我正在学习数学课程,我们必须做一些整数分解作为问题的中间步骤。我决定编写一个Python程序来为我做这个(我们没有测试我们的因素能力,所以这完全在板上)。该计划如下:
#!/usr/bin/env python3
import math
import sys
# Return a list representing the prime factorization of n. The factorization is
# found using trial division (highly inefficient).
def factorize(n):
def factorize_helper(n, min_poss_factor):
if n <= 1:
return []
prime_factors = []
smallest_prime_factor = -1
for i in range(min_poss_factor, math.ceil(math.sqrt(n)) + 1):
if n % i == 0:
smallest_prime_factor = i
break
if smallest_prime_factor != -1:
return [smallest_prime_factor] \
+ factorize_helper(n // smallest_prime_factor,
smallest_prime_factor)
else:
return [n]
if n < 0:
print("Usage: " + sys.argv[0] + " n # where n >= 0")
return []
elif n == 0 or n == 1:
return [n]
else:
return factorize_helper(n, 2)
if __name__ == "__main__":
factorization = factorize(int(sys.argv[1]))
if len(factorization) > 0:
print(factorization)
我一直在教自己一些Haskell,所以我决定尝试在Haskell中重写程序。该计划如下:
import System.Environment
-- Return a list containing all factors of n at least x.
factorize' :: (Integral a) => a -> a -> [a]
factorize' n x = smallestFactor
: (if smallestFactor == n
then []
else factorize' (n `quot` smallestFactor) smallestFactor)
where
smallestFactor = getSmallestFactor n x
getSmallestFactor :: (Integral a) => a -> a -> a
getSmallestFactor n x
| n `rem` x == 0 = x
| x > (ceiling . sqrt . fromIntegral $ n) = n
| otherwise = getSmallestFactor n (x+1)
-- Return a list representing the prime factorization of n.
factorize :: (Integral a) => a -> [a]
factorize n = factorize' n 2
main = do
argv <- getArgs
let n = read (argv !! 0) :: Int
let factorization = factorize n
putStrLn $ show (factorization)
return ()
(注意:这需要64位环境。在32位上,导入Data.Int
并使用Int64
作为read (argv !! 0)
上的类型注释
在我写完之后,我决定比较两者的性能,认识到有更好的算法,但两个程序使用的算法基本相同。例如,我做了以下几点:
$ ghc --make -O2 factorize.hs
$ /usr/bin/time -f "%Uu %Ss %E" ./factorize 89273487253497
[3,723721,41117819]
0.18u 0.00s 0:00.23
然后,计划Python程序:
$ /usr/bin/time -f "%Uu %Ss %E" ./factorize.py 89273487253497
[3, 723721, 41117819]
0.09u 0.00s 0:00.09
当然,每次运行其中一个程序时,时间会略有不同,但它们总是在这个范围内,Python程序比编译的Haskell程序快几倍。在我看来,Haskell版本应该能够更快地运行,我希望你能给我一个如何改进它的想法,以便就是这种情况。
我已经看到了一些关于优化Haskell程序的技巧,如this question的答案,但似乎无法让我的程序运行得更快。循环比递归更快吗? Haskell的I / O特别慢吗?我在实际实现算法时犯了错误吗?理想情况下,我想要一个仍然相对容易阅读的Haskell的优化版本
答案 0 :(得分:13)
如果你只计算limit = ceiling . sqrt . fromIntegral $ n
一次,而不是每次迭代计算一次,那么我看到Haskell版本更快:
limit = ceiling . sqrt . fromIntegral $ n
smallestFactor = getSmallestFactor x
getSmallestFactor x
| n `rem` x == 0 = x
| x > limit = n
| otherwise = getSmallestFactor (x+1)
使用这个版本,我看到了:
$ time ./factorizePy.py 89273487253497
[3, 723721, 41117819]
real 0m0.236s
user 0m0.171s
sys 0m0.062s
$ time ./factorizeHs 89273487253497
[3,723721,41117819]
real 0m0.190s
user 0m0.000s
sys 0m0.031s
答案 1 :(得分:3)
除了Cactus制作的关键点之外,还有一些空间用于重构和严格注释,以避免产生不必要的thunk。请特别注意factorize
是懒惰的:
factorize' undefined undefined = undefined : undefined
这不是必需的,并迫使GHC分配几个thunk。其他地方的懒惰也是如此。我希望你会得到更好的表现:
{-# LANGUAGE BangPatterns #-}
factorize' :: Integral a => a -> a -> [a]
factorize' n x
| smallestFactor == n = [smallestFactor]
| otherwise = smallestFactor : factorize' (n `quot` smallestFactor) smallestFactor
where
smallestFactor = getSmallestFactor n (ceiling . sqrt . fromIntegral $ n) x
getSmallestFactor n !limit x
| n `rem` x == 0 = x
| x > limit = n
| otherwise = getSmallestFactor n limit (x+1)
-- Return a list representing the prime factorization of n.
factorize :: Integral a => a -> [a]
factorize n = factorize' n 2
我让getSmallestFactor
同时将n
和限制作为参数。这可以防止getSmallestFactor
被分配为堆上的闭包。我不确定这是否值得多余的论点改组;你可以尝试两种方式。