





import numpy as np

class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		List[str]      ,    snake_case    :		Optional[int]=5      ,    snake_case    :		Tuple=5				):

      """simple docstring"""





      __lowerCAmelCase								=					depth
      __lowerCAmelCase								=					0
      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					min_leaf_size
      __lowerCAmelCase								=					None





    def 						A		( self    :		Optional[int]      ,    snake_case    :		List[str]      ,    snake_case    :		str				):

      """simple docstring"""





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

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





    def 						A		( self    :		Optional[int]      ,    snake_case    :		List[Any]      ,    snake_case    :		List[str]				):

      """simple docstring"""





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

      if self.depth == 1:
        __lowerCAmelCase								=					np.mean(snake_case				)
        return

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

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

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

      return





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		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(snake_case				)
        else:
          return self.left.predict(snake_case				)
      else:
        print('''Error: Decision tree not yet trained'''				)
        return None


class 	__snake_case					:






    '''simple docstring'''





    @staticmethod
    def 						A		( snake_case    :		Any      ,    snake_case    :		List[str]				):

      """simple docstring"""





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

      return float(squared_error_sum / labels.size				)


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					np.arange(-1.0  ,				1.0  ,				0.005)
  __lowerCAmelCase								=					np.sin(__lowercase)

  __lowerCAmelCase								=					DecisionTree(depth=10  ,				min_leaf_size=10)
  tree.train(__lowercase  ,				__lowercase)

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







def        __SCREAMING_SNAKE_CASE	(				__lowercase:		int):



  '''simple docstring'''






  if not isinstance(__lowercase  ,				__lowercase):
    __lowerCAmelCase								=					F'''Input value of [number={number}] must be an integer'''
    raise TypeError(__lowercase)
  if number < 0 or number % 2 == 1:
    return False
  __lowerCAmelCase								=					int(number**0.5)
  return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
