def test_complex(repeat, diff_ratio, num_trial, size): """ diff_ratio used for simulator num_trial = num_trials to find center - use minimum pair """ sim_avg=0.0 num_successful_clustering=0 cluster_score_avg=0.0 inaccurate_min_pair_counter = 0 for i in range(repeat): test_matrix,origin_tracker=haplotype_simulator(1000,size,100,3,diff_ratio) # find_minimum_pair_new(test_matrix, size, num_trial) test_center, sim_score, center_one_row, center_two_row = find_minimum_pair_new(test_matrix, size, num_trial) if origin_tracker[center_one_row] == origin_tracker[center_two_row]: inaccurate_min_pair_counter = inaccurate_min_pair_counter + 1 sim_avg = sim_avg + sim_score print ms_vector = learn.cluster.MeanShift(seeds=test_center).fit_predict(test_matrix) cluster_score = cluster_checker(origin_tracker,ms_vector) if cluster_score == 0.0: num_successful_clustering = num_successful_clustering + 1 pair_ind="" if origin_tracker[center_one_row] == origin_tracker[center_two_row]: pair_ind = "WRONG PAIR" else: pair_ind = "RIGHT PAIR" print "Trial # ", i, "Error Percentage ",cluster_score, "n(NONZERO) ", np.count_nonzero(test_center[0]), np.count_nonzero(test_center[1]), pair_ind cluster_score_avg = cluster_score_avg + cluster_score print "average simliarty = ", sim_avg/repeat, "and # successful clustering = ", num_successful_clustering print "average success rate = ", 1-(cluster_score_avg/repeat), "# wrong min pair = ", inaccurate_min_pair_counter
def test(repeat): sim_avg=0.0; num_successful_clustering=0; for i in range(repeat): test_matrix,origin_tracker=haplotype_simulator(1000,1000,100,2,3) print origin_tracker test_center=[] test_center.append(test_matrix[0]) test_center.append(test_matrix[500]) sim_avg = sim_avg + similarity(test_center) # test_bandwidth=learn.cluster.estimate_bandwidth(test_matrix, quantile=0.5) ms_vector = learn.cluster.MeanShift(seeds=test_center).fit_predict(test_matrix) if ms_vector[0] == ms_vector[251] and ms_vector[523]==ms_vector[809]: num_successful_clustering=num_successful_clustering+1 print "average simliarty = ", sim_avg/repeat, "and # successful clustering = ", num_successful_clustering
def test(repeat): sim_avg = 0.0 num_successful_clustering = 0 for i in range(repeat): test_matrix, origin_tracker = haplotype_simulator( 1000, 1000, 100, 2, 3) print origin_tracker test_center = [] test_center.append(test_matrix[0]) test_center.append(test_matrix[500]) sim_avg = sim_avg + similarity(test_center) # test_bandwidth=learn.cluster.estimate_bandwidth(test_matrix, quantile=0.5) ms_vector = learn.cluster.MeanShift( seeds=test_center).fit_predict(test_matrix) if ms_vector[0] == ms_vector[251] and ms_vector[523] == ms_vector[809]: num_successful_clustering = num_successful_clustering + 1 print "average simliarty = ", sim_avg / repeat, "and # successful clustering = ", num_successful_clustering
def test_complex(repeat, diff_ratio, num_trial, size): """ diff_ratio used for simulator num_trial = num_trials to find center - use minimum pair """ sim_avg = 0.0 num_successful_clustering = 0 cluster_score_avg = 0.0 inaccurate_min_pair_counter = 0 for i in range(repeat): test_matrix, origin_tracker = haplotype_simulator( 1000, size, 100, 3, diff_ratio) # find_minimum_pair_new(test_matrix, size, num_trial) test_center, sim_score, center_one_row, center_two_row = find_minimum_pair_new( test_matrix, size, num_trial) if origin_tracker[center_one_row] == origin_tracker[center_two_row]: inaccurate_min_pair_counter = inaccurate_min_pair_counter + 1 sim_avg = sim_avg + sim_score print ms_vector = learn.cluster.MeanShift( seeds=test_center).fit_predict(test_matrix) cluster_score = cluster_checker(origin_tracker, ms_vector) if cluster_score == 0.0: num_successful_clustering = num_successful_clustering + 1 pair_ind = "" if origin_tracker[center_one_row] == origin_tracker[center_two_row]: pair_ind = "WRONG PAIR" else: pair_ind = "RIGHT PAIR" print "Trial # ", i, "Error Percentage ", cluster_score, "n(NONZERO) ", np.count_nonzero( test_center[0]), np.count_nonzero(test_center[1]), pair_ind cluster_score_avg = cluster_score_avg + cluster_score print "average simliarty = ", sim_avg / repeat, "and # successful clustering = ", num_successful_clustering print "average success rate = ", 1 - ( cluster_score_avg / repeat), "# wrong min pair = ", inaccurate_min_pair_counter