@Appender(DiscreteModel.fit_regularized.__doc__)
def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs):
    _validate_l1_method(method)
    if ((np.size(alpha) == 1) and (alpha != 0)):
        k_params = (self.k_exog + self.k_inflate)
        alpha = (alpha * np.ones(k_params))
    extra = (self.k_extra - self.k_inflate)
    alpha_p = (alpha[:(- (self.k_extra - extra))] if (self.k_extra and (np.size(alpha) > 1)) else alpha)
    if (start_params is None):
        offset = (getattr(self, 'offset', 0) + getattr(self, 'exposure', 0))
        if ((np.size(offset) == 1) and (offset == 0)):
            offset = None
        start_params = self.model_main.fit_regularized(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=0, callback=callback, alpha=alpha_p, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs).params
        start_params = np.append(np.ones(self.k_inflate), start_params)
    cntfit = super(CountModel, self).fit_regularized(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)
    discretefit = self.result_class_reg(self, cntfit)
    return self.result_class_reg_wrapper(discretefit)
@Appender(DiscreteModel.fit_regularized.__doc__)
def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs):
    _validate_l1_method(method)
    if ((np.size(alpha) == 1) and (alpha != 0)):
        k_params = (self.k_exog + self.k_inflate)
        alpha = (alpha * np.ones(k_params))
    extra = (self.k_extra - self.k_inflate)
    alpha_p = (alpha[:(- (self.k_extra - extra))] if (self.k_extra and (np.size(alpha) > 1)) else alpha)
    if (start_params is None):
        offset = (getattr(self, 'offset', 0) + getattr(self, 'exposure', 0))
        if ((np.size(offset) == 1) and (offset == 0)):
            offset = None
        start_params = self.model_main.fit_regularized(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=0, callback=callback, alpha=alpha_p, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs).params
        start_params = np.append(np.ones(self.k_inflate), start_params)
    cntfit = super(CountModel, self).fit_regularized(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)
    discretefit = self.result_class_reg(self, cntfit)
    return self.result_class_reg_wrapper(discretefit)