"""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)






_A									=		{
    0: 'underwear',
    1: 'pants',
    2: 'belt',
    3: 'suit',
    4: 'shoe',
    5: 'socks',
    6: 'shirt',
    7: 'tie',
    8: 'watch',
}

_A									=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_A									=		[0 for x in range(len(graph))]
_A									=		[]

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     while stack:
          __UpperCamelCase       				=stack.pop()
          print(SCREAMING_SNAKE_CASE__     ,	clothes[current_clothing]							)
          order += 1

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     stack.append(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     for v in range(len(SCREAMING_SNAKE_CASE__							)							):
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)





