1AI::Categorizer::LearneUrs:e:rWeCkoan(t3r)ibuted Perl DoAcIu:m:eCnattaetgioornizer::Learner::Weka(3)
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NAME

6       AI::Categorizer::Learner::Weka - Pass-through wrapper to Weka system
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SYNOPSIS

9         use AI::Categorizer::Learner::Weka;
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11         # Here $k is an AI::Categorizer::KnowledgeSet object
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13         my $nb = new AI::Categorizer::Learner::Weka(...parameters...);
14         $nb->train(knowledge_set => $k);
15         $nb->save_state('filename');
16
17         ... time passes ...
18
19         $nb = AI::Categorizer::Learner->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         }
25

DESCRIPTION

27       This class doesn't implement any machine learners of its own, it merely
28       passes the data through to the Weka machine learning system
29       (http://www.cs.waikato.ac.nz/~ml/weka/).  This can give you access to a
30       collection of machine learning algorithms not otherwise implemented in
31       "AI::Categorizer".
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33       Currently this is a simple command-line wrapper that calls "java"
34       subprocesses.  In the future this may be converted to an "Inline::Java"
35       wrapper for better performance (faster running times).  However, if
36       you're looking for really great performance, you're probably looking in
37       the wrong place - this Weka wrapper is intended more as a way to try
38       lots of different machine learning methods.
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METHODS

41       This class inherits from the "AI::Categorizer::Learner" class, so all
42       of its methods are available unless explicitly mentioned here.
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44   new()
45       Creates a new Weka Learner and returns it.  In addition to the
46       parameters accepted by the "AI::Categorizer::Learner" class, the Weka
47       subclass accepts the following parameters:
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49       java_path
50           Specifies where the "java" executable can be found on this system.
51           The default is simply "java", meaning that it will search your
52           "PATH" to find java.
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54       java_args
55           Specifies a list of any additional arguments to give to the java
56           process.  Commonly it's necessary to allocate more memory than the
57           default, using an argument like "-Xmx130MB".
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59       weka_path
60           Specifies the path to the "weka.jar" file containing the Weka
61           bytecode.  If Weka has been installed somewhere in your java
62           "CLASSPATH", you needn't specify a "weka_path".
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64       weka_classifier
65           Specifies the Weka class to use for a categorizer.  The default is
66           "weka.classifiers.NaiveBayes".  Consult your Weka documentation for
67           a list of other classifiers available.
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69       weka_args
70           Specifies a list of any additional arguments to pass to the Weka
71           classifier class when building the categorizer.
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73       tmpdir
74           A directory in which temporary files will be written when training
75           the categorizer and categorizing new documents.  The default is
76           given by "File::Spec->tmpdir".
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78   train(knowledge_set => $k)
79       Trains the categorizer.  This prepares it for later use in categorizing
80       documents.  The "knowledge_set" parameter must provide an object of the
81       class "AI::Categorizer::KnowledgeSet" (or a subclass thereof),
82       populated with lots of documents and categories.  See
83       AI::Categorizer::KnowledgeSet for the details of how to create such an
84       object.
85
86   categorize($document)
87       Returns an "AI::Categorizer::Hypothesis" object representing the
88       categorizer's "best guess" about which categories the given document
89       should be assigned to.  See AI::Categorizer::Hypothesis for more
90       details on how to use this object.
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92   save_state($path)
93       Saves the categorizer for later use.  This method is inherited from
94       "AI::Categorizer::Storable".
95

AUTHOR

97       Ken Williams, ken@mathforum.org
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100       Copyright 2000-2003 Ken Williams.  All rights reserved.
101
102       This library is free software; you can redistribute it and/or modify it
103       under the same terms as Perl itself.
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SEE ALSO

106       AI::Categorizer(3)
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110perl v5.36.0                      2023-01-19 AI::Categorizer::Learner::Weka(3)
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