On Thu, Jul 12, 2018 at 2:22 PM Chris Angelico email@example.com wrote:
On Fri, Jul 13, 2018 at 4:11 AM, Steven D'Aprano firstname.lastname@example.org wrote:
There is no reason why primality testing can't be deterministic up to 2**64, and probabilistic with a ludicrously small chance of false positives beyond that. The implementation I use can be expected to fail on average once every 18 thousand years if you did nothing but test primes every millisecond of the day, 24 hours a day. That's good enough for most purposes :-)
What about false negatives? Guaranteed none? The failure mode of the function should, IMO, be a defined and documented aspect of it.
Miller-Rabin or other pseudo-primality tests do not produce false negatives IIUC.
I'm more concerned in the space/time tradeoff in the primes() generator. I like this implementation for teaching purposes, but I'm well aware that it grows in memory usage rather quickly (the one Tim Peters posted is probably a better balance; but it depends on how many of these you create and how long you run them).
from math import sqrt, ceil
def up_to(seq, lim): for n in seq: if n < lim: yield n else: break
def sieve_generator(): "Pretty good Sieve; skip the even numbers, stop at sqrt(candidate)" yield 2 candidate = 3 found =  while True: lim = int(ceil(sqrt(candidate))) if all(candidate % prime != 0 for prime in up_to(found, lim)): yield candidate found.append(candidate) candidate += 2
So then how do you implement isprime(). One naive way is to compare it against elements of sieve_generator() until we are equal or larger than the test element. But that's not super fast. A pseudo-primality test is much faster (except for in the first few hundred thousand primes, maybe).