



import math

def        __a			(   __lowerCAmelCase   )     ->     list[int]:
					SCREAMING_SNAKE_CASE							:     Any						=				[]
					SCREAMING_SNAKE_CASE							:     List[str]						=				2
					SCREAMING_SNAKE_CASE							:     str						=				int(math.sqrt(__lowerCAmelCase   )   )  # Size of every segment
					SCREAMING_SNAKE_CASE							:     Any						=				[True] * (end + 1)
					SCREAMING_SNAKE_CASE							:     List[Any]						=				[]

					while start <= end:
										if temp[start] is True:
															in_prime.append(__lowerCAmelCase   )
															for i in range(start * start      ,	end + 1      ,	__lowerCAmelCase   ):
																				SCREAMING_SNAKE_CASE							:     List[Any]						=				False
										start += 1
					prime += in_prime

					SCREAMING_SNAKE_CASE							:     str						=				end + 1
					SCREAMING_SNAKE_CASE							:     Dict						=				min(2 * end      ,	__lowerCAmelCase   )

					while low <= n:
										SCREAMING_SNAKE_CASE							:     int						=				[True] * (high - low + 1)
										for each in in_prime:
															SCREAMING_SNAKE_CASE							:     Any						=				math.floor(low / each   ) * each
															if t < low:
																				t += each

															for j in range(__lowerCAmelCase      ,	high + 1      ,	__lowerCAmelCase   ):
																				SCREAMING_SNAKE_CASE							:     int						=				False

										for j in range(len(__lowerCAmelCase   )   ):
															if temp[j] is True:
																				prime.append(j + low   )

										SCREAMING_SNAKE_CASE							:     Dict						=				high + 1
										SCREAMING_SNAKE_CASE							:     str						=				min(high + end      ,	__lowerCAmelCase   )

					return prime



print(sieve(10**6))




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()