dataset = by_days[user]["Monday"] + by_days[user]["Tuesday"] + by_days[user]["Wednesday"] + by_days[user]["Thursday"] 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
import datetime
results_radiation[day] = [] for day in days: dataset = datasets[user][day] random.shuffle(dataset) combinations = Utils.break_dataset_in_folds(dataset, 5) for combination in combinations: 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[day].append(float(correct)/len(test)) #--------------------------------------------------------------------------- all_check_ins = train + test all_venues = [x["venue_id"] for x in all_check_ins] n_values = Counter(all_venues) coordinates = {}
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)
results_radiation[day] = [] for day in days: dataset = datasets[user][day] random.shuffle(dataset) combinations = Utils.break_dataset_in_folds(dataset, 5) for combination in combinations: 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[day].append(float(correct) / len(test)) # --------------------------------------------------------------------------- all_check_ins = train + test all_venues = [x["venue_id"] for x in all_check_ins] n_values = Counter(all_venues) coordinates = {}