at all random and may not even be unpredictable.  Even most
>>>> sources of "quantum" randomness (which is at least possibly "truly
>>>> random", although I doubt it) aren't flat, so that they carry the
>>>> signature of their generation process unless/until you manage to
>>>> transform them into something flat (difficult unless you KNOW the
>>>> distribution they are producing).  Pseudorandom number generators have
>>>> the serious advantage of being amenable to at least some theoretical
>>>> analysis (so you can "guarantee" flatness out to some high
>>>> dimensionality, say) as well as empirical testing with e.g. dieharder.
>>>> HTH,
>>>>
>>>>    rgb
>>>>> Thanks,
>>>>> David Mathog
>>>>> mathog at caltech.edu
>>>>> Manager, Sequence Analysis Facility, Biology Division, Caltech
>>>> 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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>> 
>> 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
>> 
>

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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