'''simple docstring'''


import numpy as np






class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__( self      ,		snake_case_=5      ,		snake_case_=5						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[Any]   =      depth
       UpperCAmelCase_							:   Any   =      0
       UpperCAmelCase_							:   str   =      None
       UpperCAmelCase_							:   Optional[int]   =      None
       UpperCAmelCase_							:   Union[str, Any]   =      min_leaf_size
       UpperCAmelCase_							:   Dict   =      None


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

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

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


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       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:
            UpperCAmelCase_							:   List[str]   =      np.mean(_UpperCamelCase						)
            return

       if self.depth == 1:
            UpperCAmelCase_							:   Union[str, Any]   =      np.mean(_UpperCamelCase						)
            return

       UpperCAmelCase_							:   Dict   =      0
       UpperCAmelCase_							:   Optional[int]   =      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:
                 UpperCAmelCase_							:   Union[str, Any]   =      self.mean_squared_error(x[:i]      ,		np.mean(y[:i]						)						)
                 UpperCAmelCase_							:   str   =      self.mean_squared_error(x[i:]      ,		np.mean(y[i:]						)						)
                 UpperCAmelCase_							:   int   =      error_left + error_right
                 if error < min_error:
                      UpperCAmelCase_							:   List[str]   =      i
                      UpperCAmelCase_							:   Union[str, Any]   =      error

       if best_split != 0:
            UpperCAmelCase_							:   str   =      x[:best_split]
            UpperCAmelCase_							:   str   =      y[:best_split]
            UpperCAmelCase_							:   str   =      x[best_split:]
            UpperCAmelCase_							:   Dict   =      y[best_split:]

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

       return






  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''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(_UpperCamelCase						)
            else:
                 return self.left.predict(_UpperCamelCase						)
       else:
            print('Error: Decision tree not yet trained'						)
            return None







class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  @staticmethod
  def _UpperCamelCase     ( snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      float(0						)
       for label in labels:
            squared_error_sum += (label - prediction) ** 2

       return float(squared_error_sum / labels.size						)







def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   Optional[Any]   =      np.arange(-1.0	,     1.0	,     0.005   )
     UpperCAmelCase_							:   List[Any]   =      np.sin(UpperCAmelCase__   )

     UpperCAmelCase_							:   Optional[int]   =      DecisionTree(depth=10	,     min_leaf_size=10   )
     tree.train(UpperCAmelCase__	,     UpperCAmelCase__   )

     UpperCAmelCase_							:   Dict   =      (np.random.rand(10   ) * 2) - 1
     UpperCAmelCase_							:   Any   =      np.array([tree.predict(UpperCAmelCase__   ) for x in test_cases]   )
     UpperCAmelCase_							:   Dict   =      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 enum import Enum


class   lowercase			(   a   ):
							lowercase__  :				List[str]									=							24
							lowercase__  :				int									=							21
							lowercase__  :				Union[str, Any]									=							18
							lowercase__  :				Dict									=							15
							lowercase__  :				Any									=							12
							lowercase__  :				List[Any]									=							9
							lowercase__  :				Optional[int]									=							6
							lowercase__  :				List[Any]									=							3
							lowercase__  :				int									=							2
							lowercase__  :				Optional[int]									=							1
							lowercase__  :				Dict									=							-1
							lowercase__  :				Any									=							-2
							lowercase__  :				Tuple									=							-3
							lowercase__  :				Optional[Any]									=							-6
							lowercase__  :				Tuple									=							-9
							lowercase__  :				List[Any]									=							-12
							lowercase__  :				Any									=							-15
							lowercase__  :				List[Any]									=							-18
							lowercase__  :				int									=							-21
							lowercase__  :				Optional[Any]									=							-24



class   lowercase			(   a   ):
							lowercase__  :				Union[str, Any]									=							8
							lowercase__  :				List[Any]									=							7
							lowercase__  :				List[str]									=							6
							lowercase__  :				Dict									=							5
							lowercase__  :				List[Any]									=							4
							lowercase__  :				Union[str, Any]									=							3
							lowercase__  :				Union[str, Any]									=							2
							lowercase__  :				Optional[Any]									=							1





def 		__lowerCamelCase							(UpperCAmelCase__   :							float					,       UpperCAmelCase__   :							str | SIUnit					,       UpperCAmelCase__   :							str | SIUnit					,       ):
					if isinstance(UpperCAmelCase__					,       UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				SIUnit[known_prefix.lower()]
					if isinstance(UpperCAmelCase__					,       UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				SIUnit[unknown_prefix.lower()]
					SCREAMING_SNAKE_CASE				=				known_amount * (
					    1_0 ** (known_prefix.value - unknown_prefix.value)
					)
					return unknown_amount





def 		__lowerCamelCase							(UpperCAmelCase__   :							float					,       UpperCAmelCase__   :							str | BinaryUnit					,       UpperCAmelCase__   :							str | BinaryUnit					,       ):
					if isinstance(UpperCAmelCase__					,       UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				BinaryUnit[known_prefix.lower()]
					if isinstance(UpperCAmelCase__					,       UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				BinaryUnit[unknown_prefix.lower()]
					SCREAMING_SNAKE_CASE				=				known_amount * (
					    2 ** ((known_prefix.value - unknown_prefix.value) * 1_0)
					)
					return unknown_amount


if __name__ == "__main__":
							import doctest

							doctest.testmod()




