def initialize_with_standard_model(self, standard_model):
        super(DiscreteTimeNetworkHawkesModelGammaMixture, self).\
            initialize_with_standard_model(standard_model)

        # Set the mean field parameters
        self.bias_model.mf_alpha = np.clip(100 * self.bias_model.lambda0, 1e-8, np.inf)
        self.bias_model.mf_beta  = 100 * np.ones(self.K)

        # Weight model
        self.weight_model.mf_kappa_0 = self.weight_model.nu_0 * self.weight_model.W.copy()
        self.weight_model.mf_v_0     = self.weight_model.nu_0 * np.ones((self.K, self.K))

        self.weight_model.mf_kappa_1 = 100 * self.weight_model.W.copy()
        self.weight_model.mf_v_1     = 100 * np.ones((self.K, self.K))

        self.weight_model.mf_p       = 0.8 * self.weight_model.A + 0.2 * (1-self.weight_model.A)

        # Set mean field parameters of the impulse model
        self.impulse_model.mf_gamma = 100 * self.impulse_model.g.copy('C')

        # Set network mean field parameters
        # if self.C > 1:
        #     self.network.mf_m = 0.2 / (self.C-1) * np.ones((self.K, self.C))
        #     for c in xrange(self.C):
        #         self.network.mf_m[self.network.c == c, c] = 0.8
        # else:
        #     self.network.mf_m = np.ones((self.K, self.C))

        # Update the parents.
        # for _,_,_,p in self.data_list:
        #     p.resample(self.bias_model, self.weight_model, self.impulse_model)
        #     p.meanfieldupdate(self.bias_model, self.weight_model, self.impulse_model)
dataset/ETHPy150Open slinderman/pyhawkes/pyhawkes/models.py/DiscreteTimeNetworkHawkesModelGammaMixture.initialize_with_standard_model/original