





'''simple docstring'''






from sklearn.metrics import accuracy_score

import datasets


snake_case_					  =	'\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


snake_case_					  =	'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> 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])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


snake_case_					  =	'\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,_KWARGS_DESCRIPTION				)
class      SCREAMING_SNAKE_CASE__      (    datasets.Metric				):




    def   __lowerCamelCase				( self      ):






          """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    ,					lowercase__    ,					lowercase__    ,					lowercase__=True    ,					lowercase__=None      ):






          """simple docstring"""






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




'''simple docstring'''






def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[int]  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       List[str]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(boundary[1] - boundary[0]) / steps
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	boundary[0]
      SCREAMING_SNAKE_CASE_				:      int					=	boundary[1]
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	make_points(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	0.0
      y += (h / 3.0) * f(SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Tuple					=	2
      for i in x_i:
            y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE_   )
            cnt += 1
      y += (h / 3.0) * f(SCREAMING_SNAKE_CASE_   )
      return y


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[Any]  ,  SCREAMING_SNAKE_CASE_					:     List[Any]  ,  SCREAMING_SNAKE_CASE_					:     Optional[Any]   )							->       List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	a + h
      while x < (b - h):
            yield x
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	x + h


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str   )							->       List[Any]:  # enter your function here
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(x - 0) * (x - 0)
      return y


def  __lowerCamelCase      (				)							->       int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	0.0  # Lower bound of integration
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	1.0  # Upper bound of integration
      SCREAMING_SNAKE_CASE_				:      str					=	10.0  # define number of steps or resolution
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	[a, b]  # define boundary of integration
      SCREAMING_SNAKE_CASE_				:      List[str]					=	method_a(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_   )
      print(F"y = {y}"   )


if __name__ == "__main__":
      main()

