AI-NaiveBayes

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INSTALL  view on Meta::CPAN

Installing AI-NaiveBayes is straightforward.

## Installation with cpanm

If you have cpanm, you only need one line:

    % cpanm AI::NaiveBayes

If it does not have permission to install modules to the current perl, cpanm
will automatically set up and install to a local::lib in your home directory.
See the local::lib documentation (https://metacpan.org/pod/local::lib) for
details on enabling it in your environment.

## Installing with the CPAN shell

Alternatively, if your CPAN shell is set up, you should just be able to do:

    % cpan AI::NaiveBayes

## Manual installation

As a last resort, you can manually install it. Download the tarball, untar it,
then build it:

    % perl Makefile.PL
    % make && make test

README.pod  view on Meta::CPAN

# ABSTRACT: A Bayesian classifier

=encoding utf8

=head1 SYNOPSIS

    # AI::NaiveBayes objects are created by AI::NaiveBayes::Learner
    # but for quick start you can use the 'train' class method
    # that is a shortcut using default AI::NaiveBayes::Learner settings

    my $classifier = AI::NaiveBayes->train( 
        {
            attributes => {
                sheep => 1, very => 1,  valuable => 1, farming => 1
            },
            labels => ['farming']
        },
        {
            attributes => {

README.pod  view on Meta::CPAN

=over 4

=item new( model => $model )

Internal. See L<AI::NaiveBayes::Learner> to learn how to create a C<AI::NaiveBayes>
classifier from training data.

=item train( LIST of HASHREFS )

Shortcut for creating a trained classifier using L<AI::NaiveBayes::Learner> default
settings. 
Arguments are passed to the C<add_example> method of the L<AI::NaiveBayes::Learner>
object one by one.

=item classify( HASHREF )

Classifies a feature-vector of the form:

    { feature1 => weight1, feature2 => weight2, ... }
    
The result is a C<AI::NaiveBayes::Classification> object.

lib/AI/NaiveBayes.pm  view on Meta::CPAN

AI::NaiveBayes - A Bayesian classifier

=head1 VERSION

version 0.04

=head1 SYNOPSIS

    # AI::NaiveBayes objects are created by AI::NaiveBayes::Learner
    # but for quick start you can use the 'train' class method
    # that is a shortcut using default AI::NaiveBayes::Learner settings

    my $classifier = AI::NaiveBayes->train( 
        {
            attributes => {
                sheep => 1, very => 1,  valuable => 1, farming => 1
            },
            labels => ['farming']
        },
        {
            attributes => {

lib/AI/NaiveBayes.pm  view on Meta::CPAN

=over 4

=item new( model => $model )

Internal. See L<AI::NaiveBayes::Learner> to learn how to create a C<AI::NaiveBayes>
classifier from training data.

=item train( LIST of HASHREFS )

Shortcut for creating a trained classifier using L<AI::NaiveBayes::Learner> default
settings. 
Arguments are passed to the C<add_example> method of the L<AI::NaiveBayes::Learner>
object one by one.

=item classify( HASHREF )

Classifies a feature-vector of the form:

    { feature1 => weight1, feature2 => weight2, ... }

The result is a C<AI::NaiveBayes::Classification> object.

lib/AI/NaiveBayes/Learner.pm  view on Meta::CPAN

__PACKAGE__->meta->make_immutable;

1;

=pod

=encoding UTF-8

=head1 NAME

AI::NaiveBayes::Learner - Build AI::NaiveBayes classifier from a set of training examples.

=head1 VERSION

version 0.04

=head1 SYNOPSIS

    my $learner = AI::NaiveBayes::Learner->new(features_kept => 0.5);
    $learner->add_example(
        attributes => { sheep => 1, very => 1, valuable => 1, farming => 1 },

lib/AI/NaiveBayes/Learner.pm  view on Meta::CPAN

This software is copyright (c) 2012 by Opera Software ASA.

This is free software; you can redistribute it and/or modify it under
the same terms as the Perl 5 programming language system itself.

=cut

__END__

# ABSTRACT: Build AI::NaiveBayes classifier from a set of training examples.