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 sklearn or numpy?

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

Answer 1

The normalization of data is important for the fast and smooth training of our machine learning models. Scikit learn, a library of python has sklearn.preprocessing.normalize, that helps to normalize the data easily.

For example:

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

Hope this answer helps

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