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LuasKernel.K

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LuasKernel.K#

LuasKernel.K(hp: Any, x_l1: Array, x_l2: Array, x_t1: Array, x_t2: Array, **kwargs) → Array[source]#

Generates the full covariance matrix K formed from the sum of two kronecker products:

\[K = K_l \otimes K_t + S_l \otimes S_t\]

Not needed for any calculations with the LuasKernel but useful for creating a GeneralKernel object with the same kernel function as a LuasKernel.

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_l1 (JAXArray) – The first array containing wavelength/vertical dimension regression variable(s) for the observed locations. May be of shape (N_l,) or (d_l,N_l) for d_l different wavelength/vertical regression variables.

  • x_l2 (JAXArray) – Second array containing wavelength/vertical dimension regression variable(s).

  • x_t1 (JAXArray) – The first array containing time/horizontal dimension regression variable(s) for the observed locations. May be of shape (N_t,) or (d_t,N_t) for d_t different time/horizontal regression variables.

  • x_t2 (JAXArray) – Second array containing time/horizontal dimension regression variable(s).

Returns:

The full covariance matrix K of shape (N_l*N_t, N_l*N_t).

Return type:

JAXArray

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