这是我能想到的最好的算法。

def get_primes(n):
    numbers = set(range(n, 1, -1))
    primes = []
    while numbers:
        p = numbers.pop()
        primes.append(p)
        numbers.difference_update(set(range(p*2, n+1, p)))
    return primes

>>> timeit.Timer(stmt='get_primes.get_primes(1000000)', setup='import   get_primes').timeit(1)
1.1499958793645562

还能做得更快吗?

这段代码有一个缺陷:由于numbers是一个无序集,不能保证numbers.pop()将从集合中移除最低的数字。尽管如此,它还是适用于(至少对我来说)一些输入数字:

>>> sum(get_primes(2000000))
142913828922L
#That's the correct sum of all numbers below 2 million
>>> 529 in get_primes(1000)
False
>>> 529 in get_primes(530)
True

这里有一个来自Python Cookbook的非常简洁的示例——该URL的最快版本是:

import itertools
def erat2( ):
    D = {  }
    yield 2
    for q in itertools.islice(itertools.count(3), 0, None, 2):
        p = D.pop(q, None)
        if p is None:
            D[q*q] = q
            yield q
        else:
            x = p + q
            while x in D or not (x&1):
                x += p
            D[x] = p

这就给出了

def get_primes_erat(n):
  return list(itertools.takewhile(lambda p: p<n, erat2()))

在shell提示符(正如我喜欢做的那样)中测量这段代码在pri.py中,我观察到:

$ python2.5 -mtimeit -s'import pri' 'pri.get_primes(1000000)'
10 loops, best of 3: 1.69 sec per loop
$ python2.5 -mtimeit -s'import pri' 'pri.get_primes_erat(1000000)'
10 loops, best of 3: 673 msec per loop

所以看起来食谱解决方案的速度是原来的两倍多。


这个算法很快,但它有一个严重的缺陷:

>>> sorted(get_primes(530))
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73,
79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163,
167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251,
257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349,
353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443,
449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 527, 529]
>>> 17*31
527
>>> 23*23
529

您假设numbers.pop()将返回集合中最小的数字,但这根本不能保证。集合是无序的,pop()删除并返回任意元素,因此不能使用它从剩余数字中选择下一个质数。


警告:timeit结果可能会因硬件或设备的差异而有所不同 Python的版本。

下面是一个比较一些实现的脚本: ambi_sieve_plain, rwh_primes, rwh_primes1, rwh_primes2, sieveOfAtkin, sieveOfEratosthenes, sundaram3, sieve_wheel_30, Ambi_sieve(需要numpy) Primesfrom3to(需要numpy) Primesfrom2to(需要numpy)

非常感谢stephan让我注意到sieve_wheel_30。 罗伯特·威廉·汉克斯的《质数from2to》、《质数from3to》、《rwh_质数》、《rwh_primes1》和《rwh_primes2》都要归功于他。

在使用psyco测试的普通Python方法中,n=1000000时, Rwh_primes1是测试中最快的。

+---------------------+-------+
| Method              | ms    |
+---------------------+-------+
| rwh_primes1         | 43.0  |
| sieveOfAtkin        | 46.4  |
| rwh_primes          | 57.4  |
| sieve_wheel_30      | 63.0  |
| rwh_primes2         | 67.8  |    
| sieveOfEratosthenes | 147.0 |
| ambi_sieve_plain    | 152.0 |
| sundaram3           | 194.0 |
+---------------------+-------+

在测试的普通Python方法中,如果不使用psyco, n=1000000, Rwh_primes2是最快的。

+---------------------+-------+
| Method              | ms    |
+---------------------+-------+
| rwh_primes2         | 68.1  |
| rwh_primes1         | 93.7  |
| rwh_primes          | 94.6  |
| sieve_wheel_30      | 97.4  |
| sieveOfEratosthenes | 178.0 |
| ambi_sieve_plain    | 286.0 |
| sieveOfAtkin        | 314.0 |
| sundaram3           | 416.0 |
+---------------------+-------+

在所有测试的方法中,允许numpy,当n=1000000时, Primesfrom2to是测试中最快的。

+---------------------+-------+
| Method              | ms    |
+---------------------+-------+
| primesfrom2to       | 15.9  |
| primesfrom3to       | 18.4  |
| ambi_sieve          | 29.3  |
+---------------------+-------+

使用命令测量时间:

python -mtimeit -s"import primes" "primes.{method}(1000000)"

将{method}替换为每个方法名。

primes.py:

#!/usr/bin/env python
import psyco; psyco.full()
from math import sqrt, ceil
import numpy as np

def rwh_primes(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Returns  a list of primes < n """
    sieve = [True] * n
    for i in xrange(3,int(n**0.5)+1,2):
        if sieve[i]:
            sieve[i*i::2*i]=[False]*((n-i*i-1)/(2*i)+1)
    return [2] + [i for i in xrange(3,n,2) if sieve[i]]

def rwh_primes1(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Returns  a list of primes < n """
    sieve = [True] * (n/2)
    for i in xrange(3,int(n**0.5)+1,2):
        if sieve[i/2]:
            sieve[i*i/2::i] = [False] * ((n-i*i-1)/(2*i)+1)
    return [2] + [2*i+1 for i in xrange(1,n/2) if sieve[i]]

def rwh_primes2(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Input n>=6, Returns a list of primes, 2 <= p < n """
    correction = (n%6>1)
    n = {0:n,1:n-1,2:n+4,3:n+3,4:n+2,5:n+1}[n%6]
    sieve = [True] * (n/3)
    sieve[0] = False
    for i in xrange(int(n**0.5)/3+1):
      if sieve[i]:
        k=3*i+1|1
        sieve[      ((k*k)/3)      ::2*k]=[False]*((n/6-(k*k)/6-1)/k+1)
        sieve[(k*k+4*k-2*k*(i&1))/3::2*k]=[False]*((n/6-(k*k+4*k-2*k*(i&1))/6-1)/k+1)
    return [2,3] + [3*i+1|1 for i in xrange(1,n/3-correction) if sieve[i]]

def sieve_wheel_30(N):
    # http://zerovolt.com/?p=88
    ''' Returns a list of primes <= N using wheel criterion 2*3*5 = 30

Copyright 2009 by zerovolt.com
This code is free for non-commercial purposes, in which case you can just leave this comment as a credit for my work.
If you need this code for commercial purposes, please contact me by sending an email to: info [at] zerovolt [dot] com.'''
    __smallp = ( 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59,
    61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139,
    149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227,
    229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311,
    313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401,
    409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491,
    499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599,
    601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683,
    691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797,
    809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887,
    907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997)

    wheel = (2, 3, 5)
    const = 30
    if N < 2:
        return []
    if N <= const:
        pos = 0
        while __smallp[pos] <= N:
            pos += 1
        return list(__smallp[:pos])
    # make the offsets list
    offsets = (7, 11, 13, 17, 19, 23, 29, 1)
    # prepare the list
    p = [2, 3, 5]
    dim = 2 + N // const
    tk1  = [True] * dim
    tk7  = [True] * dim
    tk11 = [True] * dim
    tk13 = [True] * dim
    tk17 = [True] * dim
    tk19 = [True] * dim
    tk23 = [True] * dim
    tk29 = [True] * dim
    tk1[0] = False
    # help dictionary d
    # d[a , b] = c  ==> if I want to find the smallest useful multiple of (30*pos)+a
    # on tkc, then I need the index given by the product of [(30*pos)+a][(30*pos)+b]
    # in general. If b < a, I need [(30*pos)+a][(30*(pos+1))+b]
    d = {}
    for x in offsets:
        for y in offsets:
            res = (x*y) % const
            if res in offsets:
                d[(x, res)] = y
    # another help dictionary: gives tkx calling tmptk[x]
    tmptk = {1:tk1, 7:tk7, 11:tk11, 13:tk13, 17:tk17, 19:tk19, 23:tk23, 29:tk29}
    pos, prime, lastadded, stop = 0, 0, 0, int(ceil(sqrt(N)))
    # inner functions definition
    def del_mult(tk, start, step):
        for k in xrange(start, len(tk), step):
            tk[k] = False
    # end of inner functions definition
    cpos = const * pos
    while prime < stop:
        # 30k + 7
        if tk7[pos]:
            prime = cpos + 7
            p.append(prime)
            lastadded = 7
            for off in offsets:
                tmp = d[(7, off)]
                start = (pos + prime) if off == 7 else (prime * (const * (pos + 1 if tmp < 7 else 0) + tmp) )//const
                del_mult(tmptk[off], start, prime)
        # 30k + 11
        if tk11[pos]:
            prime = cpos + 11
            p.append(prime)
            lastadded = 11
            for off in offsets:
                tmp = d[(11, off)]
                start = (pos + prime) if off == 11 else (prime * (const * (pos + 1 if tmp < 11 else 0) + tmp) )//const
                del_mult(tmptk[off], start, prime)
        # 30k + 13
        if tk13[pos]:
            prime = cpos + 13
            p.append(prime)
            lastadded = 13
            for off in offsets:
                tmp = d[(13, off)]
                start = (pos + prime) if off == 13 else (prime * (const * (pos + 1 if tmp < 13 else 0) + tmp) )//const
                del_mult(tmptk[off], start, prime)
        # 30k + 17
        if tk17[pos]:
            prime = cpos + 17
            p.append(prime)
            lastadded = 17
            for off in offsets:
                tmp = d[(17, off)]
                start = (pos + prime) if off == 17 else (prime * (const * (pos + 1 if tmp < 17 else 0) + tmp) )//const
                del_mult(tmptk[off], start, prime)
        # 30k + 19
        if tk19[pos]:
            prime = cpos + 19
            p.append(prime)
            lastadded = 19
            for off in offsets:
                tmp = d[(19, off)]
                start = (pos + prime) if off == 19 else (prime * (const * (pos + 1 if tmp < 19 else 0) + tmp) )//const
                del_mult(tmptk[off], start, prime)
        # 30k + 23
        if tk23[pos]:
            prime = cpos + 23
            p.append(prime)
            lastadded = 23
            for off in offsets:
                tmp = d[(23, off)]
                start = (pos + prime) if off == 23 else (prime * (const * (pos + 1 if tmp < 23 else 0) + tmp) )//const
                del_mult(tmptk[off], start, prime)
        # 30k + 29
        if tk29[pos]:
            prime = cpos + 29
            p.append(prime)
            lastadded = 29
            for off in offsets:
                tmp = d[(29, off)]
                start = (pos + prime) if off == 29 else (prime * (const * (pos + 1 if tmp < 29 else 0) + tmp) )//const
                del_mult(tmptk[off], start, prime)
        # now we go back to top tk1, so we need to increase pos by 1
        pos += 1
        cpos = const * pos
        # 30k + 1
        if tk1[pos]:
            prime = cpos + 1
            p.append(prime)
            lastadded = 1
            for off in offsets:
                tmp = d[(1, off)]
                start = (pos + prime) if off == 1 else (prime * (const * pos + tmp) )//const
                del_mult(tmptk[off], start, prime)
    # time to add remaining primes
    # if lastadded == 1, remove last element and start adding them from tk1
    # this way we don't need an "if" within the last while
    if lastadded == 1:
        p.pop()
    # now complete for every other possible prime
    while pos < len(tk1):
        cpos = const * pos
        if tk1[pos]: p.append(cpos + 1)
        if tk7[pos]: p.append(cpos + 7)
        if tk11[pos]: p.append(cpos + 11)
        if tk13[pos]: p.append(cpos + 13)
        if tk17[pos]: p.append(cpos + 17)
        if tk19[pos]: p.append(cpos + 19)
        if tk23[pos]: p.append(cpos + 23)
        if tk29[pos]: p.append(cpos + 29)
        pos += 1
    # remove exceeding if present
    pos = len(p) - 1
    while p[pos] > N:
        pos -= 1
    if pos < len(p) - 1:
        del p[pos+1:]
    # return p list
    return p

