How to normalize an array in NumPy?

Question

I would like to have the norm of one NumPy array. More specifically, I am looking for an equivalent version of this function

def normalize(v):
    norm = np.linalg.norm(v)
    if norm == 0: 
       return v
    return v / norm

Is there something like that in skearn or numpy?

This function works in a situation where v is the 0 vector.

Answer 1

If you're using scikit-learn you can use sklearn.preprocessing.normalize:

import numpy as np
from sklearn.preprocessing import normalize

x = np.random.rand(1000)*10
norm1 = x / np.linalg.norm(x)
norm2 = normalize(x[:,np.newaxis], axis=0).ravel()
print np.all(norm1 == norm2)
# True