1AI::Categorizer::LearneUrs(e3r)Contributed Perl DocumentAaIt:i:oCnategorizer::Learner(3)
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NAME

6       AI::Categorizer::Learner - Abstract Machine Learner Class
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SYNOPSIS

9        use AI::Categorizer::Learner::NaiveBayes;  # Or other subclass
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11        # Here $k is an AI::Categorizer::KnowledgeSet object
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13        my $nb = new AI::Categorizer::Learner::NaiveBayes(...parameters...);
14        $nb->train(knowledge_set => $k);
15        $nb->save_state('filename');
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17        ... time passes ...
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19        $nb = AI::Categorizer::Learner::NaiveBayes->restore_state('filename');
20        my $c = new AI::Categorizer::Collection::Files( path => ... );
21        while (my $document = $c->next) {
22          my $hypothesis = $nb->categorize($document);
23          print "Best assigned category: ", $hypothesis->best_category, "\n";
24          print "All assigned categories: ", join(', ', $hypothesis->categories), "\n";
25        }
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DESCRIPTION

28       The "AI::Categorizer::Learner" class is an abstract class that will
29       never actually be directly used in your code.  Instead, you will use a
30       subclass like "AI::Categorizer::Learner::NaiveBayes" which implements
31       an actual machine learning algorithm.
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33       The general description of the Learner interface is documented here.
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METHODS

36       new()
37           Creates a new Learner and returns it.  Accepts the following
38           parameters:
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40           knowledge_set
41               A Knowledge Set that will be used by default during the
42               "train()" method.
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44           verbose
45               If true, the Learner will display some diagnostic output while
46               training and categorizing documents.
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48       train()
49       train(knowledge_set => $k)
50           Trains the categorizer.  This prepares it for later use in
51           categorizing documents.  The "knowledge_set" parameter must provide
52           an object of the class "AI::Categorizer::KnowledgeSet" (or a
53           subclass thereof), populated with lots of documents and categories.
54           See AI::Categorizer::KnowledgeSet for the details of how to create
55           such an object.  If you provided a "knowledge_set" parameter to
56           "new()", specifying one here will override it.
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58       categorize($document)
59           Returns an "AI::Categorizer::Hypothesis" object representing the
60           categorizer's "best guess" about which categories the given
61           document should be assigned to.  See AI::Categorizer::Hypothesis
62           for more details on how to use this object.
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64       categorize_collection(collection => $collection)
65           Categorizes every document in a collection and returns an
66           Experiment object representing the results.  Note that the
67           Experiment does not contain knowledge of the assigned categories
68           for every document, only a statistical summary of the results.
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70       knowledge_set()
71           Gets/sets the internal "knowledge_set" member.  Note that since the
72           knowledge set may be enormous, some Learners may throw away their
73           knowledge set after training or after restoring state from a file.
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75       $learner->save_state($path)
76           Saves the Learner for later use.  This method is inherited from
77           "AI::Categorizer::Storable".
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79       $class->restore_state($path)
80           Returns a Learner saved in a file with "save_state()".  This method
81           is inherited from "AI::Categorizer::Storable".
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AUTHOR

84       Ken Williams, ken@mathforum.org
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87       Copyright 2000-2003 Ken Williams.  All rights reserved.
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89       This library is free software; you can redistribute it and/or modify it
90       under the same terms as Perl itself.
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SEE ALSO

93       AI::Categorizer(3)
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97perl v5.36.0                      2022-07-22       AI::Categorizer::Learner(3)
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