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_
Return the per-file header as a string.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/linear_model/stochastic_gradient.py/BaseSGDClassifier._fit_multiclass--crossover with--dataset/ETHPy150Open deanhiller/databus/webapp/play1.3.x/python/Lib/zipfile.py/ZipInfo.FileHeader