Numpy:带有负轴的 np.sum [英] Numpy: np.sum with negative axis

问题描述

我想知道如果轴为负数，则从最后一个轴到第一个轴计数."在 docs，我已经测试过这些:

<预><代码>>>>吨数组([[1, 2],[3, 4]])>>>np.sum(t,axis=1)数组([3, 7])>>>np.sum(t,axis=0)数组([4, 6])>>>np.sum(t,axis=-2)数组([4, 6])

仍然困惑，我需要一些易于理解的解释.

解决方案

首先看一下长度为 2 的列表上的列表索引:

<预><代码>>>>L = ['一'，'二']>>>L[-1] # 最后一个元素'二'>>>L[-2] # 倒数第二个元素'一'>>>L[-3] # 越界——这个列表中只有两个元素# IndexError: 列表索引超出范围

axis 参数是类似的，除了它指定 ndarray 的维度.如果使用非方形数组会更容易看出:

<预><代码>>>>t = np.arange(1,11).reshape(2,5)>>>吨数组([[ 1, 2, 3, 4, 5],[ 6, 7, 8, 9, 10]])>>>t.ndim # 二维数组2>>>t.shape # 长度为 t.ndim 的元组(2, 5)

那么让我们看看调用 sum 的各种方法:

<预><代码>>>>t.sum() # 所有元素55>>>t.sum(axis=0) # 在第 0 个轴上求和，即列数组([ 7, 9, 11, 13, 15])>>>t.sum(axis=1) # 对第一个轴即行求和数组([15, 40])>>>t.sum(axis=-2) # 对 -2th 轴求和，即再次列 (-2 % ndim == 0)数组([ 7, 9, 11, 13, 15])

尝试 t.sum(axis=-3) 会出错，因为这个数组中只有 2 个维度.不过，您可以在 3d 阵列上使用它.

I wonder what does "If axis is negative it counts from the last to the first axis." mean in the docs, I've test these:

>>> t
array([[1, 2],
       [3, 4]])
>>> np.sum(t, axis=1)
array([3, 7])
>>> np.sum(t, axis=0)
array([4, 6])
>>> np.sum(t, axis=-2)
array([4, 6])

Still confused, I need some easily understood explanation.

解决方案

First look at list indexing on a length-2 list:

>>> L = ['one', 'two']
>>> L[-1]  # last element
'two'
>>> L[-2]  # second-to-last element
'one'
>>> L[-3]  # out of bounds - only two elements in this list
# IndexError: list index out of range

The axis argument is analogous, except it's specifying the dimension of the ndarray. It will be easier to see if using a non-square array:

>>> t = np.arange(1,11).reshape(2,5)
>>> t
array([[ 1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10]])
>>> t.ndim  # two-dimensional array
2
>>> t.shape  # a tuple of length t.ndim
(2, 5)

So let's look at the various ways to call sum:

>>> t.sum()  # all elements
55
>>> t.sum(axis=0)  # sum over 0th axis i.e. columns
array([ 7,  9, 11, 13, 15])
>>> t.sum(axis=1)  # sum over 1st axis i.e. rows
array([15, 40])
>>> t.sum(axis=-2)  # sum over -2th axis i.e. columns again (-2 % ndim == 0)
array([ 7,  9, 11, 13, 15])

Trying t.sum(axis=-3) will be an error, because you only have 2 dimensions in this array. You could use it on a 3d array, though.

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