def meanfieldupdate_p(self, stepsize=1.0):
        """
        Update p given the network parameters and the current variational
        parameters of the weight distributions.
        :return:
        """
        logit_p = self.network.expected_log_p() - self.network.expected_log_notp()
        logit_p += self.network.kappa * self.network.expected_log_v() - gammaln(self.network.kappa)
        logit_p += self.mf_kappa_1 * np.log(self.mf_v_1) - gammaln(self.mf_kappa_1)
        logit_p += gammaln(self.kappa_0) - self.kappa_0 * np.log(self.nu_0)
        logit_p += self.mf_kappa_0 * np.log(self.mf_v_0) - gammaln(self.mf_kappa_0)

        # p_hat = logistic(logit_p)
        # self.mf_p = (1.0 - stepsize) * self.mf_p + stepsize * p_hat

        logit_p_hat = (1-stepsize) * logit(self.mf_p) + \
                       stepsize * logit_p
        # self.mf_p = logistic(logit_p_hat)
        self.mf_p = np.clip(logistic(logit_p_hat), 1e-8, 1-1e-8)
dataset/ETHPy150Open slinderman/pyhawkes/pyhawkes/internals/weights.py/GammaMixtureWeights.meanfieldupdate_p/swapped operands of `-`