


"""simple docstring"""





import math





def        a_  (   _lowerCAmelCase						:				int  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			[True] * n
       lowercase__    :	Optional[Any]     					=			False
       lowercase__    :	Dict     					=			False
       lowercase__    :	Dict     					=			True

       for i in range(3     ,				int(n**0.5 + 1  )     ,				2  ):
              lowercase__    :	int     					=			i * 2
              while index < n:
                     lowercase__    :	Any     					=			False
                     lowercase__    :	List[str]     					=			index + i

       lowercase__    :	Optional[Any]     					=			[2]

       for i in range(3     ,				_lowerCAmelCase     ,				2  ):
              if is_prime[i]:
                     primes.append(_lowerCAmelCase  )

       return primes





def        a_  (   _lowerCAmelCase						:				int = 9999_6666_3333  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			math.floor(math.sqrt(_lowerCAmelCase  )  ) + 100
       lowercase__    :	Dict     					=			prime_sieve(_lowerCAmelCase  )

       lowercase__    :	str     					=			0
       lowercase__    :	Tuple     					=			0
       lowercase__    :	Any     					=			primes[prime_index]

       while (last_prime**2) <= limit:
              lowercase__    :	Optional[Any]     					=			primes[prime_index + 1]

              lowercase__    :	str     					=			last_prime**2
              lowercase__    :	Dict     					=			next_prime**2

              # Get numbers divisible by lps(current)
              lowercase__    :	List[str]     					=			lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              lowercase__    :	List[Any]     					=			upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              lowercase__    :	Union[str, Any]     					=			0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              lowercase__    :	str     					=			next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
       print(solution())


"""simple docstring"""





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						:				dict  ):





       '''simple docstring'''

       return (data["data"], data["target"])





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			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()

