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`

?

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