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    print "Saving data to $output_file\n";
    csv( in => $aoa, out => $output_file, encoding => ":encoding(utf-8)" );
    print "Done saving!\n";

}

=head2 &_fill_predicted_values ( $self, $stimuli_validate, $predicted_index, $aoa )

This is where the filling in of the predicted values takes place. Take note that the parameters naming are the same as the ones used in the C<validate> and C<test> method.

This subroutine should be called in the procedural way.

=cut

sub _fill_predicted_values {
    my ( $self, $stimuli_validate, $predicted_index, $aoa ) = @_;

    # CSV processing is all according to the documentation of Text::CSV
    open my $data_fh, "<:encoding(UTF-8)", $stimuli_validate 
        or croak "Can't open $stimuli_validate: $!";
    
    my $csv = Text::CSV->new( {auto_diag => 1, binary => 1} );
    
    my $attrib = $csv->getline($data_fh);
    
    $csv->column_names( $attrib );

    # individual row
    my $row = 0;
    while ( my $data = $csv->getline_hr($data_fh) ) {
        
        if ( _calculate_output( $self, $data )  >= $self->threshold ) {
            # write 1 into aoa
            $aoa->[ $row ][ $predicted_index ] = 1;
        } else {
            #write 0 into aoa
            $aoa->[ $row ][ $predicted_index ] = 0;
        }
        
        $row++;
    }
    
    close $data_fh;
    
    $aoa;
}

=head1 RESULTS RELATED SUBROUTINES/METHODS

This part is related to generating the confusion matrix.

=head2 get_exam_results ( ... )

The parameters and usage are the same as C<get_confusion_matrix>. See the next method.

=head2 get_confusion_matrix ( \%options )

Returns the confusion matrix in the form of a hash. The hash will contain these keys: C<true_positive>, C<true_negative>, C<false_positive>, C<false_negative>, C<accuracy>, C<sensitivity>. More stats like C<precision>, C<specificity> and C<F1_Score> ...

If you are trying to manipulate the confusion matrix hash or something, take note that all the stats are in percentage (%) in decimal (if any) except the total entries.

For C<%options>, the followings are needed unless mentioned:

=over 4

=item full_data_file => $filled_test_file

This is the CSV file filled with the predicted values. 

Make sure that you don't do anything to the actual and predicted output in this file after testing the nerve. These two columns must contain binary values only!

=item actual_output_header => $actual_column_name

=item predicted_output_header => $predicted_column_name

The binary values are treated as follows:

=over 4

=item C<0> is negative

=item C<1> is positive

=back

=item more_stats => 1

Optional.

Setting it to C<1> will process more stats that are usually not so important eg. C<precision>, C<specificity> and C<F1_Score>

=back

=cut

sub get_exam_results {

    my ( $self, $info ) = @_;
    
    $self->get_confusion_matrix( $info );
}

sub get_confusion_matrix {

    my ( $self, $info ) = @_;

    my %c_matrix = _collect_stats( $info ); # processes total_entries, accuracy, sensitivity etc
    
    %c_matrix;
}


=head2 &_collect_stats ( \%options )

Generates a hash of confusion matrix based on C<%options> given in the C<get_confusion_matrix> method.

=cut

sub _collect_stats {
    my $info = shift;