code: def main():
      parser = argparse.ArgumentParser(description='DistanceClassifier for classification based on distance measure in feature space.',
                                       add_help=False)
      parser.add_argument('INPUT_FILE', type=str, help='Data file to perform DistanceClassifier on; ensure that the class label column is labeled as "class".')
      parser.add_argument('-h', '--help', action='help', help='Show this help message and exit.')
      parser.add_argument('-is', action='store', dest='INPUT_SEPARATOR', default='\t',
                          type=str, help='Character used to separate columns in the input file.')
      parser.add_argument('-d', action='store', dest='D', default='mahalanobis',choices = ['mahalanobis','euclidean'],
                          type=str, help='Distance metric to use.')
      parser.add_argument('-v', action='store', dest='VERBOSITY', default=1, choices=[0, 1, 2],
                          type=int, help='How much information DistanceClassifier communicates while it is running: 0 = none, 1 = minimal, 2 = all.')
      parser.add_argument('-s', action='store', dest='RANDOM_STATE', default=0,
                          type=int, help='Random state for train/test split.')
      parser.add_argument('--version', action='version', version='DistanceClassifier {version}'.format(version=__version__),
                          help='Show DistanceClassifier\'s version number and exit.')
      args = parser.parse_args()
      if args.VERBOSITY >= 2:
          print('\nDistanceClassifier settings:')
          for arg in sorted(args.__dict__):
              print('{}\t=\t{}'.format(arg, args.__dict__[arg]))
          print('')
      input_data = pd.read_csv(args.INPUT_FILE, sep=args.INPUT_SEPARATOR)
      if 'Class' in input_data.columns.values:
          input_data.rename(columns={'Label': 'label'}, inplace=True)
      RANDOM_STATE = args.RANDOM_STATE if args.RANDOM_STATE > 0 else None
      dc = DistanceClassifier(d = args.D)
      dc.fit(input_data.drop('label',axis=1).values, input_data['label'].values)
      print(dc.score(input_data.drop('label',axis=1).values, input_data['label'].values))