




'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase_				:     List[Any]     			=						'''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase_				:     List[Any]     			=						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase_				:     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}
}
'''





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


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                        '''references''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                    }
                    if self.config_name == '''multilabel'''
                    else {
                        '''predictions''': datasets.Value('''int32'''     ),
                        '''references''': datasets.Value('''int32'''     ),
                    }     )			,     reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']			,     )




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=None     )					->     Any:



            '''simple docstring'''




            return {
                "accuracy": float(
                    accuracy_score(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     normalize=__SCREAMING_SNAKE_CASE			,     sample_weight=__SCREAMING_SNAKE_CASE     )     )
            }












'''simple docstring'''



from __future__ import annotations







def 	_UpperCamelCase       (_lowerCamelCase							:   int      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
     else:
          __snake_case			    =     capacity - weights[counter - 1]
          __snake_case			    =     values[counter - 1] + knapsack(
              _lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          __snake_case			    =     knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          return max(_lowerCamelCase      ,		_lowerCamelCase		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






