



"""simple docstring"""







import numpy as np





class    UpperCamelCase     :




	"""simple docstring"""






	def __init__(			self							,UpperCAmelCase_=5							,UpperCAmelCase_=5					):
			_lowercase							:      Optional[Any]			=					depth
			_lowercase							:      str			=					0
			_lowercase							:      List[Any]			=					None
			_lowercase							:      Union[str, Any]			=					None
			_lowercase							:      Dict			=					min_leaf_size
			_lowercase							:      Optional[Any]			=					None






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_					):
			if labels.ndim != 1:
					print("""Error: Input labels must be one dimensional"""					)

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






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_					):
			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:
					_lowercase							:      str			=					np.mean(UpperCAmelCase_					)
					return

			if self.depth == 1:
					_lowercase							:      Union[str, Any]			=					np.mean(UpperCAmelCase_					)
					return

			_lowercase							:      Optional[int]			=					0
			_lowercase							:      Any			=					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:
							_lowercase							:      List[Any]			=					self.mean_squared_error(x[:i]							,np.mean(y[:i]					)					)
							_lowercase							:      List[Any]			=					self.mean_squared_error(x[i:]							,np.mean(y[i:]					)					)
							_lowercase							:      str			=					error_left + error_right
							if error < min_error:
									_lowercase							:      Union[str, Any]			=					i
									_lowercase							:      Tuple			=					error

			if best_split != 0:
					_lowercase							:      Tuple			=					x[:best_split]
					_lowercase							:      int			=					y[:best_split]
					_lowercase							:      int			=					x[best_split:]
					_lowercase							:      Optional[int]			=					y[best_split:]

					_lowercase							:      Optional[int]			=					x[best_split]
					_lowercase							:      Any			=					DecisionTree(
					    depth=self.depth - 1							,min_leaf_size=self.min_leaf_size					)
					_lowercase							:      Tuple			=					DecisionTree(
					    depth=self.depth - 1							,min_leaf_size=self.min_leaf_size					)
					self.left.train(UpperCAmelCase_							,UpperCAmelCase_					)
					self.right.train(UpperCAmelCase_							,UpperCAmelCase_					)
			else:
					_lowercase							:      Tuple			=					np.mean(UpperCAmelCase_					)

			return






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			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    UpperCamelCase     :




	"""simple docstring"""






	@staticmethod
	def 					lowerCamelCase__ (			UpperCAmelCase_							,UpperCAmelCase_					):
			_lowercase							:      str			=					float(0					)
			for label in labels:
					squared_error_sum += (label - prediction) ** 2

			return float(squared_error_sum / labels.size					)







def       __SCREAMING_SNAKE_CASE      (   ):
		_lowercase							:      int			=					np.arange(-1.0					,							1.0					,							0.0_0_5	)
		_lowercase							:      int			=					np.sin(__UpperCAmelCase	)

		_lowercase							:      Any			=					DecisionTree(depth=10					,							min_leaf_size=10	)
		tree.train(__UpperCAmelCase					,							__UpperCAmelCase	)

		_lowercase							:      Any			=					(np.random.rand(10	) * 2) - 1
		_lowercase							:      Union[str, Any]			=					np.array([tree.predict(__UpperCAmelCase	) for x in test_cases]	)
		_lowercase							:      Optional[Any]			=					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'''



from __future__ import annotations







def 	_UpperCamelCase       (_lowerCamelCase							:   int      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
     else:
          __snake_case			    =     capacity - weights[counter - 1]
          __snake_case			    =     values[counter - 1] + knapsack(
              _lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          __snake_case			    =     knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          return max(_lowerCamelCase      ,		_lowerCamelCase		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






