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Partida anterior do FC Borjomi. O gráfico de desempenho e forma do FC Borjomi é um algoritmo exclusivo do Sofascore que geramos a partir das últimas 10 partidas, estatísticas, análises detalhadas e nosso próprio conhecimento da equipe. Portanto, confira a seguir: horário aposta loterias caixa online JOGAR. Para ver a programação e os resultados de futebol de hoje, visite nossa página de placar ao vivo de futebol. Contact us if you need any help. FC Zestafoni , 13:00 UTC'de itibarıyla 10 Ağu 2023 tarihinde başlayarak FC Borjomi ile başa baş gidiyor. Creditos apostas esportivas e poker depositos.
Aposte com responsabilidade. Gambling Therapy. 18+ FC Borjomi live scores, players, season schedule and today’s results are available on Sofascore. We may have video highlights with goals and news for some FC Borjomi matches, but only if they play their match in one of the most popular football leagues. There are also all FC Borjomi scheduled matches that they are going to play in the future. Levan Sharikadze, Giorgi Tkeshelashvili, Vili Isiani, Vaja Matiashvili, Levan Koshadze, Giorgi Shalikashvili, Sandro Chanturishvili, Luka Lezhava, Sevasti Todua, Papuna Mosemghvdlishvili, Valiko Gakharia, Alan Vianney, Nikoloz Khintibidze, Saba Nadiradze, Nika Tsivtsivadze, Vazha Kikava, Shalva Nadiradze, Giorgi Tsertsvadze, Bakar Abzhandadze, Giorgi Enukidze, Tsotne Keburia, Giorgi Gogelia, Bachana Beruchashvili, Saba Baghdavadze, Giorgi Chvritidze, Vache Tsiklauri, Chinedu Uzoma Nwoloki, Saba Martiashvili, Mamuka Kapanadze. You can click on players from the roster above and see available personal information such as nationality, date of birth, height, preferred foot, position, player value, transfer history etc. Próxima partida do FC Borjomi. Partida anterior do FC Borjomi. O gráfico de desempenho e forma do FC Borjomi é um algoritmo exclusivo do Sofascore que geramos a partir das últimas 10 partidas, estatísticas, análises detalhadas e nosso próprio conhecimento da equipe.
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PMC6941312 . PMID31898477. ^ Chicco D, Toetsch N, Jurman G (February 2021). ”The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation”. BioData Mining . 14 (13): 1-22. doi:10.1186/s13040-021-00244-z. Aposta esportiva best.Catia Cunha Designs.
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>>> from torch import rand , randint >>> # Example plotting multiple values >>> from torchmetrics.classification import MultilabelFBetaScore >>> metric = MultilabelFBetaScore ( num_labels = 3 , beta = 2.0 ) >>> values = [ ] >>> for _ in range ( 10 ): . values . append ( metric ( randint ( 2 , ( 20 , 3 )), randint ( 2 , ( 20 , 3 )))) >>> fig_ , ax_ = metric . plot ( values ) fbeta_score¶ Compute F-score metric. :rtype: Tensor. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the task argument to either 'binary' , 'multiclass' or multilabel . See the documentation of binary_fbeta_score() , multiclass_fbeta_score() and multilabel_fbeta_score() for the specific details of each argument influence and examples. binary_fbeta_score¶ Compute F-score metric for binary tasks. Accepts the following input tensors: If multidim_average is set to global , the metric returns a scalar value. If multidim_average is set to samplewise , the metric returns (N,) vector consisting of a scalar value per sample. >>> from torch import tensor >>> from torchmetrics.functional.classification import binary_fbeta_score >>> target = tensor ([ 0 , 1 , 0 , 1 , 0 , 1 ]) >>> preds = tensor ([ 0 , 0 , 1 , 1 , 0 , 1 ]) >>> binary_fbeta_score ( preds , target , beta = 2.0 ) tensor(0.6667) >>> from torchmetrics.functional.classification import binary_fbeta_score >>> target = tensor ([ 0 , 1 , 0 , 1 , 0 , 1 ]) >>> preds = tensor ([ 0.11 , 0.22 , 0.84 , 0.73 , 0.33 , 0.92 ]) >>> binary_fbeta_score ( preds , target , beta = 2.0 ) tensor(0.6667) >>> from torchmetrics.functional.classification import binary_fbeta_score >>> target = tensor ([[[ 0 , 1 ], [ 1 , 0 ], [ 0 , 1 ]], [[ 1 , 1 ], [ 0 , 0 ], [ 1 , 0 ]]]) >>> preds = tensor ([[[ 0.59 , 0.91 ], [ 0.91 , 0.99 ], [ 0.63 , 0.04 ]], . [[ 0.38 , 0.04 ], [ 0.86 , 0.780 ], [ 0.45 , 0.37 ]]]) >>> binary_fbeta_score ( preds , target , beta = 2.0 , multidim_average = 'samplewise' ) tensor([0.5882, 0.0000]) torchmetrics.functional.classification. multiclass_fbeta_score ( preds , target , beta , num_classes , average = 'macro' , top_k = 1 , multidim_average = 'global' , ignore_index = None , validate_args = True ) [source] ¶ \[F_ = (1 + \beta^2) * \frac * \text> ) + \text>\] preds : (N, . ) (int tensor) or (N, C, ..) (float tensor).
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