


import numpy as np

class  UpperCamelCase__ :






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






      def 	__lowercase(     self					:   Dict,		__lowerCamelCase					:   Optional[Any],		__lowerCamelCase					:   Any   )  -> Any:
           if labels.ndim != 1:
                print('''Error: Input labels must be one dimensional'''   )

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






      def 	__lowercase(     self					:   List[Any],		__lowerCamelCase					:   List[str],		__lowerCamelCase					:   Tuple   )  -> List[Any]:
           if x.ndim != 1:
                print('''Error: Input data set must be one dimensional'''   )
                return
           if len(__lowerCamelCase   ) != len(__lowerCamelCase   ):
                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(__lowerCamelCase   ) < 2 * self.min_leaf_size:
                UpperCamelCase__							:  Any								=     np.mean(__lowerCamelCase   )
                return

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

           UpperCamelCase__							:  Optional[int]								=     0
           UpperCamelCase__							:  Any								=     self.mean_squared_error(__lowerCamelCase,		np.mean(__lowerCamelCase   )   ) * 2
           for i in range(len(__lowerCamelCase   )   ):
                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__							:  Union[str, Any]								=     self.mean_squared_error(x[i:],		np.mean(y[i:]   )   )
                     UpperCamelCase__							:  List[str]								=     error_left + error_right
                     if error < min_error:
                          UpperCamelCase__							:  str								=     i
                          UpperCamelCase__							:  Union[str, Any]								=     error

           if best_split != 0:
                UpperCamelCase__							:  Dict								=     x[:best_split]
                UpperCamelCase__							:  Any								=     y[:best_split]
                UpperCamelCase__							:  int								=     x[best_split:]
                UpperCamelCase__							:  Dict								=     y[best_split:]

                UpperCamelCase__							:  Any								=     x[best_split]
                UpperCamelCase__							:  Tuple								=     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(__lowerCamelCase,		__lowerCamelCase   )
                self.right.train(__lowerCamelCase,		__lowerCamelCase   )
           else:
                UpperCamelCase__							:  Any								=     np.mean(__lowerCamelCase   )

           return






      def 	__lowercase(     self					:   Optional[int],		__lowerCamelCase					:   Tuple   )  -> int:
           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(__lowerCamelCase   )
                else:
                     return self.left.predict(__lowerCamelCase   )
           else:
                print('''Error: Decision tree not yet trained'''   )
                return None

class  UpperCamelCase__ :






      @staticmethod
      def 	__lowercase(     __lowerCamelCase					:   int,		__lowerCamelCase					:   str   )  -> List[Any]:
           UpperCamelCase__							:  Union[str, Any]								=     float(0   )
           for label in labels:
                squared_error_sum += (label - prediction) ** 2

           return float(squared_error_sum / labels.size   )







def 						_lowercase       (     )  ->   Optional[Any]:





     '''simple docstring'''





     UpperCamelCase__							:  List[str]								=     np.arange(-1.0		,1.0		,0.0_0_5			)
     UpperCamelCase__							:  Union[str, Any]								=     np.sin(__lowerCamelCase			)

     UpperCamelCase__							:  int								=     DecisionTree(depth=10		,min_leaf_size=10			)
     tree.train(__lowerCamelCase		,__lowerCamelCase			)

     UpperCamelCase__							:  Union[str, Any]								=     (np.random.rand(10			) * 2) - 1
     UpperCamelCase__							:  Optional[Any]								=     np.array([tree.predict(__lowerCamelCase			) for x in test_cases]			)
     UpperCamelCase__							:  Optional[Any]								=     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)








import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


_SCREAMING_SNAKE_CASE :					Optional[int]			=	2 << 20  # 2mb
_SCREAMING_SNAKE_CASE :					Optional[Any]			=	get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):






      def 	__lowercase(     self					:   List[str]   )  -> List[Any]:
           UpperCamelCase__							:  int								=     self._options.get_all_options().get('''runner'''   )
           return runner in [None, "DirectRunner", "PortableRunner"]







def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					List[str]=False			)  ->   Optional[int]:





     '''simple docstring'''





     UpperCamelCase__							:  Any								=     FileSystems
     if fs.exists(__lowerCamelCase			):
          if force_upload:
               logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'			)
          else:
               logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'			)
               return
     with fs.create(__lowerCamelCase			) as remote_file:
          with open(__lowerCamelCase		,'''rb'''			) as local_file:
               UpperCamelCase__							:  List[Any]								=     local_file.read(__lowerCamelCase			)
               while chunk:
                    remote_file.write(__lowerCamelCase			)
                    UpperCamelCase__							:  Union[str, Any]								=     local_file.read(__lowerCamelCase			)







def 						_lowercase       (     __lowerCamelCase			:					Any		,__lowerCamelCase			:					int		,__lowerCamelCase			:					str=False			)  ->   Tuple:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[int]								=     FileSystems
     if os.path.exists(__lowerCamelCase			):
          if force_download:
               logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'			)
          else:
               logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'			)
               return
     with fs.open(__lowerCamelCase			) as remote_file:
          with open(__lowerCamelCase		,'''wb'''			) as local_file:
               UpperCamelCase__							:  Any								=     remote_file.read(__lowerCamelCase			)
               while chunk:
                    local_file.write(__lowerCamelCase			)
                    UpperCamelCase__							:  Dict								=     remote_file.read(__lowerCamelCase			)




