Nayruden's /dev/random

--[[
    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.
   
    Parameters:
   
        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.
           
    Returns:
   
        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.
           
    Example:

        :EditDistance( "Tuesday", "Teusday" ) -- One transposition.
        :EditDistance( "kitten", "sitting" ) -- Two substitutions and a deletion.

        returns...

        :1
        :3
           
    Notes:
   
        * 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
            <http://en.wikipedia.org/w/index.php?title=Damerau%E2%80%93Levenshtein_distance&oldid=351641537>.
        * 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.
           
    Revisions:

        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
    end
   
    -- Convert string arguments to arrays of ints (ASCII values)
    if type( s ) == "string" then
        s = { string.byte( s, 1, s_len ) }
    end
   
    if type( t ) == "string" then
        t = { string.byte( t, 1, t_len ) }
    end
   
    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
    end
    for j=0, t_len do
        d[ j ] = j -- Initialize cost of insertion
    end
   
    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
                )
            end
           
            if lim and val < best then
                best = val
            end
        end
       
        if lim and best >= lim then
            return lim
        end
    end
   
    return d[ #d ]
end