pyemma.coordinates.estimation.covariance.LaggedCovariance

class pyemma.coordinates.estimation.covariance.LaggedCovariance(*args, **kwargs)

__init__(c00=True, c0t=False, ctt=False, remove_constant_mean=None, remove_data_mean=False, reversible=False, bessel=True, sparse_mode='auto', modify_data=False, lag=0, weights=None, stride=1, skip=0, chunksize=NotImplemented, ncov_max=inf, column_selection=None, diag_only=False)

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

Methods

_Loggable__create_logger()
_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__([c00, c0t, ctt, …])	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().
_check_estimated()
_cleanup_logger(logger_id, logger_name)
_estimate(iterable[, partial_fit])
_get_classes_to_inspect()	gets classes self derives from which 1.
_get_interpolation_map(cls)
_get_param_names()	Get parameter names for the estimator
_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.
_init_covar(partial_fit, n_chunks)
_logger_is_active(level)	@param level: int log level (debug=10, info=20, warn=30, error=40, critical=50)
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
estimate(X[, chunksize])	Estimates the model given the data X
fit(X[, y])	Estimates parameters - for compatibility with sklearn.
get_params([deep])	Get parameters for this estimator.
load(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
partial_fit(X)	incrementally update the estimates
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
set_params(**params)	Set the parameters of this estimator.

Attributes

C00_	Instantaneous covariance matrix
C0t_	Time-lagged covariance matrix
Ctt_	Covariance matrix of the time shifted data
_Estimator__serialize_fields
_LaggedCovariance__serialize_fields
_LaggedCovariance__serialize_version
_Loggable__ids
_Loggable__refs
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
__dict__
__doc__
__module__
__weakref__	list of weak references to the object (if defined)
_estimated
_loglevel_CRITICAL
_loglevel_DEBUG
_loglevel_ERROR
_loglevel_INFO
_loglevel_WARN
_save_data_producer
column_selection
cov
cov_tau
logger	The logger for this class instance
mean
mean_tau
model	The model estimated by this Estimator
name	The name of this instance
nsave
weights