pyemma.coordinates.estimation.covariance.LaggedCovariance¶

class pyemma.coordinates.estimation.covariance.LaggedCovariance(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)¶

__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)¶

Methods

`__init__`([c00, c0t, ctt, …])
`estimate`(X[, chunksize])
`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 object of this class from a file.
`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.

C00_¶: Instantaneous covariance matrix

C0t_¶: Time-lagged covariance matrix

Ctt_¶: Covariance matrix of the time shifted data

fit(X, y=None)¶

Estimates parameters - for compatibility with sklearn.

Parameters:	X (object) – A reference to the data from which the model will be estimated
Returns:	estimator – The estimator (self) with estimated model.
Return type:	object

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:	deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params – Parameter names mapped to their values.
Return type:	mapping of string to any

load(file_name, model_name='default')¶

loads a previously saved object of this class from a file.

Parameters:	file_name (str or file like object (has to provide read method)) – The file like object tried to be read for a serialized object. model_name (str, default='default') – if multiple models are contained in the file, these can be accessed by their name. Use func:`pyemma.list_models` to get a representation of all stored models.
Returns:	obj
Return type:	the de-serialized object

logger¶: The logger for this class instance

model¶: The model estimated by this Estimator

name¶: The name of this instance

partial_fit(X)¶

incrementally update the estimates

Parameters:	X (array, list of arrays, PyEMMA reader) – input data.

save(file_name, model_name='default', overwrite=False, save_streaming_chain=False)¶

saves the current state of this object to given file and name.

Parameters:

file_name (str) – path to desired output file
model_name (str, default='default') – creates a group named ‘model_name’ in the given file, which will contain all of the data. If the name already exists, and overwrite is False (default) will raise a RuntimeError.
overwrite (bool, default=False) – Should overwrite existing model names?
save_streaming_chain (boolean, default=False) – if True, the data_producer(s) of this object will also be saved in the given file.

Examples

>>> import pyemma, numpy as np
>>> from pyemma.util.contexts import named_temporary_file
>>> m = pyemma.msm.MSM(P=np.array([[0.1, 0.9], [0.9, 0.1]]))

>>> with named_temporary_file() as file: 
...    m.save(file, 'simple') 
...    inst_restored = pyemma.load(file, 'simple') 
>>> np.testing.assert_equal(m.P, inst_restored.P) 

set_params(**params)¶: Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. :returns: :rtype: self