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Helmut Wollmersdorfer
and 2 contributors

NAME

Set::Similarity - similarity measures for sets

Set-Similarity Coverage Status Kwalitee Score CPAN version

SYNOPSIS

 use Set::Similarity::Dice;
 
 # object method
 my $dice = Set::Similarity::Dice->new;
 my $similarity = $dice->similarity('Photographer','Fotograf');
 
 # class method
 my $dice = 'Set::Similarity::Dice';
 my $similarity = $dice->similarity('Photographer','Fotograf');
 
 # from 2-grams
 my $width = 2;
 my $similarity = $dice->similarity('Photographer','Fotograf',$width);
 
 # from arrayref of tokens
 my $similarity = $dice->similarity(['a','b'],['b']);

 # from hashref of features 
 my $bird = {
   wings    => true,
   eyes     => true,
   feathers => true,
   hairs    => false,
   legs     => true,
   arms     => false,
 };
 my $mammal = {
   wings    => false,
   eyes     => true,
   feathers => false,
   hairs    => true,
   legs     => true,
   arms     => true, 
 };
 my $similarity = $dice->similarity($bird,$mammal);
 
 # from arrayref sets
 my $bird = [qw(
   wings
   eyes
   feathers
   legs
 )];
 my $mammal = [qw(
   eyes
   hairs
   legs
   arms
 )];
 my $similarity = $dice->from_sets($bird,$mammal);

DESCRIPTION

This is the base class including mainly helper and convenience methods.

Overlap coefficient

( A intersect B ) / min(A,B)

Jaccard Index

The Jaccard coefficient measures similarity between sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets

( A intersect B ) / (A union B)

The Tanimoto coefficient is the ratio of the number of features common to both sets to the total number of features, i.e.

( A intersect B ) / ( A + B - ( A intersect B ) ) # the same as Jaccard

The range is 0 to 1 inclusive.

Dice coefficient

The Dice coefficient is the number of features in common to both sets relative to the average size of the total number of features present, i.e.

( A intersect B ) / 0.5 ( A + B ) # the same as sorensen

The weighting factor comes from the 0.5 in the denominator. The range is 0 to 1.

METHODS

All methods can be used as class or object methods.

new

  $object = Set::Similarity->new();

similarity

  my $similarity = $object->similarity($any1,$any1,$width);
  

$any can be an arrayref, a hashref or a string. Strings are tokenized into n-grams of width $width.

$width must be integer, or defaults to 1.

from_tokens

  my $similarity = $object->from_tokens(['a','b'],['b']);

from_sets

  my $similarity = $object->from_sets(['a'],['b']);
  

Croaks if called directly. This method should be implemented in a child module.

intersection

  my $intersection_size = $object->intersection(['a'],['b']);
  

uniq

  my @uniq = $object->uniq(['a','b']);
  

Transforms an arrayref of strings into an array of unique elements.

combined_length

  my $set_size_sum = $object->combined_length(['a'],['b']);

min

  my $min_set_size = $object->min(['a'],['b']);
  

ngrams

  my @monograms = $object->ngrams('abc');
  my @bigrams = $object->ngrams('abc',2);

_any

  my $arrayref = $object->_any($any,$width);
  

SEE ALSO

Set::Similarity::Cosine

Set::Similarity::Dice

Set::Similarity::Jaccard

Set::Similarity::Overlap

Bag::Similarity doing the same for bags or multisets.

Text::Levenshtein for distance measures of strings, and a very overview of similar modules,

http://en.wikipedia.org/wiki/String_metric for an overview of similarity measures.

Cluster::Similarity for clusters.

SOURCE REPOSITORY

http://github.com/wollmers/Set-Similarity

AUTHOR

Helmut Wollmersdorfer, <helmut.wollmersdorfer@gmail.com>

Kwalitee Score

COPYRIGHT AND LICENSE

Copyright (C) 2013-2015 by Helmut Wollmersdorfer

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.