"""simple docstring"""




from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCamelCase_    :		Any							=	100

lowerCamelCase_    :		List[str]							=	set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCamelCase_    :		int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
	if prime not in primes:
		continue
	primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					if number_to_partition < 0:
										return set()
					elif number_to_partition == 0:
										return {1}

					__a		 =       set()
					__a		 =       42
					__a		 =       42

					for prime in primes:
										if prime > number_to_partition:
															continue
										for sub in partition(number_to_partition - prime						):
															ret.add(sub * prime						)

					return ret
def 							lowerCAmelCase(     __lowerCamelCase = 5000						):
					for number_to_partition in range(1				,    __lowerCamelCase						):
										if len(partition(__lowerCamelCase						)						) > number_unique_partitions:
															return number_to_partition
					return None


if __name__ == "__main__":
	print(F'''{solution() = }''')





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 							lowerCAmelCase(     __lowerCamelCase						):
					return (data["data"], data["target"])
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       XGBRegressor(verbosity=0				,    random_state=42						)
					xgb.fit(__lowerCamelCase				,    __lowerCamelCase						)
					# Predict target for test data
					__a		 =       xgb.predict(__lowerCamelCase						)
					__a		 =       predictions.reshape(len(__lowerCamelCase						)				,    1						)
					return predictions
def 							lowerCAmelCase(     ):
					__a		 =       fetch_california_housing()
					__a ,				__a		 =       data_handling(__lowerCamelCase						)
					__a ,				__a ,				__a ,				__a		 =       train_test_split(
					    __lowerCamelCase				,    __lowerCamelCase				,    test_size=0.25				,    random_state=1						)
					__a		 =       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()




