code: 
  
  
  
  
  
  import numpy as np
  
  
  
  
  class 				_lowerCamelCase :
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase=5				,						UpperCAmelCase=5   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[str]        =       depth
          __snake_case   :				Tuple        =       0
          __snake_case   :				Optional[int]        =       None
          __snake_case   :				List[str]        =       None
          __snake_case   :				int        =       min_leaf_size
          __snake_case   :				Tuple        =       None
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if labels.ndim != 1:
            print("Error: Input labels must be one dimensional"   )
  
          return np.mean((labels - prediction) ** 2   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''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:
            __snake_case   :				Any        =       np.mean(UpperCAmelCase   )
            return
  
          if self.depth == 1:
            __snake_case   :				Tuple        =       np.mean(UpperCAmelCase   )
            return
  
          __snake_case   :				Optional[Any]        =       0
          __snake_case   :				str        =       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:
              __snake_case   :				Optional[int]        =       self.mean_squared_error(x[:i]				,						np.mean(y[:i]   )   )
              __snake_case   :				str        =       self.mean_squared_error(x[i:]				,						np.mean(y[i:]   )   )
              __snake_case   :				int        =       error_left + error_right
              if error < min_error:
                __snake_case   :				List[Any]        =       i
                __snake_case   :				str        =       error
  
          if best_split != 0:
            __snake_case   :				int        =       x[:best_split]
            __snake_case   :				List[Any]        =       y[:best_split]
            __snake_case   :				Any        =       x[best_split:]
            __snake_case   :				Any        =       y[best_split:]
  
            __snake_case   :				Dict        =       x[best_split]
            __snake_case   :				List[Any]        =       DecisionTree(
                depth=self.depth - 1				,						min_leaf_size=self.min_leaf_size   )
            __snake_case   :				Tuple        =       DecisionTree(
                depth=self.depth - 1				,						min_leaf_size=self.min_leaf_size   )
            self.left.train(UpperCAmelCase				,						UpperCAmelCase   )
            self.right.train(UpperCAmelCase				,						UpperCAmelCase   )
          else:
            __snake_case   :				Union[str, Any]        =       np.mean(UpperCAmelCase   )
  
          return
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''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 				_lowerCamelCase :
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[Any]        =       float(0   )
          for label in labels:
            squared_error_sum += (label - prediction) ** 2
  
          return float(squared_error_sum / labels.size   )
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	) ->    Dict:
    __snake_case   :				str        =       np.arange(-1.0   ,     1.0   ,     0.0_0_5				)
    __snake_case   :				Tuple        =       np.sin(lowercase__				)
  
    __snake_case   :				Optional[int]        =       DecisionTree(depth=10   ,     min_leaf_size=10				)
    tree.train(lowercase__   ,     lowercase__				)
  
    __snake_case   :				List[Any]        =       (np.random.rand(10				) * 2) - 1
    __snake_case   :				List[Any]        =       np.array([tree.predict(lowercase__				) for x in test_cases]				)
    __snake_case   :				str        =       np.mean((predictions - test_cases) ** 2				)
  
    print("Test values: " + str(lowercase__				)				)
    print("Predictions: " + str(lowercase__				)				)
    print("Average error: " + str(lowercase__				)				)
  
  
  if __name__ == "__main__":
       main()
       import doctest
  
       doctest.testmod(name='''mean_squarred_error''', verbose=True)
  
  
style_context: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  _UpperCamelCase       = {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}
  
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _UpperCamelCase       = [
           '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''NatForImageClassification''',
           '''NatModel''',
           '''NatPreTrainedModel''',
           '''NatBackbone''',
       ]
  
  if TYPE_CHECKING:
       from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_nat import (
                NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                NatBackbone,
                NatForImageClassification,
                NatModel,
                NatPreTrainedModel,
            )
  
  else:
       import sys
  
       _UpperCamelCase       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  