this post was submitted on 26 Aug 2023
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Given an input
c
, outputs the number of distinct lists of stringslst
such that:''.join(lst) == c
s
inlst
,s
consists of an arbitrary character followed by one or more characters from '0123456789'Sure hope I didn't mess this up, because I think the fundamental idea is quite elegant! Should run successfully for all "reasonable" inputs (as in, the numeric output fits in a uint64 and the input isn't ridiculously long). Fundamental algorithm is O(n) if you assume all arithmetic is O(1). (If not, then I don't know what the time complexity is, and I don't feel like working it out.)
Idea:
a00010 -> [a000, 10] -> [length 4, length 2] -> F(4) * F(2)
01a102b0305 -> [01, a102, b0305] -> [length 2, length 4, length 5] -> F(2) * F(4) * F(5)
where F(n) = fibonacci(n - 1) is the number of ways to partition a string of length n into a list of strings of length ≥2.
F(2) = 1 = fibonacci(1), F(3) = 1 = fibonacci(2), and F(n) = F(n - 2) + F(n - 1), so F is indeed just an offset version of the Fibonacci sequence.
To see why F(n) = F(n - 2) + F(n - 1), here are the ways to split up 'abcde': ['ab'] + (split up 'cde'), ['abc'] + (split up 'de'), and ['abcde'], corresponding to F(5) = F(3) + F(2) + 1.
And the ways to split up 'abcdef': ['ab'] + (split up 'cdef'), ['abc'] + (split up 'def'), ['abcd'] + (split up 'ef'), and ['abcdef'], corresponding to F(6) = F(4) + F(3) + F(2) + 1 = F(4) + F(5) = F(6 - 2) + F(6 - 1).
The same logic generalizes to all n >= 4.
Without memoization, I believe the Fibonacci sequence is O(2^N). It's dependent on how long a sequence of digits is in the input, so your worst case is like O(2^N) if the string is mostly digits and best case being O(N) if there are only 1 or 2 digit sequences.
Someone can correct me if I'm wrong.
My implementation is memoized by
functools.cache
, but that is a concern when it comes to recursive Fibonacci. That, and stack overflows, which are also a problem for my code (but, again, not for "reasonable" inputs -- fibonacci(94) already exceeds 2^64).Time complexity-wise, I was more thinking about the case where the numbers get so big that addition, multiplication, etc. can no longer be modelled as taking constant time. Especially if
math.prod
andenumerate
are implemented in ways that are less efficient for huge integers (I haven't thoroughly checked, and I'm not planning to).That's pretty cool. I haven't dived too deep into python, so I should of looked up the library when you attached the
@cache
decorator. I learned something.