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.IX Title "AI::Categorizer::Learner::Weka 3"
.TH AI::Categorizer::Learner::Weka 3 "2008-01-14" "perl v5.8.8" "User Contributed Perl Documentation"
.SH "NAME"
AI::Categorizer::Learner::Weka \- Pass\-through wrapper to Weka system
.SH "SYNOPSIS"
.IX Header "SYNOPSIS"
.Vb 1
\&  use AI::Categorizer::Learner::Weka;
.Ve
.PP
.Vb 1
\&  # Here $k is an AI::Categorizer::KnowledgeSet object
.Ve
.PP
.Vb 3
\&  my $nb = new AI::Categorizer::Learner::Weka(...parameters...);
\&  $nb->train(knowledge_set => $k);
\&  $nb->save_state('filename');
.Ve
.PP
.Vb 1
\&  ... time passes ...
.Ve
.PP
.Vb 6
\&  $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, "\en";
\&  }
.Ve
.SH "DESCRIPTION"
.IX Header "DESCRIPTION"
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 \f(CW\*(C`AI::Categorizer\*(C'\fR.
.PP
Currently this is a simple command-line wrapper that calls \f(CW\*(C`java\*(C'\fR
subprocesses.  In the future this may be converted to an
\&\f(CW\*(C`Inline::Java\*(C'\fR 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.
.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 unless explicitly mentioned here.
.Sh "\fInew()\fP"
.IX Subsection "new()"
Creates a new Weka Learner and returns it.  In addition to the
parameters accepted by the \f(CW\*(C`AI::Categorizer::Learner\*(C'\fR class, the
Weka subclass accepts the following parameters:
.IP "java_path" 4
.IX Item "java_path"
Specifies where the \f(CW\*(C`java\*(C'\fR executable can be found on this system.
The default is simply \f(CW\*(C`java\*(C'\fR, meaning that it will search your
\&\f(CW\*(C`PATH\*(C'\fR to find java.
.IP "java_args" 4
.IX Item "java_args"
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 \f(CW\*(C`\-Xmx130MB\*(C'\fR.
.IP "weka_path" 4
.IX Item "weka_path"
Specifies the path to the \f(CW\*(C`weka.jar\*(C'\fR file containing the Weka
bytecode.  If Weka has been installed somewhere in your java
\&\f(CW\*(C`CLASSPATH\*(C'\fR, you needn't specify a \f(CW\*(C`weka_path\*(C'\fR.
.IP "weka_classifier" 4
.IX Item "weka_classifier"
Specifies the Weka class to use for a categorizer.  The default is
\&\f(CW\*(C`weka.classifiers.NaiveBayes\*(C'\fR.  Consult your Weka documentation for a
list of other classifiers available.
.IP "weka_args" 4
.IX Item "weka_args"
Specifies a list of any additional arguments to pass to the Weka
classifier class when building the categorizer.
.IP "tmpdir" 4
.IX Item "tmpdir"
A directory in which temporary files will be written when training the
categorizer and categorizing new documents.  The default is given by
\&\f(CW\*(C`File::Spec\->tmpdir\*(C'\fR.
.ie n .Sh "train(knowledge_set => $k)"
.el .Sh "train(knowledge_set => \f(CW$k\fP)"
.IX Subsection "train(knowledge_set => $k)"
Trains the categorizer.  This prepares it for later use in
categorizing documents.  The \f(CW\*(C`knowledge_set\*(C'\fR parameter must provide
an object of the class \f(CW\*(C`AI::Categorizer::KnowledgeSet\*(C'\fR (or a subclass
thereof), populated with lots of documents and categories.  See
AI::Categorizer::KnowledgeSet for the details of how to create such
an object.
.Sh "categorize($document)"
.IX Subsection "categorize($document)"
Returns an \f(CW\*(C`AI::Categorizer::Hypothesis\*(C'\fR object representing the
categorizer's \*(L"best guess\*(R" about which categories the given document
should be assigned to.  See AI::Categorizer::Hypothesis for more
details on how to use this object.
.Sh "save_state($path)"
.IX Subsection "save_state($path)"
Saves the categorizer for later use.  This method is inherited from
\&\f(CW\*(C`AI::Categorizer::Storable\*(C'\fR.
.SH "AUTHOR"
.IX Header "AUTHOR"
Ken Williams, ken@mathforum.org
.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)