f = open(kmeans_predicted_results_file, 'rt') reader = csv.reader(f) for row in reader: user = row[0] songs = row[1:] kmeans_predicted_results.append(songs) test_results = [] f = open(test_results_file, 'rt') reader = csv.reader(f) for row in reader: user = row[0] songs = row[1:] test_results.append(songs) sumR += map.kdd_mapk(test_results, kmeans_predicted_results, 500) avg = sumR / (trials*1.0) results.append(avg) print c, avg print clusters print results # Print the results to a file with open("data/results/graph.txt", "w") as f: dString = "" for item in clusters: dString += str(item) + ',' f.write(dString[:-1]) f.write('\n')
reader = csv.reader(f) for row in reader: user = row[0] songs = row[1:] user_cf_mr_test_results.append(songs) f = open(user_cf_mr_predict_results_file, 'rt') reader = csv.reader(f) for row in reader: user = row[0] songs = row[1:] user_cf_mr_predict_results.append(songs) f = open(user_cf_predict_results_file, 'rt') reader = csv.reader(f) for row in reader: user = row[0] songs = row[1:] user_cf_predict_results.append(songs) # print "kmeans" # print map.kdd_mapk(kmeans_test_results, kmeans_predicted_results, 500) print "user_cf_mr" print map.kdd_mapk(user_cf_mr_test_results, user_cf_mr_predict_results,500) print "user_cf" print map.kdd_mapk(user_cf_mr_test_results, user_cf_predict_results,500)