new_test = np.asarray(test_set)
predictions = []
chunk = train_set[2500:]
'''find prob for each test point, compare to expected, then re-fit HMM with it'''
for idx, x in enumerate(chunk):
_, pst_prob = model.score_samples([x])
max_ind = pst_prob.argmax()
trn = model._get_transmat()[max_ind]
'''Get the max one for now. Maybe use some other method later one'''
max_trn = trn.argmax()
cov = model._get_covars()[max_trn]
mns = model._get_means()[max_trn]
rd = np.random.multivariate_normal(mns, cov)
int_rd = [int(x) for x in rd]
predictions += [int_rd]
# retrain HMM with new data point
moving_idx = 30-idx
mov_train_set = []
if moving_idx < 1:
mov_train_set = []
else: mov_train_set = train_set[-moving_idx:]
mov_new_set_idx = 0
if idx >= 30:
mov_new_set_idx = idx-30
