LuasKernel.eigendecomp_no_stored_values#
- LuasKernel.eigendecomp_no_stored_values(hp: Any, x_l: Array, x_t: Array, stored_values: Any | None = {}) Any [source]#
Required calculations for the decomposition of the overall matrix
K
where the previously stored decomposition ofK
cannot be used for the calculation of a new decomposition. This avoids checking if any of the matrices have changed but may result in performing the same eigendecomposition calculations multiple times.We can decompose the inverse of
K
into the matrices:\[K^{-1} = [W_l \otimes W_t] D^{-1} [W_l^T \otimes W_t^T]\]Where this function will calculate
W_l
,W_t
andD_inv
and stored them in thestored_values
PyTree for future log likelihood calculations.Note
Values still need to be stored for any log likelihood calculations so this method does not save memory over
eigendecomp_use_stored_values
. It may however reduce runtimes by avoiding checking if matrices have changed so it could be beneficial if all hyperparameters are being varied simultaneously for each calculation.- Parameters:
hp (Pytree) – Hyperparameters needed to build the covariance matrices
Kl
,Kt
,Sl
,St
. Will be unaffected if additional mean function parameters are also included.x_l (JAXArray) – Array containing wavelength/vertical dimension regression variable(s) for the observed locations. May be of shape
(N_l,)
or(d_l,N_l)
ford_l
different wavelength/vertical regression variables.x_t (JAXArray) – Array containing time/horizontal dimension regression variable(s) for the observed locations. May be of shape
(N_t,)
or(d_t,N_t)
ford_t
different time/horizontal regression variables.stored_values (PyTree) – This may contain stored values from the decomposition of
K
but this method will not make use of it. This dictionary will simply be overwritten with new stored values from the decomposition ofK
.
- Returns:
Stored values from the decomposition of the covariance matrices. For
LuasKernel
this consists of values computed using the eigendecomposition of each matrix and also the log determinant ofK
.- Return type:
PyTree