Fastest Way To Get Hamming Distance For Integer Array

Let a and b be vectors of the same size with 8-bit integers (0-255). I want to compute the number of bits where those vectors differs i.e. a Hamming distance between vectors formed

Solution 1:

Approach #1 : We could broadcast them into binary bits & count number of different bits, like so -

def hamming_distance(a, b):
    r = (1 << np.arange(8))[:,None]
    return np.count_nonzero( (a & r) != (b & r) )

Sample run -

In [144]: a = [127,255]
     ...: b = [127,240]
     ...: 

In [145]: hamming_distance(a, b)
Out[145]: 4

Approach #2 : Using bitwise-xor operation, we can find out the number of different binary bits between a and b -

def hamming_distance_v2(a, b):
    r = (1 << np.arange(8))[:,None]
    return np.count_nonzero((np.bitwise_xor(a,b) & r) != 0)

Solution 2:

If you are going to call the distance function many times during one execution of your program, you can gain some speed by using a precomputed table of bit counts. Here's (yet another) version of the Hamming distance function:

# _nbits[k] is the number of 1s in the binary representation of k for 0 <= k < 256.
_nbits = np.array(
      [0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3,
       4, 2, 3, 3, 4, 3, 4, 4, 5, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4,
       4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 1, 2, 2, 3, 2,
       3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5,
       4, 5, 5, 6, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4,
       5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3,
       3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 2,
       3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6,
       4, 5, 5, 6, 5, 6, 6, 7, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5,
       6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 3, 4, 4, 5, 4, 5,
       5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6,
       7, 7, 8], dtype=np.uint8)


defhamming_distance1(a, b):
    c = np.bitwise_xor(a, b)
    n = _nbits[c].sum()
    return n

In the following, a and b are the Python lists of length 32 given in a comment to the question. divakar_hamming_distance() and divakar_hamming_distance_v2() are from @Divakar's answer.

Here are the timings of @Divakar's functions:

In [116]: %timeit divakar_hamming_distance(a, b)
The slowest run took 5.57times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 11.3 µs per loop

In [117]: %timeit divakar_hamming_distance_v2(a, b)
The slowest run took 5.35times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 10.3 µs per loop

hamming_distance1(a, b) is a bit faster:

In [118]: %timeit hamming_distance1(a, b)
The slowest run took 6.04 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 7.42 µs per loop

On my computer, initializing _nbits takes about 11 µs, so there is no advantage to using hamming_distance1 if you only call the function once. If you call it three or more times, there is a net gain in performance.

If the inputs are already numpy arrays, all the functions are significantly faster:

In [119]: aa = np.array(a)

In [120]: bb = np.array(b)

In [121]: %timeit divakar_hamming_distance_v2(aa, bb)
The slowest run took 8.22 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 5.72 µs per loop

In [122]: %timeit hamming_distance1(aa, bb)
The slowest run took 12.67 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 2.77 µs per loop

Of course, if you always do that immediately before you compute the Hamming distance, the time to do the conversion must be included in the overall timing. However, if you write the code that generates a and b to take advantage of numpy earlier, you might already have them as numpy arrays by the time you compute the Hamming distance.

---

(I also experimented a bit with a 2-d array of precomputed Hamming distances between 8 bit values--an array with shape (256, 256)--but the initialization cost is higher and the performance gains are small.)

Solution 3:

maybe not the most efficient way, but the easiest imo is to convert your ouptut array to strings in binary form, then take the sum of all the characters converted back to ints...

import numpy as np

output = np.random.randint(0,63,10)
hamming = ['{:b}'.format(x).count('1') for x inoutput]

Solution 4:

sum(bin(x).count("1") for x in np.bitwise_xor(a,b))