def fit(self, X, y):
        if isinstance(y, np.ndarray) and len(y.shape) > 1 and y.shape[1] > 1:
            raise NotImplementedError('Multilabel and multi-output'
                                      ' classification is not supported.')

        if self.voting not in ('soft', 'hard'):
            raise ValueError("Voting must be 'soft' or 'hard'; got (voting=%r)"
                             % self.voting)

        if self.weights and len(self.weights) != len(self.clfs):
            raise ValueError('Number of classifiers and weights must be equal'
                             '; got %d weights, %d clfs'
                             % (len(self.weights), len(self.clfs)))

        self.le_ = LabelEncoder()
        self.le_.fit(y)
        self.classes_ = self.le_.classes_
        self.clfs_ = [clone(clf) for clf in self.clfs]

        if self.verbose > 0:
            print("Fitting %d classifiers..." % (len(self.clfs)))

        for clf in self.clfs_:

            if self.verbose > 0:
                i = self.clfs_.index(clf) + 1
                print("Fitting clf%d: %s (%d/%d)" %
                      (i, _name_estimators((clf,))[0][0], i, len(self.clfs_)))

            if self.verbose > 2:
                if hasattr(clf, 'verbose'):
                    clf.set_params(verbose=self.verbose - 2)

            if self.verbose > 1:
                print(_name_estimators((clf,))[0][1])

            clf.fit(X, self.le_.transform(y))
        return self
If HORIZON_IMAGES_ALLOW_UPLOAD is false, the image_file field widget
dataset/ETHPy150Open rasbt/mlxtend/mlxtend/classifier/ensemble_vote.py/EnsembleVoteClassifier.fit--crossover with--dataset/ETHPy150Open CiscoSystems/avos/openstack_dashboard/dashboards/project/images/images/tests.py/CreateImageFormTests.test_image_upload_disabled