Gaussian Process Regression (GPR).
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Initialize a GPR regression analysis.
Parameters : | kernel : Kernel
sigma_noise :
lm :
retrainable :
enable_ca : None or list of str
disable_ca : None or list of str
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str
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Compute gradient of the log marginal likelihood. This version use a more compact formula provided by Williams and Rasmussen book.
Compute gradient of the log marginal likelihood when hyperparameters are in logscale. This version use a more compact formula provided by Williams and Rasmussen book.
Compute log marginal likelihood using self.train_fv and self.targets.
Returns a sensitivity analyzer for GPR.
Parameters : | flavor : str
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Set hyperparameters’ values.
Note that ‘hyperparameter’ is a sequence so the order of its values is important. First value must be sigma_noise, then other kernel’s hyperparameters values follow in the exact order the kernel expect them to be.