多个逻辑参数的Numpy`logical_or` [英] Numpy `logical_or` for more than two arguments

问题描述

Numpy的logical_or函数需要不超过两个数组进行比较.如何找到两个以上数组的并集? (关于Numpy的logical_and并获得两个以上数组的交集，可能会问相同的问题.)

Numpy's logical_or function takes no more than two arrays to compare. How can I find the union of more than two arrays? (The same question could be asked with regard to Numpy's logical_and and obtaining the intersection of more than two arrays.)

推荐答案

如果您要查询

If you're asking about numpy.logical_or, then no, as the docs explicitly say, the only parameters are x1, x2, and optionally out:

numpy. logical_or ( x1, x2[, out] )= <ufunc 'logical_or'>

numpy.logical_or(x1, x2[, out]) = <ufunc 'logical_or'>

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您当然可以像这样将多个logical_or调用链接在一起:

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You can of course chain together multiple logical_or calls like this:

>>> x = np.array([True, True, False, False])
>>> y = np.array([True, False, True, False])
>>> z = np.array([False, False, False, False])
>>> np.logical_or(np.logical_or(x, y), z)
array([ True,  True,  True,  False], dtype=bool)

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在NumPy中推广这种链接的方法是使用

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The way to generalize this kind of chaining in NumPy is with reduce:

>>> np.logical_or.reduce((x, y, z))
array([ True,  True,  True,  False], dtype=bool)

当然，如果您使用一个多维数组而不是单独的数组，这也将起作用—实际上，这就是使用 meant 的方式:

And of course this will also work if you have one multi-dimensional array instead of separate arrays—in fact, that's how it's meant to be used:

>>> xyz = np.array((x, y, z))
>>> xyz
array([[ True,  True, False, False],
       [ True, False,  True, False],
       [False, False, False, False]], dtype=bool)
>>> np.logical_or.reduce(xyz)
array([ True,  True,  True,  False], dtype=bool)

但是，用NumPy术语来说，三个等长一维数组的元组是 array_like ，并且可以用作2D数组.

But a tuple of three equal-length 1D arrays is an array_like in NumPy terms, and can be used as a 2D array.

在NumPy之外，您还可以使用Python的reduce:

Outside of NumPy, you can also use Python's reduce:

>>> functools.reduce(np.logical_or, (x, y, z))
array([ True,  True,  True,  False], dtype=bool)

但是，与NumPy的reduce不同，Python并不是经常需要的.在大多数情况下，有一种更简单的处理方式-例如，将多个Python or运算符链接在一起，不要将reduce放在operator.or_之上，而只需使用any.而且当没有 时，使用显式循环通常更易读.

However, unlike NumPy's reduce, Python's is not often needed. For most cases, there's a simpler way to do things—e.g., to chain together multiple Python or operators, don't reduce over operator.or_, just use any. And when there isn't, it's usually more readable to use an explicit loop.

实际上NumPy的 any 可以也可以用于这种情况，尽管它并不那么琐碎；如果您未明确为其指定轴，则最终将得到标量而不是数组.所以:

And in fact NumPy's any can be used for this case as well, although it's not quite as trivial; if you don't explicitly give it an axis, you'll end up with a scalar instead of an array. So:

>>> np.any((x, y, z), axis=0)
array([ True,  True,  True,  False], dtype=bool)

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如您所料， logical_and 相似-您可以将其链接，np.reduce，functools.reduce或替换为

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As you might expect, logical_and is similar—you can chain it, np.reduce it, functools.reduce it, or substitute all with an explicit axis.

关于其他操作，例如 ?同样，除了在这种情况下没有适用的all/any类型的函数外，其他都是相同的. (你叫什么?odd?)

What about other operations, like logical_xor? Again, same deal… except that in this case there is no all/any-type function that applies. (What would you call it? odd?)

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