pyemma.coordinates.transform.VAMPModel

class pyemma.coordinates.transform.VAMPModel(*args, **kwargs)

__init__(mean_0=None, mean_t=None, C00=None, Ctt=None, C0t=None, dim=None, epsilon=1e-06, scaling=None)

Initialize self. See help(type(self)) for accurate signature.

Methods

_SerializableMixIn__interpolate(state, klass)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	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.
__hash__()	Return hash(self).
__init__([mean_0, mean_t, C00, Ctt, C0t, …])	Initialize self.
__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().
_cumvar(singular_values)
_diagonalize()	Performs SVD on covariance matrices and save left, right singular vectors and values in the model.
_dimension(rank0, rankt, dim, singular_values)	output dimension
_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.
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
dimension()	output dimension
expectation(observables, statistics[, …])	Compute future expectation of observable or covariance using the approximated Koopman operator.
get_model_params([deep])	Get parameters for this model.
load(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
score([test_model, score_method])	Compute the VAMP score for this model or the cross-validation score between self and a second model.
set_model_params(mean_0, mean_t, C00, Ctt, …)
update_model_params(**params)	Update given model parameter if they are set to specific values

Attributes

C00
C0t
Ctt
U	Tranformation matrix that represents the linear map from mean-free feature space to the space of left singular functions.
V	Tranformation matrix that represents the linear map from mean-free feature space to the space of right singular functions.
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
_VAMPModel__serialize_fields
_VAMPModel__serialize_version
__dict__
__doc__
__module__
__weakref__	list of weak references to the object (if defined)
_save_data_producer
cumvar	cumulative kinetic variance
scaling	Scaling of projection.
singular_values	The singular values of the half-weighted Koopman matrix