![]() |
Multivariate Pattern Analysis in Python |
Bases: mvpa.clfs.base.Classifier
Gaussian Process Regression (GPR).
Note
Available state variables:
(States enabled by default are listed with +)
Initialize a GPR regression analysis.
Parameters: |
|
---|
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.labels.
Returns a sensitivity analyzer for GPR.
Parameters: |
|
---|
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.
Bases: mvpa.measures.base.Sensitivity
SensitivityAnalyzer that reports the weights GPR trained on a given Dataset.
In case of KernelLinear compute explicitly the coefficients of the linear regression, together with their variances (if requested).
Note that the intercept is not computed.
Note
Available state variables:
(States enabled by default are listed with +)
Initialize the analyzer with the classifier it shall use.
Parameters: |
|
---|