AI-NaiveBayes

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

        }

        if ($self->limit_features) {
            my %old  = %{$model->{probs}{$label}};
            my @features = sort { abs($old{$a}) <=> abs($old{$b}) } keys(%old);
            my $limit = min($self->features_kept, 0+@features);
            if ($limit < 1) {
                $limit = int($limit * keys(%old));
            }
            my @top = @features[0..$limit-1];
            my %kept = map { $_ => $old{$_} } @top;
            $model->{probs}{$label} = \%kept;
        }
    }
    my $classifier_class = $self->classifier_class;
    return $classifier_class->new( model => $model );
}

sub add_hash {
    my ($first, $second) = @_;
    $first //= {};

t/01-learner.t  view on Meta::CPAN

$learner->add_example( attributes => _hash(qw(one two three four)),
		   labels => ['farming'] );
$learner->add_example( attributes => _hash(qw(five six seven eight)),
		   labels => ['farming'] );
$learner->add_example( attributes => _hash(qw(one two three four)),
		   labels => ['farming'] );
$model = $learner->classifier->model;
is keys %{$model->{probs}{farming}}, 4, 'half features kept';
is join(" ", sort { $a cmp $b } keys %{$model->{probs}{farming}}), 'four one three two';

sub _hash { +{ map {$_,1} @_ } }

t/02-predict.t  view on Meta::CPAN

$classifier = $lr->classifier;

# Predict
$s = $classifier->classify( _hash(qw(jakis tekst po polsku)) );
$h = $s->label_sums;
ok(abs( 3 - $h->{farming} / $h->{vampire} ) < 0.01, 'Prior probabillities' );


################################################################
sub _hash { +{ map {$_,1} @_ } }

t/default_training.t  view on Meta::CPAN

    {
        attributes => _hash(qw(vampires cannot see their images mirrors)),
        labels => ['vampire']
    },
);

isa_ok( $classifier, 'AI::NaiveBayes' );


################################################################
sub _hash { +{ map {$_,1} @_ } }