
'''simple docstring'''



import numpy as np





class 			A							:



			def __init__(					self       ,     lowerCamelCase__=5       ,     lowerCamelCase__=5       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       depth
									lowercase__							 =       0
									lowercase__							 =       None
									lowercase__							 =       None
									lowercase__							 =       min_leaf_size
									lowercase__							 =       None



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:




									'''simple docstring'''



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

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



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:




									'''simple docstring'''



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

									if self.depth == 1:
															lowercase__							 =       np.mean(lowerCamelCase__       )
															return

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

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

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

									return



			def A__ (					self       ,     lowerCamelCase__       )			->		Optional[Any]:




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





class 			A							:



			@staticmethod
			def A__ (					lowerCamelCase__       ,     lowerCamelCase__       )			->		Tuple:




									'''simple docstring'''



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

									return float(squared_error_sum / labels.size       )





def  _A		(							):
						lowercase__							 =       np.arange(-1.0		,		1.0		,		0.0_0_5	)
						lowercase__							 =       np.sin(lowercase__	)

						lowercase__							 =       DecisionTree(depth=10		,		min_leaf_size=10	)
						tree.train(lowercase__		,		lowercase__	)

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

'''simple docstring'''



def  _A		(							lowercase__	):

						# validate:
						if not isinstance(lowercase__		,		lowercase__	) or not all(
						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
												raise ValueError("""The citations should be a list of non negative integers."""	)

						citations.sort()
						lowercase__							 =       len(lowercase__	)

						for i in range(lowercase__	):
												if citations[len_citations - 1 - i] <= i:
																		return i

						return len_citations


if __name__ == "__main__":
				import doctest

				doctest.testmod()






