Numpy 用一个向量数组点一个矩阵，得到一个新的向量数组

Question

Say we have an array of 2D vectors (describing a square shape), and a matrix (scale along y axis):

vecs = np.array([[1, 0],
                 [1, 1],
                 [0, 1],
                 [0, 0]])
mat = np.array([[1, 0],
                [0, 2]])

I want to get a new array of vectors, where each vector from vecs is dot multiplied with mat . Now I do it like this:

new_vecs = vecs.copy()
for i, vec in enumerate(vecs):
    new_vecs[i] = np.dot(mat, vec)

This produces the desired result:

>>> print(new_vecs)
[[1 0]
 [1 2]
 [0 2]
 [0 0]]

What are better ways to do this?

Answer 1

The dot product np.dot will multiply matrices of any shape with each other, as long as their shapes line up: np.dot((a,b), (b,c)) -> (a,c) . So if you invert the order, Numpy does this for you in one call:

In [3]: np.dot(vecs, mat)
Out[3]:
array([[1, 0],
       [1, 2],
       [0, 2],
       [0, 0]])

Answer 2

You can use the following:

np.dot(mat , vecs.T).T