How to normalize a numpy array to a unit vector

normalization, numpy, python, scikit-learn, statistics

Solution

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

Problem

I would like to convert a NumPy array to a unit vector. More specifically, I am looking for an equivalent version of this normalisation function: ``` def normalize(v): norm = np.linalg.norm(v) if norm == 0: return v return v / norm ``` This function handles the situation where vector `v` has the norm value of 0. Is there any similar functions provided in `sklearn` or `numpy`?

Original source