





# using dfs for finding eulerian path traversal


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Any   ,    _lowercase     : Dict   ,    _lowercase     : List[Any]   ,    _lowercase     : int=None			)   ->							List[str]:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		(path or []) + [u]
		for v in graph[u]:
				if visited_edge[u][v] is False:
						lowercase__   ,				lowercase__					:      Union[str, Any]      =		True, True
						lowercase__					:      List[str]      =		dfs(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase			)
		return path


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]   ,    _lowercase     : Dict			)   ->							int:




		'''simple docstring'''
		lowercase__					:      str      =		0
		lowercase__					:      List[Any]      =		-1
		for i in range(_lowercase			):
				if i not in graph.keys():
						continue
				if len(graph[i]			) % 2 == 1:
						odd_degree_nodes += 1
						lowercase__					:      Optional[Any]      =		i
		if odd_degree_nodes == 0:
				return 1, odd_node
		if odd_degree_nodes == 2:
				return 2, odd_node
		return 3, odd_node


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




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		[[False for _ in range(max_node + 1			)] for _ in range(max_node + 1			)]
		lowercase__   ,				lowercase__					:      Optional[Any]      =		check_circuit_or_path(_lowercase   ,    _lowercase			)
		if check == 3:
				print('graph is not Eulerian'			)
				print('no path'			)
				return
		lowercase__					:      Optional[int]      =		1
		if check == 2:
				lowercase__					:      Dict      =		odd_node
				print('graph has a Euler path'			)
		if check == 1:
				print('graph has a Euler cycle'			)
		lowercase__					:      Optional[int]      =		dfs(_lowercase   ,    _lowercase   ,    _lowercase			)
		print(_lowercase			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							Optional[int]:




		'''simple docstring'''
		lowercase__					:      Any      =		{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
		lowercase__					:      Tuple      =		{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
		lowercase__					:      Any      =		{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
		lowercase__					:      Optional[Any]      =		{1: [2, 3], 2: [1, 3], 3: [1, 2]}
		lowercase__					:      Any      =		{
		    1: [],
		    2: []
		    # all degree is zero
		}
		lowercase__					:      List[Any]      =		10
		check_euler(_lowercase   ,    _lowercase			)
		check_euler(_lowercase   ,    _lowercase			)
		check_euler(_lowercase   ,    _lowercase			)
		check_euler(_lowercase   ,    _lowercase			)
		check_euler(_lowercase   ,    _lowercase			)


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()


