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I wanted to cut cycles on one of my programs that needs string approximation and the Text::LevenshteinXS module wouldn't install (no 5.8 on the server) so I wrote my own copy of the levenshtein algorithm. benchmarking against Text::Levenshtein showed 15-35% speed gain. I was thinking of uploading this to cpan, since i've seen many something/Fastsomething modules up there. The biggest difference in the algorithm itself (not the short circuiting) is the min function...why people want to loop over an array when they KNOW there are only going to be 3 paramaters really confounds me.
Think this fast module is worthy of cpan?
package Text::FastLevenshtein; use strict; use Exporter; use vars qw ($VERSION @ISA @EXPORT @EXPORT_OK %EXPORT_TAGS); $VERSION = '0.02'; @ISA = qw(Exporter); @EXPORT = (); @EXPORT_OK = qw(&distance); %EXPORT_TAGS = (); sub _min { my $min = $_[0]; $min = $_[1] if $_[1] < $min; $min = $_[2] if $_[2] < $min; return $min; } sub distance($$) { my $word1 = shift; my $word2 = shift; return 0 if $word1 eq $word2; my @d; my $len1 = length $word1; my $len2 = length $word2; $d[0][0] = 0; for (1 .. $len1) { $d[$_][0] = $_; return $_ if $_!=$len1 && substr($word1,$_) eq substr( +$word2,$_); } for (1 .. $len2) { $d[0][$_] = $_; return $_ if $_!=$len2 && substr($word1,$_) eq substr( +$word2,$_); } for my $i (1 .. $len1) { my $w1 = substr($word1,$i-1,1); for (1 .. $len2) { $d[$i][$_] = _min($d[$i-1][$_]+1, $d[$i][$_-1] ++1, $d[$i-1][$_-1]+($w1 eq substr($word2,$_-1,1) ? 0 : 1)); } } return $d[$len1][$len2]; } 1; __END__

In reply to improved levenshtein by bageler

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