def cvs_to_tica(cv_coordinates, drop):
from deeptime.decomposition import TICA
data = []
cvs = list([i for i in range(cv_coordinates.shape[2]) if i not in drop])
for i in range(cv_coordinates.shape[0]):
data.append(cv_coordinates[i, :, cvs].T)
tica = TICA(lagtime=1)
data = tica.fit(data, lagtime=1,
progress=True).fetch_model().transform(data)
return data
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
def tica(data, lag, weights=None, **params):
from deeptime.decomposition import TICA
return TICA(var_cutoff=0.95, lagtime=lag,
**params).fit_from_timeseries(
data, weights=weights).fetch_model()
ax.set_title(title)
ax.set_aspect('equal')
ax.set_xlabel('x')
ax.set_ylabel('y')
data = ellipsoids(seed=17)
discrete_trajectory = data.discrete_trajectory(n_steps=1000)
feature_trajectory = data.map_discrete_to_observations(discrete_trajectory)
vamp = VAMP(dim=1, lagtime=1)
vamp = vamp.fit(feature_trajectory).fetch_model()
vamp_projection = vamp.transform(feature_trajectory)
dxy_vamp = vamp.singular_vectors_left[:, 0] # dominant vamp component
tica = TICA(dim=1, lagtime=1)
tica = tica.fit(feature_trajectory).fetch_model()
tica_projection = tica.transform(feature_trajectory)
dxy_tica = tica.singular_vectors_left[:, 0] # dominant tica component
pca = PCA(n_components=1)
pca.fit(feature_trajectory)
pca_projection = pca.transform(feature_trajectory)
dxy_pca = pca.components_[0] # dominant pca component
f = plt.figure(constrained_layout=False, figsize=(14, 14))
gs = f.add_gridspec(nrows=2, ncols=3)
ax_projections = f.add_subplot(gs[0, :])
ax_tica = f.add_subplot(gs[1, 0])
ax_vamp = f.add_subplot(gs[1, 1])
ax_pca = f.add_subplot(gs[1, 2])