=pod

=head1 NAME

String::Similarity::Group - take a list of strings and segregate by similarity

=head1 SYNOPSIS

   use String::Similarity::Group ':all';

   my @elements = qw/victory victori matrix latrix ooland/;   
   
   my @groups   = groups( 0.8, \@elements );
   # ( [ 'victory', 'victori' ], [ 'matrix', 'latrix' ] )
   
   my @loners   = loners( 0.8, \@elements );
   # ( 'ooland' )

   # Which of the elements closest matches a string?
   my($element, $score) = similarest(\@elements, 'oland');
   # ( 'ooland', 0.83 )
   


=head1 DESCRIPTION

Imagine you have a list of filenames, and you want to group them by similarity.
You can simply pass at list of strings, the min similarity to match, and you 
get an array of groups ( array refs of similar elements).

Or if you have a list of strings, and you want to know which is most similar to your
control string.

If you have a list of names of people, and you want to know which are the most unique 
of the bunch.

=head1 SUBS

None exported by default.

=head2 groups()

First argument is similarity minimum.
Second argument is an array ref ( of the strings in question).


Returns array. 
Each element of this array is an array ref, of a group of elements, 
that match at least as your similarity minimum argument.
If an element did not contain at least one match, it is left out.

=head3 lazy matching vs hard matching

As we group, the first element is the 'group leader', by default, as 
we test elements, we test only to each group leader, and pick the highest
matching. This decreases the number of similarity procedures exponentially,
and still provides great results (tests included in distro).

You may however want to have stricter, or lazier- matching..

If you use lazy matching, we stop at the first positive match to classify an element
onto a group. With hard matching (default), we continue evaluating every element
until we have the best match possible- to classify.
Hard matching is important when you have a low similarity minimun set- to get more accurate
results.

=head4 groups()

Default. Finds closest group leader, to group by.

=head4 groups_hard()

Thorough grouping.
Finds closest element, in every group, to group by.

=head4 groups_lazy()

Laziest grouping, tests to first matching group leader, without comparing the others.

=cut





=head2 loners()

First argument is similarity min.
Second argument is an array ref to the strings in question.

Returns array of every element that does not group.

If an element did contain more than one match, it is left out.
For example, if your list has very different strings, and you set the min high..

   my @elements = qw/victory victori couples singling/;   
   my @loners = loners( 0.9, \@elements );
   scalar @loners == 2 or die;
   # @loners contains couples, singing

Another way to explain what this does: I have a list of 50 names of people. Which 
are the most unique ones?

=cut




=head2 similarest()

Arguments are array ref, string, and optionally a similarity minimum.
Returns array, first element is element that matches highest, second element is the
similarity score.

For example, you have a list of names, you want to see which of these names is most 
alike the name 'Paul'..

   my @names = qw/Paula James Marcus Gregg/;
   my( $closest_name, $score ) = similarest(  \@names, 'Paul' );

What if you only want to get a result of the similarity is at least 0.8 ?

   my @names = qw/Paula James Marcus Gregg/;
   my( $closest_name, $score ) = similarest(  \@names, 'Paul', 0.8 );

If none matches (if all score to 0, or all scores are below your similarity minimum 
argument), returns undef.

=cut



=head1 similarity minimum

See L<String::Similarity>.
Minimum required to be a positive match, float from 0.00 to 1.00.

=head2 Varying the similarity minimum

If you relax or tighten the similarity minimum, you get different results.
   
   my @groups   = groups( 0.80, [qw/victory victori matrix latrix ooland] );
   # ( [ 'victory', 'victori' ], [ 'matrix', 'latrix' ] )
   
If you set the minimum very low, it means we tolerate just about any match...

   my @groups   = groups( 0.05, [qw/victory victori matrix latrix ooland] );
   # ( [ 'victory', 'victori', 'matrix', 'latrix', 'ooland' ] )

Thus we deem there to be one group, in this case, not very useful.

=cut




=head1 CAVEATS

This is alpha software. Still in development.

=head1 DISCUSSION

If you use the same element twice, it is ignored. This is beacause of the internals.
That means if you do this:

   my @groups   = groups( 0.8, [qw/victory victory] );

Nothing is returned!

If you lower the similarity minimum a lot, we get high groupings, one group tends to accumulate
a lot more matches than other groups. This is because inside, we test against every element
in a group to make a positive group match.

Should this work differently? See any bugs? Have any suggestions? Please contact the AUTHOR.

=head1 SEE ALSO

L<String::Similarity>

=head1 AUTHOR

Leo Charre leocharre at cpan dot org

=head1 LICENSE

This package is free software; you can redistribute it and/or modify it under the same terms as Perl itself, i.e., under the terms of the "Artistic License" or the "GNU General Public License".

=head1 DISCLAIMER

This package is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

See the "GNU General Public License" for more details.

=cut


