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GeneralKernel.decomp_fn

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) for d_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) for d_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 of K.

Returns:

Stored values from the decomposition of the covariance matrix consisting of the Cholesky factor and the log determinant of K.

Return type:

PyTree

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