pyemma.coordinates.estimation.covariance.LaggedCovariance¶
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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, column_selection=None, diag_only=False)¶ -
__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
__init__
([c00, c0t, ctt, …])Initialize self.
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
Instantaneous covariance matrix
Time-lagged covariance matrix
Covariance matrix of the time shifted data
column_selection
cov
cov_tau
The logger for this class instance
mean
mean_tau
The model estimated by this Estimator
The name of this instance
nsave
weights
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C00_
¶ Instantaneous covariance matrix
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C0t_
¶ Time-lagged covariance matrix
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Ctt_
¶ Covariance matrix of the time shifted data
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estimate
(X, chunksize=None, **kwargs)¶ Estimates the model given the data X
- Parameters
X (object) – A reference to the data from which the model will be estimated
params (dict) – New estimation parameter values. The parameters must that have been announced in the __init__ method of this estimator. The present settings will overwrite the settings of parameters given in the __init__ method, i.e. the parameter values after this call will be those that have been used for this estimation. Use this option if only one or a few parameters change with respect to the __init__ settings for this run, and if you don’t need to remember the original settings of these changed parameters.
- Returns
estimator – The estimated estimator with the model being available.
- Return type
object
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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
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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
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classmethod
load
(file_name, model_name='default')¶ Loads a previously saved PyEMMA object from disk.
- 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
pyemma.list_models()
to get a representation of all stored models.
- Returns
obj
- Return type
the de-serialized object
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logger
¶ The logger for this class instance
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model
¶ The model estimated by this Estimator
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name
¶ The name of this instance
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partial_fit
(X)¶ incrementally update the estimates
- Parameters
X (array, list of arrays, PyEMMA reader) – input data.
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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: # doctest: +SKIP ... m.save(file, 'simple') # doctest: +SKIP ... inst_restored = pyemma.load(file, 'simple') # doctest: +SKIP >>> np.testing.assert_equal(m.P, inst_restored.P) # doctest: +SKIP
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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
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