def test_calculate_mean():
"""Test if mean over CMI estimates is calculated correctly."""
dat = Data()
dat.generate_mute_data(100, 5)
res_0 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_res_0.pkl'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0)
cmi = comp._calculate_cmi_all_links(dat)
cmi_mean = comp._calculate_mean([cmi, cmi])
for t in comp.union['targets']:
assert (cmi_mean[t] == cmi[t]).all(), ('Error in mean of CMI for '
'target {0}'.format(t))
def test_calculate_mean():
"""Test if mean over CMI estimates is calculated correctly."""
data = Data()
data.generate_mute_data(100, 5)
res_0 = pickle.load(
open(os.path.join(os.path.dirname(__file__), 'data/mute_results_0.p'),
'rb'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0)
cmi = comp._calculate_cmi_all_links(data)
cmi_mean = comp._calculate_mean([cmi, cmi])
for t in comp.union.targets_analysed:
assert (cmi_mean[t] == cmi[t]).all(), ('Error in mean of CMI for '
'target {0}'.format(t))
if len(cmi[t]) == 0: # skip if no links in results
continue
assert (cmi_mean[t] == cmi[t][0]).all(), (
'Error in mean of CMI for target {0} - actual: ({1}), expected: '
'({2})'.format(t, cmi_mean[t], cmi[t][0]))
def test_calculate_mean():
"""Test if mean over CMI estimates is calculated correctly."""
data = Data()
data.generate_mute_data(100, 5)
res_0 = np.load(os.path.join(os.path.dirname(__file__),
'data/mute_results_0.p'))
comp_settings = {
'cmi_estimator': 'JidtKraskovCMI',
'n_perm_max_stat': 50,
'n_perm_min_stat': 50,
'n_perm_omnibus': 200,
'n_perm_max_seq': 50,
'tail': 'two',
'n_perm_comp': 6,
'alpha_comp': 0.2,
'stats_type': 'dependent'
}
comp = NetworkComparison()
comp._initialise(comp_settings)
comp._create_union(res_0)
cmi = comp._calculate_cmi_all_links(data)
cmi_mean = comp._calculate_mean([cmi, cmi])
for t in comp.union.targets_analysed:
assert (cmi_mean[t] == cmi[t]).all(), ('Error in mean of CMI for '
'target {0}'.format(t))
