def train_hmm_and_keep_track_of_log_likelihood(hmm, obs, n_iter=1, **kwargs):
hmm.fit(obs, n_iter=1, **kwargs)
loglikelihoods = []
for n in xrange(n_iter):
hmm.fit(obs, n_iter=1, init_params='', **kwargs)
loglikelihoods.append(sum(hmm.score(x) for x in obs))
return loglikelihoods
def train_hmm_and_keep_track_of_log_likelihood(hmm, obs, n_iter=1, **kwargs):
hmm.fit(obs, n_iter=1, **kwargs)
loglikelihoods = []
for n in xrange(n_iter):
hmm.fit(obs, n_iter=1, init_params='', **kwargs)
loglikelihoods.append(sum(hmm.score(x) for x in obs))
return loglikelihoods
def test_1():
# creates a 4-state HMM on the ALA2 data. Nothing fancy, just makes
# sure the code runs without erroring out
dataset = AlanineDipeptide().get()
trajectories = dataset.trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = GaussianHMM(n_states=4, n_init=3)
hmm.fit(sequences)
assert len(hmm.timescales_ == 3)
assert np.any(hmm.timescales_ > 50)