

'''simple docstring'''
from __future__ import annotations
class  _snake_case						:






			def __init__(   self       ,_snake_case			):
				UpperCAmelCase_			:	str      =   vertices
				UpperCAmelCase_			:	Optional[Any]      =   [[0] * vertices for _ in range(_snake_case			)]






			def 						UpperCamelCase__				(   self       ,_snake_case			):
				print("Vertex \t Distance from Source"			)
				for vertex in range(self.vertices			):
					print(_snake_case       ,"\t\t"       ,distances_from_source[vertex]			)






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case			):
				UpperCAmelCase_			:	Tuple      =   1E7
				UpperCAmelCase_			:	int      =   0

				# Search not nearest vertex not in the shortest path tree
				for vertex in range(self.vertices			):
					if distances_from_source[vertex] < minimum and visited[vertex] is False:
						UpperCAmelCase_			:	Dict      =   distances_from_source[vertex]
						UpperCAmelCase_			:	Dict      =   vertex
				return min_index






			def 						UpperCamelCase__				(   self       ,_snake_case			):
				UpperCAmelCase_			:	Any      =   [int(1E7			)] * self.vertices  # distances from the source
				UpperCAmelCase_			:	str      =   0
				UpperCAmelCase_			:	Any      =   [False] * self.vertices

				for _ in range(self.vertices			):
					UpperCAmelCase_			:	List[str]      =   self.minimum_distance(_snake_case       ,_snake_case			)
					UpperCAmelCase_			:	Any      =   True

					# Update dist value of the adjacent vertices
					# of the picked vertex only if the current
					# distance is greater than new distance and
					# the vertex in not in the shortest path tree
					for v in range(self.vertices			):
						if (
						    self.graph[u][v] > 0
						    and visited[v] is False
						    and distances[v] > distances[u] + self.graph[u][v]
						):
							UpperCAmelCase_			:	int      =   distances[u] + self.graph[u][v]

				self.print_solution(_snake_case			)


if __name__ == "__main__":
					_lowerCamelCase	 =			Graph(9)
					_lowerCamelCase	 =			[
					    [0, 4, 0, 0, 0, 0, 0, 8, 0],
					    [4, 0, 8, 0, 0, 0, 0, 11, 0],
					    [0, 8, 0, 7, 0, 4, 0, 0, 2],
					    [0, 0, 7, 0, 9, 14, 0, 0, 0],
					    [0, 0, 0, 9, 0, 10, 0, 0, 0],
					    [0, 0, 4, 14, 10, 0, 2, 0, 0],
					    [0, 0, 0, 0, 0, 2, 0, 1, 6],
					    [8, 11, 0, 0, 0, 0, 1, 0, 7],
					    [0, 0, 2, 0, 0, 0, 6, 7, 0],
					]
					graph.dijkstra(0)











"""simple docstring"""




from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase_           ="""\
@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}
}
"""

UpperCamelCase_           ="""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


UpperCamelCase_           ="""
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.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

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

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""




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






				def 				snake_case	( self		:    Optional[int]							)	->      List[Any]:




										'''simple docstring'''



										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_absolute_error.html'''
										    ], )






				def 				snake_case	( self		:    Dict							)	->      Union[str, Any]:




										'''simple docstring'''



										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 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    Dict, lowerCAmelCase__		:    Union[str, Any], lowerCAmelCase__		:    Tuple=None, lowerCAmelCase__		:    Tuple="uniform_average"							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    mean_absolute_error(lowerCAmelCase__, lowerCAmelCase__, sample_weight=lowerCAmelCase__, multioutput=lowerCAmelCase__							)

										return {"mae": mae_score}



