from __future__ import annotations

from math import pi







def       A__   (						snake_case_			:		float							,					snake_case_			:		float							,					snake_case_			:		float   ):

 if (inductance, frequency, reactance).count(0   ) != 1:
  raise ValueError('''One and only one argument must be 0'''   )
 if inductance < 0:
  raise ValueError('''Inductance cannot be negative'''   )
 if frequency < 0:
  raise ValueError('''Frequency cannot be negative'''   )
 if reactance < 0:
  raise ValueError('''Inductive reactance cannot be negative'''   )
 if inductance == 0:
  return {"inductance": reactance / (2 * pi * frequency)}
 elif frequency == 0:
  return {"frequency": reactance / (2 * pi * inductance)}
 elif reactance == 0:
  return {"reactance": 2 * pi * frequency * inductance}
 else:
  raise ValueError('''Exactly one argument must be 0'''   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def        __a			(   __lowerCAmelCase   )     ->     tuple:
					return (data["data"], data["target"])

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     np.ndarray:
					SCREAMING_SNAKE_CASE							:     int						=				XGBRegressor(verbosity=0      ,	random_state=42   )
					xgb.fit(__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Predict target for test data
					SCREAMING_SNAKE_CASE							:     List[Any]						=				xgb.predict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				predictions.reshape(len(__lowerCAmelCase   )      ,	1   )
					return predictions

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Dict						=				fetch_california_housing()
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				data_handling(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				train_test_split(
					    __lowerCAmelCase      ,	__lowerCAmelCase      ,	test_size=0.25      ,	random_state=1   )
					SCREAMING_SNAKE_CASE							:     int						=				xgboost(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Error printing
					print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )
					print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()