用平坦的子范围替换数字列表 [英] Replace a list of numbers with flat sub-ranges
问题描述
给出一个数字列表,如下所示:
Given a list of numbers, like this:
lst = [0, 10, 15, 17]
我想要一个列表,其中包含i -> i + 3
中所有i
的i -> i + 3
元素.如果范围重叠,我希望将它们合并.
I'd like a list that has elements from i -> i + 3
for all i
in lst
. If there are overlapping ranges, I'd like them merged.
因此,对于上面的示例,我们首先得到:
So, for the example above, we first get:
[0, 1, 2, 3, 10, 11, 12, 13, 15, 16, 17, 18, 17, 18, 19, 20]
但是对于最后两个组,范围重叠,因此在合并它们时,您具有:
But for the last 2 groups, the ranges overlap, so upon merging them, you have:
[0, 1, 2, 3, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20]
这是我想要的输出.
这就是我想到的:
from collections import OrderedDict
res = list(OrderedDict.fromkeys([y for x in lst for y in range(x, x + 4)]).keys())
print(res) = [0, 1, 2, 3, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20]
但是,这很慢(10000 loops, best of 3: 56 µs per loop
).如果可能,我想要一个numpy解决方案,或者比这更快的python解决方案.
However, this is slow (10000 loops, best of 3: 56 µs per loop
). I'd like a numpy solution if possible, or a python solution that's faster than this.
推荐答案
Approach #1 : One approach based on broadcasted
summation and then using np.unique
to get unique numbers -
np.unique(np.asarray(lst)[:,None] + np.arange(4))
方法2::另一个基于广播的求和,然后进行掩蔽-
Approach #2 : Another based on broadcasted summation and then masking -
def mask_app(lst, interval_len = 4):
arr = np.array(lst)
r = np.arange(interval_len)
ranged_vals = arr[:,None] + r
a_diff = arr[1:] - arr[:-1]
valid_mask = np.vstack((a_diff[:,None] > r, np.ones(interval_len,dtype=bool)))
return ranged_vals[valid_mask]
运行时测试
原始方法-
from collections import OrderedDict
def org_app(lst):
list(OrderedDict.fromkeys([y for x in lst for y in range(x, x + 4)]).keys())
时间-
In [409]: n = 10000
In [410]: lst = np.unique(np.random.randint(0,4*n,(n))).tolist()
In [411]: %timeit org_app(lst)
...: %timeit np.unique(np.asarray(lst)[:,None] + np.arange(4))
...: %timeit mask_app(lst, interval_len = 4)
...:
10 loops, best of 3: 32.7 ms per loop
1000 loops, best of 3: 1.03 ms per loop
1000 loops, best of 3: 671 µs per loop
In [412]: n = 100000
In [413]: lst = np.unique(np.random.randint(0,4*n,(n))).tolist()
In [414]: %timeit org_app(lst)
...: %timeit np.unique(np.asarray(lst)[:,None] + np.arange(4))
...: %timeit mask_app(lst, interval_len = 4)
...:
1 loop, best of 3: 350 ms per loop
100 loops, best of 3: 14.7 ms per loop
100 loops, best of 3: 9.73 ms per loop
转换为array
的瓶颈似乎是两种发布方法的瓶颈,尽管此后看来回报很丰厚.只是为了了解最后一个数据集在转换上花费的时间-
The bottleneck with the two posted approaches seems like is with the conversion to array
, though that seems to be paying off well afterwards. Just to give a sense of the time spent on the conversion for the last dataset -
In [415]: %timeit np.array(lst)
100 loops, best of 3: 5.6 ms per loop
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