def test_mlmsm_pipeline(self):
file = mdshare.fetch('hmm-doublewell-2d-100k.npz', working_directory='data')
with np.load(file) as fh:
data = fh['trajectory']
transition_matrix = fh['transition_matrix']
pipeline = Pipeline(steps=[
('tica', TICA(dim=1, lagtime=1)),
('cluster', KMeans(n_clusters=2, max_iter=500)),
('counts', TransitionCountEstimator(lagtime=1, count_mode="sliding"))
])
pipeline.fit(data)
counts = pipeline[-1].fetch_model().submodel_largest()
mlmsm = MaximumLikelihoodMSM().fit(counts).fetch_model()
P = mlmsm.pcca(2).coarse_grained_transition_matrix
mindist = min(np.linalg.norm(P - transition_matrix), np.linalg.norm(P - transition_matrix.T))
assert mindist < 0.05
def test_mlmsm_pipeline(self):
hmm = HiddenMarkovModel(transition_model=MarkovStateModel([[.8, .2],
[.1, .9]]),
output_model=GaussianOutputModel(
n_states=2,
means=[-10, 10],
sigmas=[.1, .1]))
htraj, traj = hmm.simulate(10000)
transition_matrix = hmm.transition_model.transition_matrix
pipeline = Pipeline(steps=[(
'tica', TICA(dim=1, lagtime=1)
), (
'cluster', KMeans(n_clusters=2, max_iter=500)
), ('counts',
TransitionCountEstimator(lagtime=1, count_mode="sliding"))])
pipeline.fit(traj[..., None])
counts = pipeline[-1].fetch_model().submodel_largest()
mlmsm = MaximumLikelihoodMSM().fit(counts).fetch_model()
P = mlmsm.pcca(2).coarse_grained_transition_matrix
mindist = min(np.linalg.norm(P - transition_matrix),
np.linalg.norm(P - transition_matrix.T))
assert mindist < 0.05