




from sklearn.neural_network import MLPClassifier

SCREAMING_SNAKE_CASE__ :   List[Any]						=     [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     [0, 1, 0, 0]


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

clf.fit(X, y)


SCREAMING_SNAKE_CASE__ :   List[str]						=     [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
SCREAMING_SNAKE_CASE__ :   Any						=     clf.predict(test)

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Optional[Any]:
       return list(_SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()












SCREAMING_SNAKE_CASE__ :   str						=     {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}

def   A   (   _SCREAMING_SNAKE_CASE					)			->				List[str]:
       return c.isdigit()

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Optional[Any]:
       lowerCamelCase     :   Tuple    				=      []

       # iterate over the string in reverse order
       for c in expression.split()[::-1]:
              # push operand to stack
              if is_operand(_SCREAMING_SNAKE_CASE					):
                     stack.append(int(_SCREAMING_SNAKE_CASE					)					)

              else:
                     # pop values from stack can calculate the result
                     # push the result onto the stack again
                     lowerCamelCase     :   str    				=      stack.pop()
                     lowerCamelCase     :   Optional[int]    				=      stack.pop()
                     stack.append(calc[c](_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)					)

       return stack.pop()


# Driver code
if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ :   Optional[int]						=     '+ 9 * 2 6'
       print(evaluate(test_expression))

       SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     '/ * 10 2 + 4 1 '
       print(evaluate(test_expression))






