return None
def infer(self, params):
self.calc_dominance_distributions('in', params)
self.calc_dominance_distributions('out', params)
for user in self.users.iter():
if user['location_point'] == None:
user['location_point'] = self.infer_one(user['id'], params)
def get_users(self):
return self.users
if __name__ == '__main__':
import sys
from lib.users import Users
from lib.graph import Graph
if len(sys.argv) < 3:
print '[usage]: python %s [users file path] [graph file path]' % sys.argv[0]
exit()
users = Users()
users.load_file(sys.argv[1])
graph = Graph()
graph.load_file(sys.argv[2])
lmm = LMM(users, graph)
lmm.infer()
print lmm.get_users()
exit()
args = {}
for i in range(1, len(sys.argv)):
key, value = sys.argv[i].split(':')
args[key] = value
test_users = Users()
test_users.load_file(args['test'])
training_users = Users()
training_users.load_file(args['training'])
ev = Evaluation(test_users)
if args['method'] == 'naiveg':
graph = Graph()
graph.load_file(args['graph'])
method = NaiveG(training_users, graph)
elif args['method'] == 'naivec':
db = DB(args['dbuser'], args['dbpass'], args['dbname'])
tweets = Tweets(db)
venues = Venues(db)
method = NaiveC(training_users, tweets, venues)
elif args['method'] == 'li':
db = DB(args['dbuser'], args['dbpass'], args['dbname'])
tweets = Tweets(db)
venues = Venues(db)
graph = Graph()
graph.load_file(args['graph'])
method = UDI(training_users, tweets, venues, graph)
elif args['method'] == 'jurgens':
graph = Graph()
