def update(self, model):
        self.model = model

        self.sn2 = self.model.get_noise()

        # Sample representer points
        self.sampling_acquisition.update(model)
        self.sample_representer_points()

        # Omega values which are needed for the innovations
        # by sampling from a uniform grid
        self.W = norm.ppf(np.linspace(1. / (self.Np + 1),
                                    1 - (self.Np + 1) / 1.,
                                    self.Np))[np.newaxis, :]

        # Compute current posterior belief at the representer points
        self.Mb, self.Vb = self.model.predict(self.zb, full_cov=True)
        self.pmin = mc_part.joint_pmin(self.Mb, self.Vb, self.Nf)
        self.logP = np.log(self.pmin)
dataset/ETHPy150Open automl/RoBO/robo/acquisition/information_gain_mc.py/InformationGainMC.update/swapped operands of `/`