二进制numpy数组之间的快速汉明距离计算 [英] Fast hamming distance computation between binary numpy arrays

问题描述

我有两个长度相同的numpy数组，其中包含二进制值

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])

我想尽可能快地计算它们之间的汉明距离，因为我要进行数百万次这样的距离计算.

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

这是一个简单但缓慢的选项(摘自维基百科):

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

我想出了更快的选择，灵感来自堆栈溢出的一些答案.

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

我想知道是否有更快的方法(可能使用cython)进行计算?

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

推荐答案

有一个现成的numpy函数可以击败len((a != b).nonzero()[0]);)

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

np.count_nonzero(a!=b)

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