def fit(self, data, callback: Callable = None):
"""
Performs the estimation on either a count matrix or a previously estimated TransitionCountModel.
Parameters
----------
data : (N,N) count matrix or TransitionCountModel or MaximumLikelihoodMSM or MarkovStateModel
a count matrix or a transition count model that was estimated from data
callback: callable, optional, default=None
Function to be called to indicate progress of sampling.
Returns
-------
self : BayesianMSM
Reference to self.
"""
from deeptime.markov import TransitionCountModel
if isinstance(data, TransitionCountModel) and data.counting_mode is not None \
and "effective" not in data.counting_mode:
raise ValueError(
"The transition count model was not estimated using an effective counting method, "
"therefore counts are likely to be strongly correlated yielding wrong confidences."
)
if isinstance(data, Estimator):
if data.has_model:
data = data.fetch_model()
else:
raise ValueError(
"Can only use estimators as input if they have been fit previously."
)
if isinstance(data, TransitionCountModel) or is_square_matrix(data):
msm = MaximumLikelihoodMSM(
reversible=self.reversible,
stationary_distribution_constraint=self.
stationary_distribution_constraint,
sparse=self.sparse,
maxiter=self.maxiter,
maxerr=self.maxerr).fit(data).fetch_model()
elif isinstance(data, MarkovStateModel):
msm = data
else:
raise ValueError(
"Unsupported input data, can only be count matrix (or TransitionCountModel, "
"TransitionCountEstimator) or a MarkovStateModel instance or an estimator producing "
"Markov state models.")
return self.fit_from_msm(msm, callback=callback)
def test_is_square_matrix():
assert_(is_square_matrix(np.ones((5, 5))))
assert_(not is_square_matrix(np.ones((3, 5))))