GeneralKernel.generate_noise#
- GeneralKernel.generate_noise(hp: Any, x_l: Array, x_t: Array, size: int | None = 1, stored_values: Any | None = {}) Array [source]#
Generate noise with the covariance matrix returned by this kernel using the input hyperparameters
hp
.- 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.size (int, optional) – The number of different draws of noise to generate. Defaults to 1.
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:
Generate noise of shape
(N_l, N_t)
ifsize = 1
or(N_l, N_t, size)
if size > 1.- Return type:
JAXArray