NumPy:创建布尔数组，例如"repeat".但在多个维度 [英] NumPy: create bool array like "repeat" but in multiple dimensions

问题描述

我正在寻找与sum()相反的东西.我们开始:

I'm looking for sort of the opposite of sum() I guess. Here we go:

x = array([
    [False, False, False, False, False],
    [ True, False, False, False, False],
    [ True,  True, False, False, False],
    [ True,  True,  True, False, False]])

x.sum(axis=1)
Out: array([0, 1, 2, 3])

所以我想朝相反的方向:从[0,1,2,3]到类似x的数组(我可以在x中指定我想要的列数，当然要大于5).

So I want to go the opposite direction: from [0,1,2,3] to an array like x (I can specify the number of columns I want in x, of course, above it's 5).

理想情况下，该解决方案也应该适用于更高的尺寸，并且我当然也不想在Python中循环，因为输入内容可能比此示例更长.也就是说，这是一个使用循环的解决方案:

The solution should ideally work for higher dimensions too, and I don't want to loop in Python of course, because the input could be longer than this example. That said, here's a solution using a loop:

s = np.array([0, 1, 2, 3])
y = np.zeros((len(s), 5), np.bool)
for row,col in enumerate(s):
    y[row,0:col] = True

推荐答案

IIUC-但我不确定我可以这样做-您可以使用arange和广播比较:

IIUC -- and I'm not sure that I do -- you could use arange and a broadcasting comparison:

>>> v = np.array([0,1,3,2])
>>> np.arange(5) < v[...,None]
array([[False, False, False, False, False],
       [ True, False, False, False, False],
       [ True,  True,  True, False, False],
       [ True,  True, False, False, False]], dtype=bool)

或二维:

>>> v = np.array([[1,2],[0,2]])
>>> np.arange(5) < v[...,None]
array([[[ True, False, False, False, False],
        [ True,  True, False, False, False]],

       [[False, False, False, False, False],
        [ True,  True, False, False, False]]], dtype=bool)
>>> ((np.arange(5) < v[...,None]).sum(2) == v).all()
True

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