def loglike(self, endog, mu, freq_weights=1., scale=1.):
        r"""
        The log-likelihood function in terms of the fitted mean response.

        Parameters
        ----------
        endog : array-like
            Endogenous response variable
        mu : array-like
            The fitted mean response values
        freq_weights : array-like
            1d array of frequency weights. The default is 1.
        scale : float
            The scale parameter. The default is 1.

        Returns
        -------
        llf : float
            The value of the loglikelihood function evaluated at
            (endog,mu,freq_weights,scale) as defined below.

        Notes
        -----
        Defined as:

        .. math::

           llf = \sum_i freq\_weights_i * (Y_i * \log{(\alpha * e^{\eta_i} /
                 (1 + \alpha * e^{\eta_i}))} - \log{(1 + \alpha * e^{\eta_i})}/
                 \alpha + Constant)

        where :math:`Constant` is defined as:

        .. math::

           Constant = \ln \Gamma{(Y_i + 1/ \alpha )} - \ln \Gamma(Y_i + 1) -
                      \ln \Gamma{(1/ \alpha )}
        """
        lin_pred = self._link(mu)
        constant = (special.gammaln(endog + self.alpha / 1) -
                    special.gammaln(endog+1)-special.gammaln(1/self.alpha))
        exp_lin_pred = np.exp(lin_pred)
        return np.sum((endog * np.log(self.alpha * exp_lin_pred /
                                      (1 + self.alpha * exp_lin_pred)) -
                      np.log(1 + self.alpha * exp_lin_pred) /
                      self.alpha + constant) * freq_weights)
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/genmod/families/family.py/NegativeBinomial.loglike/swapped operands of `/`