





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)








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__)

