Az persze nem mondható el róla, hogy kenterbe verné a piacvezető angol bukmékerekét, de így is igen bőséges a felhozatal. Az európai és az egyéb kontinensbeli top ligák által prezentált népszerű csapatsportok (foci, amerikai foci, jéghoki, kosárlabda, röplabda, baseball stb.) mellett elég sok egyéb sport eseményeire is lehet fogadni a CampoBet sportfogadás oldalán. A fogadóiroda in-play betting (Élő Fogadás) részlege relatíve jónak mondható, mivel itt szinte minden fontosabb sporteseményre lehet különböző piacokon fogadni a kiemelt sportágak esetében. Brady e o tight end Rob casino forum Gronkowski tornaram-se a segunda dupla quarterback -recebedor a alcançar a marca de cem passes para touchdown , juntando-se a Peyton Manning e Marvin Harrison. A szorzók (odds) meglehetősen jónak mondhatók, és egyes sporteseményeknél jóval kedvezőbbek is, mint számos nevesebb buki esetében. A bukméker nem fejlesztett még ki natív mobil alkalmazást a rendszeréhez.
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The check() method will remove this many dimensions when computing validity. alias of _DependentProperty. alias of _GreaterThanEq. alias of _IntegerInterval. alias of _HalfOpenInterval. alias of _Multinomial. Constraint Registry ¶ biject_to(constraint) looks up a bijective Transform from constraints.real to the given constraint . The returned transform is guaranteed to have .bijective = True and should implement .log_abs_det_jacobian() . Jogos que apostas são online.You can locate a lot of worthy events on the site from the action of the Aussies, international cricket tournaments, English county championship, T20 world cup, cricket world cup, Big Bash League, CPL, etc.
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If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally, we convert to int tensor with thresholding using the value in threshold . target ( Tensor ): An int tensor of shape (N, C, . ) . mlfbs ( Tensor ): A tensor whose returned shape depends on the average and multidim_average arguments: beta ¶ ( float ) – Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight num_labels ¶ ( int ) – Integer specifing the number of labels threshold ¶ ( float ) – Threshold for transforming probability to binary (0,1) predictions average ¶ ( Optional [ Literal [ 'micro' , 'macro' , 'weighted' , 'none' ]]) – Defines the reduction that is applied over labels. Should be one of the following: micro : Sum statistics over all labels macro : Calculate statistics for each label and average them weighted : calculates statistics for each label and computes weighted average using their support ”none” or None : calculates statistic for each label and applies no reduction. >>> from torch import tensor >>> from torchmetrics.classification import MultilabelFBetaScore >>> target = tensor ([[ 0 , 1 , 0 ], [ 1 , 0 , 1 ]]) >>> preds = tensor ([[ 0 , 0 , 1 ], [ 1 , 0 , 1 ]]) >>> metric = MultilabelFBetaScore ( beta = 2.0 , num_labels = 3 ) >>> metric ( preds , target ) tensor(0.6111) >>> mlfbs = MultilabelFBetaScore ( beta = 2.0 , num_labels = 3 , average = None ) >>> mlfbs ( preds , target ) tensor([1.0000, 0.0000, 0.8333]) >>> from torchmetrics.classification import MultilabelFBetaScore >>> target = tensor ([[ 0 , 1 , 0 ], [ 1 , 0 , 1 ]]) >>> preds = tensor ([[ 0.11 , 0.22 , 0.84 ], [ 0.73 , 0.33 , 0.92 ]]) >>> metric = MultilabelFBetaScore ( beta = 2.0 , num_labels = 3 ) >>> metric ( preds , target ) tensor(0.6111) >>> mlfbs = MultilabelFBetaScore ( beta = 2.0 , num_labels = 3 , average = None ) >>> mlfbs ( preds , target ) tensor([1.0000, 0.0000, 0.8333]) >>> from torchmetrics.classification import MultilabelFBetaScore >>> 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 ]], .
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