
import sys

sys.set_int_max_str_digits(0)  # type: ignore


def       a			(   a  )    ->bool:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   [0] * 11
			SCREAMING_SNAKE_CASE    	=   [0] * 11

			# mark last 9 numbers
			for _ in range(9  ):
						SCREAMING_SNAKE_CASE    	=   1
						SCREAMING_SNAKE_CASE    	=   number // 10
			# flag
			SCREAMING_SNAKE_CASE    	=   True

			# check last 9 numbers for pandigitality

			for x in range(9  ):
						if not check_last[x + 1]:
									SCREAMING_SNAKE_CASE    	=   False
			if not f:
						return f

			# mark first 9 numbers
			SCREAMING_SNAKE_CASE    	=   int(str(a  )[:9]  )

			for _ in range(9  ):
						SCREAMING_SNAKE_CASE    	=   1
						SCREAMING_SNAKE_CASE    	=   number // 10

			# check first 9 numbers for pandigitality

			for x in range(9  ):
						if not check_front[x + 1]:
									SCREAMING_SNAKE_CASE    	=   False
			return f


def       a			(   a  )    ->bool:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   [0] * 11

			# mark last 9 numbers
			for _ in range(9  ):
						SCREAMING_SNAKE_CASE    	=   1
						SCREAMING_SNAKE_CASE    	=   number // 10
			# flag
			SCREAMING_SNAKE_CASE    	=   True

			# check last 9 numbers for pandigitality

			for x in range(9  ):
						if not check_last[x + 1]:
									SCREAMING_SNAKE_CASE    	=   False
			return f


def       a			(   )    ->int:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   1
			SCREAMING_SNAKE_CASE    	=   1
			SCREAMING_SNAKE_CASE    	=   2
			# temporary Fibonacci numbers

			SCREAMING_SNAKE_CASE    	=   1
			SCREAMING_SNAKE_CASE    	=   1
			SCREAMING_SNAKE_CASE    	=   2
			# temporary Fibonacci numbers mod 1e9

			# mod m=1e9, done for fast optimisation
			SCREAMING_SNAKE_CASE    	=   [0] * 100_0000
			SCREAMING_SNAKE_CASE    	=   10_0000_0000

			for x in range(100_0000  ):
						SCREAMING_SNAKE_CASE    	=   (aa + ba) % m
						SCREAMING_SNAKE_CASE    	=   ba % m
						SCREAMING_SNAKE_CASE    	=   ca % m
						if checka(a  ):
									SCREAMING_SNAKE_CASE    	=   1

			for x in range(100_0000  ):
						SCREAMING_SNAKE_CASE    	=   a + b
						SCREAMING_SNAKE_CASE    	=   b
						SCREAMING_SNAKE_CASE    	=   c
						# perform check only if in tocheck
						if tocheck[x + 3] and check(a  ):
									return x + 3  # first 2 already done
			return -1


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

import numpy as np





class 						lowerCamelCase			:







	def __init__(    self			:Any      ,    lowercase			:List[Any]=5      ,    lowercase			:Optional[Any]=5 )		->			Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   depth
				SCREAMING_SNAKE_CASE    	=   0
				SCREAMING_SNAKE_CASE    	=   None
				SCREAMING_SNAKE_CASE    	=   None
				SCREAMING_SNAKE_CASE    	=   min_leaf_size
				SCREAMING_SNAKE_CASE    	=   None







	def    snake_case__    (    self			:str      ,    lowercase			:int      ,    lowercase			:int )		->			str:







				"""simple docstring"""






				if labels.ndim != 1:
							print('''Error: Input labels must be one dimensional''' )

				return np.mean((labels - prediction) ** 2 )







	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:int      ,    lowercase			:Dict )		->			Tuple:







				"""simple docstring"""






				if x.ndim != 1:
							print('''Error: Input data set must be one dimensional''' )
							return
				if len(lowercase ) != len(lowercase ):
							print('''Error: X and y have different lengths''' )
							return
				if y.ndim != 1:
							print('''Error: Data set labels must be one dimensional''' )
							return

				if len(lowercase ) < 2 * self.min_leaf_size:
							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
							return

				if self.depth == 1:
							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
							return

				SCREAMING_SNAKE_CASE    	=   0
				SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(lowercase      ,    np.mean(lowercase ) ) * 2
				for i in range(len(lowercase ) ):
							if len(x[:i] ) < self.min_leaf_size:
										continue
							elif len(x[i:] ) < self.min_leaf_size:
										continue
							else:
										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[:i]      ,    np.mean(y[:i] ) )
										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[i:]      ,    np.mean(y[i:] ) )
										SCREAMING_SNAKE_CASE    	=   error_left + error_right
										if error < min_error:
													SCREAMING_SNAKE_CASE    	=   i
													SCREAMING_SNAKE_CASE    	=   error

				if best_split != 0:
							SCREAMING_SNAKE_CASE    	=   x[:best_split]
							SCREAMING_SNAKE_CASE    	=   y[:best_split]
							SCREAMING_SNAKE_CASE    	=   x[best_split:]
							SCREAMING_SNAKE_CASE    	=   y[best_split:]

							SCREAMING_SNAKE_CASE    	=   x[best_split]
							SCREAMING_SNAKE_CASE    	=   DecisionTree(
							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
							SCREAMING_SNAKE_CASE    	=   DecisionTree(
							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
							self.left.train(lowercase      ,    lowercase )
							self.right.train(lowercase      ,    lowercase )
				else:
							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )

				return







	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:List[Any] )		->			str:







				"""simple docstring"""






				if self.prediction is not None:
							return self.prediction
				elif self.left or self.right is not None:
							if x >= self.decision_boundary:
										return self.right.predict(lowercase )
							else:
										return self.left.predict(lowercase )
				else:
							print('''Error: Decision tree not yet trained''' )
							return None





class 						lowerCamelCase			:







	@staticmethod
	def    snake_case__    (    lowercase			:Optional[int]      ,    lowercase			:Optional[Any] )		->			Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   float(0 )
				for label in labels:
							squared_error_sum += (label - prediction) ** 2

				return float(squared_error_sum / labels.size )



def       a			(   )    ->Any:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   np.arange(-1.0		,	1.0		,	0.0_05  )
			SCREAMING_SNAKE_CASE    	=   np.sin(a  )

			SCREAMING_SNAKE_CASE    	=   DecisionTree(depth=10		,	min_leaf_size=10  )
			tree.train(a		,	a  )

			SCREAMING_SNAKE_CASE    	=   (np.random.rand(10  ) * 2) - 1
			SCREAMING_SNAKE_CASE    	=   np.array([tree.predict(a  ) for x in test_cases]  )
			SCREAMING_SNAKE_CASE    	=   np.mean((predictions - test_cases) ** 2  )

			print('''Test values: ''' + str(a  )  )
			print('''Predictions: ''' + str(a  )  )
			print('''Average error: ''' + str(a  )  )


if __name__ == "__main__":
							main()
							import doctest

							doctest.testmod(name='mean_squarred_error', verbose=True)