def _predict(self, estimate, label):
        class_name = self.__class__.__name__
        doc_string = """
          Predict the %s at certain point in time.

          Parameters:
            time: a scalar or an array of times to predict the value of %s at.

          Returns:
            predictions: a scalar if time is a scalar, a numpy array if time in an array.
          """ % (class_name, class_name)

        def predict(time):
            predictor = lambda t: getattr(self, estimate).ix[:t].iloc[-1][label]
            try:
                return np.array([predictor(t) for t in time])
            except __HOLE__:
                return predictor(time)

        predict.__doc__ = doc_string
        return predict
dataset/ETHPy150Open CamDavidsonPilon/lifelines/lifelines/fitters/__init__.py/UnivariateFitter._predict