def sieveOfEratosthenes(n):
    """sieveOfEratosthenes(n): return the list of the primes < n."""
    # Code from: <dickinsm@gmail.com>, Nov 30 2006
    # http://groups.google.com/group/comp.lang.python/msg/f1f10ced88c68c2d
    if n <= 2:
        return []
    sieve = range(3, n, 2)
    top = len(sieve)
    for si in sieve:
        if si:
            bottom = (si*si - 3) // 2
            if bottom >= top:
                break
            sieve[bottom::si] = [0] * -((bottom - top) // si)
    return [2] + [el for el in sieve if el]

def sieveOfAtkin(end):
    """sieveOfAtkin(end): return a list of all the prime numbers <end
    using the Sieve of Atkin."""
    # Code by Steve Krenzel, <Sgk284@gmail.com>, improved
    # Code: https://web.archive.org/web/20080324064651/http://krenzel.info/?p=83
    # Info: http://en.wikipedia.org/wiki/Sieve_of_Atkin
    assert end > 0
    lng = ((end-1) // 2)
    sieve = [False] * (lng + 1)

    x_max, x2, xd = int(sqrt((end-1)/4.0)), 0, 4
    for xd in xrange(4, 8*x_max + 2, 8):
        x2 += xd
        y_max = int(sqrt(end-x2))
        n, n_diff = x2 + y_max*y_max, (y_max << 1) - 1
        if not (n & 1):
            n -= n_diff
            n_diff -= 2
        for d in xrange((n_diff - 1) << 1, -1, -8):
            m = n % 12
            if m == 1 or m == 5:
                m = n >> 1
                sieve[m] = not sieve[m]
            n -= d

    x_max, x2, xd = int(sqrt((end-1) / 3.0)), 0, 3
    for xd in xrange(3, 6 * x_max + 2, 6):
        x2 += xd
        y_max = int(sqrt(end-x2))
        n, n_diff = x2 + y_max*y_max, (y_max << 1) - 1
        if not(n & 1):
            n -= n_diff
            n_diff -= 2
        for d in xrange((n_diff - 1) << 1, -1, -8):
            if n % 12 == 7:
                m = n >> 1
                sieve[m] = not sieve[m]
            n -= d

    x_max, y_min, x2, xd = int((2 + sqrt(4-8*(1-end)))/4), -1, 0, 3
    for x in xrange(1, x_max + 1):
        x2 += xd
        xd += 6
        if x2 >= end: y_min = (((int(ceil(sqrt(x2 - end))) - 1) << 1) - 2) << 1
        n, n_diff = ((x*x + x) << 1) - 1, (((x-1) << 1) - 2) << 1
        for d in xrange(n_diff, y_min, -8):
            if n % 12 == 11:
                m = n >> 1
                sieve[m] = not sieve[m]
            n += d

    primes = [2, 3]
    if end <= 3:
        return primes[:max(0,end-2)]

    for n in xrange(5 >> 1, (int(sqrt(end))+1) >> 1):
        if sieve[n]:
            primes.append((n << 1) + 1)
            aux = (n << 1) + 1
            aux *= aux
            for k in xrange(aux, end, 2 * aux):
                sieve[k >> 1] = False

    s  = int(sqrt(end)) + 1
    if s  % 2 == 0:
        s += 1
    primes.extend([i for i in xrange(s, end, 2) if sieve[i >> 1]])

    return primes

def ambi_sieve_plain(n):
    s = range(3, n, 2)
    for m in xrange(3, int(n**0.5)+1, 2): 
        if s[(m-3)/2]: 
            for t in xrange((m*m-3)/2,(n>>1)-1,m):
                s[t]=0
    return [2]+[t for t in s if t>0]

def sundaram3(max_n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/2073279#2073279
    numbers = range(3, max_n+1, 2)
    half = (max_n)//2
    initial = 4

    for step in xrange(3, max_n+1, 2):
        for i in xrange(initial, half, step):
            numbers[i-1] = 0
        initial += 2*(step+1)

        if initial > half:
            return [2] + filter(None, numbers)

################################################################################
# Using Numpy:
def ambi_sieve(n):
    # http://tommih.blogspot.com/2009/04/fast-prime-number-generator.html
    s = np.arange(3, n, 2)
    for m in xrange(3, int(n ** 0.5)+1, 2): 
        if s[(m-3)/2]: 
            s[(m*m-3)/2::m]=0
    return np.r_[2, s[s>0]]

def primesfrom3to(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Returns a array of primes, p < n """
    assert n>=2
    sieve = np.ones(n/2, dtype=np.bool)
    for i in xrange(3,int(n**0.5)+1,2):
        if sieve[i/2]:
            sieve[i*i/2::i] = False
    return np.r_[2, 2*np.nonzero(sieve)[0][1::]+1]    

def primesfrom2to(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Input n>=6, Returns a array of primes, 2 <= p < n """
    sieve = np.ones(n/3 + (n%6==2), dtype=np.bool)
    sieve[0] = False
    for i in xrange(int(n**0.5)/3+1):
        if sieve[i]:
            k=3*i+1|1
            sieve[      ((k*k)/3)      ::2*k] = False
            sieve[(k*k+4*k-2*k*(i&1))/3::2*k] = False
    return np.r_[2,3,((3*np.nonzero(sieve)[0]+1)|1)]

if __name__=='__main__':
    import itertools
    import sys

    def test(f1,f2,num):
        print('Testing {f1} and {f2} return same results'.format(
            f1=f1.func_name,
            f2=f2.func_name))
        if not all([a==b for a,b in itertools.izip_longest(f1(num),f2(num))]):
            sys.exit("Error: %s(%s) != %s(%s)"%(f1.func_name,num,f2.func_name,num))

    n=1000000
    test(sieveOfAtkin,sieveOfEratosthenes,n)
    test(sieveOfAtkin,ambi_sieve,n)
    test(sieveOfAtkin,ambi_sieve_plain,n) 
    test(sieveOfAtkin,sundaram3,n)
    test(sieveOfAtkin,sieve_wheel_30,n)
    test(sieveOfAtkin,primesfrom3to,n)
    test(sieveOfAtkin,primesfrom2to,n)
    test(sieveOfAtkin,rwh_primes,n)
    test(sieveOfAtkin,rwh_primes1,n)         
    test(sieveOfAtkin,rwh_primes2,n)

运行脚本测试,确保所有实现都给出相同的结果。


对于最快的代码,numpy解决方案是最好的。不过,出于纯粹的学术原因,我发布了我的纯python版本,它比上面发布的食谱版本快不到50%。由于我将整个列表放在内存中,所以需要足够的空间来容纳所有内容,但它的可伸缩性似乎相当好。

def daniel_sieve_2(maxNumber):
    """
    Given a number, returns all numbers less than or equal to
    that number which are prime.
    """
    allNumbers = range(3, maxNumber+1, 2)
    for mIndex, number in enumerate(xrange(3, maxNumber+1, 2)):
        if allNumbers[mIndex] == 0:
            continue
        # now set all multiples to 0
        for index in xrange(mIndex+number, (maxNumber-3)/2+1, number):
            allNumbers[index] = 0
    return [2] + filter(lambda n: n!=0, allNumbers)

结果是:

