'''simple docstring'''






def    _SCREAMING_SNAKE_CASE		(			__snake_case       : list							):
     _A								=     1.3
     _A								=     len(__snake_case							)
     _A								=     False

     while not completed:
          # Update the gap value for a next comb
          _A								=     int(gap / shrink_factor							)
          if gap <= 1:
               _A								=     True

          _A								=     0
          while index + gap < len(__snake_case							):
               if data[index] > data[index + gap]:
                    # Swap values
                    _A		,					_A								=     data[index + gap], data[index]
                    _A								=     False
               index += 1

     return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _UpperCAmelCase						:    Tuple     					=					input('''Enter numbers separated by a comma:\n''').strip()
   _UpperCAmelCase						:    Union[str, Any]     					=					[int(item) for item in user_input.split(''',''')]
   print(comb_sort(unsorted))




'''simple docstring'''






from sklearn.neural_network import MLPClassifier

_UpperCAmelCase						:    str     					=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCAmelCase						:    Any     					=					[0, 1, 0, 0]


_UpperCAmelCase						:    List[Any]     					=					MLPClassifier(
    solver='''lbfgs''', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


_UpperCAmelCase						:    Optional[Any]     					=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_UpperCAmelCase						:    Tuple     					=					clf.predict(test)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]							):
     return list(__snake_case							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



