




'''simple docstring'''





import numpy as np

class      _a       :






     def __init__(				self      ,_SCREAMING_SNAKE_CASE=5      ,_SCREAMING_SNAKE_CASE=5					)							->    List[str]:
          _snake_case							=   depth
          _snake_case							=   0
          _snake_case							=   None
          _snake_case							=   None
          _snake_case							=   min_leaf_size
          _snake_case							=   None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Optional[int]:
          if labels.ndim != 1:
               print("Error: Input labels must be one dimensional"					)

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Any:
          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        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Union[str, Any]:
          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      _a       :






     @staticmethod
     def        _lowercase			(				_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							=   float(0					)
          for label in labels:
               squared_error_sum += (label - prediction) ** 2

          return float(squared_error_sum / labels.size					)







def 			__a			(     )		->				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)








'''simple docstring'''





import argparse
import os


UpperCamelCase_		:    Union[str, Any]									=		{
    '''pytorch''': {
        '''1.13''': {
            '''torch''': '''1.13.1''',
            '''torchvision''': '''0.14.1''',
            '''torchaudio''': '''0.13.1''',
            '''python''': 3.9,
            '''cuda''': '''cu116''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu116'''
            ),
            '''base_image''': '''nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.12''': {
            '''torch''': '''1.12.1''',
            '''torchvision''': '''0.13.1''',
            '''torchaudio''': '''0.12.1''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.11''': {
            '''torch''': '''1.11.0''',
            '''torchvision''': '''0.12.0''',
            '''torchaudio''': '''0.11.0''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.10''': {
            '''torch''': '''1.10.2''',
            '''torchvision''': '''0.11.3''',
            '''torchaudio''': '''0.10.2''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        '''1.9''': {
            '''torch''': '''1.9.1''',
            '''torchvision''': '''0.10.1''',
            '''torchaudio''': '''0.9.1''',
            '''python''': 3.9,
            '''cuda''': '''cu111''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu111'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
    '''tensorflow''': {
        '''2.11''': {
            '''tensorflow''': '''2.11.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.11.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.10''': {
            '''tensorflow''': '''2.10.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.10.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.9''': {
            '''tensorflow''': '''2.9.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.9.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.8''': {
            '''tensorflow''': '''2.8.2''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.8.2''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.7''': {
            '''tensorflow''': '''2.7.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.7.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.6''': {
            '''tensorflow''': '''2.6.5''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.6.5''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.5''': {
            '''tensorflow''': '''2.5.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.5.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
}


if __name__ == "__main__":
   UpperCamelCase_		:    Optional[Any]									=		argparse.ArgumentParser('''Choose the framework and version to install''')
   parser.add_argument(
       '''--framework''', help='''The framework to install. Should be `torch` or `tensorflow`''', type=str, required=True
   )
   parser.add_argument('''--version''', help='''The version of the framework to install.''', type=str, required=True)
   UpperCamelCase_		:    Tuple									=		parser.parse_args()

   UpperCamelCase_		:    Optional[int]									=		past_versions_testing[args.framework][args.version]

   os.system(F'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')
   print(F'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')

   UpperCamelCase_		:    List[Any]									=		''''''
   if args.framework == "pytorch":
      UpperCamelCase_		:    str									=		info['''cuda''']
   os.system(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')
   print(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')


