




"""simple docstring"""





import numpy as np

class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=5 ,SCREAMING_SNAKE_CASE_				:       int=5 ):
										snake_case__    :   Optional[Any]							     =   depth
										snake_case__    :   Dict							     =   0
										snake_case__    :   Any							     =   None
										snake_case__    :   int							     =   None
										snake_case__    :   Tuple							     =   min_leaf_size
										snake_case__    :   Tuple							     =   None

				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Tuple ):
										if labels.ndim != 1:
																print('''Error: Input labels must be one dimensional''' )

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

				def  _lowerCamelCase   (				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Tuple ):
										if x.ndim != 1:
																print('''Error: Input data set must be one dimensional''' )
																return
										if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
																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(SCREAMING_SNAKE_CASE_ ) < 2 * self.min_leaf_size:
																snake_case__    :   Any							     =   np.mean(SCREAMING_SNAKE_CASE_ )
																return

										if self.depth == 1:
																snake_case__    :   Dict							     =   np.mean(SCREAMING_SNAKE_CASE_ )
																return

										snake_case__    :   Any							     =   0
										snake_case__    :   Optional[int]							     =   self.mean_squared_error(SCREAMING_SNAKE_CASE_ ,np.mean(SCREAMING_SNAKE_CASE_ ) ) * 2
										for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
																if len(x[:i] ) < self.min_leaf_size:
																						continue
																elif len(x[i:] ) < self.min_leaf_size:
																						continue
																else:
																						snake_case__    :   Union[str, Any]							     =   self.mean_squared_error(x[:i] ,np.mean(y[:i] ) )
																						snake_case__    :   Tuple							     =   self.mean_squared_error(x[i:] ,np.mean(y[i:] ) )
																						snake_case__    :   Any							     =   error_left + error_right
																						if error < min_error:
																												snake_case__    :   Any							     =   i
																												snake_case__    :   Union[str, Any]							     =   error

										if best_split != 0:
																snake_case__    :   List[Any]							     =   x[:best_split]
																snake_case__    :   Optional[Any]							     =   y[:best_split]
																snake_case__    :   str							     =   x[best_split:]
																snake_case__    :   Dict							     =   y[best_split:]

																snake_case__    :   List[str]							     =   x[best_split]
																snake_case__    :   int							     =   DecisionTree(
																    depth=self.depth - 1 ,min_leaf_size=self.min_leaf_size )
																snake_case__    :   int							     =   DecisionTree(
																    depth=self.depth - 1 ,min_leaf_size=self.min_leaf_size )
																self.left.train(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
																self.right.train(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
										else:
																snake_case__    :   Tuple							     =   np.mean(SCREAMING_SNAKE_CASE_ )

										return





				def  _lowerCamelCase   (				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ):
										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(SCREAMING_SNAKE_CASE_ )
																else:
																						return self.left.predict(SCREAMING_SNAKE_CASE_ )
										else:
																print('''Error: Decision tree not yet trained''' )
																return None







class  __lowercase    :


				'''simple docstring'''


				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   str							     =   float(0 )
										for label in labels:
																squared_error_sum += (label - prediction) ** 2

										return float(squared_error_sum / labels.size )







def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   int							     =   np.arange(-1.0 ,     1.0 ,     0.005						)
						snake_case__    :   List[Any]							     =   np.sin(lowerCamelCase						)

						snake_case__    :   str							     =   DecisionTree(depth=10 ,     min_leaf_size=10						)
						tree.train(lowerCamelCase ,     lowerCamelCase						)

						snake_case__    :   Optional[Any]							     =   (np.random.rand(10						) * 2) - 1
						snake_case__    :   Any							     =   np.array([tree.predict(lowerCamelCase						) for x in test_cases]						)
						snake_case__    :   Optional[Any]							     =   np.mean((predictions - test_cases) ** 2						)

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


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

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








"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   Any							     =   data
										snake_case__    :   Node | None							     =   None





				def __str__(				self				:       Union[str, Any] ):
										return F'''{self.data}'''







class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       Optional[int] ):
										snake_case__    :   Node | None							     =   None
										snake_case__    :   Node | None							     =   None

				def __iter__(				self				:       Optional[int] ):
										snake_case__    :   Optional[int]							     =   self.front
										while node:
																yield node.data
																snake_case__    :   Optional[int]							     =   node.next

				def __len__(				self				:       Any ):
										return len(tuple(iter(self ) ) )

				def __str__(				self				:       Union[str, Any] ):
										return " <- ".join(str(SCREAMING_SNAKE_CASE_ ) for item in self )

				def  _lowerCamelCase   (				self				:       List[str] ):
										return len(self ) == 0

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   int							     =   Node(SCREAMING_SNAKE_CASE_ )
										if self.is_empty():
																snake_case__    :   List[Any]							     =   node
										else:
																assert isinstance(self.rear ,SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Union[str, Any]							     =   node
																snake_case__    :   int							     =   node

				def  _lowerCamelCase   (				self				:       int ):
										if self.is_empty():
																raise IndexError('''dequeue from empty queue''' )
										assert isinstance(self.front ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   List[str]							     =   self.front
										snake_case__    :   str							     =   self.front.next
										if self.front is None:
																snake_case__    :   Optional[Any]							     =   None
										return node.data





				def  _lowerCamelCase   (				self				:       Optional[int] ):
										snake_case__    :   Optional[Any]							     =   None


if __name__ == "__main__":
							from doctest import testmod

							testmod()


