numpy:将向量添加到矩阵列明智 [英] Numpy: add a vector to matrix column wise

问题描述

a
Out[57]: 
array([[1, 2],
       [3, 4]])

b
Out[58]: 
 array([[5, 6],
       [7, 8]])

In[63]: a[:,-1] + b
Out[63]: 
array([[ 7, 10],
       [ 9, 12]])

这是逐行加法.我如何将它们按列明智地添加以获得

This is row wise addition. How do I add them column wise to get

In [65]: result
Out[65]: 
array([[ 7,  8],
       [11, 12]])

我不想转置整个数组，添加然后转回.还有其他办法吗?

I don't want to transpose the whole array, add and then transpose back. Is there any other way?

推荐答案

在a[:,-1]的末尾添加新轴，使其形状为(2,1).然后，添加b将会沿着列(广播 第二个轴)而不是行(这是默认值).

Add a newaxis to the end of a[:,-1], so that it has shape (2,1). Addition with b would then broadcast along the column (the second axis) instead of the rows (which is the default).

In [47]: b + a[:,-1][:, np.newaxis]
Out[47]: 
array([[ 7,  8],
       [11, 12]])

---

a[:,-1]具有形状(2,). b具有形状(2,2).广播默认情况下会在左侧的 中添加新的坐标轴.因此，当NumPy计算a[:,-1] + b时，其广播机制会导致a[:,-1]的形状更改为(1,2)并广播为(2,2)，并沿其长度1轴(即沿其行)的值进行广播.

---

a[:,-1] has shape (2,). b has shape (2,2). Broadcasting adds new axes on the left by default. So when NumPy computes a[:,-1] + b its broadcasting mechanism causes a[:,-1]'s shape to be changed to (1,2) and broadcasted to (2,2), with the values along its axis of length 1 (i.e. along its rows) to be broadcasted.

相反，a[:,-1][:, np.newaxis]具有形状(2,1).因此，广播将其形状更改为(2,2)，并沿其长度为1的轴(即沿其列)的值进行广播.

In contrast, a[:,-1][:, np.newaxis] has shape (2,1). So broadcasting changes its shape to (2,2) with the values along its axis of length 1 (i.e. along its columns) to be broadcasted.

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