def main():
sim, friends, train, test = get_usersim(), get_friends(), get_train(), get_test()
score = 0
for line in test:
user, artist = line.split()[:2]
# get 10 recommended artists for every user
recommended = recommend(sim, train, friends, user, n=34)
hit = int(artist in recommended) # determine whether the artist was recommended/predicted or not
score += hit
print("Score: {} from {}\nAccuracy: {:.2f}%".format(score, len(test), 100*score/len(test)))
def main():
train, test = get_train(), get_test()
all_artists = list(train)
score = 0
for line in test:
user, artist = line.split()[:2]
# get 10 random artists for every user
shuffle(all_artists)
recommended = all_artists[:10]
hit = int(artist in recommended) # determine whether the artist was recommended/predicted or not
score += hit
print("Score: {} from {}\nAccuracy: {:.2f}%".format(score, len(test), 100*score/len(test)))
from collections import defaultdict from get import get_artist_ID, get_itemsim, get_usersim, get_friends, get_train, get_test from sys import stdin sim = get_itemsim() artist_ID = get_artist_ID() train = get_train() for line in stdin: artist = line.strip() scores = [] for sim_artist in sim[artist]: similarity_score = sim[artist][sim_artist] scores.append((similarity_score,sim_artist)) topNsimilar = sorted(scores,reverse=1)[:10] for tup in topNsimilar: print(artist_ID[tup[1]], tup[0])