A bit late to the party but here's a Rust solution that is probably maybe correct: https://pastebin.com/Eaes7hU9
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Very late, but C solution using fib: Git
#include
#include
#include
size_t fib(size_t n)
{
/* n=93 is the last fib(n) that fits in u64. */
static size_t cache[94] = { 0, 1 };
static size_t top_valid = 1;
size_t a, b;
if (top_valid >= n) {
return cache[n];
} else if (n >= sizeof(cache) / sizeof(*cache)) {
fputs("Fib overflow\n", stderr);
exit(EXIT_FAILURE);
}
a = cache[top_valid - 1];
b = cache[top_valid];
for (; top_valid < n; top_valid += 2) {
a = cache[top_valid + 1] = a + b;
b = cache[top_valid + 2] = a + b;
}
return cache[n];
}
int main(int argc, char **argv)
{
char *p, c;
size_t combos = 1;
int digits = 0;
if (argc != 2) {
fputs("Bad invocation.\n", stderr);
}
if (!*(p = argv[1])) {
goto done;
}
for (;;) {
c = *++p;
if (c < '0' || c > '9') {
combos *= fib(digits);
digits = 0;
if (!c) {
break;
}
} else {
digits++;
}
}
done:
printf("%zu\n", combos);
return 0;
}
Zig would probably be a good language for a solution if there is a known maximum number of combinations, since it has large fixed-width types and comptime.
So, I also realized its the fibonacci sequence for number of combinations of numbers. All I care about is counting sequential digits, we skip a character after every sequence. We just need to account for if the first character is a number and we are good. I did this in ruby. I didn't really try for anything bonus points wise.
Pastebin because formatting doesn't like &
or <
My code also outputs 0
if the sequence itself is invalid(ends on a non-digit character, or has two non-digit characters in a row)
Given an input c
, outputs the number of distinct lists of strings lst
such that:
''.join(lst) == c
- for every string
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.)
from functools import cache
from itertools import pairwise
from math import prod
@cache
def fibonacci(n: int) -> int:
if n == 0:
return 0
if n == 1:
return 1
return fibonacci(n - 1) + fibonacci(n - 2)
def main(compressed: str) -> int:
is_fragment_start = [i == 0 or c not in '0123456789' for i, c in enumerate(compressed)]
fragment_start_positions = [i for i, s in enumerate(is_fragment_start) if s]
fragment_lengths = [stop - start for start, stop in pairwise(fragment_start_positions + [len(compressed)])]
return prod(fibonacci(fragment_length - 1) for fragment_length in fragment_lengths)
if __name__ == '__main__':
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('compressed')
print(main(parser.parse_args().compressed))
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
and enumerate
are implemented in ways that are less efficient for huge integers (I haven't thoroughly checked, and I'm not planning to).
functools.cache
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.