pyemma.msm.HMSM

class pyemma.msm.HMSM(*args, **kwargs)

Hidden Markov model on discrete states.

Parameters

hmm (DiscreteHMM) – Hidden Markov Model

__init__(P, pobs, pi=None, dt_model='1 step')

Parameters

• Pcoarse (ndarray (m,m)) – coarse-grained or hidden transition matrix

• Pobs (ndarray (m,n)) – observation probability matrix from hidden to observable discrete states

• dt_model (str, optional, default='1 step') – time step of the model

Methods

_SerializableMixIn__interpolate(state, klass)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(other)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()
__gt__(value, /)	Return self>value.
__init__(P, pobs[, pi, dt_model])	param Pcoarse coarse-grained or hidden transition matrix
__init_subclass__(*args, **kwargs)	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__my_getstate__()
__my_setstate__(state)
__ne__(value, /)	Return self!=value.
__new__(cls, *args, **kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_assert_in_active(A)	Checks if set A is within the active set
_assert_metastable()	Tests if pcca object is available, or else raises a ValueError.
_committor_backward(P, A, B[, mu])
_committor_forward(P, A, B)
_compute_eigendecomposition(neig)	Conducts the eigenvalue decomposition and stores k eigenvalues, left and right eigenvectors
_compute_eigenvalues(neig)	Conducts the eigenvalue decomposition and stores k eigenvalues, left and right eigenvectors
_ensure_eigendecomposition([neig])	Ensures that eigendecomposition has been performed with at least neig eigenpairs
_ensure_eigenvalues([neig])	Ensures that at least neig eigenvalues have been computed
_get_classes_to_inspect()	gets classes self derives from which 1.
_get_interpolation_map(cls)
_get_model_param_names()	Get parameter names for the model
_get_private_field(cls, name[, default])
_get_serialize_fields(cls)
_get_state_of_serializeable_fields(klass, state)	:return a dictionary {k:v} for k in self.serialize_fields and v=getattr(self, k)
_get_version(cls[, require])
_get_version_for_class_from_state(state, klass)	retrieves the version of the current klass from the state mapping from old locations to new ones.
_mfpt(P, A, B[, mu])
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
committor_backward(A, B)	Backward committor from set A to set B
committor_forward(A, B)	Forward committor (also known as p_fold or splitting probability) from set A to set B
correlation(a[, b, maxtime, k, ncv])	Time-correlation for equilibrium experiment.
eigenvalues([k])	Compute the transition matrix eigenvalues
eigenvectors_left([k])	Compute the left transition matrix eigenvectors
eigenvectors_right([k])	Compute the right transition matrix eigenvectors
expectation(a)	Equilibrium expectation value of a given observable.
fingerprint_correlation(a[, b, k, ncv])	Dynamical fingerprint for equilibrium time-correlation experiment.
fingerprint_relaxation(p0, a[, k, ncv])	Dynamical fingerprint for perturbation/relaxation experiment.
get_model_params([deep])	Get parameters for this model.
load(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
mfpt(A, B)	Mean first passage times from set A to set B, in units of the input trajectory time step
pcca(m)	Runs PCCA++ [1]_ to compute a metastable decomposition of MSM states
propagate(p0, k)	Propagates the initial distribution p0 k times
relaxation(p0, a[, maxtime, k, ncv])	Simulates a perturbation-relaxation experiment.
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
set_model_params([P, pobs, pi, reversible, …])	param P coarse-grained or hidden transition matrix
simulate(N[, start, stop, dt])	Generates a realization of the Hidden Markov Model
submodel([states, obs])	Returns a HMM with restricted state space
timescales([k])	The relaxation timescales corresponding to the eigenvalues
transition_matrix_obs([k])	Computes the transition matrix between observed states
update_model_params(**params)	Update given model parameter if they are set to specific values

Attributes

P	The transition matrix on the active set.
_HMSM__serialize_version
_MSM__serialize_fields
_MSM__serialize_version
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
__dict__
__doc__
__hash__
__module__
__weakref__	list of weak references to the object (if defined)
_save_data_producer
dt_model	Description of the physical time corresponding to the lag.
eigenvectors_left_obs
eigenvectors_right_obs
is_reversible	Returns whether the MSM is reversible
is_sparse	Returns whether the MSM is sparse
lifetimes	Lifetimes of states of the hidden transition matrix
metastable_assignments	Computes the assignment to metastable sets for observable states
metastable_distributions	Returns the output probability distributions. Identical to
metastable_memberships	Computes the memberships of observable states to metastable sets by
metastable_sets	Computes the metastable sets of observable states within each
n_metastable	Number of states chosen for PCCA++ computation.
neig	number of eigenvalues to compute.
nstates	Number of active states on which all computations and estimations are done
nstates_obs
observation_probabilities	returns the output probability matrix
pi	The stationary distribution on the MSM states
reversible	Returns whether the MSM is reversible
sparse	Returns whether the MSM is sparse
stationary_distribution	The stationary distribution on the MSM states
stationary_distribution_obs
timestep_model	Physical time corresponding to one transition matrix step, e.g.
transition_matrix	The transition matrix on the active set.