class CovarianceModel¶
- class deeptime.covariance.CovarianceModel(cov_00: Optional[ndarray] = None, cov_0t: Optional[ndarray] = None, cov_tt: Optional[ndarray] = None, mean_0: Optional[ndarray] = None, mean_t: Optional[ndarray] = None, bessels_correction: bool = True, symmetrized: bool = False, lagtime: Optional[int] = None, data_mean_removed: bool = False)¶
A model which in particular carries the estimated covariances, means from a
Covariance
.- Parameters:
cov_00 ((n, n) ndarray, optional, default=None) – The instantaneous covariances if computed (see
Covariance.compute_c00
).cov_0t ((n, n) ndarray, optional, default=None) – The time-lagged covariances if computed (see
Covariance.compute_c0t
).cov_tt ((n, n) ndarray, optional, default=None) – The time-lagged instantaneous covariances if computed (see
Covariance.compute_ctt
).mean_0 ((n,) ndarray, optional, default=None) – The instantaneous means if computed.
mean_t ((n,) ndarray, optional, default=None) – The time-shifted means if computed.
bessels_correction (bool, optional, default=True) – Whether Bessel’s correction was used during estimation.
lagtime (int, default=None) – The lagtime that was used during estimation.
data_mean_removed (bool, default=False) – Whether the data mean was removed. This can have an influence on the effective VAMP score.
Attributes
Whether Bessel's correction was applied during estimation.
The instantaneous covariances.
The time-shifted covariances.
The time-shifted instantaneous covariances.
Whether the data mean was removed.
The lagtime at which estimation was performed.
The instantaneous means.
The time-shifted means.
Whether correlations and second moments are symmetrized in time.
Methods
copy
()Makes a deep copy of this model.
get_params
([deep])Get the parameters.
set_params
(**params)Set the parameters of this estimator.
whiten
(data[, epsilon, method])Whiten a (T, N)-shaped chunk of data by transforming it into the PCA basis.
- copy() Model ¶
Makes a deep copy of this model.
- Returns:
A new copy of this model.
- Return type:
copy
- get_params(deep=False)¶
Get the parameters.
- Returns:
params – Parameter names mapped to their values.
- Return type:
mapping of string to any
- 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 latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
object
- whiten(data: ndarray, epsilon=1e-10, method='QR') ndarray ¶
Whiten a (T, N)-shaped chunk of data by transforming it into the PCA basis. In case of rank deficiency this reduces the dimension.
- Parameters:
data ((T,N) ndarray) – The data to be whitened.
epsilon (float, optional, default=1e-10) – Truncation parameter. See
deeptime.numeric.spd_inv_sqrt()
.method (str, optional, default='QR') – Decomposition method. See
deeptime.numeric.spd_inv_sqrt()
.
- Returns:
whitened_data – Whitened data.
- Return type:
(T, n) ndarray
- property bessels_correction: bool¶
Whether Bessel’s correction was applied during estimation.
- Type:
bool
- property cov_00: Optional[ndarray]¶
The instantaneous covariances.
- Type:
(n, n) ndarray or None
- property cov_0t: Optional[ndarray]¶
The time-shifted covariances.
- Type:
(n, n) ndarray or None
- property cov_tt: Optional[ndarray]¶
The time-shifted instantaneous covariances.
- Type:
(n, n) ndarray or None
- property data_mean_removed: bool¶
Whether the data mean was removed.
- Type:
bool
- property lagtime: Optional[int]¶
The lagtime at which estimation was performed.
- Type:
int or None
- property mean_0: Optional[ndarray]¶
The instantaneous means.
- Type:
(n,) ndarray or None
- property mean_t: Optional[ndarray]¶
The time-shifted means.
- Type:
(n,) ndarray or None
- property symmetrized: bool¶
Whether correlations and second moments are symmetrized in time.
- Type:
bool