"""simple docstring"""







import numpy as np


class      _lowercase						:

				def __init__(       self    :      int    ,      _lowercase    :      Optional[Any]=5    ,      _lowercase    :      int=5       ):


										'''simple docstring'''
										UpperCAmelCase__		=						depth
										UpperCAmelCase__		=						0
										UpperCAmelCase__		=						None
										UpperCAmelCase__		=						None
										UpperCAmelCase__		=						min_leaf_size
										UpperCAmelCase__		=						None

				def     A	(       self    :      int    ,      _lowercase    :      Optional[Any]    ,      _lowercase    :      Union[str, Any]       ):


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

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

				def     A	(       self    :      Dict    ,      _lowercase    :      Optional[int]    ,      _lowercase    :      List[Any]       ):


										'''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:
																UpperCAmelCase__		=						np.mean(_lowercase       )
																return

										if self.depth == 1:
																UpperCAmelCase__		=						np.mean(_lowercase       )
																return

										UpperCAmelCase__		=						0
										UpperCAmelCase__		=						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:
																						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(_lowercase    ,      _lowercase       )
																self.right.train(_lowercase    ,      _lowercase       )
										else:
																UpperCAmelCase__		=						np.mean(_lowercase       )

										return

				def     A	(       self    :      List[Any]    ,      _lowercase    :      int       ):


										'''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      _lowercase						:

				@staticmethod
				def     A	(       _lowercase    :      int    ,      _lowercase    :      Optional[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[Any]:
						UpperCAmelCase__		=						np.arange(-1.0						,     1.0						,     0.0_05     )
						UpperCAmelCase__		=						np.sin(UpperCAmelCase__     )

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

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


"""simple docstring"""







class      _lowercase						:

				def __init__(       self    :      Tuple    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						data
										UpperCAmelCase__   		=						None
										UpperCAmelCase__   		=						None


def 					a__ (						UpperCAmelCase__    :     BinaryTreeNode | None						,     UpperCAmelCase__    :     int     )->		BinaryTreeNode | None:
						if node is None:
												UpperCAmelCase__   		=						BinaryTreeNode(UpperCAmelCase__     )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												UpperCAmelCase__   		=						insert(node.left_child						,     UpperCAmelCase__     )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												UpperCAmelCase__   		=						insert(node.right_child						,     UpperCAmelCase__     )
						return node


def 					a__ (						UpperCAmelCase__    :     None | BinaryTreeNode     )->		list[int]:  # if node is None,return
						if node:
												UpperCAmelCase__   		=						inorder(node.left_child     )
												UpperCAmelCase__   		=						[*inorder_array, node.data]
												UpperCAmelCase__   		=						inorder_array + inorder(node.right_child     )
						else:
												UpperCAmelCase__   		=						[]
						return inorder_array


def 					a__ (						)->		BinaryTreeNode | None:
						UpperCAmelCase__   		=						insert(UpperCAmelCase__						,     1_5     )
						insert(UpperCAmelCase__						,     1_0     )
						insert(UpperCAmelCase__						,     2_5     )
						insert(UpperCAmelCase__						,     6     )
						insert(UpperCAmelCase__						,     1_4     )
						insert(UpperCAmelCase__						,     2_0     )
						insert(UpperCAmelCase__						,     6_0     )
						return root


def 					a__ (						)->		None:
						# main function
						UpperCAmelCase__   		=						make_tree()
						print("""Printing values of binary search tree in Inorder Traversal."""     )
						inorder(UpperCAmelCase__     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()

