




'''simple docstring'''



import numpy as np





class     lowerCAmelCase   :


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE=5    ,  __SCREAMING_SNAKE_CASE=5  )      ->  List[str]:



            '''simple docstring'''




            __snake_case    					=  depth
            __snake_case    					=  0
            __snake_case    					=  None
            __snake_case    					=  None
            __snake_case    					=  min_leaf_size
            __snake_case    					=  None


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  Tuple:



            '''simple docstring'''




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

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


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  str:



            '''simple docstring'''




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

            if self.depth == 1:
                 __snake_case    					=  np.mean(__SCREAMING_SNAKE_CASE  )
                 return

            __snake_case    					=  0
            __snake_case    					=  self.mean_squared_error(__SCREAMING_SNAKE_CASE    ,  np.mean(__SCREAMING_SNAKE_CASE  )  ) * 2
            for i in range(len(__SCREAMING_SNAKE_CASE  )  ):
                 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(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
                 self.right.train(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
            else:
                 __snake_case    					=  np.mean(__SCREAMING_SNAKE_CASE  )

            return




       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



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





class     lowerCAmelCase   :


       @staticmethod
       def       lowerCAmelCase      (   __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  str:



            '''simple docstring'''




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

            return float(squared_error_sum / labels.size  )







def 			_UpperCamelCase ()->   Optional[Any]:




     '''simple docstring'''





     __snake_case    					=  np.arange(-1.0   ,	1.0   ,	0.005	)
     __snake_case    					=  np.sin(_lowerCamelCase	)

     __snake_case    					=  DecisionTree(depth=10   ,	min_leaf_size=10	)
     tree.train(_lowerCamelCase   ,	_lowerCamelCase	)

     __snake_case    					=  (np.random.rand(10	) * 2) - 1
     __snake_case    					=  np.array([tree.predict(_lowerCamelCase	) for x in test_cases]	)
     __snake_case    					=  np.mean((predictions - test_cases) ** 2	)

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


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

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












'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


UpperCAmelCase_       :  int      =		logging.get_logger(__name__)





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  None:



            '''simple docstring'''




            warnings.warn(
                '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MaskFormerImageProcessor instead.'''    ,  __SCREAMING_SNAKE_CASE    ,  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )






