Re: number generator

"Nick Craig-Wood" <nick@xxxxxxxxxxxxxx> wrote:

Paul Rubin <http> wrote:
The fencepost method still seems to be simplest:

t = sorted(random.sample(xrange(1,50), 4))
print [(j-i) for i,j in zip([0]+t, t+[50])]

Mmm, nice.

Here is another effort which is easier to reason about the
distribution produced but not as efficient.

def real(N, M):
while 1:
t = [ random.random() for i in range(N) ]
factor = M / sum(t)
t = [ int(round(x * factor)) for x in t]
if sum(t) == M:
break
print "again"
assert len(t) == N
assert sum(t) == M
return t

It goes round the while loop on average 0.5 times.

If 0 isn't required then just test for it and go around the loop again
if found. That of course skews the distribution in difficult to
calculate ways!


I have been wondering about the following as this thread unrolled:

Is it possible to devise a test that can distinguish between sets
of:

- five random numbers that add to 50, and
- four random numbers and a fudge number that add to 50?

My stats are way too small and rusty to attempt to answer
the question, but it seems intuitively a very difficult thing.

- Hendrik

.

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