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Function: EditDistance
Finds the edit distance between two strings or tables. Edit distance is the minimum number of
edits needed to transform one string or table into the other.
s - A *string* or *table*.
t - Another *string* or *table* to compare against s.
lim - An *optional number* to limit the function to a maximum edit distance. If specified
and the function detects that the edit distance is going to be larger than limit, limit
is returned immediately.
A *number* specifying the minimum edits it takes to transform s into t or vice versa. Will
not return a higher number than lim, if specified.
:EditDistance( "Tuesday", "Teusday" ) -- One transposition.
:EditDistance( "kitten", "sitting" ) -- Two substitutions and a deletion.
* Complexity is O( (#t+1) * (#s+1) ) when lim isn't specified.
* This function can be used to compare array-like tables as easily as strings.
* The algorithm used is Damerau–Levenshtein distance, which calculates edit distance based
off number of subsitutions, additions, deletions, and transpositions.
* Source code for this function is based off the Wikipedia article for the algorithm
* This function is case sensitive when comparing strings.
* If this function is being used several times a second, you should be taking advantage of
the lim parameter.
* Using this function to compare against a dictionary of 250,000 words took about 0.6
seconds on my machine for the word "Teusday", around 10 seconds for very poorly
spelled words. Both tests used lim.
v1.00 - Initial.
function EditDistance( s, t, lim )
local s_len, t_len = #s, #t -- Calculate the sizes of the strings or arrays
if lim and math.abs( s_len - t_len ) >= lim then -- If sizes differ by lim, we can stop here
return lim
-- Convert string arguments to arrays of ints (ASCII values)
if type( s ) == "string" then
s = { string.byte( s, 1, s_len ) }
if type( t ) == "string" then
t = { string.byte( t, 1, t_len ) }
local min = math.min -- Localize for performance
local num_columns = t_len + 1 -- We use this a lot
local d = {} -- (s_len+1) * (t_len+1) is going to be the size of this array
-- This is technically a 2D array, but we're treating it as 1D. Remember that 2D access in the
-- form my_2d_array[ i, j ] can be converted to my_1d_array[ i * num_columns + j ], where
-- num_columns is the number of columns you had in the 2D array assuming row-major order and
-- that row and column indices start at 0 (we're starting at 0).
for i=0, s_len do
d[ i * num_columns ] = i -- Initialize cost of deletion
for j=0, t_len do
d[ j ] = j -- Initialize cost of insertion
for i=1, s_len do
local i_pos = i * num_columns
local best = lim -- Check to make sure something in this row will be below the limit
for j=1, t_len do
local add_cost = (s[ i ] ~= t[ j ] and 1 or 0)
local val = min(
d[ i_pos - num_columns + j ] + 1, -- Cost of deletion
d[ i_pos + j - 1 ] + 1, -- Cost of insertion
d[ i_pos - num_columns + j - 1 ] + add_cost -- Cost of substitution, it might not cost anything if it's the same
d[ i_pos + j ] = val
-- Is this eligible for tranposition?
if i > 1 and j > 1 and s[ i ] == t[ j - 1 ] and s[ i - 1 ] == t[ j ] then
d[ i_pos + j ] = min(
val, -- Current cost
d[ i_pos - num_columns - num_columns + j - 2 ] + add_cost -- Cost of transposition
if lim and val < best then
best = val
if lim and best >= lim then
return lim
return d[ #d ]

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@reidoda reidoda commented Oct 19, 2018

Hi I'd like to use this code for a project at my company. Could you choose an open source license so that I can know whether or not you will permit this? I'm happy to speak directly with you about how it will be used.


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@arjunae arjunae commented May 14, 2019

Hey reidoda,
Also been unsure about license questions especially in gists. - especially those well written ones like above.

So far i found (uses lua's free license)
... a forum post at facepunch (with unknown license status)
and finally one with a MIT License:

Hope that helps.

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