training_percentage = 0.8 # % of data used for training the model
sleep_stages_train, sleep_stages_test = np.split(
sleep_stages, [int(training_percentage * sleep_stages.shape[0])])
epoch_codes_train, epoch_codes_test = np.split(
epoch_codes, [int(training_percentage * epoch_codes.shape[0])])
#print(epoch_codes_train)
#print("Now printing weird stuff...")
#weird = Counter(zip(sleep_stages_train, epoch_codes_train)).items()
#print(weird)
hmm = HMM(nr_states, nr_groups)
#print(sleep_stages_train)
#print(epoch_codes_train)
hmm.train(sleep_stages_train, epoch_codes_train)
x = hmm.get_state_sequence(epoch_codes_test)
'''
sleep_stages_train = np.array(sleep_stages_train)
epoch_codes_train = np.array(epoch_codes_train)
sleep_stages_train = np.reshape(sleep_stages_train, ((115,1)))
epoch_codes_train = np.reshape(epoch_codes_train, ((115,1)))
print(sleep_stages_train.shape)
print(epoch_codes_train.shape)
print(epoch_codes_train)
model = RandomForestClassifier()
model.fit(sleep_stages_train, epoch_codes_train)
x = model.predict(epoch_codes_test)
'''
