cnb.py 文件源码

def _partial_fit(self, X, y, classes=None, first_partial_fit=None):

        if first_partial_fit and not classes:
            raise ValueError("classes must be passed on the first call "
                         "to partial_fit.")

        if not self.is_fitted:
            self.alpha_sum_ = X.shape[1] * self.alpha

        if classes:
            self.classes_ = classes

        lb = LabelBinarizer()
        y_one_hot = lb.fit_transform(y)
        self.class_count_ = np.sum(y_one_hot, axis=0)

        if not self.classes_:
            self.classes_ = lb.classes_

        #self._class_log_prob()
        self._update_complement_features(X, y_one_hot)
        self.is_fitted = True