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)