>>>mine = timeit.Timer("daniel_sieve_2(1000000)",
...                    "from sieves import daniel_sieve_2")
>>>prev = timeit.Timer("get_primes_erat(1000000)",
...                    "from sieves import get_primes_erat")
>>>print "Mine: {0:0.4f} ms".format(min(mine.repeat(3, 1))*1000)
Mine: 428.9446 ms
>>>print "Previous Best {0:0.4f} ms".format(min(prev.repeat(3, 1))*1000)
Previous Best 621.3581 ms

对于足够大的N,真正最快的解决方案是下载一个预先计算的质数列表,将其存储为元组,并执行如下操作:

for pos,i in enumerate(primes):
    if i > N:
        print primes[:pos]

如果只有N >个质数[-1],则计算更多的质数并将新列表保存在代码中,以便下次同样快。

要跳出思维定势。


使用Sundaram的Sieve,我想我打破了pure-Python的记录:

def sundaram3(max_n):
    numbers = range(3, max_n+1, 2)
    half = (max_n)//2
    initial = 4

    for step in xrange(3, max_n+1, 2):
        for i in xrange(initial, half, step):
            numbers[i-1] = 0
        initial += 2*(step+1)

        if initial > half:
            return [2] + filter(None, numbers)

Comparasion:

C:\USERS>python -m timeit -n10 -s "import get_primes" "get_primes.get_primes_erat(1000000)"
10 loops, best of 3: 710 msec per loop

C:\USERS>python -m timeit -n10 -s "import get_primes" "get_primes.daniel_sieve_2(1000000)"
10 loops, best of 3: 435 msec per loop

C:\USERS>python -m timeit -n10 -s "import get_primes" "get_primes.sundaram3(1000000)"
10 loops, best of 3: 327 msec per loop

假设N < 9,080,191, Miller-Rabin's Primality检验的确定性实现

import sys

def miller_rabin_pass(a, n):
    d = n - 1
    s = 0
    while d % 2 == 0:
        d >>= 1
        s += 1

    a_to_power = pow(a, d, n)
    if a_to_power == 1:
        return True
    for i in range(s-1):
        if a_to_power == n - 1:
            return True
        a_to_power = (a_to_power * a_to_power) % n
    return a_to_power == n - 1


def miller_rabin(n):
    if n <= 2:
        return n == 2

    if n < 2_047:
        return miller_rabin_pass(2, n)

    return all(miller_rabin_pass(a, n) for a in (31, 73))


n = int(sys.argv[1])
primes = [2]
for p in range(3,n,2):
  if miller_rabin(p):
    primes.append(p)
print len(primes)

根据维基百科(http://en.wikipedia.org/wiki/Miller -Rabin_primality_test)上的文章,对于a = 37和73,测试N < 9,080,191足以判断N是否为合数。

我从原始米勒-拉宾测试的概率实现中改编了源代码:https://www.literateprograms.org/miller-rabin_primality_test__python_.html


如果你可以控制N,列出所有质数的最快方法就是预先计算它们。认真对待。预计算是一种被忽视的优化方法。


下面是我在Python中通常用来生成质数的代码:

$ python -mtimeit -s'import sieve' 'sieve.sieve(1000000)' 
10 loops, best of 3: 445 msec per loop
$ cat sieve.py
from math import sqrt

def sieve(size):
 prime=[True]*size
 rng=xrange
 limit=int(sqrt(size))

 for i in rng(3,limit+1,+2):
  if prime[i]:
   prime[i*i::+i]=[False]*len(prime[i*i::+i])

 return [2]+[i for i in rng(3,size,+2) if prime[i]]

if __name__=='__main__':
 print sieve(100)

它不能与这里发布的更快的解决方案竞争,但至少它是纯python。

谢谢你提出这个问题。我今天真的学到了很多东西。


我猜最快的方法是在代码中硬编码质数。

因此,为什么不编写一个缓慢的脚本,生成另一个源文件,其中包含所有数字,然后在运行实际程序时导入该源文件呢?

当然,只有当你在编译时知道N的上限时,这才有效,但这是(几乎)所有项目欧拉问题的情况。

 

PS:我可能错了,虽然解析源的硬连接质数比计算它们要慢,但据我所知,Python是从编译的.pyc文件运行的,所以在这种情况下,读取一个包含所有质数到N的二进制数组应该是非常快的。


更快、更内存的纯Python代码:

def primes(n):
    """ Returns  a list of primes < n """
    sieve = [True] * n
    for i in range(3,int(n**0.5)+1,2):
        if sieve[i]:
            sieve[i*i::2*i]=[False]*((n-i*i-1)//(2*i)+1)
    return [2] + [i for i in range(3,n,2) if sieve[i]]

或者从半筛子开始

def primes1(n):
    """ Returns  a list of primes < n """
    sieve = [True] * (n//2)
    for i in range(3,int(n**0.5)+1,2):
        if sieve[i//2]:
            sieve[i*i//2::i] = [False] * ((n-i*i-1)//(2*i)+1)
    return [2] + [2*i+1 for i in range(1,n//2) if sieve[i]]

更快,内存更明智的numpy代码:

import numpy
def primesfrom3to(n):
    """ Returns a array of primes, 3 <= p < n """
    sieve = numpy.ones(n//2, dtype=bool)
    for i in range(3,int(n**0.5)+1,2):
        if sieve[i//2]:
            sieve[i*i//2::i] = False
    return 2*numpy.nonzero(sieve)[0][1::]+1

从三分之一筛子开始的一种更快的变化:

import numpy
def primesfrom2to(n):
    """ Input n>=6, Returns a array of primes, 2 <= p < n """
    sieve = numpy.ones(n//3 + (n%6==2), dtype=bool)
    for i in range(1,int(n**0.5)//3+1):
        if sieve[i]:
            k=3*i+1|1
            sieve[       k*k//3     ::2*k] = False
            sieve[k*(k-2*(i&1)+4)//3::2*k] = False
    return numpy.r_[2,3,((3*numpy.nonzero(sieve)[0][1:]+1)|1)]

上面代码的纯python版本(很难编码)将是:

def primes2(n):
    """ Input n>=6, Returns a list of primes, 2 <= p < n """
    n, correction = n-n%6+6, 2-(n%6>1)
    sieve = [True] * (n//3)
    for i in range(1,int(n**0.5)//3+1):
      if sieve[i]:
        k=3*i+1|1
        sieve[      k*k//3      ::2*k] = [False] * ((n//6-k*k//6-1)//k+1)
        sieve[k*(k-2*(i&1)+4)//3::2*k] = [False] * ((n//6-k*(k-2*(i&1)+4)//6-1)//k+1)
    return [2,3] + [3*i+1|1 for i in range(1,n//3-correction) if sieve[i]]

不幸的是,pure-python不采用更简单和更快的numpy方式进行赋值,并且在循环中调用len(),如[False]*len(sieve[((k*k)//3)::2*k])太慢。所以我不得不即兴修正输入(避免更多的数学运算),并做一些极端的(痛苦的)数学魔术。

我个人认为numpy(它被广泛使用)不是Python标准库的一部分是很遗憾的,而且Python开发人员似乎完全忽略了语法和速度方面的改进。


很抱歉打扰,但erat2()在算法中有一个严重的缺陷。

在搜索下一个合成时,我们只需要测试奇数。 Q p都是奇数;那么q+p是偶数,不需要检验,但q+2*p总是奇数。这消除了while循环条件中的“if even”测试,并节省了大约30%的运行时。

当我们在它:而不是优雅的'D.pop(q,None)'获取和删除方法,使用'if q in D: p=D[q],del D[q]',这是两倍的速度!至少在我的机器上(P3-1Ghz)。 所以我建议这个聪明算法的实现:

def erat3( ):
    from itertools import islice, count

    # q is the running integer that's checked for primeness.
    # yield 2 and no other even number thereafter
    yield 2
    D = {}
    # no need to mark D[4] as we will test odd numbers only
    for q in islice(count(3),0,None,2):
        if q in D:                  #  is composite
            p = D[q]
            del D[q]
            # q is composite. p=D[q] is the first prime that
            # divides it. Since we've reached q, we no longer
            # need it in the map, but we'll mark the next
            # multiple of its witnesses to prepare for larger
            # numbers.
            x = q + p+p        # next odd(!) multiple
            while x in D:      # skip composites
                x += p+p
            D[x] = p
        else:                  # is prime
            # q is a new prime.
            # Yield it and mark its first multiple that isn't
            # already marked in previous iterations.
            D[q*q] = q
            yield q

使用Numpy实现的半筛子略有不同:

http://rebrained.com/?p=458

import math
import numpy
def prime6(upto):
    primes=numpy.arange(3,upto+1,2)
    isprime=numpy.ones((upto-1)/2,dtype=bool)
    for factor in primes[:int(math.sqrt(upto))]:
        if isprime[(factor-2)/2]: isprime[(factor*3-2)/2:(upto-1)/2:factor]=0
    return numpy.insert(primes[isprime],0,2)

有人能把这个和其他时间比较一下吗?在我的机器上,它似乎与其他Numpy半筛相当。


到目前为止,我尝试过的最快的方法是基于Python烹饪书erat2函数:

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:
            x = q + 2*p
            while x in D:
                x += 2*p
            D[x] = p

关于加速的解释,请看下面的答案。


第一次使用python,所以我在这里使用的一些方法可能看起来有点麻烦。我只是直接将我的c++代码转换为python,这就是我所拥有的(尽管在python中有点慢)

#!/usr/bin/env python
import time

def GetPrimes(n):

    Sieve = [1 for x in xrange(n)]

    Done = False
    w = 3

    while not Done:

        for q in xrange (3, n, 2):
            Prod = w*q
            if Prod < n:
                Sieve[Prod] = 0
            else:
                break

        if w > (n/2):
            Done = True
        w += 2

    return Sieve



start = time.clock()

d = 10000000
Primes = GetPrimes(d)

count = 1 #This is for 2

for x in xrange (3, d, 2):
    if Primes[x]:
        count+=1

elapsed = (time.clock() - start)
print "\nFound", count, "primes in", elapsed, "seconds!\n"

pythonw Primes.py 在12.799119秒内找到664579个质数!

