Help-Site Computer Manuals
Software
Hardware
Programming
Networking
  Algorithms & Data Structures   Programming Languages   Revision Control
  Protocols
  Cameras   Computers   Displays   Keyboards & Mice   Motherboards   Networking   Printers & Scanners   Storage
  Windows   Linux & Unix   Mac

AI::Categorizer::FeatureVector
Features vs. Values

AI::Categorizer::FeatureVector - Features vs. Values


NAME

AI::Categorizer::FeatureVector - Features vs. Values


SYNOPSIS


  my $f1 = new AI::Categorizer::FeatureVector

    (features => {howdy => 2, doody => 3});

  my $f2 = new AI::Categorizer::FeatureVector

    (features => {doody => 1, whopper => 2});

   

  @names = $f1->names;

  $x = $f1->length;

  $x = $f1->sum;

  $x = $f1->includes('howdy');

  $x = $f1->value('howdy');

  $x = $f1->dot($f2);

  

  $f3 = $f1->clone;

  $f3 = $f1->intersection($f2);

  $f3 = $f1->add($f2);

  

  $h = $f1->as_hash;

  $h = $f1->as_boolean_hash;

  

  $f1->normalize;


DESCRIPTION

This class implements a ``feature vector'', which is a flat data structure indicating the values associated with a set of features. At its base level, a FeatureVector usually represents the set of words in a document, with the value for each feature indicating the number of times each word appears in the document. However, the values are arbitrary so they can represent other quantities as well, and FeatureVectors may also be combined to represent the features of multiple documents.


METHODS

...


AUTHOR

Ken Williams, ken@mathforum.org


COPYRIGHT

Copyright 2000-2003 Ken Williams. All rights reserved.

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


SEE ALSO

AI::Categorizer(3), Storable(3)

Programminig
Wy
Wy
yW
Wy
Programming
Wy
Wy
Wy
Wy