classifier.py 文件源码

def fit(self, X, y, check_input=True):
        self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
        Y = self._label_binarizer.fit_transform(y)
        if self._label_binarizer.y_type_.startswith('multilabel'):
            # we don't (yet) support multi-label classification in ENet
            raise ValueError(
                "%s doesn't support multi-label classification" % (
                    self.__class__.__name__))

        # Y = column_or_1d(Y, warn=True)
        super(ElasticNetClassifier, self).fit(X, Y)
        if self.classes_.shape[0] > 2:
            ndim = self.classes_.shape[0]
        else:
            ndim = 1
        self.coef_ = self.coef_.reshape(ndim, -1)

        return self