Donbeo Donbeo - 1 month ago 49
Python Question

how to normalize array numpy?

I would like to have a norm 1 numpy array.
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 situation where v is the 0 vector.

Answer

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