




"""simple docstring"""





def        _UpperCamelCase     (      UpperCamelCase				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			list[list[int]]												=       [[0 for _ in range(UpperCamelCase				)] for _ in range(m + 1				)]
			for i in range(m + 1				):
						__UpperCAmelCase						:			Tuple												=       1

			for n in range(m + 1				):
						for k in range(1					,							UpperCamelCase				):
									memo[n][k] += memo[n][k - 1]
									if n - k > 0:
												memo[n][k] += memo[n - k - 1][k]

			return memo[m][m - 1]


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										try:
															A				=							int(input("""Enter a number: """).strip())
															print(partition(n))
										except ValueError:
															print("""Please enter a number.""")
					else:
										try:
															A				=							int(sys.argv[1])
															print(partition(n))
										except ValueError:
															print("""Please pass a number.""")



'''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    lowerCAmelCase       (		UpperCamelCase__					: dict							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray							):


	"""simple docstring"""
	__UpperCAmelCase      					=      XGBRegressor(verbosity=0     ,       random_state=4_2							)
	xgb.fit(UpperCamelCase__     ,       UpperCamelCase__							)
	# Predict target for test data
	__UpperCAmelCase      					=      xgb.predict(UpperCamelCase__							)
	__UpperCAmelCase      					=      predictions.reshape(len(UpperCamelCase__							)     ,       1							)
	return predictions





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      fetch_california_housing()
	__UpperCAmelCase					,      __UpperCAmelCase      					=      data_handling(UpperCamelCase__							)
	__UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase      					=      train_test_split(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       test_size=0.25     ,       random_state=1							)
	__UpperCAmelCase      					=      xgboost(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	# Error printing
	print(f"""Mean Absolute Error : {mean_absolute_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)
	print(f"""Mean Square Error  : {mean_squared_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()
