code: def percent_point(self, y, 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)
docs: 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.