


import numpy as np







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase=5			,						__lowerCamelCase=5)      ->    str:
            _A				:	Tuple       =			depth
            _A				:	List[Any]       =			0
            _A				:	Any       =			None
            _A				:	List[str]       =			None
            _A				:	Optional[int]       =			min_leaf_size
            _A				:	Any       =			None


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    int:
            if labels.ndim != 1:
                   print("Error: Input labels must be one dimensional")

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


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            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:
                   _A				:	Optional[int]       =			np.mean(__lowerCamelCase)
                   return

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

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

            if best_split != 0:
                   _A				:	List[str]       =			x[:best_split]
                   _A				:	Optional[Any]       =			y[:best_split]
                   _A				:	Tuple       =			x[best_split:]
                   _A				:	Optional[Any]       =			y[best_split:]

                   _A				:	int       =			x[best_split]
                   _A				:	int       =			DecisionTree(
                       depth=self.depth - 1			,						min_leaf_size=self.min_leaf_size)
                   _A				:	Dict       =			DecisionTree(
                       depth=self.depth - 1			,						min_leaf_size=self.min_leaf_size)
                   self.left.train(__lowerCamelCase			,						__lowerCamelCase)
                   self.right.train(__lowerCamelCase			,						__lowerCamelCase)
            else:
                   _A				:	Optional[int]       =			np.mean(__lowerCamelCase)

            return


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    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(__lowerCamelCase)
                   else:
                          return self.left.predict(__lowerCamelCase)
            else:
                   print("Error: Decision tree not yet trained")
                   return None







class 					lowerCAmelCase__	:




     '''simple docstring'''


     @staticmethod
     def 			_lowerCamelCase			(						__lowerCamelCase			,						__lowerCamelCase)      ->    Dict:
            _A				:	Optional[int]       =			float(0)
            for label in labels:
                   squared_error_sum += (label - prediction) ** 2

            return float(squared_error_sum / labels.size)




def 							_UpperCAmelCase    ():
       _A				:	List[Any]       =			np.arange(-1.0    ,							1.0    ,							0.0_05						)
       _A				:	List[str]       =			np.sin(UpperCamelCase__						)

       _A				:	str       =			DecisionTree(depth=10    ,							min_leaf_size=10						)
       tree.train(UpperCamelCase__    ,							UpperCamelCase__						)

       _A				:	Optional[Any]       =			(np.random.rand(10						) * 2) - 1
       _A				:	List[str]       =			np.array([tree.predict(UpperCamelCase__						) for x in test_cases]						)
       _A				:	Optional[int]       =			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 warnings

from .generation import FlaxGenerationMixin







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     # warning at import time
     warnings.warn(
         "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
         "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."					,   a					,   )
