[英]Broadcast operation between arrays of shape (N, M) and (N,)
This is a rather simple question but I can't seem to find the answer. 这是一个相当简单的问题,但我似乎找不到答案。 Consider two simple arrays:
考虑两个简单的数组:
import numpy as np
a = np.random.uniform(0., 1., (2, 1000))
b = np.random.uniform(0., 1., (2,))
I want to perform the operation a - b
so that the final array is ([[a[0] - b[0], a[1] - b[1]])
and I get 我想要执行操作
a - b
以便最终数组为([[a[0] - b[0], a[1] - b[1]])
,我得到
ValueError: operands could not be broadcast together with shapes (2,1000) (2,)
How can I perform this (or some other) operation? 如何执行此(或其他)操作?
According to the General Broadcasting Rules : 根据一般广播规则 :
When operating on two arrays, NumPy compares their shapes element-wise.
在两个数组上进行操作时,NumPy逐元素比较其形状。 It starts with the trailing dimensions, and works its way forward.
它从尾随尺寸开始,一直向前发展。 Two dimensions are compatible when
两种尺寸兼容
- they are equal, or
它们相等,或者
- one of them is 1
其中之一是1
So there's the error because the last dimension of a
(1000) and b
(2) can not be broadcasted; 出现错误是因为无法广播
a
(1000)和b
(2)的最后维度; You can convert b
to a 2d array of shape (2, 1)
so that 1
-> (can broadcast to) 1000
, 2
-> (can broadcast to) 2
: 你可以转换
b
到形状的2D阵列(2, 1)
使得1
- >(可广播到) 1000
, 2
- >(可广播到) 2
:
a - b[:,None]
#array([[ 0.06475683, -0.43773571, -0.62561564, ..., 0.05205518,
# -0.1209487 , 0.16334639],
# [ 0.58443617, 0.28764136, 0.75789299, ..., 0.18159133,
# 0.28548633, -0.12037869]])
Or 要么
a - b.reshape(2,1)
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