#!/usr/bin/env python
import time

def GetPrimes2(n):

    Sieve = [1 for x in xrange(n)]

    for q in xrange (3, n, 2):
        k = q
        for y in xrange(k*3, n, k*2):
            Sieve[y] = 0

    return Sieve



start = time.clock()

d = 10000000
Primes = GetPrimes2(d)

count = 1 #This is for 2

for x in xrange (3, d, 2):
    if Primes[x]:
        count+=1

elapsed = (time.clock() - start)
print "\nFound", count, "primes in", elapsed, "seconds!\n"

pythonw Primes2.py 在10.230172秒内找到664579个质数!

#!/usr/bin/env python
import time

def GetPrimes3(n):

    Sieve = [1 for x in xrange(n)]

    for q in xrange (3, n, 2):
        k = q
        for y in xrange(k*k, n, k << 1):
            Sieve[y] = 0

    return Sieve



start = time.clock()

d = 10000000
Primes = GetPrimes3(d)

count = 1 #This is for 2

for x in xrange (3, d, 2):
    if Primes[x]:
        count+=1

elapsed = (time.clock() - start)
print "\nFound", count, "primes in", elapsed, "seconds!\n"

python Primes2.py 在7.113776秒内找到664579个质数!


我可能迟到了,但必须为此添加自己的代码。它使用大约n/2的空间,因为我们不需要存储偶数,我还使用bitarray python模块,进一步大幅减少内存消耗,并允许计算所有高达1,000,000,000的质数

from bitarray import bitarray
def primes_to(n):
    size = n//2
    sieve = bitarray(size)
    sieve.setall(1)
    limit = int(n**0.5)
    for i in range(1,limit):
        if sieve[i]:
            val = 2*i+1
            sieve[(i+i*val)::val] = 0
    return [2] + [2*i+1 for i, v in enumerate(sieve) if v and i > 0]

python -m timeit -n10 -s "import euler" "euler.primes_to(1000000000)"
10 loops, best of 3: 46.5 sec per loop

这是在64bit 2.4GHZ MAC OSX 10.8.3上运行的


我知道比赛已经结束好几年了。...

尽管如此,这是我对纯python质数筛子的建议,基于在向前处理筛子时使用适当的步骤省略2、3和5的倍数。尽管如此,在N<10^9时,它实际上比@Robert William Hanks的优解rwh_primes2和rwh_primes1要慢。通过使用大于1.5* 10^8的ctypes.c_ushort筛分数组,可以在某种程度上适应内存限制。

10^6

$ python -mtimeit -s"import primeSieveSpeedComp" "primeSieveSpeedComp. primesieveseq (1000000)" 10个循环,最好的3:46.7毫秒每循环

import primeSieveSpeedComp (primeSieveSpeedComp) “primeSieveSpeedComp.rwh_primes1(1000000)”10个循环,最好的3:43.2 每回路Msec $ python -m timeit -s"import primeSieveSpeedComp" “primeSieveSpeedComp.rwh_primes2(1000000)”10圈,最好成绩是3:34.5 每回路Msec

10^7

$ python -mtimeit -s"import primeSieveSpeedComp" "primeSieveSpeedComp. primesieveseq (10000000)" 10个循环,最好是3:530毫秒每循环

import primeSieveSpeedComp (primeSieveSpeedComp) “primeSieveSpeedComp.rwh_primes1(10000000)”10圈,3:494的最佳成绩 每回路Msec $ python -m timeit -s"import primeSieveSpeedComp" “primeSieveSpeedComp.rwh_primes2(10000000)”10圈,最好的3:375 每回路Msec

10^8

$ python -mtimeit -s"import primeSieveSpeedComp" "primeSieveSpeedComp. primesieveseq (100000000)" 10圈,最好的3:5.55秒每圈

import primeSieveSpeedComp (primeSieveSpeedComp) “primeSieveSpeedComp.rwh_primes1(100000000)”10圈,最好成绩是3:5.33 秒/循环 $ python -m timeit -s"import primeSieveSpeedComp" “primeSieveSpeedComp.rwh_primes2(100000000)”10圈,最好的3:3.95 秒/循环

10^9

$ python -mtimeit -s"import primeSieveSpeedComp" "primeSieveSpeedComp. primesieveseq (1000000000)" 10圈,最好的3圈:每圈61.2秒

$ python -mtimeit -n 3 -s"import primeSieveSpeedComp" “primeSieveSpeedComp.rwh_primes1(1000000000)”3圈,最好的3:97.8 秒/循环 $ python -m timeit -s"import primeSieveSpeedComp" “primeSieveSpeedComp.rwh_primes2(1000000000)”10个循环,3个最好: 每循环41.9秒

您可以将下面的代码复制到ubuntu primeSieveSpeedComp中以查看此测试。

def primeSieveSeq(MAX_Int):
    if MAX_Int > 5*10**8:
        import ctypes
        int16Array = ctypes.c_ushort * (MAX_Int >> 1)
        sieve = int16Array()
        #print 'uses ctypes "unsigned short int Array"'
    else:
        sieve = (MAX_Int >> 1) * [False]
        #print 'uses python list() of long long int'
    if MAX_Int < 10**8:
        sieve[4::3] = [True]*((MAX_Int - 8)/6+1)
        sieve[12::5] = [True]*((MAX_Int - 24)/10+1)
    r = [2, 3, 5]
    n = 0
    for i in xrange(int(MAX_Int**0.5)/30+1):
        n += 3
        if not sieve[n]:
            n2 = (n << 1) + 1
            r.append(n2)
            n2q = (n2**2) >> 1
            sieve[n2q::n2] = [True]*(((MAX_Int >> 1) - n2q - 1) / n2 + 1)
        n += 2
        if not sieve[n]:
            n2 = (n << 1) + 1
            r.append(n2)
            n2q = (n2**2) >> 1
            sieve[n2q::n2] = [True]*(((MAX_Int >> 1) - n2q - 1) / n2 + 1)
        n += 1
        if not sieve[n]:
            n2 = (n << 1) + 1
            r.append(n2)
            n2q = (n2**2) >> 1
            sieve[n2q::n2] = [True]*(((MAX_Int >> 1) - n2q - 1) / n2 + 1)
        n += 2
        if not sieve[n]:
            n2 = (n << 1) + 1
            r.append(n2)
            n2q = (n2**2) >> 1
            sieve[n2q::n2] = [True]*(((MAX_Int >> 1) - n2q - 1) / n2 + 1)
        n += 1
        if not sieve[n]:
            n2 = (n << 1) + 1
            r.append(n2)
            n2q = (n2**2) >> 1
            sieve[n2q::n2] = [True]*(((MAX_Int >> 1) - n2q - 1) / n2 + 1)
        n += 2
        if not sieve[n]:
            n2 = (n << 1) + 1
            r.append(n2)
            n2q = (n2**2) >> 1
            sieve[n2q::n2] = [True]*(((MAX_Int >> 1) - n2q - 1) / n2 + 1)
        n += 3
        if not sieve[n]:
            n2 = (n << 1) + 1
            r.append(n2)
            n2q = (n2**2) >> 1
            sieve[n2q::n2] = [True]*(((MAX_Int >> 1) - n2q - 1) / n2 + 1)
        n += 1
        if not sieve[n]:
            n2 = (n << 1) + 1
            r.append(n2)
            n2q = (n2**2) >> 1
            sieve[n2q::n2] = [True]*(((MAX_Int >> 1) - n2q - 1) / n2 + 1)
    if MAX_Int < 10**8:
        return [2, 3, 5]+[(p << 1) + 1 for p in [n for n in xrange(3, MAX_Int >> 1) if not sieve[n]]]
    n = n >> 1
    try:
        for i in xrange((MAX_Int-2*n)/30 + 1):
            n += 3
            if not sieve[n]:
                r.append((n << 1) + 1)
            n += 2
            if not sieve[n]:
                r.append((n << 1) + 1)
            n += 1
            if not sieve[n]:
                r.append((n << 1) + 1)
            n += 2
            if not sieve[n]:
                r.append((n << 1) + 1)
            n += 1
            if not sieve[n]:
                r.append((n << 1) + 1)
            n += 2
            if not sieve[n]:
                r.append((n << 1) + 1)
            n += 3
            if not sieve[n]:
                r.append((n << 1) + 1)
            n += 1
            if not sieve[n]:
                r.append((n << 1) + 1)
    except:
        pass
    return r

这是使用存储列表查找质数的一种优雅而简单的解决方案。从4个变量开始,你只需要测试除数的奇数质数,你只需要测试你要测试的质数的一半(测试9,11,13是否能整除17没有意义)。它将先前存储的质数作为除数进行测试。

    # Program to calculate Primes
 primes = [1,3,5,7]
for n in range(9,100000,2):
    for x in range(1,(len(primes)/2)):
        if n % primes[x] == 0:
            break
    else:
        primes.append(n)
print primes

