GeneralKernel.decomp_fn#
- GeneralKernel.decomp_fn(hp: Any, x_l: Array, x_t: Array, stored_values: Any | None = {}) Any [source]#
Builds the full covariance matrix K and uses the decomposition function specified at initialisation to return the Cholesky factor and the log determinant of K.
- Parameters:
hp (Pytree) – Hyperparameters needed to build the covariance matrix
K
. Will be unaffected if additional mean function parameters are also included.x_l (JAXArray) – Array containing wavelength/vertical dimension regression variable(s). 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). May be of shape
(N_t,)
or(d_t,N_t)
ford_t
different time/horizontal regression variables.stored_values (PyTree) – Stored values from the decomposition of the covariance matrix. For
GeneralKernel
this consists of the Cholesky factor and the log determinant ofK
.
- Returns:
Stored values from the decomposition of the covariance matrix consisting of the Cholesky factor and the log determinant of
K
.- Return type:
PyTree