John Machin wrote:
> You don't need to use random sampling. Paul Rubin has shown how it can
> be done deterministically. The following is a generalisation of his
> code; it generates all possible assemblies of size n from a list of
> parts. Is this helpful?
>
> def all_size_n_knickers(rqd_size, pieces):
> npieces = len(pieces)
> knicker_count = npieces ** rqd_size
> austen = [npieces ** (rqd_size-k-1) for k in xrange(rqd_size)]
> for i in xrange(knicker_count):
> knicker = [pieces[j] for j in [(i // d) % npieces for d in austen]]
> yield knicker
>
> for alist in all_size_n_knickers(4, 'abc'):
> print ''.join(alist)
>
> print
> print list(all_size_n_knickers(2, [1, 42, 666]))
Just testing out my ELCH JythonConsole :-)
def unint(i,symbols):
res = []
radix = len(symbols)
while i:
i,r = divmod(i,radix)
res.append(symbols[r])
return res[::-1]
start = int('10000',3)
finish = int('20000',3)
for i in range(start,finish):
print ''.join(unint(i,'abc')[1:])
This makes me wonder why we still don't have something like the unint
function above in the standard distribution.
Anton
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