

import numpy as np



class 						lowercase_					:



      def __init__(    self   :       Optional[Any]    ,      A__   :       str=5    ,      A__   :       int=5	)					->  Dict:
        _snake_case		=							depth
        _snake_case		=							0
        _snake_case		=							None
        _snake_case		=							None
        _snake_case		=							min_leaf_size
        _snake_case		=							None



      def 		UpperCamelCase_	(    self   :       Any    ,      A__   :       Any    ,      A__   :       int	)					->  Tuple:
        if labels.ndim != 1:
          print('''Error: Input labels must be one dimensional'''	)

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



      def 		UpperCamelCase_	(    self   :       Any    ,      A__   :       Optional[int]    ,      A__   :       Dict	)					->  Optional[int]:
        if x.ndim != 1:
          print('''Error: Input data set must be one dimensional'''	)
          return
        if len(UpperCamelCase__	) != len(UpperCamelCase__	):
          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(UpperCamelCase__	) < 2 * self.min_leaf_size:
          _snake_case		=							np.mean(UpperCamelCase__	)
          return

        if self.depth == 1:
          _snake_case		=							np.mean(UpperCamelCase__	)
          return

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

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

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

        return







      def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       Optional[Any]	)					->  str:
        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(UpperCamelCase__	)
          else:
            return self.left.predict(UpperCamelCase__	)
        else:
          print('''Error: Decision tree not yet trained'''	)
          return None



class 						lowercase_					:



      @staticmethod
      def 		UpperCamelCase_	(    A__   :       Any    ,      A__   :       Tuple	)					->  int:
        _snake_case		=							float(0	)
        for label in labels:
          squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size	)



def    snake_case_()							->			List[str]:



  """simple docstring"""






  _snake_case		=							np.arange(-1.0				, 1.0				, 0.005  )
  _snake_case		=							np.sin(_UpperCamelCase  )

  _snake_case		=							DecisionTree(depth=10				, min_leaf_size=10  )
  tree.train(_UpperCamelCase				, _UpperCamelCase  )

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



from __future__ import annotations

from typing import Generic, TypeVar

__A    	=			TypeVar('''T''')



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       str    ,      A__   :       T	)					->  Union[str, Any]:
        _snake_case		=							data  # Assign data
        _snake_case		=							None  # Initialize next as null
        _snake_case		=							None  # Initialize prev as null



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       List[str]	)					->  None:
        _snake_case		=							None



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       T	)					->  None:
        if self.head is None:
          _snake_case		=							Node(A__	)
        else:
          _snake_case		=							Node(A__	)
          _snake_case		=							new_node
          _snake_case		=							self.head
          _snake_case		=							None
          _snake_case		=							new_node



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  T | None:
        if self.head is None:
          return None
        else:
          assert self.head is not None
          _snake_case		=							self.head.data
          _snake_case		=							self.head.next
          if self.head is not None:
            _snake_case		=							None
          return temp



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  T | None:
        return self.head.data if self.head is not None else None



      def __len__(    self   :       Optional[int]	)					->  int:
        _snake_case		=							self.head
        _snake_case		=							0
        while temp is not None:
          count += 1
          _snake_case		=							temp.next
        return count



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  bool:
        return self.head is None







      def 		UpperCamelCase_	(    self   :       Dict	)					->  None:
        print('''stack elements are:'''	)
        _snake_case		=							self.head
        while temp is not None:
          print(temp.data    ,      end='''->'''	)
          _snake_case		=							temp.next


# Code execution starts here
if __name__ == "__main__":
 # Start with the empty stack
 __A    	=			Stack()

 # Insert 4 at the beginning. So stack becomes 4->None
 print('''Stack operations using Doubly LinkedList''')
 stack.push(4)

 # Insert 5 at the beginning. So stack becomes 4->5->None
 stack.push(5)

 # Insert 6 at the beginning. So stack becomes 4->5->6->None
 stack.push(6)

 # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
 stack.push(7)

 # Print the stack
 stack.print_stack()

 # Print the top element
 print('''\nTop element is ''', stack.top())

 # Print the stack size
 print('''Size of the stack is ''', len(stack))

 # pop the top element
 stack.pop()

 # pop the top element
 stack.pop()

 # two elements have now been popped off
 stack.print_stack()

 # Print True if the stack is empty else False
 print('''\nstack is empty:''', stack.is_empty())

