AI::Categorizer::Learner::Weka - Pass-through wrapper to Weka system
use AI::Categorizer::Learner::Weka; # Here $k is an AI::Categorizer::KnowledgeSet object my $nb = new AI::Categorizer::Learner::Weka(...parameters...); $nb->train(knowledge_set => $k); $nb->save_state('filename'); ... time passes ... $nb = AI::Categorizer::Learner->restore_state('filename'); my $c = new AI::Categorizer::Collection::Files( path => ... ); while (my $document = $c->next) { my $hypothesis = $nb->categorize($document); print "Best assigned category: ", $hypothesis->best_category, "\n"; }
This class doesn't implement any machine learners of its own, it merely passes the data through to the Weka machine learning system (http://www.cs.waikato.ac.nz/~ml/weka/). This can give you access to a collection of machine learning algorithms not otherwise implemented in AI::Categorizer.
AI::Categorizer
Currently this is a simple command-line wrapper that calls java subprocesses. In the future this may be converted to an Inline::Java wrapper for better performance (faster running times). However, if you're looking for really great performance, you're probably looking in the wrong place - this Weka wrapper is intended more as a way to try lots of different machine learning methods.
java
Inline::Java
This class inherits from the AI::Categorizer::Learner class, so all of its methods are available unless explicitly mentioned here.
AI::Categorizer::Learner
Creates a new Weka Learner and returns it. In addition to the parameters accepted by the AI::Categorizer::Learner class, the Weka subclass accepts the following parameters:
Specifies where the java executable can be found on this system. The default is simply java, meaning that it will search your PATH to find java.
PATH
Specifies a list of any additional arguments to give to the java process. Commonly it's necessary to allocate more memory than the default, using an argument like -Xmx130MB.
-Xmx130MB
Specifies the path to the weka.jar file containing the Weka bytecode. If Weka has been installed somewhere in your java CLASSPATH, you needn't specify a weka_path.
weka.jar
CLASSPATH
weka_path
Specifies the Weka class to use for a categorizer. The default is weka.classifiers.NaiveBayes. Consult your Weka documentation for a list of other classifiers available.
weka.classifiers.NaiveBayes
Specifies a list of any additional arguments to pass to the Weka classifier class when building the categorizer.
A directory in which temporary files will be written when training the categorizer and categorizing new documents. The default is given by File::Spec->tmpdir.
File::Spec->tmpdir
Trains the categorizer. This prepares it for later use in categorizing documents. The knowledge_set parameter must provide an object of the class AI::Categorizer::KnowledgeSet (or a subclass thereof), populated with lots of documents and categories. See AI::Categorizer::KnowledgeSet for the details of how to create such an object.
knowledge_set
AI::Categorizer::KnowledgeSet
Returns an AI::Categorizer::Hypothesis object representing the categorizer's "best guess" about which categories the given document should be assigned to. See AI::Categorizer::Hypothesis for more details on how to use this object.
AI::Categorizer::Hypothesis
Saves the categorizer for later use. This method is inherited from AI::Categorizer::Storable.
AI::Categorizer::Storable
Ken Williams, ken@mathforum.org
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.
AI::Categorizer(3)
To install AI::Categorizer, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::Categorizer
CPAN shell
perl -MCPAN -e shell install AI::Categorizer
For more information on module installation, please visit the detailed CPAN module installation guide.