
import numpy as np


class 							lowercase   :



		def __init__(					self							, A_=5							, A_=5			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	depth
				UpperCamelCase            =	0
				UpperCamelCase            =	None
				UpperCamelCase            =	None
				UpperCamelCase            =	min_leaf_size
				UpperCamelCase            =	None



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   int:
				"""simple docstring"""
				if labels.ndim != 1:
						print('Error: Input labels must be one dimensional'			)

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



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   Tuple:
				"""simple docstring"""
				if x.ndim != 1:
						print('Error: Input data set must be one dimensional'			)
						return
				if len(A_			) != len(A_			):
						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(A_			) < 2 * self.min_leaf_size:
						UpperCamelCase            =	np.mean(A_			)
						return

				if self.depth == 1:
						UpperCamelCase            =	np.mean(A_			)
						return

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

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

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

				return



		def __UpperCamelCase					(					self							, A_			)    ->   List[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(A_			)
						else:
								return self.left.predict(A_			)
				else:
						print('Error: Decision tree not yet trained'			)
						return None






class 							lowercase   :



		@staticmethod
		def __UpperCamelCase					(					A_							, A_			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	float(0			)
				for label in labels:
						squared_error_sum += (label - prediction) ** 2

				return float(squared_error_sum / labels.size			)






def  A			(   )  ->    List[str]:
		'''simple docstring'''

		UpperCamelCase            =	np.arange(-1.0       ,			1.0       ,			0.0_0_5					)
		UpperCamelCase            =	np.sin(lowercase					)

		UpperCamelCase            =	DecisionTree(depth=10       ,			min_leaf_size=10					)
		tree.train(lowercase       ,			lowercase					)

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

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


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

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


from __future__ import annotations


def  A			(   lowercase       ,			lowercase					)  ->    int:
		'''simple docstring'''

		UpperCamelCase      ,    UpperCamelCase            =	0, len(lowercase					) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
					print(double_linear_search(list(range(100)), 40))
