def percent_point(self, y, V):
        """Compute the inverse of conditional cumulative distribution :math:`C(u|v)^-1`

        Args:
            y: `np.ndarray` value of :math:`C(u|v)`.
            v: `np.ndarray` given value of v.
        """
        self.check_fit()

        if self.theta < 0:
            return V

        else:
            result = []
            for _y, _V in zip(y, V):
                minimum = fminbound(self.partial_derivative_scalar, EPSILON, 1.0, args=(_y, _V))
                if isinstance(minimum, np.ndarray):
                    minimum = minimum[0]

                result.append(minimum)

            return np.array(result)
Compute the inverse of conditional cumulative distribution :math:`C(u|v)^-1`

        Args:
            y: `np.ndarray` value of :math:`C(u|v)`.
            v: `np.ndarray` given value of v.