Algorithm-AdaBoost

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lib/Algorithm/AdaBoost.pm  view on Meta::CPAN

sub final_classifier {
  args my $self;
  Carp::croak 'The classifier is not trained' unless $self->trained;
  return $self->{final_classifier};
}

sub train {
  args
    my $self,
    my $num_iterations => 'Int',
    my $training_set => +{ isa => 'ArrayRef', optional => 1 },
    my $weak_classifier_generator => +{ isa => 'CodeRef', optional => 1 };

  $training_set //= $self->training_set
    // Carp::croak('Given no training set.');
  $weak_classifier_generator //= $self->weak_classifier_generator
    // Carp::croak('Given no weak classifier generator.');
  my $num_training_set = @$training_set;

  # Initial distribution is uniform.
  my $distribution = [ (1 / $num_training_set) x $num_training_set ];

  my ($weak_classifier, $weight);
  my @weak_classifiers;
  while ($num_iterations--) {
    # Construct a weak classifier which classifies data on the distribution.
    $weak_classifier = $weak_classifier_generator->(
      distribution => $distribution,
      training_set => $training_set,
    );
    $weight = $self->calculate_classifier_weight(
      classifier => $weak_classifier,
      distribution => $distribution,
    );
    push @weak_classifiers, +{

lib/Algorithm/AdaBoost.pm  view on Meta::CPAN

  # Training phase.
  my $learner = Alogrithm::AdaBoost->new(
    training_set => [
      +{ feature => [...], label => 1, },
      +{ feature => [...], label => -1, },
      +{ feature => [...], label => -1, },
      ...
    ],
    weak_classifier_generator => \&my_poor_learning_algorithm,
  );
  $learner->train(num_iterations => 1_000);

  # Now you have a boost-ed classifier (Algorithm::AdaBoost::Classifier).
  my $classifier = $learner->final_classifier;
  given ($classifier->classify([...])) {
    when ($_ > 0) { say 'The data belongs to class 1.' }
    when ($_ < 0) { say 'The data belongs to class 2.' }
    default { warn 'The data cannot be classified.' }
  }

=head1 DESCRIPTION

lib/Algorithm/AdaBoost.pm  view on Meta::CPAN

Returns the last constructed classifier.

=head2 train

Constructs a stronger classifier from given training set and weak learning algorithm.

This method takes 1 mandatory parameter:

=over 2

=item num_iterations

Specifies how many training iterations to be excuted (i.e., how many weak classifiers to be generated).

=back

and 2 optional parameters:

=over 2

=item training_set

=item weak_classifier_generator

t/01_basic.t  view on Meta::CPAN

my $learner = new_ok 'Algorithm::AdaBoost' => [
  training_set => \@training_set,
  weak_classifier_generator => \&generate_weak_classifier,
];
ok(
  +(not $learner->trained),
  '|trained| should be false before the learner |train|-ed.'
);

$learner->train(num_iterations => 1000);
ok($learner->trained, 'A classifier is constructed successfully.');

my $classifier = $learner->final_classifier;

my $correct = 0;
for my $test_data (@test_set) {
  my $answer = $classifier->classify($test_data->{feature}) < 0 ? -1 : 1;
  ++$correct if $answer == $test_data->{label};
}
my $accuracy = $correct / @test_set;