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        for my $param (split ',', $args{print}){
            if(!exists $allowed{$param}){
                $errors .= "Error: unknown print parameter '$param'\n";
            }
        }
    }
    if($errors){
        $errors .= 'use "analogize --help" for detailed usage information';
        chomp $errors;
        pod2usage($errors);
    }
}

__END__

=pod

=encoding UTF-8

=head1 NAME

analogize - classify data with AM from the command line

=head1 VERSION

version 3.13

=head1 SYNOPSIS

analogize --format <format> [--exemplars <file>] [--test <file>]
[--project <dir>] [--print <config_info,statistical_summary,
analogical_set_summary,gang_summary,gang_detailed>]
[--help]

=head1 DESCRIPTION

Classify data with analogical modeling from the command line.
Required arguments are B<format> and either B<exemplars> or
B<project>. You can use old AM::Parallel projects (a directory
containing C<data> and C<test> files) or specify individual data
and test files. By default, only the accuracy of the predicted
outcomes is printed. More detail may be printed using the B<print>
option.

=head1 OPTIONS

=over

=item B<format>

specify either commas or nocommas format for exemplar and test data files
(C<=> should be used for "null" variables). See L<Algorithm::AM::DataSet/dataset_from_file>
for details on the two formats.

=item C<exemplars>, C<data> or C<train>

path to the file containing the examplar/training data

=item C<project>

path to an AM::Parallel-style project (ignores 'outcome' file); this
should be a directory containing a file called C<data> containing known
exemplars and C<test> containing test exemplars. If the C<test> file does
not exist, then a leave-one-out scheme is used for testing using the
exemplars in the C<data> file.

=item C<test>

path to the file containing the test data. If none is specified,
performs leave-one-out classification with the exemplar set.

=item C<print>

reports to print, separated by commas (be careful not to add spaces between report names!).
For example, C<--print analogical_set_summary,gang_summary> would print
analogical sets and gang summaries.

Available options are:

=over

=item C<config_info>

Describes the configuration used and some simple information about the data,
i.e. cardinality, etc.

=item C<statistical_summary>

A statistical summary of the classification results, including
all predicted outcomes with their scores and percentages and
the total score for all outcomes. Whether the predicted class is
correct, incorrect, or a tie is also included, if the test item
had a known class.

=item C<analogical_set_summary>

The analogical set, showing all items that contributed to the predicted
outcome, along with the amount contributed by each item (score and
percentage overall).

=item C<gang_summary>

A summary of the gang effects on the outcome prediction.

=item C<gang_detailed>

Same as C<gang_summary>, but also includes lists of exemplars for each
gang.

=back

=item C<include_given>

Allow a test item to be included in the data set during classification.
If false (default), test items will be removed from the dataset during
classification.

=item C<include_nulls>

Treat null variables in a test item as regular variables. If false (default),
these variables will be excluded and not considered during classification.