parameters: (self, X, y, alpha, C, learning_rate,
                          sample_weight, n_iter)
docstring: Fit a multi-class classifier by combining binary classifiers
  
          Each binary classifier predicts one class versus all others. This
          strategy is called OVA: One Versus All.
docstring_summary: Fit a multi-class classifier by combining binary classifiers
docstring_tokens: Fit a multi - class classifier by combining binary classifiers
function: def _fit_multiclass(self, X, y, alpha, C, learning_rate,
                          sample_weight, n_iter):
          """Fit a multi-class classifier by combining binary classifiers
  
          Each binary classifier predicts one class versus all others. This
          strategy is called OVA: One Versus All.
          """
          # Use joblib to fit OvA in parallel.
          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.,
                                  sample_weight)
              for i in range(len(self.classes_)))
  
          for i, (_, intercept) in enumerate(result):
              self.intercept_[i] = intercept
  
          self.t_ += n_iter * X.shape[0]
  
          if self.average > 0:
              if self.average <= self.t_ - 1.0:
                  self.coef_ = self.average_coef_
                  self.intercept_ = self.average_intercept_
              else:
                  self.coef_ = self.standard_coef_
                  self.standard_intercept_ = np.atleast_1d(self.intercept_)
                  self.intercept_ = self.standard_intercept_
function_tokens: def _fit_multiclass ( self , X , y , alpha , C , learning_rate , sample_weight , n_iter ) : # Use joblib to fit OvA in parallel. 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. , sample_weight ) for i in range ( len ( self . classes_ ) ) ) for i , ( _ , intercept ) in enumerate ( result ) : self . intercept_ [ i ] = intercept self . t_ += n_iter * X . shape [ 0 ] if self . average > 0 : if self . average <= self . t_ - 1.0 : self . coef_ = self . average_coef_ self . intercept_ = self . average_intercept_ else : self . coef_ = self . standard_coef_ self . standard_intercept_ = np . atleast_1d ( self . intercept_ ) self . intercept_ = self . standard_intercept_