algorithm,cluster-analysis,computational-geometry,hamming-distance

One of the previously asked questions has some good discussions, so you can refer to that, Nearest neighbors in high-dimensional data? Other than this, you can also look at, http://web.cs.swarthmore.edu/~adanner/cs97/s08/papers/dahl_wootters.pdf Few papers which analyze different approaches, http://www.jmlr.org/papers/volume11/radovanovic10a/radovanovic10a.pdf https://www.cse.ust.hk/~yike/sigmod09-lsb.pdf...

You can consider the bit-positions separately. That gives you 32 (or some other number) of easier problems, where you still have to calculate the sum of all pairs of hamming distances, except now it's over 1-bit numbers. The hamming distance between two 1-bit numbers is their XOR. And now it...

mysql,string,binary,hamming-distance,bitcount

Working code: SELECT BIT_COUNT( CONV( hash, 2, 10 ) ^ 0b0000000101100111111100011110000011100000111100011011111110011011 ) ...

binary,bioinformatics,hamming-distance

Just calculate the distance for each pair: L1 L2 L3 L4 L1 0 3 2 0 L2 3 0 4 2 L3 2 4 0 3 L4 0 2 3 0 How to represent the matrix depends on the programming language you use. Some kind of two-dimensional array (or at...

r,sequence,dplyr,hamming-distance

Here is another dplyr solution that does not require any transformation of the data into long/wide forms: library(dplyr) sek = rbind(c(1, 'a', 'a', 'a'), c(1, 'a', 'a', 'a'), c(2, 'b', 'b', 'b'), c(2, 'c', 'b', 'b')) %>% data.frame colnames(sek) <- c('Group', paste('t', 1:3, sep = '')) sek %>% group_by(Group) %>%...

python,binary,bit,hamming-distance

Implement it: def hamming2(s1, s2): """Calculate the Hamming distance between two bit strings""" assert len(s1) == len(s2) return sum(c1 != c2 for c1, c2 in zip(s1, s2)) And test it: assert hamming2("1010", "1111") == 2 assert hamming2("1111", "0000") == 4 assert hamming2("1111", "1111") == 0 ...

SELECT CONVERT((CONV('b4124b0d195b2507', 16, 10)), SIGNED) ^ CONVERT((CONV('eae26aebf1f139f9', 16, 10)), SIGNED) is what you want The conv as per the docs http://dev.mysql.com/doc/refman/5.0/en/mathematical-functions.html#function_conv Returns a string representation of the number N, converted from base from_base to base to_base You need to convert back to numbers to xor...

bit-manipulation,bit,bitwise-operators,hamming-distance

You can simply do this : int need=__builtin_popcountll(A^B); cout<<need; ...

python,runtime,timeit,hamming-distance

The key is making less method lookups and function calls: def hamming_distance_4(s_1, s_2): return sum(i != j for i, j in i.izip(s_1, s_2)) runs at ham_4 1.10134792328 in my system. ham_2 and ham_3 makes lookups inside the loops, so they are slower....

python,algorithm,graph-algorithm,hamming-distance

Assuming you store your dictionary in a set(), so that lookup is O(1) in the average (worst case O(n)). You can generate all the valid words at hamming distance 1 from a word: >>> def neighbours(word): ... for j in range(len(word)): ... for d in string.ascii_lowercase: ... word1 = ''.join(d...