
"""simple docstring"""





class A_      :




  '''simple docstring'''


  class A_      :




    '''simple docstring'''
    SCREAMING_SNAKE_CASE__     :		int    	=						"""_prev""", """_data""", """_next"""







    def __init__(						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

       """simple docstring"""
       UpperCAmelCase_   :   int     	=       link_p
       UpperCAmelCase_   :   List[Any]     	=       element
       UpperCAmelCase_   :   Any     	=       link_n







    def 			UpperCamelCase__   (						self    ):

       """simple docstring"""
       return (
           F""" Prev -> {self._prev is not None}, Next -> {self._next is not None}"""
       )







  def __init__(						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       self._Node(lowercase_							,  lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   int     	=       self._Node(lowercase_							,  lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   Any     	=       self._trailer
     UpperCAmelCase_   :   Optional[Any]     	=       self._header
     UpperCAmelCase_   :   Optional[Any]     	=       0







  def __len__(						self    ):

     """simple docstring"""
     return self._size







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.__len__() == 0







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     # Create new_node by setting it's prev.link -> header
     # setting it's next.link -> trailer
     UpperCAmelCase_   :   Optional[int]     	=       self._Node(lowercase_							,  lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       new_node
     UpperCAmelCase_   :   List[str]     	=       new_node
     self._size += 1
     return self







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       node._prev
     UpperCAmelCase_   :   Union[str, Any]     	=       node._next

     UpperCAmelCase_   :   List[Any]     	=       successor
     UpperCAmelCase_   :   Optional[int]     	=       predecessor
     self._size -= 1
     UpperCAmelCase_   :   Dict     	=       node._data
     UpperCAmelCase_   :   List[str]     	=       None
     del node
     return temp




class A_      (_DoublyLinkedBase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if self.is_empty():
        raise Exception("List is empty"    )
     return self._header._next._data







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if self.is_empty():
        raise Exception("List is empty"    )
     return self._trailer._prev._data







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     return self._insert(self._header							,  lowercase_							,  self._header._next    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     return self._insert(self._trailer._prev							,  lowercase_							,  self._trailer    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if self.is_empty():
        raise IndexError("remove_first from empty list"    )
     return self._delete(self._header._next    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if self.is_empty():
        raise IndexError("remove_first from empty list"    )
     return self._delete(self._trailer._prev    )







'''simple docstring'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

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







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               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(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

        if self.depth == 1:
               _UpperCAmelCase    :		Dict     	=					np.mean(a_				)
               return

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

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

               _UpperCAmelCase    :		List[str]     	=					x[best_split]
               _UpperCAmelCase    :		List[Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               _UpperCAmelCase    :		Union[str, Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               self.left.train(a_    ,a_				)
               self.right.train(a_    ,a_				)
        else:
               _UpperCAmelCase    :		Any     	=					np.mean(a_				)

        return







 def _snake_case					(				self    ,a_				)						->  List[Any]:
        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(a_				)
               else:
                      return self.left.predict(a_				)
        else:
               print("""Error: Decision tree not yet trained"""				)
               return None







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		Any     	=					(np.random.rand(10						) * 2) - 1
       _UpperCAmelCase    :		Dict     	=					np.array([tree.predict(lowerCAmelCase_						) for x in test_cases]						)
       _UpperCAmelCase    :		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)



