def fit(self, X, y, sample_weight=None):
        """Build a survival support vector machine model from training data.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features)
            Data matrix.

        y : structured array, shape = (n_samples,)
            A structured array containing the binary event indicator
            as first field, and time of event or time of censoring as
            second field.

        sample_weight : array-like, shape = (n_samples,), optional
            Array of weights that are assigned to individual
            samples. If not provided,
            then each sample is given unit weight.

        Returns
        -------
        self
        """
        random_state = check_random_state(self.random_state)

        x_pairs, y_pairs = self._get_survival_pairs(X, y, random_state)

        self.C = self.alpha
        return super().fit(x_pairs, y_pairs, sample_weight=sample_weight)
Build a survival support vector machine model from training data.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features)
            Data matrix.

        y : structured array, shape = (n_samples,)
            A structured array containing the binary event indicator
            as first field, and time of event or time of censoring as
            second field.

        sample_weight : array-like, shape = (n_samples,), optional
            Array of weights that are assigned to individual
            samples. If not provided,
            then each sample is given unit weight.

        Returns
        -------
        self