我需要一个滚动窗口(又名滑动窗口)可迭代的序列/迭代器/生成器。(默认的Python迭代可以被认为是一种特殊情况,其中窗口长度为1。)我目前正在使用以下代码。我怎样才能做得更优雅和/或更有效?

def rolling_window(seq, window_size):
    it = iter(seq)
    win = [it.next() for cnt in xrange(window_size)] # First window
    yield win
    for e in it: # Subsequent windows
        win[:-1] = win[1:]
        win[-1] = e
        yield win

if __name__=="__main__":
    for w in rolling_window(xrange(6), 3):
        print w

"""Example output:   
   [0, 1, 2]
   [1, 2, 3]
   [2, 3, 4]
   [3, 4, 5]
"""

对于window_size == 2的特定情况(即,在序列中迭代相邻的重叠对),请参见如何从列表中迭代重叠(当前,下一个)值对?


当前回答

多个迭代器!

def window(seq, size, step=1):
    # initialize iterators
    iters = [iter(seq) for i in range(size)]
    # stagger iterators (without yielding)
    [next(iters[i]) for j in range(size) for i in range(-1, -j-1, -1)]
    while(True):
        yield [next(i) for i in iters]
        # next line does nothing for step = 1 (skips iterations for step > 1)
        [next(i) for i in iters for j in range(step-1)]

next(it)在序列结束时引发StopIteration,出于一些我无法理解的很酷的原因,yield语句在这里除外它,函数返回,忽略没有形成完整窗口的剩余值。

无论如何,这是目前为止行数最少的解决方案,它唯一的要求是seq实现__iter__或__getitem__,并且除了@dansalmo的解决方案之外,不依赖于itertools或集合:)

其他回答

我喜欢t ():

from itertools import tee, izip

def window(iterable, size):
    iters = tee(iterable, size)
    for i in xrange(1, size):
        for each in iters[i:]:
            next(each, None)
    return izip(*iters)

for each in window(xrange(6), 3):
    print list(each)

给:

[0, 1, 2]
[1, 2, 3]
[2, 3, 4]
[3, 4, 5]
def rolling_window(list, degree):
    for i in range(len(list)-degree+1):
        yield [list[i+o] for o in range(degree)]

这是一个滚动平均函数

在旧版本的Python文档中有一个itertools示例:

from itertools import islice

def window(seq, n=2):
    "Returns a sliding window (of width n) over data from the iterable"
    "   s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ...                   "
    it = iter(seq)
    result = tuple(islice(it, n))
    if len(result) == n:
        yield result
    for elem in it:
        result = result[1:] + (elem,)
        yield result

文档中的那个更简洁一点,我想它使用了itertools来达到更好的效果。


如果你的迭代器是一个简单的列表/元组,用指定的窗口大小滑动它的简单方法是:

seq = [0, 1, 2, 3, 4, 5]
window_size = 3

for i in range(len(seq) - window_size + 1):
    print(seq[i: i + window_size])

输出:

[0, 1, 2]
[1, 2, 3]
[2, 3, 4]
[3, 4, 5]

多个迭代器!

def window(seq, size, step=1):
    # initialize iterators
    iters = [iter(seq) for i in range(size)]
    # stagger iterators (without yielding)
    [next(iters[i]) for j in range(size) for i in range(-1, -j-1, -1)]
    while(True):
        yield [next(i) for i in iters]
        # next line does nothing for step = 1 (skips iterations for step > 1)
        [next(i) for i in iters for j in range(step-1)]

next(it)在序列结束时引发StopIteration,出于一些我无法理解的很酷的原因,yield语句在这里除外它,函数返回,忽略没有形成完整窗口的剩余值。

无论如何,这是目前为止行数最少的解决方案,它唯一的要求是seq实现__iter__或__getitem__,并且除了@dansalmo的解决方案之外,不依赖于itertools或集合:)

更新

Kelly发现这是一个重复的答案。但我在这里留下这个作为反例,因为我包含了一个毫无意义的最小值。

所以如果你想用min来避免IndexError,没有必要,range会帮你处理这种情况。


旧的答案

奇怪的是,当n > len(l)返回[]时,下面的句柄在语义上是正确的。

>>> l = [0, 1, 2, 3, 4]

>>> n = 2
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> [[0, 1], [1, 2], [2, 3], [3, 4]]
>>>
>>> n = 3
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> [[0, 1, 2], [1, 2, 3], [2, 3, 4]]
>>>
>>> n = 4
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> [[0, 1, 2, 3], [1, 2, 3, 4]]
>>>
>>> n = 5
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> [[0, 1, 2, 3, 4]]
>>>
>>> n = 10 # n > len(l)
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> []