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lib/AI/Classifier/Text/FileLearner.pm  view on Meta::CPAN

    my $learner = $self->learner;
    while ( my $data  = $self->next ) {
        normalize( $data->{features} );
        $self->weight_terms($data);
        $learner->add_example( 
            attributes => $data->{features},
            labels     => $data->{categories}
        );
    }
}


sub classifier {
    my $self = shift;
    $self->teach_it;
    return AI::Classifier::Text->new(
        classifier => $self->learner->classifier,
        analyzer => $self->analyzer,
    );
}


sub weight_terms {
    my ( $self, $doc ) = @_;
    my $f = $doc->{features};
    given ($self->term_weighting) {
        when ('n') {
            my $max_tf = max values %$f;
            $_ = 0.5 + 0.5 * $_ / $max_tf for values %$f;
        }
        when ('b') {
            $_ = $_ ? 1 : 0 for values %$f;
        }
        when (undef){
        }
        default {
            croak 'Unknown weighting type: '.$self->term_weighting;
        }
    }
}

# this doesn't quite fit the current model (it requires the entire collection
# of documents to be in memory at once), but it may be useful to someone, someday
# so let's just leave it here
sub collection_weighting {
    my (@documents, $subtrahend) = @_;
    $subtrahend //= 0;

    my $num_docs   = +@documents;

    my %frequency;
    for my $doc (@documents) {
        for my $k (keys %{$doc->{attributes}}) {
            $frequency{$k}++;
        }
    }

    foreach my $doc (@documents) {
        my $f = $doc->{attributes};
        for (keys %$f) {
            $f->{$_} *= log($num_docs / ($frequency{$_} // 0) - $subtrahend);
        }
    }
}

sub euclidean_length {
    my $f = shift;

    my $total = 0;
    foreach (values %$f) {
        $total += $_**2;
    }

    return sqrt($total);
}

sub scale {
    my ($f, $scalar) = @_;

    $_ *= $scalar foreach values %$f;

    return $f;
}

sub normalize {
    my $attrs = shift;

    my $length = euclidean_length($attrs);

    return $length ? scale($attrs, 1/$length) : $attrs;
}

1;

=pod

=head1 NAME

AI::Classifier::Text::FileLearner - Training data reader for AI::NaiveBayes

=head1 VERSION

version 0.03

=head1 SYNOPSIS

    use AI::Classifier::Text::FileLearner;

    my $learner = AI::Classifier::Text::FileLearner->new( training_dir => 't/data/training_set_ordered/' );

    my $classifier = $learner->classifier;

=head1 DESCRIPTION

This is a trainer of text classifiers.  It traverses a directory filled,
interprets the subdirectories in it as category names, reads all files in them and adds them
as examples for the classifier being trained.

head1 METHODS

=over 4