随着时间的推移,我收集了几个质数筛子。我电脑上最快的是这样的:

from time import time
# 175 ms for all the primes up to the value 10**6
def primes_sieve(limit):
    a = [True] * limit
    a[0] = a[1] = False
    #a[2] = True
    for n in xrange(4, limit, 2):
        a[n] = False
    root_limit = int(limit**.5)+1
    for i in xrange(3,root_limit):
        if a[i]:
            for n in xrange(i*i, limit, 2*i):
                a[n] = False
    return a

LIMIT = 10**6
s=time()
primes = primes_sieve(LIMIT)
print time()-s

这是你和别人比较的方式。

# You have to list primes upto n
nums = xrange(2, n)
for i in range(2, 10):
    nums = filter(lambda s: s==i or s%i, nums)
print nums

这么简单……


我对这个问题反应迟钝,但这似乎是一个有趣的练习。我使用numpy,这可能是作弊,我怀疑这个方法是最快的,但它应该是清楚的。它筛选一个仅引用其下标的布尔数组,并从所有True值的下标中引出质数。不需要取模。

import numpy as np
def ajs_primes3a(upto):
    mat = np.ones((upto), dtype=bool)
    mat[0] = False
    mat[1] = False
    mat[4::2] = False
    for idx in range(3, int(upto ** 0.5)+1, 2):
        mat[idx*2::idx] = False
    return np.where(mat == True)[0]

如果你不想重新发明轮子,你可以安装符号数学库symphony(是的,它与Python 3兼容)

pip install sympy

然后使用质数函数

from sympy import sieve
primes = list(sieve.primerange(1, 10**6))

编写自己的质数查找代码很有指导意义,但手边有一个快速可靠的库也很有用。我围绕c++库primesieve编写了一个包装器,命名为primesieve-python

试试pip install primesieve吧

import primesieve
primes = primesieve.generate_primes(10**8)

我很好奇对比一下速度。


如果你接受itertools,但不接受numpy,这里有一个针对Python 3的rwh_primes2的改编版本,它在我的机器上运行速度大约是原来的两倍。唯一的实质性变化是使用bytearray而不是列表来表示布尔值,并使用压缩而不是列表推导来构建最终列表。(如果可以的话,我会把这句话作为moarningsun之类的评论。)

import itertools
izip = itertools.zip_longest
chain = itertools.chain.from_iterable
compress = itertools.compress
def rwh_primes2_python3(n):
    """ Input n>=6, Returns a list of primes, 2 <= p < n """
    zero = bytearray([False])
    size = n//3 + (n % 6 == 2)
    sieve = bytearray([True]) * size
    sieve[0] = False
    for i in range(int(n**0.5)//3+1):
      if sieve[i]:
        k=3*i+1|1
        start = (k*k+4*k-2*k*(i&1))//3
        sieve[(k*k)//3::2*k]=zero*((size - (k*k)//3 - 1) // (2 * k) + 1)
        sieve[  start ::2*k]=zero*((size -   start  - 1) // (2 * k) + 1)
    ans = [2,3]
    poss = chain(izip(*[range(i, n, 6) for i in (1,5)]))
    ans.extend(compress(poss, sieve))
    return ans

比较:

>>> timeit.timeit('primes.rwh_primes2(10**6)', setup='import primes', number=1)
0.0652179726976101
>>> timeit.timeit('primes.rwh_primes2_python3(10**6)', setup='import primes', number=1)
0.03267321276325674

and

>>> timeit.timeit('primes.rwh_primes2(10**8)', setup='import primes', number=1)
6.394284538007014
>>> timeit.timeit('primes.rwh_primes2_python3(10**8)', setup='import primes', number=1)
3.833829450302801

这些都是经过编写和测试的。所以没有必要重新发明轮子。

python -m timeit -r10 -s"from sympy import sieve" "primes = list(sieve.primerange(1, 10**6))"

打破了12.2秒的记录!

10 loops, best of 10: 12.2 msec per loop

如果这还不够快,你可以试试PyPy:

pypy -m timeit -r10 -s"from sympy import sieve" "primes = list(sieve.primerange(1, 10**6))"

结果是:

10 loops, best of 10: 2.03 msec per loop

得到247张赞成票的答案列出了15.9毫秒的最佳解决方案。 比较这个! !


我测试了一些unutbu的功能,我用饥饿的百万数字计算它

获胜者是使用numpy库的函数,

注意:做一个内存利用率测试也很有趣:)

示例代码

完整的代码在我的github存储库

#!/usr/bin/env python

import lib
import timeit
import sys
import math
import datetime

import prettyplotlib as ppl
import numpy as np

import matplotlib.pyplot as plt
from prettyplotlib import brewer2mpl

primenumbers_gen = [
    'sieveOfEratosthenes',
    'ambi_sieve',
    'ambi_sieve_plain',
    'sundaram3',
    'sieve_wheel_30',
    'primesfrom3to',
    'primesfrom2to',
    'rwh_primes',
    'rwh_primes1',
    'rwh_primes2',
]

def human_format(num):
    # https://stackoverflow.com/questions/579310/formatting-long-numbers-as-strings-in-python?answertab=active#tab-top
    magnitude = 0
    while abs(num) >= 1000:
        magnitude += 1
        num /= 1000.0
    # add more suffixes if you need them
    return '%.2f%s' % (num, ['', 'K', 'M', 'G', 'T', 'P'][magnitude])


if __name__=='__main__':

    # Vars
    n = 10000000 # number itereration generator
    nbcol = 5 # For decompose prime number generator
    nb_benchloop = 3 # Eliminate false positive value during the test (bench average time)
    datetimeformat = '%Y-%m-%d %H:%M:%S.%f'
    config = 'from __main__ import n; import lib'
    primenumbers_gen = {
        'sieveOfEratosthenes': {'color': 'b'},
        'ambi_sieve': {'color': 'b'},
        'ambi_sieve_plain': {'color': 'b'},
         'sundaram3': {'color': 'b'},
        'sieve_wheel_30': {'color': 'b'},
# # #        'primesfrom2to': {'color': 'b'},
        'primesfrom3to': {'color': 'b'},
        # 'rwh_primes': {'color': 'b'},
        # 'rwh_primes1': {'color': 'b'},
        'rwh_primes2': {'color': 'b'},
    }


    # Get n in command line
    if len(sys.argv)>1:
        n = int(sys.argv[1])

    step = int(math.ceil(n / float(nbcol)))
    nbs = np.array([i * step for i in range(1, int(nbcol) + 1)])
    set2 = brewer2mpl.get_map('Paired', 'qualitative', 12).mpl_colors

    print datetime.datetime.now().strftime(datetimeformat)
    print("Compute prime number to %(n)s" % locals())
    print("")

    results = dict()
    for pgen in primenumbers_gen:
        results[pgen] = dict()
        benchtimes = list()
        for n in nbs:
            t = timeit.Timer("lib.%(pgen)s(n)" % locals(), setup=config)
            execute_times = t.repeat(repeat=nb_benchloop,number=1)
            benchtime = np.mean(execute_times)
            benchtimes.append(benchtime)
        results[pgen] = {'benchtimes':np.array(benchtimes)}

fig, ax = plt.subplots(1)
plt.ylabel('Computation time (in second)')
plt.xlabel('Numbers computed')
i = 0
for pgen in primenumbers_gen:

    bench = results[pgen]['benchtimes']
    avgs = np.divide(bench,nbs)
    avg = np.average(bench, weights=nbs)

    # Compute linear regression
    A = np.vstack([nbs, np.ones(len(nbs))]).T
    a, b = np.linalg.lstsq(A, nbs*avgs)[0]

    # Plot
    i += 1
    #label="%(pgen)s" % locals()
    #ppl.plot(nbs, nbs*avgs, label=label, lw=1, linestyle='--', color=set2[i % 12])
    label="%(pgen)s avg" % locals()
    ppl.plot(nbs, a * nbs + b, label=label, lw=2, color=set2[i % 12])
print datetime.datetime.now().strftime(datetimeformat)

ppl.legend(ax, loc='upper left', ncol=4)

# Change x axis label
ax.get_xaxis().get_major_formatter().set_scientific(False)
fig.canvas.draw()
labels = [human_format(int(item.get_text())) for item in ax.get_xticklabels()]

ax.set_xticklabels(labels)
ax = plt.gca()

plt.show()

对于Python 3

def rwh_primes2(n):
    correction = (n%6>1)
    n = {0:n,1:n-1,2:n+4,3:n+3,4:n+2,5:n+1}[n%6]
    sieve = [True] * (n//3)
    sieve[0] = False
    for i in range(int(n**0.5)//3+1):
      if sieve[i]:
        k=3*i+1|1
        sieve[      ((k*k)//3)      ::2*k]=[False]*((n//6-(k*k)//6-1)//k+1)
        sieve[(k*k+4*k-2*k*(i&1))//3::2*k]=[False]*((n//6-(k*k+4*k-2*k*(i&1))//6-1)//k+1)
    return [2,3] + [3*i+1|1 for i in range(1,n//3-correction) if sieve[i]]

这里是最快的函数之一的两个更新版本(纯Python 3.6),

from itertools import compress

def rwh_primes1v1(n):
    """ Returns  a list of primes < n for n > 2 """
    sieve = bytearray([True]) * (n//2)
    for i in range(3,int(n**0.5)+1,2):
        if sieve[i//2]:
            sieve[i*i//2::i] = bytearray((n-i*i-1)//(2*i)+1)
    return [2,*compress(range(3,n,2), sieve[1:])]

def rwh_primes1v2(n):
    """ Returns a list of primes < n for n > 2 """
    sieve = bytearray([True]) * (n//2+1)
    for i in range(1,int(n**0.5)//2+1):
        if sieve[i]:
            sieve[2*i*(i+1)::2*i+1] = bytearray((n//2-2*i*(i+1))//(2*i+1)+1)
    return [2,*compress(range(3,n,2), sieve[1:])]

