






from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCamelCase   :   Union[str, Any]      =    """
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

__UpperCamelCase   :   List[str]      =    """
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

__UpperCamelCase   :   Tuple      =    """\
@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 							__UpperCamelCase     (  datasets.Metric ):







	def 			_a (	self   :				List[str]      )		->			Optional[int]:
		"""simple docstring"""







		return datasets.MetricInfo(
		    description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
		        {
		            """predictions""": datasets.Value("""int32"""      ),
		            """references""": datasets.Value("""int32"""      ),
		        }      )							,					reference_urls=[
		        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
		    ]							,					)







	def 			_a (	self   :				Union[str, Any]							,					_lowerCAmelCase   :				str							,					_lowerCAmelCase   :				Optional[Any]							,					_lowerCAmelCase   :				Optional[Any]=None      )		->			Optional[Any]:
		"""simple docstring"""







		return {
		    "matthews_correlation": float(matthews_corrcoef(_lowerCAmelCase							,					_lowerCAmelCase							,					sample_weight=_lowerCAmelCase      )      ),
		}







class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     {}



 def  lowerCAmelCase_    (				self    :						List[str]		):
       print(self.vertex		)
       for i in self.vertex:
             print(_lowerCAmelCase							,							' -> '							,							' -> '.join([str(_lowerCAmelCase		) for j in self.vertex[i]]		)		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int		):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase		)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_      		=     [to_vertex]



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_      		=     [False] * len(self.vertex		)

       # call the recursive helper function
       for i in range(len(self.vertex		)		):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						list		):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_      		=     True

       print(_lowerCAmelCase							,							end=' '		)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)


if __name__ == "__main__":
   lowerCamelCase__					:  List[Any]      							=				Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3