
"""simple docstring"""





_a									=				[[0, 1, 1], [0, 0, 1], [1, 0, 0]]


class A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       name
     UpperCAmelCase_   :   str     	=       []
     UpperCAmelCase_   :   List[Any]     	=       []







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     self.inbound.append(lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     self.outbound.append(lowercase_    )







  def __repr__(						self    ):

     """simple docstring"""
     return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""







def       __a	(						__lowerCamelCase,						__lowerCamelCase=3,						__lowerCamelCase=0.85    ):
   UpperCAmelCase_   :   Optional[int]     	=       {}
   for node in nodes:
      UpperCAmelCase_   :   int     	=       1

   UpperCAmelCase_   :   Optional[int]     	=       {}
   for node in nodes:
      UpperCAmelCase_   :   Union[str, Any]     	=       len(node.outbound    )

   for i in range(__lowerCamelCase    ):
      print(f"""======= Iteration {i + 1} ======="""    )
      for _, node in enumerate(__lowerCamelCase    ):
         UpperCAmelCase_   :   Dict     	=       (1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound    )
      print(__lowerCamelCase    )







def       __a	(						):
   UpperCAmelCase_   :   List[Any]     	=       list(input("Enter Names of the Nodes: "    ).split()    )

   UpperCAmelCase_   :   str     	=       [Node(__lowerCamelCase    ) for name in names]

   for ri, row in enumerate(__lowerCamelCase    ):
      for ci, col in enumerate(__lowerCamelCase    ):
         if col == 1:
            nodes[ci].add_inbound(names[ri]    )
            nodes[ri].add_outbound(names[ci]    )

   print("======= Nodes ======="    )
   for node in nodes:
      print(__lowerCamelCase    )

   page_rank(__lowerCamelCase    )


if __name__ == "__main__":
    main()





from sklearn.metrics import accuracy_score

import datasets


A__										=      "\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"


A__										=      "\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"


A__										=      "\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  UpperCamelCase_      (	datasets.Metric      ):


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					int						)       ->			List[str]:
      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   _UpperCamelCase     (						self     :					Dict  ,	A_     :					List[Any]  ,	A_     :					Tuple  ,	A_     :					Optional[Any]=True  ,	A_     :					Tuple=None						)       ->			Any:
      return {
          "accuracy": float(
              accuracy_score(A_  ,	A_  ,	normalize=A_  ,	sample_weight=A_						)						)
      }


