



'''simple docstring'''






from sklearn.neural_network import MLPClassifier

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


A_				:					Any							=      MLPClassifier(
    solver="""lbfgs""", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


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




def        snake_case_   (	lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       return list(lowerCAmelCase_						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






from math import cos, sin







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=float(lowerCAmelCase_     )
     return [[scaling_factor * int(x == y     ) for x in range(2     )] for y in range(2     )]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     return [[c, -s], [s, c]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[c * c, cs], [cs, s * s]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f'    {scaling(5) = }')
print(f'  {rotation(45) = }')
print(f'{projection(45) = }')



print(f'{reflection(45) = }')

