



"""simple docstring"""


import numpy as np







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Optional[Any]  ,    lowercase_					:Dict=5  ,    lowercase_					:Tuple=5   )							->     str:
												UpperCAmelCase			 =					depth
												UpperCAmelCase			 =					0
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					min_leaf_size
												UpperCAmelCase			 =					None






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:List[Any]   )							->     Any:
												if labels.ndim != 1:
																		print('Error: Input labels must be one dimensional'   )

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






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:str  ,    lowercase_					:Union[str, Any]   )							->     Optional[Any]:
												if x.ndim != 1:
																		print('Error: Input data set must be one dimensional'   )
																		return
												if len(lowercase_   ) != len(lowercase_   ):
																		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(lowercase_   ) < 2 * self.min_leaf_size:
																		UpperCAmelCase			 =					np.mean(lowercase_   )
																		return

												if self.depth == 1:
																		UpperCAmelCase			 =					np.mean(lowercase_   )
																		return

												UpperCAmelCase			 =					0
												UpperCAmelCase			 =					self.mean_squared_error(lowercase_  ,    np.mean(lowercase_   )   ) * 2
												for i in range(len(lowercase_   )   ):
																		if len(x[:i]   ) < self.min_leaf_size:
																								continue
																		elif len(x[i:]   ) < self.min_leaf_size:
																								continue
																		else:
																								UpperCAmelCase			 =					self.mean_squared_error(x[:i]  ,    np.mean(y[:i]   )   )
																								UpperCAmelCase			 =					self.mean_squared_error(x[i:]  ,    np.mean(y[i:]   )   )
																								UpperCAmelCase			 =					error_left + error_right
																								if error < min_error:
																														UpperCAmelCase			 =					i
																														UpperCAmelCase			 =					error

												if best_split != 0:
																		UpperCAmelCase			 =					x[:best_split]
																		UpperCAmelCase			 =					y[:best_split]
																		UpperCAmelCase			 =					x[best_split:]
																		UpperCAmelCase			 =					y[best_split:]

																		UpperCAmelCase			 =					x[best_split]
																		UpperCAmelCase			 =					DecisionTree(
																		    depth=self.depth - 1  ,    min_leaf_size=self.min_leaf_size   )
																		UpperCAmelCase			 =					DecisionTree(
																		    depth=self.depth - 1  ,    min_leaf_size=self.min_leaf_size   )
																		self.left.train(lowercase_  ,    lowercase_   )
																		self.right.train(lowercase_  ,    lowercase_   )
												else:
																		UpperCAmelCase			 =					np.mean(lowercase_   )

												return






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:str   )							->     int:
												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(lowercase_   )
																		else:
																								return self.left.predict(lowercase_   )
												else:
																		print('Error: Decision tree not yet trained'   )
																		return None







class  A_     :



						"""simple docstring"""






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:int  ,    lowercase_					:List[Any]   )							->     Any:
												UpperCAmelCase			 =					float(0   )
												for label in labels:
																		squared_error_sum += (label - prediction) ** 2

												return float(squared_error_sum / labels.size   )


def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					np.arange(-1.0						,      1.0						,      0.0_0_5		)
						UpperCAmelCase			 =					np.sin(lowercase_		)

						UpperCAmelCase			 =					DecisionTree(depth=10						,      min_leaf_size=10		)
						tree.train(lowercase_						,      lowercase_		)

						UpperCAmelCase			 =					(np.random.rand(10		) * 2) - 1
						UpperCAmelCase			 =					np.array([tree.predict(lowercase_		) for x in test_cases]		)
						UpperCAmelCase			 =					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)













"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_								=	{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       lowercase_								=	[
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

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




