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.IX Title "AI::Categorizer::Learner::Guesser 3"
.TH AI::Categorizer::Learner::Guesser 3 "2008-01-14" "perl v5.8.8" "User Contributed Perl Documentation"
.SH "NAME"
AI::Categorizer::Learner::Guesser \- Simple guessing based on class probabilities
.SH "SYNOPSIS"
.IX Header "SYNOPSIS"
.Vb 1
\&  use AI::Categorizer::Learner::Guesser;
.Ve
.PP
.Vb 1
\&  # Here $k is an AI::Categorizer::KnowledgeSet object
.Ve
.PP
.Vb 3
\&  my $l = new AI::Categorizer::Learner::Guesser;
\&  $l->train(knowledge_set => $k);
\&  $l->save_state('filename');
.Ve
.PP
.Vb 1
\&  ... time passes ...
.Ve
.PP
.Vb 7
\&  $l = AI::Categorizer::Learner->restore_state('filename');
\&  my $c = new AI::Categorizer::Collection::Files( path => ... );
\&  while (my $document = $c->next) {
\&    my $hypothesis = $l->categorize($document);
\&    print "Best assigned category: ", $hypothesis->best_category, "\en";
\&    print "All assigned categories: ", join(', ', $hypothesis->categories), "\en";
\&  }
.Ve
.SH "DESCRIPTION"
.IX Header "DESCRIPTION"
This implements a simple category guesser that makes assignments based
solely on the prior probabilities of categories.  For instance, if 5%
of the training documents belong to a certain category, then the
probability of any test document being assigned to that category is
0.05.  This can be useful for providing baseline scores to compare
with other more sophisticated algorithms.
.PP
See AI::Categorizer for a complete description of the interface.
.SH "METHODS"
.IX Header "METHODS"
This class inherits from the \f(CW\*(C`AI::Categorizer::Learner\*(C'\fR class, so all
of its methods are available.
.SH "AUTHOR"
.IX Header "AUTHOR"
Ken Williams (\f(CW\*(C`<ken@mathforum.org>\*(C'\fR)
.SH "COPYRIGHT"
.IX Header "COPYRIGHT"
Copyright 2000\-2003 Ken Williams.  All rights reserved.
.PP
This library is free software; you can redistribute it and/or
modify it under the same terms as Perl itself.
.SH "SEE ALSO"
.IX Header "SEE ALSO"
\&\fIAI::Categorizer\fR\|(3)