def fit_mle(self,
                init_vals,
                print_res=True,
                method="BFGS",
                loss_tol=1e-06,
                gradient_tol=1e-06,
                maxiter=1000,
                ridge=None,
                *args):
        """
        Parameters
        ----------
        init_vals : 1D ndarray.
            The initial values to start the optimizatin process with. There
            should be one value for each utility coefficient, outside intercept
            parameter, shape parameter, and nest parameter being estimated.
        print_res : bool, optional.
            Determines whether the timing and initial and final log likelihood
            results will be printed as they they are determined.
        method : str, optional.
            Should be a valid string which can be passed to
            scipy.optimize.minimize. Determines the optimization algorithm
            which is used for this problem.
        loss_tol : float, optional.
            Determines the tolerance on the difference in objective function
            values from one iteration to the next which is needed to determine
            convergence. Default == 1e-06.
        gradient_tol : float, optional.
            Determines the tolerance on the difference in gradient values from
            one iteration to the next which is needed to determine convergence.
            Default == 1e-06.
        ridge : int, float, long, or None, optional.
            Determines whether or not ridge regression is performed. If an int,
            float or long is passed, then that scalar determines the ridge
            penalty for the optimization. Default == None.

        Returns
        -------
        None. Saves estimation results to the model instance.
        """
        msg = "This model class' fit_mle method has not been constructed."
        raise NotImplementedError(msg)
def fit_mle ( self , init_vals , print_res = True , method = "BFGS" , loss_tol = 1e-06 , gradient_tol = 1e-06 , maxiter = 1000 , ridge = None , * args ) : msg = "This model class' fit_mle method has not been constructed." raise NotImplementedError ( msg )
Parameters
        ----------
        init_vals : 1D ndarray.
            The initial values to start the optimizatin process with. There
            should be one value for each utility coefficient, outside intercept
            parameter, shape parameter, and nest parameter being estimated.
        print_res : bool, optional.
            Determines whether the timing and initial and final log likelihood
            results will be printed as they they are determined.
        method : str, optional.
            Should be a valid string which can be passed to
            scipy.optimize.minimize. Determines the optimization algorithm
            which is used for this problem.
        loss_tol : float, optional.
            Determines the tolerance on the difference in objective function
            values from one iteration to the next which is needed to determine
            convergence. Default == 1e-06.
        gradient_tol : float, optional.
            Determines the tolerance on the difference in gradient values from
            one iteration to the next which is needed to determine convergence.
            Default == 1e-06.
        ridge : int, float, long, or None, optional.
            Determines whether or not ridge regression is performed. If an int,
            float or long is passed, then that scalar determines the ridge
            penalty for the optimization. Default == None.

        Returns
        -------
        None. Saves estimation results to the model instance.