import numpy as np


class   lowercase			:




							def __init__(  self      :    Tuple		,							_UpperCamelCase      :    str=5		,							_UpperCamelCase      :    Optional[int]=5					)    -> Dict:



												'''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      :    Tuple		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    str					)    -> Optional[int]:



												'''simple docstring'''

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

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




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Union[str, Any]					)    -> Optional[int]:



												'''simple docstring'''

												if x.ndim != 1:
																	print("Error: Input data set must be one dimensional"					)
																	return
												if len(_UpperCamelCase					) != len(_UpperCamelCase					):
																	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(_UpperCamelCase					) < 2 * self.min_leaf_size:
																	SCREAMING_SNAKE_CASE				=				np.mean(_UpperCamelCase					)
																	return

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

												SCREAMING_SNAKE_CASE				=				0
												SCREAMING_SNAKE_CASE				=				self.mean_squared_error(_UpperCamelCase		,							np.mean(_UpperCamelCase					)					) * 2
												for i in range(len(_UpperCamelCase					)					):
																	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(_UpperCamelCase		,							_UpperCamelCase					)
																	self.right.train(_UpperCamelCase		,							_UpperCamelCase					)
												else:
																	SCREAMING_SNAKE_CASE				=				np.mean(_UpperCamelCase					)

												return





							def 		__snake_case(  self      :    Tuple		,							_UpperCamelCase      :    str					)    -> Tuple:



												'''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(_UpperCamelCase					)
																	else:
																						return self.left.predict(_UpperCamelCase					)
												else:
																	print("Error: Decision tree not yet trained"					)
																	return None



class   lowercase			:




							@staticmethod
							def 		__snake_case(  _UpperCamelCase      :    str		,							_UpperCamelCase      :    Tuple					)    -> Tuple:



												'''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 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				np.arange(-1.0					,       1.0					,       0.005     )
					SCREAMING_SNAKE_CASE				=				np.sin(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				DecisionTree(depth=1_0					,       min_leaf_size=1_0     )
					tree.train(UpperCAmelCase__					,       UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				(np.random.rand(1_0     ) * 2) - 1
					SCREAMING_SNAKE_CASE				=				np.array([tree.predict(UpperCAmelCase__     ) for x in test_cases]     )
					SCREAMING_SNAKE_CASE				=				np.mean((predictions - test_cases) ** 2     )

					print("Test values: " + str(UpperCAmelCase__     )     )
					print("Predictions: " + str(UpperCAmelCase__     )     )
					print("Average error: " + str(UpperCAmelCase__     )     )


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

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





import math





def 		__lowerCamelCase							(UpperCAmelCase__   :							int     ):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5					,       int(math.sqrt(UpperCAmelCase__     ) + 1     )					,       6     ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True





def 		__lowerCamelCase							(UpperCAmelCase__   :							int = 2_0_0_0_0_0_0     ):
					return sum(num for num in range(3					,       UpperCAmelCase__					,       2     ) if is_prime(UpperCAmelCase__     )     ) + 2 if n > 2 else 0


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




