code: 
  import numpy as np
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __init__(	self     :     Union[str, Any]							,							lowerCAmelCase_     :     Optional[int]=5							,							lowerCAmelCase_     :     Tuple=5					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       depth
  								SCREAMING_SNAKE_CASE_	      =       0
  								SCREAMING_SNAKE_CASE_	      =       None
  								SCREAMING_SNAKE_CASE_	      =       None
  								SCREAMING_SNAKE_CASE_	      =       min_leaf_size
  								SCREAMING_SNAKE_CASE_	      =       None
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Dict							,							lowerCAmelCase_     :     str					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if labels.ndim != 1:
  													print('''Error: Input labels must be one dimensional'''					)
  
  								return np.mean((labels - prediction) ** 2					)
  
  			def _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     List[str]							,							lowerCAmelCase_     :     Optional[Any]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								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:
  													SCREAMING_SNAKE_CASE_	      =       np.mean(lowerCAmelCase_					)
  													return
  
  								if self.depth == 1:
  													SCREAMING_SNAKE_CASE_	      =       np.mean(lowerCAmelCase_					)
  													return
  
  								SCREAMING_SNAKE_CASE_	      =       0
  								SCREAMING_SNAKE_CASE_	      =       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:
  																		SCREAMING_SNAKE_CASE_	      =       self.mean_squared_error(x[:i]							,							np.mean(y[:i]					)					)
  																		SCREAMING_SNAKE_CASE_	      =       self.mean_squared_error(x[i:]							,							np.mean(y[i:]					)					)
  																		SCREAMING_SNAKE_CASE_	      =       error_left + error_right
  																		if error < min_error:
  																							SCREAMING_SNAKE_CASE_	      =       i
  																							SCREAMING_SNAKE_CASE_	      =       error
  
  								if best_split != 0:
  													SCREAMING_SNAKE_CASE_	      =       x[:best_split]
  													SCREAMING_SNAKE_CASE_	      =       y[:best_split]
  													SCREAMING_SNAKE_CASE_	      =       x[best_split:]
  													SCREAMING_SNAKE_CASE_	      =       y[best_split:]
  
  													SCREAMING_SNAKE_CASE_	      =       x[best_split]
  													SCREAMING_SNAKE_CASE_	      =       DecisionTree(
  													    depth=self.depth - 1							,							min_leaf_size=self.min_leaf_size					)
  													SCREAMING_SNAKE_CASE_	      =       DecisionTree(
  													    depth=self.depth - 1							,							min_leaf_size=self.min_leaf_size					)
  													self.left.train(lowerCAmelCase_							,							lowerCAmelCase_					)
  													self.right.train(lowerCAmelCase_							,							lowerCAmelCase_					)
  								else:
  													SCREAMING_SNAKE_CASE_	      =       np.mean(lowerCAmelCase_					)
  
  								return
  
  
  
  
  			def _lowercase (	self     :     Any							,							lowerCAmelCase_     :     Optional[Any]					)					->					List[Any]:
  
  
  
  
  								"""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(lowerCAmelCase_					)
  													else:
  																		return self.left.predict(lowerCAmelCase_					)
  								else:
  													print('''Error: Decision tree not yet trained'''					)
  													return None
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			@staticmethod
  			def _lowercase (	lowerCAmelCase_     :     List[Any]							,							lowerCAmelCase_     :     Any					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       float(0					)
  								for label in labels:
  													squared_error_sum += (label - prediction) ** 2
  
  								return float(squared_error_sum / labels.size					)
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     )->		Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       np.arange(-1.0				,1.0				,0.0_0_5      )
  					SCREAMING_SNAKE_CASE_	      =       np.sin(UpperCAmelCase      )
  
  					SCREAMING_SNAKE_CASE_	      =       DecisionTree(depth=10				,min_leaf_size=10      )
  					tree.train(UpperCAmelCase				,UpperCAmelCase      )
  
  					SCREAMING_SNAKE_CASE_	      =       (np.random.rand(10      ) * 2) - 1
  					SCREAMING_SNAKE_CASE_	      =       np.array([tree.predict(UpperCAmelCase      ) for x in test_cases]      )
  					SCREAMING_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)
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_														=      {
      "configuration_switch_transformers": [
          "SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "SwitchTransformersConfig",
          "SwitchTransformersOnnxConfig",
      ]
  }
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "SwitchTransformersEncoderModel",
  		    "SwitchTransformersForConditionalGeneration",
  		    "SwitchTransformersModel",
  		    "SwitchTransformersPreTrainedModel",
  		    "SwitchTransformersTop1Router",
  		    "SwitchTransformersSparseMLP",
  		]
  
  
  if TYPE_CHECKING:
  		from .configuration_switch_transformers import (
  		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
  		    SwitchTransformersConfig,
  		    SwitchTransformersOnnxConfig,
  		)
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_switch_transformers import (
  				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    SwitchTransformersEncoderModel,
  				    SwitchTransformersForConditionalGeneration,
  				    SwitchTransformersModel,
  				    SwitchTransformersPreTrainedModel,
  				    SwitchTransformersSparseMLP,
  				    SwitchTransformersTopaRouter,
  				)
  
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  