\!hd "SSKrSSjrSSjrg)NcXU(aX:HR5$X:HR5$)N)meansum) target_temppredicted_temp normalizes lC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\mlxtend/evaluate/accuracy.py_compute_metricr s)-3355-2244c [R"U5n[R"U5n[R"U5n[U5[U5:wa [ S5eUS:Xa [ XVU5$US:XaPX7;a [ S5e[R "XS:HSS5n[R "Xc:HSS5n[ XVU5$US:Xao[UVs/sH?n[ [R "XX:gSS5[R "Xh:gSS55PMA sn5[URS5- $US:Xae/n [R"U5H3n X :Hn [R"X:HU 5n U RU 5 M5 [R"U 5$[S U-5es snf) aGeneral accuracy function for supervised learning. Parameters ------------ y_target : array-like, shape=[n_values] True class labels or target values. y_predicted : array-like, shape=[n_values] Predicted class labels or target values. method : str, 'standard' by default. The chosen method for accuracy computation. If set to 'standard', computes overall accuracy. If set to 'binary', computes accuracy for class pos_label. If set to 'average', computes average per-class (balanced) accuracy. If set to 'balanced', computes the scikit-learn-style balanced accuracy. pos_label : str or int, 1 by default. The class whose accuracy score is to be reported. Used only when `method` is set to 'binary' normalize : bool, True by default. If True, returns fraction of correctly classified samples. If False, returns number of correctly classified samples. Returns ------------ score: float Examples ----------- For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/evaluate/accuracy_score/ zD`y_target` and `y_predicted` don't have the same number of elements.standardbinaryz'Chosen value of pos_label doesn't existraveragebalancedzJ`method` must be "standard", "average", "balanced", or "binary". Got "%s".) npasarrayuniquelenAttributeErrorr whererfloatshaperappend ValueError) y_target y_predictedmethod pos_labelrrr unique_labelslab all_class_acccpositive_labels class_accs r accuracy_scorer&sL**X&KZZ ,NIIk*M 8}K(( U  {IFF 8   ) !JK Khh{7A> ."=q!D{IFF 9  )   )C  HH[/A6HH^2Aq9)    -%%a( )* * :  8$A&mO!1? CDI   +% ww}%% 24: ;  ' sAF<)T)r rT)numpyrr r&r r r)s5FJQ r