#NEWLINE# def _fit_multiclass( self , X , y , alpha , C , learning_rate , sample_weight , n_iter ) : #NEWLINE# #INDENT# 'Fit a multi-class classifier by combining binary classifiers\n\n        Each binary classifier predicts one class versus all others. This\n        strategy is called OVA: One Versus All.\n        ' #NEWLINE# result = Parallel ( n_jobs = self . n_jobs , backend = 'threading' , verbose = self . verbose ) ( ( delayed ( fit_binary ) ( self , i , X , y , alpha , C , learning_rate , n_iter , self . _expanded_class_weight [ i ] , 1.0 , sample_weight ) for i in range ( len ( self . classes_ ) ) ) ) #NEWLINE# for ( i , ( _ , intercept ) ) in enumerate ( result ) : #NEWLINE# #INDENT# self . intercept_ [ i ] = intercept #NEWLINE# #UNINDENT# self . t_ += ( n_iter * X . shape [ 0 ] ) #NEWLINE# if ( self . average > 0 ) : #NEWLINE# #INDENT# if ( self . average <= ( self . t_ - 1.0 ) ) : #NEWLINE# #INDENT# self . coef_ = self . average_coef_ #NEWLINE# self . intercept_ = self . average_intercept_ #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# self . coef_ = self . standard_coef_ #NEWLINE# self . standard_intercept_ = np . atleast_1d ( self . intercept_ ) #NEWLINE# self . intercept_ = self . standard_intercept_