Estimate censoring distribution from training data.

        Parameters
        ----------
        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.

        Returns
        -------
        self

def fit(self, y):
        """Estimate censoring distribution from training data.

        Parameters
        ----------
        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.

        Returns
        -------
        self
        """
        event, time = check_y_survival(y)
        if event.all():
            self.unique_time_ = numpy.unique(time)
            self.prob_ = numpy.ones(self.unique_time_.shape[0])
        else:
            unique_time, prob = kaplan_meier_estimator(~event, time)
            self.unique_time_ = numpy.concatenate(([-numpy.infty], unique_time))
            self.prob_ = numpy.concatenate(([1.], prob))

        return self