def fit_gp_multinomial_model(model, test, pi_train=None, N_samples=100, run=1):
    if pi_train is not None:
        if isinstance(model, pgmult.gp.LogisticNormalGP):
            model.data_list[0]["psi"] = ln_pi_to_psi(pi_train) - model.mu
        elif isinstance(model, pgmult.gp.MultinomialGP):
            model.data_list[0]["psi"] = pi_to_psi(pi_train) - model.mu
            model.resample_omega()
    else:
        model.initialize_from_data()

    ### Inference
    results_base = os.path.join("results", "names", "run%03d" % run, "results")
    results_file = results_base + ".pkl.gz"
    if os.path.exists(results_file):
        with gzip.open(results_file, "r") as f:
            samples, lls, pred_lls, timestamps = pickle.load(f)

    else:
        Z_test = get_inputs(test)
        lls = [model.log_likelihood()]
        samples = [model.copy_sample()]
        pred_ll, pred_pi = model.predictive_log_likelihood(Z_test, test.data)
        pred_lls = [pred_ll]
        pred_pis = [pred_pi]
        times = [0]

        # Print initial values
        print("Initial LL: ", lls[0])
        print("Initial Pred LL: ", pred_lls[0])


        for itr in xrange(N_samples):
            print("Iteration ", itr)
            tic = time.time()
            model.resample_model(verbose=True)
            times.append(time.time()-tic)

            samples.append(model.copy_sample())
            lls.append(model.log_likelihood())
            pred_ll, pred_pi = model.predictive_log_likelihood(get_inputs(test), test.data)
            pred_lls.append(pred_ll)
            pred_pis.append(pred_pi)

            print("Log likelihood: ", lls[-1])
            print("Pred Log likelihood: ", pred_ll)

            # Save this sample
            # with gzip.open(results_file + ".itr%03d.pkl.gz" % itr, "w") as f:
            #     pickle.dump(model, f, protocol=-1)

        lls = np.array(lls)
        pred_lls = np.array(pred_lls)
        timestamps = np.cumsum(times)

    return samples, lls, pred_lls, pred_pis, timestamps


#############
#  metrics  #
#############
dataset/ETHPy150Open HIPS/pgmult/experiments/census_gp.py/fit_gp_multinomial_model/original