"""simple docstring"""
import numpy as np




class   __snake_case			:



      def __init__(		self       ,     lowercase=5       ,     lowercase=5)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Dict     				=      depth
        a__:	List[str]     				=      0
        a__:	Tuple     				=      None
        a__:	Dict     				=      None
        a__:	Optional[Any]     				=      min_leaf_size
        a__:	Tuple     				=      None



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





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

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



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     Optional[int]:





        '''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:
          a__:	int     				=      np.mean(lowercase)
          return

        if self.depth == 1:
          a__:	List[str]     				=      np.mean(lowercase)
          return

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

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

          a__:	Tuple     				=      x[best_split]
          a__:	Union[str, Any]     				=      DecisionTree(
              depth=self.depth - 1       ,     min_leaf_size=self.min_leaf_size)
          a__:	Optional[int]     				=      DecisionTree(
              depth=self.depth - 1       ,     min_leaf_size=self.min_leaf_size)
          self.left.train(lowercase       ,     lowercase)
          self.right.train(lowercase       ,     lowercase)
        else:
          a__:	Dict     				=      np.mean(lowercase)

        return



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     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(lowercase)
          else:
            return self.left.predict(lowercase)
        else:
          print('Error: Decision tree not yet trained')
          return None







class   __snake_case			:



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase)      ->     Dict:





        '''simple docstring'''





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

        return float(squared_error_sum / labels.size)







def 		__a  (      )      ->Optional[Any]:
  a__:	Dict     				=      np.arange(-1.0						,	1.0						,	0.005  )
  a__:	Optional[Any]     				=      np.sin(_SCREAMING_SNAKE_CASE  )

  a__:	Tuple     				=      DecisionTree(depth=10						,	min_leaf_size=10  )
  tree.train(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  a__:	int     				=      (np.random.rand(10  ) * 2) - 1
  a__:	List[Any]     				=      np.array([tree.predict(_SCREAMING_SNAKE_CASE  ) for x in test_cases]  )
  a__:	Tuple     				=      np.mean((predictions - test_cases) ** 2  )

  print('Test values: ' + str(_SCREAMING_SNAKE_CASE  )  )
  print('Predictions: ' + str(_SCREAMING_SNAKE_CASE  )  )
  print('Average error: ' + str(_SCREAMING_SNAKE_CASE  )  )


if __name__ == "__main__":
 main()
 import doctest

 doctest.testmod(name='mean_squarred_error', verbose=True)






"""simple docstring"""






class       lowerCamelCase_		:





				"""simple docstring"""







				class       lowerCamelCase_		:





								"""simple docstring"""







								_lowerCAmelCase   :   Dict   				=       "_prev", "_data", "_next"



								def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
												SCREAMING_SNAKE_CASE__  				=					link_p
												SCREAMING_SNAKE_CASE__  				=					element
												SCREAMING_SNAKE_CASE__  				=					link_n






								def 	lowerCAmelCase__		(    self  ):
												return (
												    f''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
												)



				def __init__(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self._Node(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					self._Node(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					self._trailer
								SCREAMING_SNAKE_CASE__  				=					self._header
								SCREAMING_SNAKE_CASE__  				=					0



				def __len__(    self  ):
								return self._size



				def 	lowerCAmelCase__		(    self  ):
								return self.__len__() == 0



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								# Create new_node by setting it's prev.link -> header
								# setting it's next.link -> trailer
								SCREAMING_SNAKE_CASE__  				=					self._Node(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					new_node
								SCREAMING_SNAKE_CASE__  				=					new_node
								self._size += 1
								return self






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					node._prev
								SCREAMING_SNAKE_CASE__  				=					node._next

								SCREAMING_SNAKE_CASE__  				=					successor
								SCREAMING_SNAKE_CASE__  				=					predecessor
								self._size -= 1
								SCREAMING_SNAKE_CASE__  				=					node._data
								SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					None
								del node
								return temp


class       lowerCamelCase_		(      _DoublyLinkedBase ):





				"""simple docstring"""







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



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



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								return self._insert(self._header							,		UpperCAmelCase__							,		self._header._next  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								return self._insert(self._trailer._prev							,		UpperCAmelCase__							,		self._trailer  )



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






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