Hamming distance

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In information theory, the Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different. In other words, it measures the minimum number of substitutions required to change one string into the other, or the minimum number of errors that could have transformed one string into the other. In a more general context, the Hamming distance is one of several string metrics for measuring the edit distance between two sequences.

A major application is in coding theory, more specifically to block codes, in which the equal-length strings are vectors over a finite field.

Video Hamming distance

Examples

The Hamming distance between:

• "karolin" and "kathrin" is 3.
• "karolin" and "kerstin" is 3.
• 1011101 and 1001001 is 2.
• 2173896 and 2233796 is 3.

Maps Hamming distance

Properties

For a fixed length n, the Hamming distance is a metric on the set of the words of length n (also known as a Hamming space), as it fulfills the conditions of non-negativity, identity of indiscernibles and symmetry, and it can be shown by complete induction that it satisfies the triangle inequality as well. The Hamming distance between two words a and b can also be seen as the Hamming weight of a - b for an appropriate choice of the - operator, much as the difference between two integers can be seen as a distance from zero on the number line.

For binary strings a and b the Hamming distance is equal to the number of ones (population count) in a XOR b. The metric space of length-n binary strings, with the Hamming distance, is known as the Hamming cube; it is equivalent as a metric space to the set of distances between vertices in a hypercube graph. One can also view a binary string of length n as a vector in ${\displaystyle \mathbb {R} ^{n}}$ by treating each symbol in the string as a real coordinate; with this embedding, the strings form the vertices of an n-dimensional hypercube, and the Hamming distance of the strings is equivalent to the Manhattan distance between the vertices.

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Error detection and error correction

The Hamming distance is used to define some essential notions in coding theory, such as error detecting and error correcting codes. In particular, a code C is said to be k-errors detecting if any two codewords c₁ and c₂ from C that have a Hamming distance less than k coincide; otherwise, a code is k-errors detecting if, and only if, the minimum Hamming distance between any two of its codewords is at least k+1.

A code C is said to be k-errors correcting if, for every word w in the underlying Hamming space H, there exists at most one codeword c (from C) such that the Hamming distance between w and c is at most k. In other words, a code is k-errors correcting if, and only if, the minimum Hamming distance between any two of its codewords is at least 2k+1. This is more easily understood geometrically as any closed balls of radius k centered on distinct codewords being disjoint. These balls are also called Hamming spheres in this context.

Thus a code with minimum Hamming distance d between its codewords can detect at most d-1 errors and can correct ?(d-1)/2? errors. The latter number is also called the packing radius or the error-correcting capability of the code.

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History and applications

The Hamming distance is named after Richard Hamming, who introduced the concept in his fundamental paper on Hamming codes Error detecting and error correcting codes in 1950. Hamming weight analysis of bits is used in several disciplines including information theory, coding theory, and cryptography.

It is used in telecommunication to count the number of flipped bits in a fixed-length binary word as an estimate of error, and therefore is sometimes called the signal distance. For q-ary strings over an alphabet of size q >= 2 the Hamming distance is applied in case of the q-ary symmetric channel, while the Lee distance is used for phase-shift keying or more generally channels susceptible to synchronization errors because the Lee distance accounts for errors of ±1. If ${\displaystyle q=2}$ or ${\displaystyle q=3}$ both distances coincide because any pair of elements from ${\textstyle \mathbb {Z} /2\mathbb {Z} }$ or ${\textstyle \mathbb {Z} /3\mathbb {Z} }$ differ by 1, but the distances are different for larger ${\displaystyle q}$.

The Hamming distance is also used in systematics as a measure of genetic distance.

However, for comparing strings of different lengths, or strings where not just substitutions but also insertions or deletions have to be expected, a more sophisticated metric like the Levenshtein distance is more appropriate.

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Algorithm example

The function hamming_distance(), implemented in Python 2.3+, computes the Hamming distance between two strings (or other iterable objects) of equal length by creating a sequence of Boolean values indicating mismatches and matches between corresponding positions in the two inputs and then summing the sequence with False and True values being interpreted as zero and one.

where the zip() function merges two equal-length collections in pairs.

The following C function will compute the Hamming distance of two integers (considered as binary values, that is, as sequences of bits). The running time of this procedure is proportional to the Hamming distance rather than to the number of bits in the inputs. It computes the bitwise exclusive or of the two inputs, and then finds the Hamming weight of the result (the number of nonzero bits) using an algorithm of Wegner (1960) that repeatedly finds and clears the lowest-order nonzero bit. Some compilers support the __builtin_popcount function which can calculate this using specialized processor hardware where available.

Or, a much faster hardware alternative (for compilers that support builtins) is to use popcount like so.

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See also

• Closest string
• Damerau-Levenshtein distance
• Euclidean distance
• Mahalanobis distance
• Jaccard index
• Sørensen similarity index
• Word ladder
• Gray code
• Levenshtein distance
• Sparse distributed memory

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References

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Further reading

•  This article incorporates public domain material from the General Services Administration document "Federal Standard 1037C".
• Wegner, Peter (1960). "A technique for counting ones in a binary computer". Communications of the ACM. 3 (5): 322. doi:10.1145/367236.367286. 
• MacKay, David J. C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge: Cambridge University Press. ISBN 0-521-64298-1. 

Source of the article : Wikipedia