How to implement an efficient infinite generator of prime numbers in Python?
This is not a homework, I am just curious.
INFINITE is the key word here.
I wish to use it as for p in primes(). I believe that this is a built-in function in Haskell.
So, the answer cannot be as naive as "Just do a Sieve".
First of all, you do not know how many consecutive primes will be consumed. Well, suppose you could concoct 100 of them at a time. Would you use the same Sieve approach as well as the frequency of prime numbers formula?
I prefer non-concurrent approach.
Thank you for reading (and writing ;) )!
I still like what I wrote up here (a Cookbook recipe with many other authors) -- it shows how a Sieve of Eratosthenes has no intrinsic limits, and the comments and discussion, I believe, make it quite clear. This was recently discussed on Stack Overflow (search for the authors' names, I guess) and somebody proposed a substantially faster (but IMHO less clear) version;-).
“If I have seen further…”
The erat2 function from the cookbook can be further sped up (by about 20-25%):
erat2a
import itertools as it
def erat2a( ):
D = { }
yield 2
for q in it.islice(it.count(3), 0, None, 2):
p = D.pop(q, None)
if p is None:
D[q*q] = q
yield q
else:
# old code here:
# x = p + q
# while x in D or not (x&1):
# x += p
# changed into:
x = q + 2*p
while x in D:
x += 2*p
D[x] = p
The not (x&1) check verifies that x is odd. However, since both q and p are odd, by adding 2*p half of the steps are avoided along with the test for oddity.
erat3
If one doesn't mind a little extra fanciness, erat2 can be sped up by 35-40% with the following changes (NB: needs Python 2.7+ or Python 3+ because of the itertools.compress function):
import itertools as it
def erat3( ):
D = { 9: 3, 25: 5 }
yield 2
yield 3
yield 5
MASK= 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0,
MODULOS= frozenset( (1, 7, 11, 13, 17, 19, 23, 29) )
for q in it.compress(
it.islice(it.count(7), 0, None, 2),
it.cycle(MASK)):
p = D.pop(q, None)
if p is None:
D[q*q] = q
yield q
else:
x = q + 2*p
while x in D or (x%30) not in MODULOS:
x += 2*p
D[x] = p
The erat3 function takes advantage of the fact that all primes (except for 2, 3, 5) modulo 30 result to only eight numbers: the ones included in the MODULOS frozenset. Thus, after yielding the initial three primes, we start from 7 and work only with the candidates.
The candidate filtering uses the itertools.compress function; the “magic” is in the MASK sequence; MASK has 15 elements (there are 15 odd numbers in every 30 numbers, as chosen by the itertools.islice function) with a 1 for every possible candidate, starting from 7. The cycle repeats as specified by the itertools.cycle function.
The introduction of the candidate filtering needs another modification: the or (x%30) not in MODULOS check. The erat2 algorithm processed all odd numbers; now that the erat3 algorithm processes only r30 candidates, we need to make sure that all D.keys() can only be such —false— candidates.
Benchmarks
Results
On an Atom 330 Ubuntu 9.10 server, versions 2.6.4 and 3.1.1+:
$ testit
up to 8192
==== python2 erat2 ====
100 loops, best of 3: 18.6 msec per loop
==== python2 erat2a ====
100 loops, best of 3: 14.5 msec per loop
==== python2 erat3 ====
Traceback (most recent call last):
…
AttributeError: 'module' object has no attribute 'compress'
==== python3 erat2 ====
100 loops, best of 3: 19.2 msec per loop
==== python3 erat2a ====
100 loops, best of 3: 14.1 msec per loop
==== python3 erat3 ====
100 loops, best of 3: 11.7 msec per loop
On an AMD Geode LX Gentoo home server, Python 2.6.5 and 3.1.2:
$ testit
up to 8192
==== python2 erat2 ====
10 loops, best of 3: 104 msec per loop
==== python2 erat2a ====
10 loops, best of 3: 81 msec per loop
==== python2 erat3 ====
Traceback (most recent call last):
…
AttributeError: 'module' object has no attribute 'compress'
==== python3 erat2 ====
10 loops, best of 3: 116 msec per loop
==== python3 erat2a ====
10 loops, best of 3: 82 msec per loop
==== python3 erat3 ====
10 loops, best of 3: 66 msec per loop
Benchmark code
A primegen.py module contains the erat2 , erat2a and erat3 functions. Here follows the testing script:
#!/bin/sh
max_num=${1:-8192}
echo up to $max_num
for python_version in python2 python3
do
for function in erat2 erat2a erat3
do
echo "==== $python_version $function ===="
$python_version -O -m timeit -c
-s "import itertools as it, functools as ft, operator as op, primegen; cmp= ft.partial(op.ge, $max_num)"
"next(it.dropwhile(cmp, primegen.$function()))"
done
done
Since the OP asks for an efficient implementation, here's a significant improvement to the active state 2002 code by David Eppstein/Alex Martelli (seen here in his answer): don't record a prime's info in the dictionary until its square is seen among the candidates . Brings space complexity down to below O(sqrt(n)) instead of O(n), for n primes produced ( π(sqrt(n log n)) ~ 2 sqrt(n log n) / log(n log n) ~ 2 sqrt(n / log n) ). Consequently, time complexity is also improved, ie it runs faster.
Creates a "sliding sieve" as a dictionary of current multiples of each base prime (ie below the sqrt of the current production point), together with their step values:
from itertools import count
# ideone.com/aVndFM
def postponed_sieve(): # postponed sieve, by Will Ness
yield 2; yield 3; yield 5; yield 7; # original code David Eppstein,
sieve = {} # Alex Martelli, ActiveState Recipe 2002
ps = postponed_sieve() # a separate base Primes Supply:
p = next(ps) and next(ps) # (3) a Prime to add to dict
q = p*p # (9) its sQuare
for c in count(9,2): # the Candidate
if c in sieve: # c's a multiple of some base prime
s = sieve.pop(c) # i.e. a composite ; or
elif c < q:
yield c # a prime
continue
else: # (c==q): # or the next base prime's square:
s=count(q+2*p,2*p) # (9+6, by 6 : 15,21,27,33,...)
p=next(ps) # (5)
q=p*p # (25)
for m in s: # the next multiple
if m not in sieve: # no duplicates
break
sieve[m] = s # original test entry: ideone.com/WFv4f
(the older, original code here was edited to incorporate changes as seen in the answer by Tim Peters, below). see also this for a related discussion.
Similar 2-3-5-7 wheel-based code runs ~ 2.15x faster (which is very close to the theoretical improvement of 3/2 * 5/4 * 7/6 = 2.1875 ).