def main():
users = db_connector.getAllWardrobeData()
#start_time = time.time()
matrix = getMatrix( users )
#print users[2]
#u = input("Enter user (0-19): ")
projectedRatings = {}
for u in range( len(users) ):
#print "- Getting kNN for User #" + str(u)
NN = kNN( np.array( matrix[u] ), 4 )[1:]
weights = assignWeights( u, NN, matrix )
#print weights
#print "- Recommendations for User #" + str(u)
#print
#print "User #" + str(u) + "'s Nearest Neighbors and Weights:"
projectedRatings[u] = getRecItems(u, weights, users)
#print (time.time() - start_time)
print projectedRatings
"""call = input("Enter user number: ")
print projectedRatings[call]"""
#print users
"""print "Angelica and Billie Jean: "
def main():
conn = db_connector.getDBConnection()
cur = conn.cursor()
wardrobes = db_connector.getAllWardrobeData()
catalogue = db_connector.getCatalogueData()
users = sorted(wardrobes.keys())
for user in users:
# comment when not updating db
#query = "DELETE FROM modifit_user_recommendations WHERE user_id = {0}"
#cur.execute(query.format(user))
print "user: "******"category: " + str(category)
# compute cosine similarities under the current category
cosineSimilarities = {}
for user2 in users:
if user2 != user and category in wardrobes[user2]:
a = getOrderedValues( wardrobes[user][category], wardrobes[user2][category] )
b = getOrderedValues( wardrobes[user2][category], wardrobes[user][category] )
cosineSimilarities[user2] = getCosineSimilarity( np.array( a ), np.array( b ) )
#print str(user2) + ": " + str(cosineSimilarities[user2])
#print kNN( cosineSimilarities, 5 )
# compute weighted ratings of items from nearest neighbors
weightedRatings = getWeightedRatings( user, category, cosineSimilarities, wardrobes )
#print weightedRatings
# pick the top 3 weighted ratings
sortedRatings = sorted(weightedRatings.items(), key=lambda x: x[1], reverse=True)
if len(sortedRatings) > 3:
sortedRatings = sortedRatings[:3]
# assign the top 3 as candidate items
candidateItems = {}
for rating in sortedRatings:
candidateItems[rating[0]] = rating[1]
#print candidateItems
# compute projected ratings of candidate items using jaccard index
# if projected rating >= 3, then item will be recommended to user
projectedRatings = {}
for item1 in candidateItems:
for item2 in wardrobes[user][category]:
score = getJaccardIndex(catalogue[category - 1][item1], catalogue[category - 1][item2])
rating = score * wardrobes[user][category][item2]
if (item1 not in projectedRatings or score > projectedRatings[item1]) and rating >= 3:
projectedRatings[item1] = rating
#print projectedRatings
# print recommendations and projected ratings
for item in sorted(projectedRatings.keys()):
print str(item) + ": " + str(projectedRatings[item])
# comment when not updating db
"""for item in catalogue[category - 1]:
if item in projectedRatings:
query = "INSERT INTO modifit_user_recommendations " \
+ "(user_id, item_id, projected_rating, user_rating) " \
+ "VALUES ({0}, {1}, {2}, 0);"
try:
cur.execute(query.format(user, item, projectedRatings[item]))
except:
print "Can't execute query"""
print
