@classmethod
def setup_class(cls):
    path = os.path.join(current_path, 'results', 'results_predict_rgnp.csv')
    results = pd.read_csv(path)
    true = {'params': np.r_[(0.3812383, 0.3564492, (- 0.0055216), 1.195482, (0.6677098 ** 2), 0.3710719, 0.4621503, 0.7002937, (- 0.3206652))], 'llf': (- 179.32354), 'llf_fit': (- 179.38684), 'llf_fit_em': (- 184.99606), 'bse_oim': np.r_[(0.1424841, 0.0994742, 0.2057086, 0.1225987, np.nan, 0.1754383, 0.1652473, 0.187409, 0.1295937)], 'smoothed0': results.iloc[3:]['switchar2_sm1'], 'smoothed1': results.iloc[3:]['switchar2_sm2'], 'predict0': results.iloc[3:]['switchar2_yhat1'], 'predict1': results.iloc[3:]['switchar2_yhat2'], 'predict_predicted': results.iloc[3:]['switchar2_pyhat'], 'predict_filtered': results.iloc[3:]['switchar2_fyhat'], 'predict_smoothed': results.iloc[3:]['switchar2_syhat']}
    super(TestHamiltonAR2Switch, cls).setup_class(true, rgnp, k_regimes=2, order=2)
@classmethod
def setup_class(cls):
    path = os.path.join(current_path, 'results', 'results_predict_rgnp.csv')
    results = pd.read_csv(path)
    true = {'params': np.r_[(0.3812383, 0.3564492, (- 0.0055216), 1.195482, (0.6677098 ** 2), 0.3710719, 0.4621503, 0.7002937, (- 0.3206652))], 'llf': (- 179.32354), 'llf_fit': (- 179.38684), 'llf_fit_em': (- 184.99606), 'bse_oim': np.r_[(0.1424841, 0.0994742, 0.2057086, 0.1225987, np.nan, 0.1754383, 0.1652473, 0.187409, 0.1295937)], 'smoothed0': results.iloc[3:]['switchar2_sm1'], 'smoothed1': results.iloc[3:]['switchar2_sm2'], 'predict0': results.iloc[3:]['switchar2_yhat1'], 'predict1': results.iloc[3:]['switchar2_yhat2'], 'predict_predicted': results.iloc[3:]['switchar2_pyhat'], 'predict_filtered': results.iloc[3:]['switchar2_fyhat'], 'predict_smoothed': results.iloc[3:]['switchar2_syhat']}
    super(TestHamiltonAR2Switch, cls).setup_class(true, rgnp, k_regimes=2, order=2)