random.shuffle(dataset) combinations = Utils.break_dataset_in_folds(dataset, 5) for combination in combinations: social_model_stanford = CorrectSocialModelStanford() if user in network: social_model_ours = SimpleSocialModel(datasets, network[user], user) train = combination['train'] test = combination['test'] #--------------------------------------------------------------------------- model = StanfordModel() model.train(train, number_of_iterations = 10) if model.parameters != None: correct = 0 for check_in in test: real_venue = check_in["venue_id"] time = check_in["date"] predicted_venue = model.predict(time, train + test) if real_venue == predicted_venue: correct += 1 results_stanford.append(float(correct)/len(test)) #------------ if user in network: social_model_stanford.fit_model(model, network[user], datasets) correct = 0 for check_in in test:
random.shuffle(dataset)
combinations = Utils.break_dataset_in_folds(dataset, 5)
for combination in combinations:
social_model_stanford = CorrectSocialModelStanford()
if user in network:
social_model_ours = SimpleSocialModel(datasets, network[user],
user)
train = combination['train']
test = combination['test']
#---------------------------------------------------------------------------
model = StanfordModel()
model.train(train, number_of_iterations=10)
if model.parameters != None:
correct = 0
for check_in in test:
real_venue = check_in["venue_id"]
time = check_in["date"]
predicted_venue = model.predict(time, train + test)
if real_venue == predicted_venue:
correct += 1
results_stanford.append(float(correct) / len(test))
#------------
if user in network:
social_model_stanford.fit_model(model, network[user],
datasets)
correct = 0