下面是一个使用python的列表推导式生成质数的有趣技术(但不是最有效的):

noprimes = [j for i in range(2, 8) for j in range(i*2, 50, i)]
primes = [x for x in range(2, 50) if x not in noprimes]

下面是Eratosthenes的一个numpy版本,具有良好的复杂度(低于排序长度为n的数组)和向量化。与@unutbu相比,用46微秒就可以找到100万以下的所有质数。

import numpy as np 
def generate_primes(n):
    is_prime = np.ones(n+1,dtype=bool)
    is_prime[0:2] = False
    for i in range(int(n**0.5)+1):
        if is_prime[i]:
            is_prime[i**2::i]=False
    return np.where(is_prime)[0]

计时:

import time    
for i in range(2,10):
    timer =time.time()
    generate_primes(10**i)
    print('n = 10^',i,' time =', round(time.time()-timer,6))

>> n = 10^ 2  time = 5.6e-05
>> n = 10^ 3  time = 6.4e-05
>> n = 10^ 4  time = 0.000114
>> n = 10^ 5  time = 0.000593
>> n = 10^ 6  time = 0.00467
>> n = 10^ 7  time = 0.177758
>> n = 10^ 8  time = 1.701312
>> n = 10^ 9  time = 19.322478

在Pure Python中最快的质数筛分:

from itertools import compress

def half_sieve(n):
    """
    Returns a list of prime numbers less than `n`.
    """
    if n <= 2:
        return []
    sieve = bytearray([True]) * (n // 2)
    for i in range(3, int(n ** 0.5) + 1, 2):
        if sieve[i // 2]:
            sieve[i * i // 2::i] = bytearray((n - i * i - 1) // (2 * i) + 1)
    primes = list(compress(range(1, n, 2), sieve))
    primes[0] = 2
    return primes

我优化了埃拉托色尼筛子的速度和内存。

基准

from time import clock
import platform

def benchmark(iterations, limit):
    start = clock()
    for x in range(iterations):
        half_sieve(limit)
    end = clock() - start
    print(f'{end/iterations:.4f} seconds for primes < {limit}')

if __name__ == '__main__':
    print(platform.python_version())
    print(platform.platform())
    print(platform.processor())
    it = 10
    for pw in range(4, 9):
        benchmark(it, 10**pw)

输出

>>> 3.6.7
>>> Windows-10-10.0.17763-SP0
>>> Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
>>> 0.0003 seconds for primes < 10000
>>> 0.0021 seconds for primes < 100000
>>> 0.0204 seconds for primes < 1000000
>>> 0.2389 seconds for primes < 10000000
>>> 2.6702 seconds for primes < 100000000

我在这里找到了一个纯Python 2素数生成器,在Willy Good的评论中,它比rwh2_primes快。

def primes235(limit):
yield 2; yield 3; yield 5
if limit < 7: return
modPrms = [7,11,13,17,19,23,29,31]
gaps = [4,2,4,2,4,6,2,6,4,2,4,2,4,6,2,6] # 2 loops for overflow
ndxs = [0,0,0,0,1,1,2,2,2,2,3,3,4,4,4,4,5,5,5,5,5,5,6,6,7,7,7,7,7,7]
lmtbf = (limit + 23) // 30 * 8 - 1 # integral number of wheels rounded up
lmtsqrt = (int(limit ** 0.5) - 7)
lmtsqrt = lmtsqrt // 30 * 8 + ndxs[lmtsqrt % 30] # round down on the wheel
buf = [True] * (lmtbf + 1)
for i in xrange(lmtsqrt + 1):
    if buf[i]:
        ci = i & 7; p = 30 * (i >> 3) + modPrms[ci]
        s = p * p - 7; p8 = p << 3
        for j in range(8):
            c = s // 30 * 8 + ndxs[s % 30]
            buf[c::p8] = [False] * ((lmtbf - c) // p8 + 1)
            s += p * gaps[ci]; ci += 1
for i in xrange(lmtbf - 6 + (ndxs[(limit - 7) % 30])): # adjust for extras
    if buf[i]: yield (30 * (i >> 3) + modPrms[i & 7])

我的结果:

$ time ./prime_rwh2.py 1e8
5761455 primes found < 1e8

real    0m3.201s
user    0m2.609s
sys     0m0.578s
$ time ./prime_wheel.py 1e8
5761455 primes found < 1e8

real    0m2.710s
user    0m2.469s
sys     0m0.219s

...在我最近的中档笔记本电脑(i5 8265U 1.6GHz)上运行Ubuntu Win 10。

这是一个mod 30轮筛,跳过倍数2,3和5。对我来说,它在2.5e9左右的时候工作得很好,那时我的笔记本电脑开始用完8G内存,需要大量交换。

我喜欢对30取余,因为它只有8个余数不是2 3 5的倍数。这允许使用移位和“&”进行乘法,除法和mod,并应该允许将一个mod 30轮的结果打包到一个字节中。我把威利的代码变成了一个分段的mod 30轮筛,以消除大N的抖动,并张贴在这里。

还有一个更快的Javascript版本,它是分段的,并使用了一个mod 210轮(没有2,3,5或7的倍数)@GordonBGood与一个深入的解释,这对我很有用。


我发现的最简单的方法是:

primes = []
for n in range(low, high + 1):
    if all(n % i for i in primes):
        primes.append(n)

我已经更新了Python 3的大部分代码,并将其扔给perfplot(我的一个项目),看看哪个实际上是最快的。事实证明,对于较大的n,从{2,3}开始的质数是最好的:


代码重现情节:

import perfplot
from math import sqrt, ceil
import numpy as np
import sympy


def rwh_primes(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Returns  a list of primes < n """
    sieve = [True] * n
    for i in range(3, int(n ** 0.5) + 1, 2):
        if sieve[i]:
            sieve[i * i::2 * i] = [False] * ((n - i * i - 1) // (2 * i) + 1)
    return [2] + [i for i in range(3, n, 2) if sieve[i]]


def rwh_primes1(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Returns  a list of primes < n """
    sieve = [True] * (n // 2)
    for i in range(3, int(n ** 0.5) + 1, 2):
        if sieve[i // 2]:
            sieve[i * i // 2::i] = [False] * ((n - i * i - 1) // (2 * i) + 1)
    return [2] + [2 * i + 1 for i in range(1, n // 2) if sieve[i]]


def rwh_primes2(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """Input n>=6, Returns a list of primes, 2 <= p < n"""
    assert n >= 6
    correction = n % 6 > 1
    n = {0: n, 1: n - 1, 2: n + 4, 3: n + 3, 4: n + 2, 5: n + 1}[n % 6]
    sieve = [True] * (n // 3)
    sieve[0] = False
    for i in range(int(n ** 0.5) // 3 + 1):
        if sieve[i]:
            k = 3 * i + 1 | 1
            sieve[((k * k) // 3)::2 * k] = [False] * (
                (n // 6 - (k * k) // 6 - 1) // k + 1
            )
            sieve[(k * k + 4 * k - 2 * k * (i & 1)) // 3::2 * k] = [False] * (
                (n // 6 - (k * k + 4 * k - 2 * k * (i & 1)) // 6 - 1) // k + 1
            )
    return [2, 3] + [3 * i + 1 | 1 for i in range(1, n // 3 - correction) if sieve[i]]


def sieve_wheel_30(N):
    # http://zerovolt.com/?p=88
    """ Returns a list of primes <= N using wheel criterion 2*3*5 = 30

Copyright 2009 by zerovolt.com
This code is free for non-commercial purposes, in which case you can just leave this comment as a credit for my work.
If you need this code for commercial purposes, please contact me by sending an email to: info [at] zerovolt [dot] com."""
    __smallp = (
        2,
        3,
        5,
        7,
        11,
        13,
        17,
        19,
        23,
        29,
        31,
        37,
        41,
        43,
        47,
        53,
        59,
        61,
        67,
        71,
        73,
        79,
        83,
        89,
        97,
        101,
        103,
        107,
        109,
        113,
        127,
        131,
        137,
        139,
        149,
        151,
        157,
        163,
        167,
        173,
        179,
        181,
        191,
        193,
        197,
        199,
        211,
        223,
        227,
        229,
        233,
        239,
        241,
        251,
        257,
        263,
        269,
        271,
        277,
        281,
        283,
        293,
        307,
        311,
        313,
        317,
        331,
        337,
        347,
        349,
        353,
        359,
        367,
        373,
        379,
        383,
        389,
        397,
        401,
        409,
        419,
        421,
        431,
        433,
        439,
        443,
        449,
        457,
        461,
        463,
        467,
        479,
        487,
        491,
        499,
        503,
        509,
        521,
        523,
        541,
        547,
        557,
        563,
        569,
        571,
        577,
        587,
        593,
        599,
        601,
        607,
        613,
        617,
        619,
        631,
        641,
        643,
        647,
        653,
        659,
        661,
        673,
        677,
        683,
        691,
        701,
        709,
        719,
        727,
        733,
        739,
        743,
        751,
        757,
        761,
        769,
        773,
        787,
        797,
        809,
        811,
        821,
        823,
        827,
        829,
        839,
        853,
        857,
        859,
        863,
        877,
        881,
        883,
        887,
        907,
        911,
        919,
        929,
        937,
        941,
        947,
        953,
        967,
        971,
        977,
        983,
        991,
        997,
    )
    # wheel = (2, 3, 5)
    const = 30
    if N < 2:
        return []
    if N <= const:
        pos = 0
        while __smallp[pos] <= N:
            pos += 1
        return list(__smallp[:pos])
    # make the offsets list
    offsets = (7, 11, 13, 17, 19, 23, 29, 1)
    # prepare the list
    p = [2, 3, 5]
    dim = 2 + N // const
    tk1 = [True] * dim
    tk7 = [True] * dim
    tk11 = [True] * dim
    tk13 = [True] * dim
    tk17 = [True] * dim
    tk19 = [True] * dim
    tk23 = [True] * dim
    tk29 = [True] * dim
    tk1[0] = False
    # help dictionary d
    # d[a , b] = c  ==> if I want to find the smallest useful multiple of (30*pos)+a
    # on tkc, then I need the index given by the product of [(30*pos)+a][(30*pos)+b]
    # in general. If b < a, I need [(30*pos)+a][(30*(pos+1))+b]
    d = {}
    for x in offsets:
        for y in offsets:
            res = (x * y) % const
            if res in offsets:
                d[(x, res)] = y
    # another help dictionary: gives tkx calling tmptk[x]
    tmptk = {1: tk1, 7: tk7, 11: tk11, 13: tk13, 17: tk17, 19: tk19, 23: tk23, 29: tk29}
    pos, prime, lastadded, stop = 0, 0, 0, int(ceil(sqrt(N)))

