我试图通过去掉子列表长度的复杂公式,进一步优化素数线程中的冠军解。相同子序列的len()太慢,因为len很昂贵,生成子序列也很昂贵。这看起来稍微加快了函数的速度,但是我还不能去掉除法,尽管我只在条件语句中做除法。当然,我可以试着简化长度计算,去掉n的起始标记优化,而不是n*n。。。
from __future__ import division
如果你为代码启用psyco,故事就完全不同了,然而Atkins筛码比这种特殊的切片技术更快。
import cProfile
def rwh_primes1(n):
# http://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 primes(n):
# http://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
# recurrence formula for length by amount1 and amount2 Tony Veijalainen 2010
""" Returns a list of primes < n """
sieve = [True] * (n//2)
amount1 = n-10
amount2 = 6
for i in xrange(3,int(n**0.5)+1,2):
if sieve[i//2]:
## can you make recurrence formula for whole reciprocal?
sieve[i*i//2::i] = [False] * (amount1//amount2+1)
amount1-=4*i+4
amount2+=4
return [2] + [2*i+1 for i in xrange(1,n//2) if sieve[i]]
numprimes=1000000
print('Profiling')
cProfile.Profile.bias = 4e-6
for test in (rwh_primes1, primes):
cProfile.run("test(numprimes)")
分析(版本之间没有太大差异)
3 function calls in 0.191 CPU seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.006 0.006 0.191 0.191 <string>:1(<module>)
1 0.185 0.185 0.185 0.185 myprimes.py:3(rwh_primes1)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
3 function calls in 0.192 CPU seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.006 0.006 0.192 0.192 <string>:1(<module>)
1 0.186 0.186 0.186 0.186 myprimes.py:12(primes)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
rwh_primes1 took 23.670 s
primes took 22.792 s
primesieve took 10.850 s
有趣的是,如果不生成素数列表,但返回筛本身,则时间大约是数字列表版本的一半。