


"""simple docstring"""
from __future__ import annotations





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   Optional[int]		,							UpperCAmelCase_				:   Optional[int]							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		vertices
														snake_case_     							=		[[0] * vertices for _ in range(a__							)]



							def 		lowerCAmelCase							(					self				:   Tuple		,							UpperCAmelCase_				:   Dict							)       ->Optional[Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   List[str]							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		1E7
														snake_case_     							=		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:
																												snake_case_     							=		distances_from_source[vertex]
																												snake_case_     							=		vertex
														return min_index



							def 		lowerCAmelCase							(					self				:   Any		,							UpperCAmelCase_				:   List[str]							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[int(1E7							)] * self.vertices  # distances from the source
														snake_case_     							=		0
														snake_case_     							=		[False] * self.vertices

														for _ in range(self.vertices							):
																					snake_case_     							=		self.minimum_distance(a__		,							a__							)
																					snake_case_     							=		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]
																												):
																																			snake_case_     							=		distances[u] + self.graph[u][v]

														self.print_solution(a__							)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     Dict		=     Graph(9)
				__SCREAMING_SNAKE_CASE				:     str		=     [
				    [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)







from sklearn.metrics import mean_absolute_error

import datasets


a					:				Optional[Any]       =      '\\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'

a					:				int       =      '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


a					:				Any       =      '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        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.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'

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

       def 					UpperCAmelCase					(      self       ):






        '''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 					UpperCAmelCase					(      self       ):






        '''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 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__=None						,    a__="uniform_average"       ):






        '''simple docstring'''






        snake_case :			str							=   mean_absolute_error(a__						,    a__						,    sample_weight=a__						,    multioutput=a__       )

        return {"mae": mae_score}






