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()