Fast hamming distance computation between binary numpy arrays

Question

I have two numpy arrays of the same length that contain binary values

import numpy as np
a=np.array([1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0])
b=np.array([1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1])

I want to compute the hamming distance between them as fast as possible since I have millions of such distance computations to make.

A simple but slow option is this (taken from wikipedia):

%timeit sum(ch1 != ch2 for ch1, ch2 in zip(a, b))
10000 loops, best of 3: 79 us per loop

I have come up with faster options, inspired by some answers here on stack overflow.

%timeit np.sum(np.bitwise_xor(a,b))
100000 loops, best of 3: 6.94 us per loop

%timeit len(np.bitwise_xor(a,b).nonzero()[0])
100000 loops, best of 3: 2.43 us per loop

I'm wondering if there are even faster ways to compute this, possibly using cython?

Answer 1

There is a ready numpy function which beats len((a != b).nonzero()[0]) ;)

np.count_nonzero(a!=b)