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)
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)