def test_GaussHMM_posterior(cases: str) -> None:
np.random.seed(12346)
cases = int(cases)
i = 1
N_decimal = 4
max_iter = 100
tol=1e-3
while i < cases:
n_samples = np.random.randint(10, 50)
hidden_states = np.random.randint(3, 6)
n_features = np.random.randint(4, 9)
X = []
lengths = []
for _ in range(n_samples):
seq_length = np.random.randint(4, 9)
this_x = np.random.rand(seq_length,n_features)
X.append(this_x)
lengths.append(seq_length)
hmm_gold = GaussianHMM(n_components=hidden_states,
covariance_type='full',
n_iter=max_iter,
tol=tol)
X_gold = np.concatenate(X)
hmm_gold.fit(X_gold, lengths)
gold_means = hmm_gold.means_
gold_pi = hmm_gold.startprob_
gold_n_features = hmm_gold.n_features
gold_transmat = hmm_gold.transmat_
gold_means = hmm_gold.means_
gold_covars = hmm_gold.covars_
hmm_mine = GaussHMM(hidden_states=hidden_states,
A=gold_transmat,
n_features=gold_n_features,
means=gold_means,
covar=gold_covars,
pi=gold_pi,
tol=tol,
max_iter=max_iter)
_,gold_posteriors = hmm_gold.score_samples(X_gold, lengths)
mine_posterior_list = [hmm_mine.posterior(this_x) for this_x in X]
mine_posterior_list = np.concatenate(mine_posterior_list)
assert_almost_equal(mine_posterior_list, gold_posteriors, decimal=N_decimal)
i+=1
print('Successfully testing the function of computing posteriors in Gaussian HMM!')
new_x = np.asarray(train_set) n_comps = 6 model = GaussianHMM(n_comps) model.fit([new_x]) hidden_states = model.predict(new_x) 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
