4D NumPy数组中每个2D矩阵的最大值

问题描述

我有一个4D数组，其定义如下:

I have a 4D array, which is defined as follows:

B = np.array(
    [[[[0.5000, 0.5625],
       [0.5000, 0.5625]],

      [[1.2500, 0.5000],
       [0.5625, 0.6875]],

      [[0.5625, 0.6250],
       [0.5000, 0.5625]]]]
)

我想获取每个2D矩阵的最大值，以便得到以下结果:

I want to take the max of each 2D matrix, such that I get a result of:

array([0.5625, 1.250, 0.6250])

类似地，我想获取每个2D矩阵的最小值，以便得到以下结果:

Similarly, I want to take the min of each 2D matrix, such that I get a result of:

array([0.5000, 0.5000, 0.5000])

但是，当执行 np.max(B，axis = 0)， np.max(B，axis = 1)， np.max(B，axis = 2)或 np.max(B，axis = 3)-这些都不能给出正确的答案.我需要指定另一个参数来执行此操作吗?

However, when doing np.max(B, axis=0), np.max(B, axis=1), np.max(B, axis=2), or np.max(B, axis=3) -- none of these gives the right answer. Is there another argument I need to specify to do this operation?

正确的解决方案不应使用任何循环，理想情况下不应使用一个函数调用.

The correct solution should not use any loops and ideally one function call.

推荐答案

我认为这个问题是对 axis 参数如何工作的误解.对于大多数这些聚合方法， axis 关键字是要投影 的一个或多个轴，即这些轴被移除".从结果.因此，在这种情况下，您想调用类似的内容:

I think the issue is a misunderstanding of how the axis argument works. For most of these aggregation methods the axis keyword is the axis (or axes) to project along, i.e. these axes are "removed" from the result. So in this case you want to call something like:

In [7]: B.max((0, 2, 3))
Out[7]: array([0.5625, 1.25  , 0.625 ])

min

In [8]: B.min((0, 2, 3))
Out[8]: array([0.5, 0.5, 0.5])

或者您可以直接调用 numpy 方法

Or you can call the numpy method directly

In [9]: np.max(B, axis=(0, 2, 3))
Out[9]: array([0.5625, 1.25  , 0.625 ])

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