def __init__(self,
reversible,
statdist_constraint,
sparse,
count_mode="sliding"):
super().__init__(statdist_constraint=statdist_constraint,
sparse=sparse,
count_mode=count_mode)
maxerr = 1e-12
if statdist_constraint:
est = MaximumLikelihoodMSM(reversible=reversible,
maxerr=maxerr,
stationary_distribution_constraint=self.
stationary_distribution,
sparse=sparse)
else:
est = MaximumLikelihoodMSM(reversible=reversible,
maxerr=maxerr,
sparse=sparse)
est.fit(self.counts)
self._msm = est.fetch_model()
self._msm_estimator = est
self._expectation = 31.73
if not reversible:
self._timescales = np.array([310.49376926, 8.48302712, 5.02649564])
else:
self._timescales = np.array([310.87, 8.5, 5.09])
def test_msm_submodel_statdist(disconnected_states, lag, reversible,
count_mode):
count_model = TransitionCountEstimator(
lagtime=lag,
count_mode=count_mode).fit(disconnected_states.dtrajs).fetch_model()
for cset in count_model.connected_sets():
submodel = count_model.submodel(cset)
estimator = MaximumLikelihoodMSM(reversible=reversible).fit(submodel)
msm = estimator.fetch_model()
C = submodel.count_matrix
P = C / np.sum(C, axis=-1)[:, None]
import scipy.linalg as salg
eigval, eigvec = salg.eig(P, left=True, right=False)
pi = np.real(eigvec)[:, np.where(
np.real(eigval) > 1. - 1e-3)[0]].squeeze()
if np.any(pi < 0):
pi *= -1.
pi = pi / np.sum(pi)
assert_array_almost_equal(msm.stationary_distribution,
pi,
decimal=1,
err_msg="Failed for cset {} with "
"cmat {}".format(cset,
submodel.count_matrix))
def estimate_markov_model(dtrajs, lag, **kw) -> MarkovStateModel:
statdist_constraint = kw.pop('statdist', None)
connectivity = kw.pop('connectivity_threshold', 0.)
sparse = kw.pop('sparse', False)
count_model = TransitionCountEstimator(lagtime=lag, count_mode="sliding", sparse=sparse).fit(dtrajs).fetch_model()
count_model = count_model.submodel_largest(probability_constraint=statdist_constraint,
connectivity_threshold=connectivity)
est = MaximumLikelihoodMSM(stationary_distribution_constraint=statdist_constraint, sparse=sparse, **kw)
est.fit(count_model)
return est.fetch_model()