hi monks,
here comes another implementation of the
Levenshtein Distance; a distance metric which measures the similarities of strings.
Info and some other implementations can be found at Levenshtein distance: calculating similarity of strings.
At the moment I need to compare two sets of strings where each set consists of approximately 2000 strings, which means approximataley 3-4 million measurements. Thus I wrote this code, in which I tried to minimize the memory usage, to make it more efficient.
sub ldist {
my @s = split //, shift;
my @t = split //, shift;
return scalar @t if scalar @s == 0;
return scalar @s if scalar @t == 0;
my (@prevColumn, @currColumn);
@prevColumn = 0..scalar(@t);
for my $s (0..$#s) {
@currColumn = ( $s + 1 );
for my $t (0..$#t) {
push @currColumn, min(
$currColumn[$t] + 1
, $prevColumn[$t+1] + 1
, $prevColumn[$t] + ($s[$s] eq $t[$t] ? 0 : 1)
);
}
@prevColumn = @currColumn;
}
pop @currColumn
}
sub min {
my $min = shift;
for (@_) {
$min = $_ if $_ < $min;
}
$min;
}
