How to normalize an array in NumPy?
Question description - 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.
The numpy normalize 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)*10norm1 = x / np.linalg.norm(x)norm2 = normalize(x[:,np.newaxis], axis=0).ravel()print(np.all(norm1 == norm2))# TrueHope this answer helps