





import sys


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]			)   ->							Dict:




		'''simple docstring'''
		lowercase__					:      Any      =		len(_lowercase			)
		lowercase__					:      Dict      =		[[0 for x in range(_lowercase			)] for x in range(_lowercase			)]
		lowercase__					:      Optional[int]      =		[[0 for x in range(_lowercase			)] for x in range(_lowercase			)]

		for chain_length in range(2   ,    _lowercase			):
				for a in range(1   ,    n - chain_length + 1			):
						lowercase__					:      List[str]      =		a + chain_length - 1

						lowercase__					:      str      =		sys.maxsize
						for c in range(_lowercase   ,    _lowercase			):
								lowercase__					:      Dict      =		(
								    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
								)
								if cost < matrix[a][b]:
										lowercase__					:      List[str]      =		cost
										lowercase__					:      Any      =		c
		return matrix, sol


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Any   ,    _lowercase     : List[str]   ,    _lowercase     : Any			)   ->							Tuple:




		'''simple docstring'''
		if i == j:
				print('A' + str(_lowercase			)   ,    end=' '			)
		else:
				print('('   ,    end=' '			)
				print_optiomal_solution(_lowercase   ,    _lowercase   ,    optimal_solution[i][j]			)
				print_optiomal_solution(_lowercase   ,    optimal_solution[i][j] + 1   ,    _lowercase			)
				print(')'   ,    end=' '			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		[30, 35, 15, 5, 10, 20, 25]
		lowercase__					:      Optional[int]      =		len(_lowercase			)
		# Size of matrix created from above array will be
		# 30*35 35*15 15*5 5*10 10*20 20*25
		lowercase__   ,				lowercase__					:      Tuple      =		matrix_chain_order(_lowercase			)

		print('No. of Operation required: ' + str(matrix[1][n - 1]			)			)
		print_optiomal_solution(_lowercase   ,    1   ,    n - 1			)


if __name__ == "__main__":
			main()









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       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict			)   ->							tuple:




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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray			)   ->							np.ndarray:




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


def       SCREAMING_SNAKE_CASE__  (       )   ->							None:




		'''simple docstring'''
		lowercase__					:      List[str]      =		fetch_california_housing()
		lowercase__   ,				lowercase__					:      Optional[int]      =		data_handling(_lowercase			)
		lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[Any]      =		train_test_split(
		    _lowercase   ,    _lowercase   ,    test_size=0.25   ,    random_state=1			)
		lowercase__					:      Any      =		xgboost(_lowercase   ,    _lowercase   ,    _lowercase			)
		# Error printing
		print(f"""Mean Absolute Error : {mean_absolute_error(_lowercase   ,    _lowercase			)}"""			)
		print(f"""Mean Square Error  : {mean_squared_error(_lowercase   ,    _lowercase			)}"""			)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()


