Normalise list of N dimensional numpy arrays

Question

I have a list of N dimensional NumPy arrays.

num_vecs = 10
dims = 2
vecs = np.random.normal(size=(num_vecs, dims))

I want to normalize them, so the magnitude/length of each vector is 1. I can easily do this with a for-loop.

norm = np.linalg.norm(vecs, axis=1)
for dd in range(dims):
 vecs[:, dd] /= norm
assert np.allclose(np.linalg.norm(vecs, axis=1), 1.)

But how do I get rid of the for-loop?

Answer 1

The trick is to use the keepdims parameter.

import numpy as np
num_vecs = 10
dims = 2
vecs = np.random.normal(size=(num_vecs, dims))
vecs /= np.linalg.norm(vecs, axis=1, keepdims=True)
assert np.allclose(np.linalg.norm(vecs, axis=1), 1.)