





import numpy as np




class  __UpperCamelCase						:





     def __init__(    self							,       _UpperCamelCase=5							,       _UpperCamelCase=5				):
        _UpperCAmelCase											=      depth
        _UpperCAmelCase											=      0
        _UpperCAmelCase											=      None
        _UpperCAmelCase											=      None
        _UpperCAmelCase											=      min_leaf_size
        _UpperCAmelCase											=      None





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase				):
        if labels.ndim != 1:
           print('''Error: Input labels must be one dimensional'''				)

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





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase				):
        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											=      np.mean(_UpperCamelCase				)
           return

        if self.depth == 1:
           _UpperCAmelCase											=      np.mean(_UpperCamelCase				)
           return

        _UpperCAmelCase											=      0
        _UpperCAmelCase											=      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											=      self.mean_squared_error(x[:i]							,       np.mean(y[:i]				)				)
              _UpperCAmelCase											=      self.mean_squared_error(x[i:]							,       np.mean(y[i:]				)				)
              _UpperCAmelCase											=      error_left + error_right
              if error < min_error:
                 _UpperCAmelCase											=      i
                 _UpperCAmelCase											=      error

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

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

        return





     def 		UpperCamelCase(    self							,       _UpperCamelCase				):
        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  __UpperCamelCase						:





     @staticmethod
     def 		UpperCamelCase(    _UpperCamelCase							,       _UpperCamelCase				):
        _UpperCAmelCase											=      float(0				)
        for label in labels:
           squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)




def     A__					(						)     ->     int:






   """simple docstring"""







   _UpperCAmelCase											=      np.arange(-1.0							,  1.0							,  0.0_0_5	)
   _UpperCAmelCase											=      np.sin(SCREAMING_SNAKE_CASE_	)

   _UpperCAmelCase											=      DecisionTree(depth=10							,  min_leaf_size=10	)
   tree.train(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_											=  {
    "configuration_oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig"],
    "processing_oneformer": ["OneFormerProcessor"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_											=  ["OneFormerImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_											=  [
       "ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "OneFormerForUniversalSegmentation",
       "OneFormerModel",
       "OneFormerPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
   from .processing_oneformer import OneFormerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_oneformer import OneFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_oneformer import (
          ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          OneFormerForUniversalSegmentation,
          OneFormerModel,
          OneFormerPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase_											=  _LazyModule(__name__, globals()["__file__"], _import_structure)