    # inner functions definition
    def del_mult(tk, start, step):
        for k in range(start, len(tk), step):
            tk[k] = False

    # end of inner functions definition
    cpos = const * pos
    while prime < stop:
        # 30k + 7
        if tk7[pos]:
            prime = cpos + 7
            p.append(prime)
            lastadded = 7
            for off in offsets:
                tmp = d[(7, off)]
                start = (
                    (pos + prime)
                    if off == 7
                    else (prime * (const * (pos + 1 if tmp < 7 else 0) + tmp)) // const
                )
                del_mult(tmptk[off], start, prime)
        # 30k + 11
        if tk11[pos]:
            prime = cpos + 11
            p.append(prime)
            lastadded = 11
            for off in offsets:
                tmp = d[(11, off)]
                start = (
                    (pos + prime)
                    if off == 11
                    else (prime * (const * (pos + 1 if tmp < 11 else 0) + tmp)) // const
                )
                del_mult(tmptk[off], start, prime)
        # 30k + 13
        if tk13[pos]:
            prime = cpos + 13
            p.append(prime)
            lastadded = 13
            for off in offsets:
                tmp = d[(13, off)]
                start = (
                    (pos + prime)
                    if off == 13
                    else (prime * (const * (pos + 1 if tmp < 13 else 0) + tmp)) // const
                )
                del_mult(tmptk[off], start, prime)
        # 30k + 17
        if tk17[pos]:
            prime = cpos + 17
            p.append(prime)
            lastadded = 17
            for off in offsets:
                tmp = d[(17, off)]
                start = (
                    (pos + prime)
                    if off == 17
                    else (prime * (const * (pos + 1 if tmp < 17 else 0) + tmp)) // const
                )
                del_mult(tmptk[off], start, prime)
        # 30k + 19
        if tk19[pos]:
            prime = cpos + 19
            p.append(prime)
            lastadded = 19
            for off in offsets:
                tmp = d[(19, off)]
                start = (
                    (pos + prime)
                    if off == 19
                    else (prime * (const * (pos + 1 if tmp < 19 else 0) + tmp)) // const
                )
                del_mult(tmptk[off], start, prime)
        # 30k + 23
        if tk23[pos]:
            prime = cpos + 23
            p.append(prime)
            lastadded = 23
            for off in offsets:
                tmp = d[(23, off)]
                start = (
                    (pos + prime)
                    if off == 23
                    else (prime * (const * (pos + 1 if tmp < 23 else 0) + tmp)) // const
                )
                del_mult(tmptk[off], start, prime)
        # 30k + 29
        if tk29[pos]:
            prime = cpos + 29
            p.append(prime)
            lastadded = 29
            for off in offsets:
                tmp = d[(29, off)]
                start = (
                    (pos + prime)
                    if off == 29
                    else (prime * (const * (pos + 1 if tmp < 29 else 0) + tmp)) // const
                )
                del_mult(tmptk[off], start, prime)
        # now we go back to top tk1, so we need to increase pos by 1
        pos += 1
        cpos = const * pos
        # 30k + 1
        if tk1[pos]:
            prime = cpos + 1
            p.append(prime)
            lastadded = 1
            for off in offsets:
                tmp = d[(1, off)]
                start = (
                    (pos + prime)
                    if off == 1
                    else (prime * (const * pos + tmp)) // const
                )
                del_mult(tmptk[off], start, prime)
    # time to add remaining primes
    # if lastadded == 1, remove last element and start adding them from tk1
    # this way we don't need an "if" within the last while
    if lastadded == 1:
        p.pop()
    # now complete for every other possible prime
    while pos < len(tk1):
        cpos = const * pos
        if tk1[pos]:
            p.append(cpos + 1)
        if tk7[pos]:
            p.append(cpos + 7)
        if tk11[pos]:
            p.append(cpos + 11)
        if tk13[pos]:
            p.append(cpos + 13)
        if tk17[pos]:
            p.append(cpos + 17)
        if tk19[pos]:
            p.append(cpos + 19)
        if tk23[pos]:
            p.append(cpos + 23)
        if tk29[pos]:
            p.append(cpos + 29)
        pos += 1
    # remove exceeding if present
    pos = len(p) - 1
    while p[pos] > N:
        pos -= 1
    if pos < len(p) - 1:
        del p[pos + 1 :]
    # return p list
    return p


def sieve_of_eratosthenes(n):
    """sieveOfEratosthenes(n): return the list of the primes < n."""
    # Code from: <dickinsm@gmail.com>, Nov 30 2006
    # http://groups.google.com/group/comp.lang.python/msg/f1f10ced88c68c2d
    if n <= 2:
        return []
    sieve = list(range(3, n, 2))
    top = len(sieve)
    for si in sieve:
        if si:
            bottom = (si * si - 3) // 2
            if bottom >= top:
                break
            sieve[bottom::si] = [0] * -((bottom - top) // si)
    return [2] + [el for el in sieve if el]


def sieve_of_atkin(end):
    """return a list of all the prime numbers <end using the Sieve of Atkin."""
    # Code by Steve Krenzel, <Sgk284@gmail.com>, improved
    # Code: https://web.archive.org/web/20080324064651/http://krenzel.info/?p=83
    # Info: http://en.wikipedia.org/wiki/Sieve_of_Atkin
    assert end > 0
    lng = (end - 1) // 2
    sieve = [False] * (lng + 1)

    x_max, x2, xd = int(sqrt((end - 1) / 4.0)), 0, 4
    for xd in range(4, 8 * x_max + 2, 8):
        x2 += xd
        y_max = int(sqrt(end - x2))
        n, n_diff = x2 + y_max * y_max, (y_max << 1) - 1
        if not (n & 1):
            n -= n_diff
            n_diff -= 2
        for d in range((n_diff - 1) << 1, -1, -8):
            m = n % 12
            if m == 1 or m == 5:
                m = n >> 1
                sieve[m] = not sieve[m]
            n -= d

    x_max, x2, xd = int(sqrt((end - 1) / 3.0)), 0, 3
    for xd in range(3, 6 * x_max + 2, 6):
        x2 += xd
        y_max = int(sqrt(end - x2))
        n, n_diff = x2 + y_max * y_max, (y_max << 1) - 1
        if not (n & 1):
            n -= n_diff
            n_diff -= 2
        for d in range((n_diff - 1) << 1, -1, -8):
            if n % 12 == 7:
                m = n >> 1
                sieve[m] = not sieve[m]
            n -= d

    x_max, y_min, x2, xd = int((2 + sqrt(4 - 8 * (1 - end))) / 4), -1, 0, 3
    for x in range(1, x_max + 1):
        x2 += xd
        xd += 6
        if x2 >= end:
            y_min = (((int(ceil(sqrt(x2 - end))) - 1) << 1) - 2) << 1
        n, n_diff = ((x * x + x) << 1) - 1, (((x - 1) << 1) - 2) << 1
        for d in range(n_diff, y_min, -8):
            if n % 12 == 11:
                m = n >> 1
                sieve[m] = not sieve[m]
            n += d

    primes = [2, 3]
    if end <= 3:
        return primes[: max(0, end - 2)]

    for n in range(5 >> 1, (int(sqrt(end)) + 1) >> 1):
        if sieve[n]:
            primes.append((n << 1) + 1)
            aux = (n << 1) + 1
            aux *= aux
            for k in range(aux, end, 2 * aux):
                sieve[k >> 1] = False

    s = int(sqrt(end)) + 1
    if s % 2 == 0:
        s += 1
    primes.extend([i for i in range(s, end, 2) if sieve[i >> 1]])

    return primes


def ambi_sieve_plain(n):
    s = list(range(3, n, 2))
    for m in range(3, int(n ** 0.5) + 1, 2):
        if s[(m - 3) // 2]:
            for t in range((m * m - 3) // 2, (n >> 1) - 1, m):
                s[t] = 0
    return [2] + [t for t in s if t > 0]


def sundaram3(max_n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/2073279#2073279
    numbers = range(3, max_n + 1, 2)
    half = (max_n) // 2
    initial = 4

    for step in range(3, max_n + 1, 2):
        for i in range(initial, half, step):
            numbers[i - 1] = 0
        initial += 2 * (step + 1)

        if initial > half:
            return [2] + filter(None, numbers)


