NumPy: how to quickly normalize many vectors?

Question

How can a list of vectors be elegantly normalized, in NumPy?

Here is an example that does not work:

from numpy import *

vectors = array([arange         


        

Answer 1

Well, unless I missed something, this does work:

vectors / norms

The problem in your suggestion is the broadcasting rules.

vectors  # shape 2, 10
norms  # shape 10

The shape do not have the same length! So the rule is to first extend the small shape by one on the left:

norms  # shape 1,10

You can do that manually by calling:

vectors / norms.reshape(1,-1)  # same as vectors/norms

If you wanted to compute vectors.T/norms, you would have to do the reshaping manually, as follows:

vectors.T / norms.reshape(-1,1)  # this works