train_gp {MagmaClustR} | R Documentation |
Learning hyper-parameters of a Gaussian Process
Description
Learning hyper-parameters of any new individual/task in Magma
is
required in the prediction procedure. This function can also be used to learn
hyper-parameters of a simple GP (just let the hyperpost
argument set
to NULL, and use prior_mean
instead). When using within Magma
,
by providing data for the new individual/task, the hyper-posterior mean and
covariance parameters, and initialisation values for the hyper-parameters,
the function computes maximum likelihood estimates of the hyper-parameters.
Usage
train_gp(
data,
prior_mean = NULL,
ini_hp = NULL,
kern = "SE",
hyperpost = NULL,
pen_diag = 1e-10
)
Arguments
data |
A tibble or data frame. Required columns: |
prior_mean |
Mean parameter of the GP. This argument can be specified under various formats, such as:
|
ini_hp |
A named vector, tibble or data frame of hyper-parameters
associated with the |
kern |
A kernel function, defining the covariance structure of the GP. Several popular kernels (see The Kernel Cookbook) are already implemented and can be selected within the following list:
|
hyperpost |
A list, containing the elements 'mean' and 'cov',
the parameters of the hyper-posterior distribution of the mean process.
Typically, this argument should come from a previous learning using
|
pen_diag |
A number. A jitter term, added on the diagonal to prevent numerical issues when inverting nearly singular matrices. |
Value
A tibble, containing the trained hyper-parameters for the kernel of the new individual/task.
Examples
TRUE