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Levenshtein distance

Compute distance for each pair. Notice that this recursive implementation is very inefficient. Wagner-Fischer algorithm is iterative and much faster. #vimscript

Start file

" Levenshtein distance between two strings
function! LD(s1, s2)
  let l1 = strlen(a:s1)
  let l2 = strlen(a:s2)
  if l1 <= 0
    return l2
  elseif l2 <= 0
    return l1
  elseif a:s1[0] ==# a:s2[0]
    return LD(a:s1[1:], a:s2[1:])
  else
    return 1 + min([
    \ LD(a:s1, a:s2[1:]), LD(a:s1[1:], a:s2), LD(a:s1[1:], a:s2[1:])
    \ ])
  endif
endfunction
" Humm... Is this useful?
"nmap M I<C-R>=LD("<C-R>a","<C-R>b")<CR><Esc>

"VIM VimGolf
"keystroke matt
"vimscript way
"reformat refactor
"challenge chatter
"colons semicolons
"golfer golfers
"order sorting
"modules models
"shift+ret ctrl+ret
"ninja manic
"destination distance
"pattern lines
"linewrapping linewrapping
"swap switch
"vice versa
"split multiples
"block blackened
"-a-b-c c-a-b
"hello_world hello world!

End file

6
8
9
5
5
4
1
5
2
5
4
7
6
0
4
4
7
5
3
2

View Diff

1,39c1,20
< " Levenshtein distance between two strings
< function! LD(s1, s2)
<   let l1 = strlen(a:s1)
<   let l2 = strlen(a:s2)
<   if l1 <= 0
<     return l2
<   elseif l2 <= 0
<     return l1
<   elseif a:s1[0] ==# a:s2[0]
<     return LD(a:s1[1:], a:s2[1:])
<   else
<     return 1 + min([
<     \ LD(a:s1, a:s2[1:]), LD(a:s1[1:], a:s2), LD(a:s1[1:], a:s2[1:])
<     \ ])
<   endif
< endfunction
< " Humm... Is this useful?
< "nmap M I<C-R>=LD("<C-R>a","<C-R>b")<CR><Esc>
< 
< "VIM VimGolf
< "keystroke matt
< "vimscript way
< "reformat refactor
< "challenge chatter
< "colons semicolons
< "golfer golfers
< "order sorting
< "modules models
< "shift+ret ctrl+ret
< "ninja manic
< "destination distance
< "pattern lines
< "linewrapping linewrapping
< "swap switch
< "vice versa
< "split multiples
< "block blackened
< "-a-b-c c-a-b
< "hello_world hello world!
---
> 6
> 8
> 9
> 5
> 5
> 4
> 1
> 5
> 2
> 5
> 4
> 7
> 6
> 0
> 4
> 4
> 7
> 5
> 3
> 2

Solutions by @Seve_py:

Created by: @mcr05 Tweet

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