python中的numpy数组的随机大小分块

加里尼

我想将索引数组划分为随机大小的块(从可能的大小的有限范围内获取),这些块也可以在彼此之间随机播放。我尝试了在这里找到的以下内容但重点放在大小相等的块上。

a = np.arange(1, 100)

def chunk(xs, n): # to chunk the array xs in n parts
    ys = list(xs)
    random.shuffle(ys)
    size = len(ys) // n
    leftovers= ys[size*n:]
    for c, xtra in enumerate(leftovers):
        yield ys[c*size:(c+1)*size] + [ xtra ]
    for c in xrange(c+1,n):
        yield ys[c*size:(c+1)*size]

换句话说,如何更改上面的函数以具有一定数量的块(随机数,并且在彼此之间随机播放),并且可变大小的取值范围是随机的,例如 [5-10]

电话

这将起作用:

from itertools import chain
import numpy as np

a = np.arange(1, 100)
def chunk(xs, nlow, nhigh, shuffle=True):
    xs = np.asarray(xs)
    if shuffle:
        # shuffle, if you want
        xs = xs.copy()
        np.random.shuffle(xs)

    # get at least enough random chunk sizes in the specified range, ie nlow <= n <= nhigh
    ns = np.random.randint(nlow, nhigh+1, size=xs.size//nlow)
    # add up the chunk sizes to get the indices at which we'll slice up the input array
    ixs = np.add.accumulate(ns)
    # truncate ixs so that its contents are all valid indices with respect to xs
    ixs = ixs[:np.searchsorted(ixs, xs.size)]

    # yield slices from the input array
    for start,end in zip(chain([None], ixs), chain(ixs, [None])):
        yield xs[start:end]

list(chunk(a, 5, 10))

输出:

[array([67, 79, 17, 62, 12, 37, 70, 24]),
 array([98, 48, 88, 59, 47]),
 array([52, 60, 89, 23, 43, 44]),
 array([ 7, 27, 33, 74, 49,  2]),
 array([ 6, 51, 40, 13, 56, 45]),
 array([31,  3, 55, 10, 11, 46,  9, 42, 34]),
 array([53, 22, 95, 41, 19, 32,  4, 69, 86]),
 array([93, 68, 57, 65, 92, 76, 28, 63, 64, 58]),
 array([91, 66, 18, 99, 21]),
 array([36, 83, 15, 78,  1, 81, 97, 84]),
 array([61, 71, 25, 94, 87, 20, 85, 38]),
 array([ 8, 96, 75, 30, 77, 14, 72, 29]),
 array([35, 90, 82, 73, 39,  5, 26, 50, 16]),
 array([80, 54])]

编辑

我的原始答案没有为最终块的大小设置下限,因此有时它会小于指定的大小(尽管永远不会更大)。据我所知,没有直接的方法可以解决这个问题。但是,通常可以通过拒绝来自该区域的任何样本来从随机分布中删除不需要的区域。换句话说,您可以通过扔掉其中没有的最后一个建议的块来确保最后一个块足够大:

def getIxs(xsize, nlow, nhigh):
    # get at least enough random chunk sizes in the specified range, ie nlow <= n <= nhigh
    ns = np.random.randint(nlow, nhigh+1, size=xsize//nlow)

    # add up the chunk sizes to get the indices at which we'll slice up the input array
    ixs = np.add.accumulate(ns)

    # truncate ixs so that its contents are all valid indices with respect to xs
    ixs = ixs[:np.searchsorted(ixs, xsize)]

    return ixs

def chunk(xs, nlow, nhigh):
    xs = np.asarray(xs)

    ixs = getIxs(xs.size, nlow, nhigh)

    # rerun getIxs until the size of the final chunk is large enough
    while (xs.size - ixs[-1]) < nlow:
        ixs = getIxs(xs.size, nlow, nhigh)

    # yield slices from the input array
    for start,end in zip(chain([None], ixs), chain(ixs, [None])):
        yield xs[start:end]

这种方法应保留每个块大小的总体随机性。

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