锁组合,用于动态锁大小
下面,我将给出两个具有不同尺寸值的示例。
锁1
# numbers are the shown values on the so in this case: 0,1,2
numbers = 5
# fields are those things i can turn to change my combination
fields = 4
所以我对我所有的期望是
0 0 0 5
0 0 1 4
0 0 2 3
0 0 3 2
0 0 4 1
0 0 5 0
0 1 0 4
0 1 1 3
0 1 2 2
0 1 3 1
0 1 4 0
0 2 0 3
0 2 1 2
0 2 2 1
0 2 3 0
0 3 0 2
0 3 1 1
0 3 2 0
0 4 0 1
0 4 1 0
0 5 0 0
1 0 0 4
1 0 1 3
1 0 2 2
1 0 3 1
1 0 4 0
1 1 0 3
1 1 1 2
1 1 2 1
1 1 3 0
1 2 0 2
1 2 1 1
1 2 2 0
1 3 0 1
1 3 1 0
1 4 0 0
2 0 0 3
2 0 1 2
2 0 2 1
2 0 3 0
2 1 0 2
2 1 1 1
2 1 2 0
2 2 0 1
2 2 1 0
2 3 0 0
3 0 0 2
3 0 1 1
3 0 2 0
3 1 0 1
3 1 1 0
3 2 0 0
4 0 0 1
4 0 1 0
4 1 0 0
5 0 0 0
我的第二把锁具有以下值:
numbers = 3
values = 3
所以我所期望的像这样
0 0 3
0 1 2
0 2 1
0 3 0
1 0 2
1 1 1
1 2 0
2 0 1
2 1 0
3 0 0
我知道这可以用类似方法完成itertools.permutations
,但是我想通过建立行而不是通过使我的RAM超载来生成行。我发现最后两行总是以相同的方式建立。所以我写了一个为我构建的函数:
def posibilities(value):
all_pos = []
for y in range(value + 1):
posibility = []
posibility.append(y)
posibility.append(value)
all_pos.append(posibility)
value -= 1
return all_pos
现在,我想以某种方式使函数中的其他值动态适合,例如Lock-2现在看起来像这样:
0 posibilities(3)
1 posibilities(2)
2 posibilities(1)
3 posibilities(0)
我知道我应该使用while
循环等等,但是我无法获得动态值的解决方案。
-
您 可以 递归执行此操作,但是通常最好避免在Python中进行递归,除非您 确实
需要,例如在处理递归数据结构(例如树)时。标准Python(又名CPython)中的递归效率不高,因为它无法进行尾部调用消除。此外,它还应用了递归限制(默认情况下为1000个级别,但用户可以修改)。要生成的序列称为弱组合,而Wikipedia文章提供了一种简单的算法,该算法易于在标准
itertools.combinations
函数的帮助下实现。#!/usr/bin/env python3 ''' Generate the compositions of num of a given width Algorithm from https://en.wikipedia.org/wiki/Composition_%28combinatorics%29#Number_of_compositions Written by PM 2Ring 2016.11.11 ''' from itertools import combinations def compositions(num, width): m = num + width - 1 last = (m,) first = (-1,) for t in combinations(range(m), width - 1): yield [v - u - 1 for u, v in zip(first + t, t + last)] # test for t in compositions(5, 4): print(t) print('- ' * 20) for t in compositions(3, 3): print(t)
输出
[0, 0, 0, 5] [0, 0, 1, 4] [0, 0, 2, 3] [0, 0, 3, 2] [0, 0, 4, 1] [0, 0, 5, 0] [0, 1, 0, 4] [0, 1, 1, 3] [0, 1, 2, 2] [0, 1, 3, 1] [0, 1, 4, 0] [0, 2, 0, 3] [0, 2, 1, 2] [0, 2, 2, 1] [0, 2, 3, 0] [0, 3, 0, 2] [0, 3, 1, 1] [0, 3, 2, 0] [0, 4, 0, 1] [0, 4, 1, 0] [0, 5, 0, 0] [1, 0, 0, 4] [1, 0, 1, 3] [1, 0, 2, 2] [1, 0, 3, 1] [1, 0, 4, 0] [1, 1, 0, 3] [1, 1, 1, 2] [1, 1, 2, 1] [1, 1, 3, 0] [1, 2, 0, 2] [1, 2, 1, 1] [1, 2, 2, 0] [1, 3, 0, 1] [1, 3, 1, 0] [1, 4, 0, 0] [2, 0, 0, 3] [2, 0, 1, 2] [2, 0, 2, 1] [2, 0, 3, 0] [2, 1, 0, 2] [2, 1, 1, 1] [2, 1, 2, 0] [2, 2, 0, 1] [2, 2, 1, 0] [2, 3, 0, 0] [3, 0, 0, 2] [3, 0, 1, 1] [3, 0, 2, 0] [3, 1, 0, 1] [3, 1, 1, 0] [3, 2, 0, 0] [4, 0, 0, 1] [4, 0, 1, 0] [4, 1, 0, 0] [5, 0, 0, 0] - - - - - - - - - - - - - - - - - - - - [0, 0, 3] [0, 1, 2] [0, 2, 1] [0, 3, 0] [1, 0, 2] [1, 1, 1] [1, 2, 0] [2, 0, 1] [2, 1, 0] [3, 0, 0]
FWIW,以上代码可以
compositions(15, 8)
在运行于Python 3.6或Python 2.6的旧2GHz
32位计算机上,在约1.6秒内生成170544个序列。(时序信息是通过使用Bashtime
命令获得的)。
FWIW,这是user3736966从此答案中获取的递归版本。我已经对其进行了修改,以使用与代码相同的参数名称,使用列表而不是元组,并与Python
3兼容。def compositions(num, width, parent=[]): if width > 1: for i in range(num, -1, -1): yield from compositions(i, width - 1, parent + [num - i]) else: yield parent + [num]
出乎意料的是,此版本比原始版本快一点,大约要花1.5秒
compositions(15, 8)
。如果您的Python版本不懂
yield from
,您可以执行以下操作:def compositions(num, width, parent=[]): if width > 1: for i in range(num, -1, -1): for t in compositions(i, width - 1, parent + [num - i]): yield t else: yield parent + [num]
要按降序生成构图,只需将
range
调用调换即即for i in range(num + 1):
。最后,这是一个不可读的单行版本。:)
def c(n, w, p=[]): yield from(t for i in range(n,-1,-1)for t in c(i,w-1,p+[n-i]))if w-1 else[p+[n]]
作为一个顽固的修补匠,我无法阻止自己制作另一个版本。:)这只是原始版本
combinations
,以及itertools文档中列出的代码。当然,实数itertools.combinations
是用C编写的,因此它比文档中所示的等效Python代码运行得更快。def compositions(num, width): r = width - 1 indices = list(range(r)) revrange = range(r-1, -1, -1) first = [-1] last = [num + r] yield [0] * r + [num] while True: for i in revrange: if indices[i] != i + num: break else: return indices[i] += 1 for j in range(i+1, r): indices[j] = indices[j-1] + 1 yield [v - u - 1 for u, v in zip(first + indices, indices + last)]
这个版本比原始版本慢50%
compositions(15, 8)
:在我的机器上大约需要2.3秒。