I would like to convert a NumPy array to a unit vector. 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
This function works in a situation where
v is the 0 vector.
If you"re using scikit-learn you can use
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
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