


import numpy as np



class      snake_case__	:
				def __init__(	self  ,   _UpperCAmelCase=5  ,   _UpperCAmelCase=5   ):




										"""simple docstring"""




										__magic_name__					:				str				=   depth
										__magic_name__					:				Optional[Any]				=   0
										__magic_name__					:				Any				=   None
										__magic_name__					:				Union[str, Any]				=   None
										__magic_name__					:				Any				=   min_leaf_size
										__magic_name__					:				int				=   None
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""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   ):




										"""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:
																__magic_name__					:				Union[str, Any]				=   np.mean(_UpperCAmelCase   )
																return

										if self.depth == 1:
																__magic_name__					:				Union[str, Any]				=   np.mean(_UpperCAmelCase   )
																return

										__magic_name__					:				Union[str, Any]				=   0
										__magic_name__					:				List[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:
																						__magic_name__					:				Any				=   self.mean_squared_error(x[:i]  ,   np.mean(y[:i]   )   )
																						__magic_name__					:				Any				=   self.mean_squared_error(x[i:]  ,   np.mean(y[i:]   )   )
																						__magic_name__					:				Optional[int]				=   error_left + error_right
																						if error < min_error:
																												__magic_name__					:				Dict				=   i
																												__magic_name__					:				Any				=   error

										if best_split != 0:
																__magic_name__					:				Union[str, Any]				=   x[:best_split]
																__magic_name__					:				Tuple				=   y[:best_split]
																__magic_name__					:				Optional[int]				=   x[best_split:]
																__magic_name__					:				Optional[Any]				=   y[best_split:]

																__magic_name__					:				str				=   x[best_split]
																__magic_name__					:				Tuple				=   DecisionTree(
																    depth=self.depth - 1  ,   min_leaf_size=self.min_leaf_size   )
																__magic_name__					:				Dict				=   DecisionTree(
																    depth=self.depth - 1  ,   min_leaf_size=self.min_leaf_size   )
																self.left.train(_UpperCAmelCase  ,   _UpperCAmelCase   )
																self.right.train(_UpperCAmelCase  ,   _UpperCAmelCase   )
										else:
																__magic_name__					:				Dict				=   np.mean(_UpperCAmelCase   )

										return





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""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      snake_case__	:
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




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

										return float(squared_error_sum / labels.size   )






def 			__SCREAMING_SNAKE_CASE							(					)							->	Union[str, Any]:
						__magic_name__					:				Optional[Any]				=   np.arange(-1.0   ,    1.0   ,    0.0_0_5					)
						__magic_name__					:				str				=   np.sin(lowercase__					)

						__magic_name__					:				Tuple				=   DecisionTree(depth=1_0   ,    min_leaf_size=1_0					)
						tree.train(lowercase__   ,    lowercase__					)

						__magic_name__					:				Union[str, Any]				=   (np.random.rand(1_0					) * 2) - 1
						__magic_name__					:				Any				=   np.array([tree.predict(lowercase__					) for x in test_cases]					)
						__magic_name__					:				Optional[int]				=   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


snake_case__  				=			{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'QDQBertForMaskedLM',
			    'QDQBertForMultipleChoice',
			    'QDQBertForNextSentencePrediction',
			    'QDQBertForQuestionAnswering',
			    'QDQBertForSequenceClassification',
			    'QDQBertForTokenClassification',
			    'QDQBertLayer',
			    'QDQBertLMHeadModel',
			    'QDQBertModel',
			    'QDQBertPreTrainedModel',
			    'load_tf_weights_in_qdqbert',
			]


if TYPE_CHECKING:
			from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_qdqbert import (
						    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    QDQBertForMaskedLM,
						    QDQBertForMultipleChoice,
						    QDQBertForNextSentencePrediction,
						    QDQBertForQuestionAnswering,
						    QDQBertForSequenceClassification,
						    QDQBertForTokenClassification,
						    QDQBertLayer,
						    QDQBertLMHeadModel,
						    QDQBertModel,
						    QDQBertPreTrainedModel,
						    load_tf_weights_in_qdqbert,
						)


else:
			import sys

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


