def test_kendall_small(): # there are ties! cres = ccfcr.kendall(prev_c, curr_c, sorted_ids_small) print cres pres =ccfpr.kendall(prev_p, curr_p, sorted_ids_small) print pres assert abs(cres[0] - pres[0]) < epsilon assert abs(cres[1] - pres[1]) < epsilon
def test_kendall_small_2(): # there is no tie! cres = ccfcr.kendall(prev_c_2, curr_c_2, sorted_ids_small_2) print cres pres =ccfpr.kendall(prev_p_2, curr_p_2, sorted_ids_small_2) print pres assert abs(cres[0] - pres[0]) < epsilon assert abs(cres[1] - pres[1]) < epsilon
def test_kendall_dissimilar(): c_res = ccfcr.kendall(prev_data_with_centrality, curr_data_with_centrality_d, sorted_ids_d) c_res_2 = ccfcr.compute_kendall(prev_data_with_centrality, prev_data_with_centrality) p_res =ccfpr.kendall(prev_data_with_position, curr_data_with_position_d, sorted_ids_d) assert abs(c_res[0] - p_res[0]) < epsilon assert False == (abs(c_res_2[0] - p_res[0]) < epsilon) assert abs(c_res[1] - p_res[1]) < epsilon
def test_kendall_similar(): c_res = ccfcr.kendall(prev_data_with_centrality, curr_data_with_centrality, sorted_ids) p_res =ccfpr.kendall(prev_data_with_position, curr_data_with_position, sorted_ids) assert abs(c_res[0] - p_res[0]) < epsilon assert abs(c_res[1] - p_res[1]) < epsilon
def test_kendall_all(): prev_data_full, curr_data_full = ccfcr.proc_kendall(prev_data, curr_data_d, sorted_ids_d) p_res =kmc.kendall_all(prev_data_full, curr_data_full) c_res = ccfcr.kendall(prev_data, curr_data_d, sorted_ids_d) assert abs(c_res[0] - p_res[0]) < epsilon assert abs(c_res[1] - p_res[1]) < epsilon