def test_max_statistic_sequential(): data = Data() data.generate_mute_data(104, 10) settings = { 'cmi_estimator': 'JidtKraskovCMI', 'n_perm_max_stat': 21, 'n_perm_min_stat': 21, 'n_perm_omnibus': 21, 'n_perm_max_seq': 21, 'max_lag_sources': 5, 'min_lag_sources': 1, 'max_lag_target': 5 } setup = MultivariateTE() setup._initialise(settings, data, sources=[0, 1], target=2) setup.current_value = (0, 4) setup.selected_vars_sources = [(1, 1), (1, 2)] setup.selected_vars_full = [(0, 1), (1, 1), (1, 2)] setup._selected_vars_realisations = np.random.rand( data.n_realisations(setup.current_value), len(setup.selected_vars_full)) setup._current_value_realisations = np.random.rand( data.n_realisations(setup.current_value), 1) [sign, p, te] = stats.max_statistic_sequential(analysis_setup=setup, data=data)
def test_max_statistic_sequential(): dat = Data() dat.generate_mute_data(104, 10) opts = { 'cmi_calc_name': 'jidt_kraskov', 'n_perm_max_stat': 21, 'n_perm_min_stat': 21, 'n_perm_omnibus': 21, 'n_perm_max_seq': 21, } setup = Multivariate_te(max_lag_sources=5, min_lag_sources=1, max_lag_target=5, options=opts) setup.current_value = (0, 4) setup.selected_vars_sources = [(1, 1), (1, 2)] setup.selected_vars_full = [(0, 1), (1, 1), (1, 2)] setup._selected_vars_realisations = np.random.rand(dat.n_realisations(setup.current_value), len(setup.selected_vars_full)) setup._current_value_realisations = np.random.rand(dat.n_realisations(setup.current_value), 1) [sign, p, te] = stats.max_statistic_sequential(analysis_setup=setup, data=dat, opts=opts)