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))