base.py 文件源码

def predict(self, X):
        """Perform classification on samples in X.

        For an one-class model, +1 or -1 is returned.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            For kernel="precomputed", the expected shape of X is
            [n_samples_test, n_samples_train]

        Returns
        -------
        y_pred : array, shape (n_samples,)
            Class labels for samples in X.
        """
        y = super(BaseSVC, self).predict(X)
        return self.classes_.take(np.asarray(y, dtype=np.intp))

    # Hacky way of getting predict_proba to raise an AttributeError when
    # probability=False using properties. Do not use this in new code; when
    # probabilities are not available depending on a setting, introduce two
    # estimators.