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package AI::NaiveBayes::Classification;
use strict;
use warnings;
use 5.010;
use Moose;
has features => (is => 'ro', isa => 'HashRef[HashRef]', required => 1);
has label_sums => (is => 'ro', isa => 'HashRef', required => 1);
has best_category => (is => 'ro', isa => 'Str', lazy_build => 1);
sub _build_best_category {
my $self = shift;
my $sc = $self->label_sums;
my ($best_cat, $best_score) = each %$sc;
while (my ($key, $val) = each %$sc) {
($best_cat, $best_score) = ($key, $val) if $val > $best_score;
}
return $best_cat;
}
sub find_predictors{
my $self = shift;
my $best_cat = $self->best_category;
my $features = $self->features;
my @predictors;
for my $feature ( keys %$features ) {
for my $cat ( keys %{ $features->{$feature } } ){
next if $cat eq $best_cat;
push @predictors, [ $feature, $features->{$feature}{$best_cat} - $features->{$feature}{$cat} ];
}
}
@predictors = sort { abs( $b->[1] ) <=> abs( $a->[1] ) } @predictors;
return $best_cat, @predictors;
}
__PACKAGE__->meta->make_immutable;
1;
__END__
# ABSTRACT: The result of a bayesian classification
=head1 SYNOPSIS
my $result = $classifier->classify({bar => 3, blurp => 2});
# $result is an AI::NaiveBayes::Classification object
say $result->best_category;
my $predictors = $result->find_predictors;
=head1 DESCRIPTION
AI::NaiveBayes::Classification represents the result of a bayesian classification,
produced by AI::NaiveBayes classifier.
=head1 METHODS
=over 4
=item C<best_category()>
Returns a string being a label that suits given document the best.
=item C<find_predictors()>
This method returns the C<best_category()>, as well as the list of all the predictors
along with their influence on the best category selected. So the second value
returned is a list of array references, where each one contains a string being a
single feature and a number describing its influence on the result. So the
second part of the result may look like this:
(
[ 'activities', 1.2511540632952 ],
[ 'over', -1.0269523272981 ],
[ 'provide', 0.8280157033269 ],
[ 'natural', 0.7361042359385 ],
[ 'against', -0.6923354975173 ],
)
=back
=head1 SEE ALSO
AI::NaiveBayes (3), AI::Classifier(3)
=cut