# Using Numpy:
def ambi_sieve(n):
    # http://tommih.blogspot.com/2009/04/fast-prime-number-generator.html
    s = np.arange(3, n, 2)
    for m in range(3, int(n ** 0.5) + 1, 2):
        if s[(m - 3) // 2]:
            s[(m * m - 3) // 2::m] = 0
    return np.r_[2, s[s > 0]]


def primesfrom3to(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Returns an array of primes, p < n """
    assert n >= 2
    sieve = np.ones(n // 2, dtype=bool)
    for i in range(3, int(n ** 0.5) + 1, 2):
        if sieve[i // 2]:
            sieve[i * i // 2::i] = False
    return np.r_[2, 2 * np.nonzero(sieve)[0][1::] + 1]


def primesfrom2to(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Input n>=6, Returns an array of primes, 2 <= p < n """
    assert n >= 6
    sieve = np.ones(n // 3 + (n % 6 == 2), dtype=bool)
    sieve[0] = False
    for i in range(int(n ** 0.5) // 3 + 1):
        if sieve[i]:
            k = 3 * i + 1 | 1
            sieve[((k * k) // 3)::2 * k] = False
            sieve[(k * k + 4 * k - 2 * k * (i & 1)) // 3::2 * k] = False
    return np.r_[2, 3, ((3 * np.nonzero(sieve)[0] + 1) | 1)]


def sympy_sieve(n):
    return list(sympy.sieve.primerange(1, n))


b = perfplot.bench(
    setup=lambda n: n,
    kernels=[
        rwh_primes,
        rwh_primes1,
        rwh_primes2,
        sieve_wheel_30,
        sieve_of_eratosthenes,
        sieve_of_atkin,
        # ambi_sieve_plain,
        # sundaram3,
        ambi_sieve,
        primesfrom3to,
        primesfrom2to,
        sympy_sieve,
    ],
    n_range=[2 ** k for k in range(3, 25)],
    xlabel="n",
)
b.save("out.png")
b.show()

这是问题解的一种变化应该比问题本身更快。它使用埃拉托色尼的静态筛,没有其他优化。

from typing import List

def list_primes(limit: int) -> List[int]:
    primes = set(range(2, limit + 1))
    for i in range(2, limit + 1):
        if i in primes:
            primes.difference_update(set(list(range(i, limit + 1, i))[1:]))
    return sorted(primes)

>>> list_primes(100)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]

在写这篇文章的时候,这是最快的工作解决方案(至少在我的机器上是这样)。它同时使用numpy和bitarray,并受到这个答案的primesfrom2to的启发。

import numpy as np
from bitarray import bitarray


def bit_primes(n):
    bit_sieve = bitarray(n // 3 + (n % 6 == 2))
    bit_sieve.setall(1)
    bit_sieve[0] = False

    for i in range(int(n ** 0.5) // 3 + 1):
        if bit_sieve[i]:
            k = 3 * i + 1 | 1
            bit_sieve[k * k // 3::2 * k] = False
            bit_sieve[(k * k + 4 * k - 2 * k * (i & 1)) // 3::2 * k] = False

    np_sieve = np.unpackbits(np.frombuffer(bit_sieve.tobytes(), dtype=np.uint8)).view(bool)
    return np.concatenate(((2, 3), ((3 * np.flatnonzero(np_sieve) + 1) | 1)))

下面是与素数from2to的比较,它之前被发现是unutbu比较中最快的解:

python3 -m timeit -s "import fast_primes" "fast_primes.bit_primes(1000000)"
200 loops, best of 5: 1.19 msec per loop

python3 -m timeit -s "import fast_primes" "fast_primes.primesfrom2to(1000000)"
200 loops, best of 5: 1.23 msec per loop

对于寻找100万以下的质数,bit_primes稍微快一些。 n值越大,差异就越大。在某些情况下,bit_primes的速度是原来的两倍多:

python3 -m timeit -s "import fast_primes" "fast_primes.bit_primes(500_000_000)"
1 loop, best of 5: 540 msec per loop

python3 -m timeit -s "import fast_primes" "fast_primes.primesfrom2to(500_000_000)"
1 loop, best of 5: 1.15 sec per loop

作为参考,以下是primesfrom2to I的最小修改版本(适用于Python 3):

def primesfrom2to(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Input n>=6, Returns a array of primes, 2 <= p < n"""
    sieve = np.ones(n // 3 + (n % 6 == 2), dtype=np.bool)
    sieve[0] = False
    for i in range(int(n ** 0.5) // 3 + 1):
        if sieve[i]:
            k = 3 * i + 1 | 1
            sieve[((k * k) // 3)::2 * k] = False
            sieve[(k * k + 4 * k - 2 * k * (i & 1)) // 3::2 * k] = False
    return np.r_[2, 3, ((3 * np.nonzero(sieve)[0] + 1) | 1)]

你有一个更快的代码和最简单的代码生成质数。 但对于更大的数字,当n=10000, 10000000时,它不起作用,可能是。pop()方法失败了

考虑:N是质数吗?

case 1: You got some factors of N, for i in range(2, N): If N is prime loop is performed for ~(N-2) times. else less number of times case 2: for i in range(2, int(math.sqrt(N)): Loop is performed for almost ~(sqrt(N)-2) times if N is prime else will break somewhere case 3: Better We Divide N With Only number of primes<=sqrt(N) Where loop is performed for only π(sqrt(N)) times π(sqrt(N)) << sqrt(N) as N increases from math import sqrt from time import * prime_list = [2] n = int(input()) s = time() for n0 in range(2,n+1): for i0 in prime_list: if n0%i0==0: break elif i0>=int(sqrt(n0)): prime_list.append(n0) break e = time() print(e-s) #print(prime_list); print(f'pi({n})={len(prime_list)}') print(f'{n}: {len(prime_list)}, time: {e-s}') Output 100: 25, time: 0.00010275840759277344 1000: 168, time: 0.0008606910705566406 10000: 1229, time: 0.015588521957397461 100000: 9592, time: 0.023436546325683594 1000000: 78498, time: 4.1965954303741455 10000000: 664579, time: 109.24591708183289 100000000: 5761455, time: 2289.130858898163

小于1000似乎很慢,但小于10^6我认为更快。

然而,我无法理解时间的复杂性。


我很惊讶居然没人提到numba。

该版本在2.47 ms±36.5µs内达到1M标记。

几年前,维基百科页面上出现了一个阿特金筛子的伪代码。这已经不存在了,参考阿特金筛似乎是一个不同的算法。一个2007/03/01版本的维基百科页面(Primer number as 2007-03-01)显示了我用作参考的伪代码。

import numpy as np
from numba import njit

@njit
def nb_primes(n):
    # Generates prime numbers 2 <= p <= n
    # Atkin's sieve -- see https://en.wikipedia.org/w/index.php?title=Prime_number&oldid=111775466
    sqrt_n = int(np.sqrt(n)) + 1

    # initialize the sieve
    s = np.full(n + 1, -1, dtype=np.int8)
    s[2] = 1
    s[3] = 1

    # put in candidate primes:
    # integers which have an odd number of
    # representations by certain quadratic forms
    for x in range(1, sqrt_n):
        x2 = x * x
        for y in range(1, sqrt_n):
            y2 = y * y
            k = 4 * x2 + y2
            if k <= n and (k % 12 == 1 or k % 12 == 5): s[k] *= -1
            k = 3 * x2 + y2
            if k <= n and (k % 12 == 7): s[k] *= -1
            k = 3 * x2 - y2
            if k <= n and x > y and k % 12 == 11: s[k] *= -1

    # eliminate composites by sieving
    for k in range(5, sqrt_n):
        if s[k]:
            k2 = k*k
            # k is prime, omit multiples of its square; this is sufficient because
            # composites which managed to get on the list cannot be square-free
            for i in range(1, n // k2 + 1):
                j = i * k2 # j ∈ {k², 2k², 3k², ..., n}
                s[j] = -1
    return np.nonzero(s>0)[0]

# initial run for "compilation" 
nb_primes(10)

时机

In[10]:
%timeit nb_primes(1_000_000)

Out[10]:
2.47 ms ± 36.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In[11]:
%timeit nb_primes(10_000_000)

Out[11]:
33.4 ms ± 373 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In[12]:
%timeit nb_primes(100_000_000)

Out[12]:
828 ms ± 5.64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

从2021年的答案开始,我还没有发现二进制数组方法对10亿以下的质数有利。

但我可以用几个技巧将质数从2加速到接近x2:

使用numexpr库将numpy表达式转换为分配较少的紧循环 取代np。有更快的选择 以某种方式操作筛选的前9个元素,因此不需要改变数组的形状

总之,在我的机器上,质数< 10亿的时间从25秒变成了14.5秒

import numexpr as ne
import numpy as np

def primesfrom2to_numexpr(n):
    # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
    """ Input n>=24, Returns a array of primes, 2 <= p < n + a few over"""
    sieve = np.zeros((n // 3 + (n % 6 == 2))//4+1, dtype=np.int32)
    ne.evaluate('sieve + 0x01010101', out=sieve)
    sieve = sieve.view('int8')
    #sieve = np.ones(n // 3 + (n % 6 == 2), dtype=np.bool_)
    sieve[0] = 0
    for i in np.arange(int(n ** 0.5) // 3 + 1):
        if sieve[i]:
            k = 3 * i + 1 | 1
            sieve[((k * k) // 3)::2 * k] = 0
            sieve[(k * k + 4 * k - 2 * k * (i & 1)) // 3::2 * k] = 0
    sieve[[0,8]] = 1
    result = np.flatnonzero(sieve)
    ne.evaluate('result * 3 + 1 + result%2', out=result)
    result[:9] = [2,3,5,7,11,13,17,19,23]
    return result