



'''simple docstring'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str | Literal[False]:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	str							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count += 1
																					UpperCAmelCase  :	Optional[Any]							  =	"""_"""
							if count > 1:
														return False
							else:
														return "".join(_lowercase	)



def    __lowerCamelCase						(      _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	List[str]							  =	[]
							while True:
														UpperCAmelCase  :	Optional[int]							  =	["""$"""] * len(_lowercase	)
														UpperCAmelCase  :	int							  =	[]
														for i in range(len(_lowercase	)	):
																					for j in range(i + 1			,       len(_lowercase	)	):
																												UpperCAmelCase  :	str							  =	compare_string(binary[i]			,       binary[j]	)
																												if k is False:
																																			UpperCAmelCase  :	Union[str, Any]							  =	"""*"""
																																			UpperCAmelCase  :	Optional[Any]							  =	"""*"""
																																			temp.append("""X"""	)
														for i in range(len(_lowercase	)	):
																					if checka[i] == "$":
																												pi.append(binary[i]	)
														if len(_lowercase	) == 0:
																					return pi
														UpperCAmelCase  :	List[Any]							  =	list(set(_lowercase	)	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Dict							  =	[]
							for minterm in minterms:
														UpperCAmelCase  :	List[str]							  =	""""""
														for _ in range(_lowercase	):
																					UpperCAmelCase  :	Dict							  =	str(minterm % 2	) + string
																					minterm //= 2
														temp.append(_lowercase	)
							return temp



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    bool:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count_n += 1
							return count_n == count



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Tuple							  =	[]
							UpperCAmelCase  :	Optional[int]							  =	[0] * len(_lowercase	)
							for i in range(len(chart[0]	)	):
														UpperCAmelCase  :	Any							  =	0
														UpperCAmelCase  :	Optional[Any]							  =	-1
														for j in range(len(_lowercase	)	):
																					if chart[j][i] == 1:
																												count += 1
																												UpperCAmelCase  :	str							  =	j
														if count == 1:
																					UpperCAmelCase  :	Optional[int]							  =	1
							for i in range(len(_lowercase	)	):
														if select[i] == 1:
																					for j in range(len(chart[0]	)	):
																												if chart[i][j] == 1:
																																			for k in range(len(_lowercase	)	):
																																										UpperCAmelCase  :	List[str]							  =	0
																					temp.append(prime_implicants[i]	)
							while True:
														UpperCAmelCase  :	int							  =	0
														UpperCAmelCase  :	Tuple							  =	-1
														UpperCAmelCase  :	Union[str, Any]							  =	0
														for i in range(len(_lowercase	)	):
																					UpperCAmelCase  :	Optional[Any]							  =	chart[i].count(1	)
																					if count_n > max_n:
																												UpperCAmelCase  :	Union[str, Any]							  =	count_n
																												UpperCAmelCase  :	Optional[Any]							  =	i

														if max_n == 0:
																					return temp

														temp.append(prime_implicants[rem]	)

														for i in range(len(chart[0]	)	):
																					if chart[rem][i] == 1:
																												for j in range(len(_lowercase	)	):
																																			UpperCAmelCase  :	Optional[Any]							  =	0



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[list[int]]:
							UpperCAmelCase  :	Optional[int]							  =	[[0 for x in range(len(_lowercase	)	)] for x in range(len(_lowercase	)	)]
							for i in range(len(_lowercase	)	):
														UpperCAmelCase  :	Tuple							  =	prime_implicants[i].count("""_"""	)
														for j in range(len(_lowercase	)	):
																					if is_for_table(prime_implicants[i]			,       binary[j]			,       _lowercase	):
																												UpperCAmelCase  :	List[Any]							  =	1

							return chart



def    __lowerCamelCase						(      )   ->    None:
							UpperCAmelCase  :	str							  =	int(input("""Enter the no. of variables\n"""	)	)
							UpperCAmelCase  :	List[Any]							  =	[
							    float(_lowercase	)
							    for x in input(
							        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""	).split()
							]
							UpperCAmelCase  :	str							  =	decimal_to_binary(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	check(_lowercase	)
							print("""Prime Implicants are:"""	)
							print(_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	prime_implicant_chart(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	selection(_lowercase			,       _lowercase	)
							print("""Essential Prime Implicants are:"""	)
							print(_lowercase	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()






'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


a     :    Union[str, Any]      						=					"""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

a     :    int      						=					"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


a     :    int      						=					"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,  _KWARGS_DESCRIPTION    )
class     UpperCamelCase_				(							datasets.Metric    ):





	def 				_lowercase(						self					)    ->    List[str]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(self._get_feature_types()					)	,	reference_urls=[
								        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
								    ]	,	)





	def 				_lowercase(						self					)    ->    List[Any]:
								if self.config_name == "multilist":
															return {
															    "predictions": datasets.Sequence(datasets.Value("""float"""					)					),
															    "references": datasets.Sequence(datasets.Value("""float"""					)					),
															}
								else:
															return {
															    "predictions": datasets.Value("""float"""					),
															    "references": datasets.Value("""float"""					),
															}






	def 				_lowercase(						self	,	A	,	A	,	A=None	,	A="uniform_average"	,	A=True					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	mean_squared_error(
								    A	,	A	,	sample_weight=A	,	multioutput=A	,	squared=A					)

								return {"mse": mse}

