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 msm_double_well(lagtime=100,
reversible=True,
**kwargs) -> MaximumLikelihoodMSM:
count_model = TransitionCountEstimator(lagtime=lagtime, count_mode="sliding")\
.fit(datasets.double_well_discrete().dtraj).fetch_model().submodel_largest()
est = MaximumLikelihoodMSM(reversible=reversible, **kwargs)
est.fit(count_model)
return est
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()