'''simple docstring'''


import numpy as np






class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__( self      ,		snake_case_=5      ,		snake_case_=5						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      depth
       UpperCAmelCase_							:   List[str]   =      0
       UpperCAmelCase_							:   List[Any]   =      None
       UpperCAmelCase_							:   Optional[Any]   =      None
       UpperCAmelCase_							:   Optional[Any]   =      min_leaf_size
       UpperCAmelCase_							:   int   =      None


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       if labels.ndim != 1:
            print('Error: Input labels must be one dimensional'						)

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


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       if x.ndim != 1:
            print('Error: Input data set must be one dimensional'						)
            return
       if len(snake_case_						) != len(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(snake_case_						) < 2 * self.min_leaf_size:
            UpperCAmelCase_							:   Optional[Any]   =      np.mean(snake_case_						)
            return

       if self.depth == 1:
            UpperCAmelCase_							:   List[Any]   =      np.mean(snake_case_						)
            return

       UpperCAmelCase_							:   Optional[int]   =      0
       UpperCAmelCase_							:   int   =      self.mean_squared_error(snake_case_      ,		np.mean(snake_case_						)						) * 2
       for i in range(len(snake_case_						)						):
            if len(x[:i]						) < self.min_leaf_size:
                 continue
            elif len(x[i:]						) < self.min_leaf_size:
                 continue
            else:
                 UpperCAmelCase_							:   Any   =      self.mean_squared_error(x[:i]      ,		np.mean(y[:i]						)						)
                 UpperCAmelCase_							:   int   =      self.mean_squared_error(x[i:]      ,		np.mean(y[i:]						)						)
                 UpperCAmelCase_							:   List[str]   =      error_left + error_right
                 if error < min_error:
                      UpperCAmelCase_							:   List[Any]   =      i
                      UpperCAmelCase_							:   Tuple   =      error

       if best_split != 0:
            UpperCAmelCase_							:   List[str]   =      x[:best_split]
            UpperCAmelCase_							:   str   =      y[:best_split]
            UpperCAmelCase_							:   Union[str, Any]   =      x[best_split:]
            UpperCAmelCase_							:   Any   =      y[best_split:]

            UpperCAmelCase_							:   List[Any]   =      x[best_split]
            UpperCAmelCase_							:   int   =      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(snake_case_      ,		snake_case_						)
            self.right.train(snake_case_      ,		snake_case_						)
       else:
            UpperCAmelCase_							:   Union[str, Any]   =      np.mean(snake_case_						)

       return






  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''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(snake_case_						)
            else:
                 return self.left.predict(snake_case_						)
       else:
            print('Error: Decision tree not yet trained'						)
            return None







class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  @staticmethod
  def _UpperCamelCase     ( snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      float(0						)
       for label in labels:
            squared_error_sum += (label - prediction) ** 2

       return float(squared_error_sum / labels.size						)







def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   Tuple   =      np.arange(-1.0	,     1.0	,     0.005   )
     UpperCAmelCase_							:   Union[str, Any]   =      np.sin(lowerCamelCase_   )

     UpperCAmelCase_							:   Optional[Any]   =      DecisionTree(depth=10	,     min_leaf_size=10   )
     tree.train(lowerCamelCase_	,     lowerCamelCase_   )

     UpperCAmelCase_							:   Optional[int]   =      (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)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__     :			Optional[Any]      			=  {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			Tuple      			=  [
     '''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

 snake_case__     :			int      			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






