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    # preprocess if $x is hashref
    $x = [
        map {
	    my $attr = $_;
	    ref($x->{$attr}) eq 'ARRAY' ? 
	        map { "$attr:$_" } @{$x->{$attr}} : "$_:$x->{$_}" 
        } keys %$x
    ] if ref($x) eq 'HASH';
    # calculate score
    my @x1 = map { $self->{x_bucket}->{$_} } @$x;
    my $lambda_f = 0;
    if (defined(my $y1 = $self->{y_bucket}->{$y})) {
        for my $x1 (@x1) {
	    if (defined($x1)) {
	        my $lambda_i = $self->{f_map}->[$y1]->[$x1];
                $lambda_f += $self->{lambda}->[$lambda_i]
		    if $lambda_i != -1;
            }
        }
    }
    return $lambda_f; 
}

sub predict {
    my $self = shift;
    my $x = shift;
    my @score = map { $self->score($x => $_) } @{$self->{y_list}};
    my ($max_score, $max_y) = (undef, undef);
    for my $y (0 .. $self->{y_num} - 1) {
        ($max_score, $max_y) = ($score[$y], $y) if not defined($max_y);
	($max_score, $max_y) = ($score[$y], $y) if $score[$y] > $max_score;
    }
    return $self->{y_list}->[$max_y];
}

1;

__END__

=head1 NAME

AI::MaxEntropy::Model - Perl extension for using Maximum Entropy Models

=head1 SYNOPSIS

  use AI::MaxEntropy::Model;

  # learn a model by AI::MaxEntropy
  require AI::MaxEntropy;
  my $me = AI::MaxEntropy->new;
  $me->see(['round', 'smooth', 'red'] => 'apple' => 2);
  $me->see(['long', 'smooth', 'yellow'] => 'banana' => 3);
  $me->see(['round', 'rough'] => 'orange' => 2);
  my $model = $me->learn;

  # make prediction on unseen data
  # ask what a red round thing is most likely to be
  my $y = $model->predict(['round', 'red']);
  # the answer apple is expected

  # print out scores of all possible labels
  for ($model->all_labels) {
      my $s = $model->score(['round', 'red'] => $_);
      print "$_: $s\n";
  }
  
  # save the model to file
  $model->save('model_file');

  # load the model from file
  $model->load('model_file');

=head1 DESCRIPTION

This module manipulates models learnt by L<AI::MaxEntropy>. For details
about Maximum Entropy learner, please refer to L<AI::MaxEntropy>.

=head1 FUNCTIONS

=head2 new

Create a new model object from a model file.

  my $model = AI::MaxEntropy::Model->new('model_file');

=head2 predict

Get the most possible label for a unlabeled sample

  ...
  
  $y = $model->predict(['round', 'red']);

=head2 score

Get scores for every possible label for a unlabeled sample

  ...

  $s = $model->score(['round', 'red'] => 'apple');

=head2 save

Dumps the model to a file.

  ...

  $model->save('model_file');

=head2 load

Loads the model from a file.

  ...

  $model->load('model_file');

=head2 all_x

Returns a list of all x.

=head2 all_labels

Returns a list of all y (labels).