How do you get the magnitude of a vector in Numpy?

Solution 1:

The function you're after is numpy.linalg.norm. (I reckon it should be in base numpy as a property of an array -- say x.norm() -- but oh well).

import numpy as np
x = np.array([1,2,3,4,5])
np.linalg.norm(x)

You can also feed in an optional ord for the nth order norm you want. Say you wanted the 1-norm:

np.linalg.norm(x,ord=1)

And so on.

Solution 2:

If you are worried at all about speed, you should instead use:

mag = np.sqrt(x.dot(x))

Here are some benchmarks:

>>> import timeit
>>> timeit.timeit('np.linalg.norm(x)', setup='import numpy as np; x = np.arange(100)', number=1000)
0.0450878
>>> timeit.timeit('np.sqrt(x.dot(x))', setup='import numpy as np; x = np.arange(100)', number=1000)
0.0181372

EDIT: The real speed improvement comes when you have to take the norm of many vectors. Using pure numpy functions doesn't require any for loops. For example:

In [1]: import numpy as np

In [2]: a = np.arange(1200.0).reshape((-1,3))

In [3]: %timeit [np.linalg.norm(x) for x in a]
100 loops, best of 3: 4.23 ms per loop

In [4]: %timeit np.sqrt((a*a).sum(axis=1))
100000 loops, best of 3: 18.9 us per loop

In [5]: np.allclose([np.linalg.norm(x) for x in a],np.sqrt((a*a).sum(axis=1)))
Out[5]: True

Solution 3:

Yet another alternative is to use the einsum function in numpy for either arrays:

In [1]: import numpy as np

In [2]: a = np.arange(1200.0).reshape((-1,3))

In [3]: %timeit [np.linalg.norm(x) for x in a]
100 loops, best of 3: 3.86 ms per loop

In [4]: %timeit np.sqrt((a*a).sum(axis=1))
100000 loops, best of 3: 15.6 µs per loop

In [5]: %timeit np.sqrt(np.einsum('ij,ij->i',a,a))
100000 loops, best of 3: 8.71 µs per loop

or vectors:

In [5]: a = np.arange(100000)

In [6]: %timeit np.sqrt(a.dot(a))
10000 loops, best of 3: 80.8 µs per loop

In [7]: %timeit np.sqrt(np.einsum('i,i', a, a))
10000 loops, best of 3: 60.6 µs per loop

There does, however, seem to be some overhead associated with calling it that may make it slower with small inputs:

In [2]: a = np.arange(100)

In [3]: %timeit np.sqrt(a.dot(a))
100000 loops, best of 3: 3.73 µs per loop

In [4]: %timeit np.sqrt(np.einsum('i,i', a, a))
100000 loops, best of 3: 4.68 µs per loop