GaussianProcess
CalibrateEmulateSample.Emulators.GaussianProcessesPackage
— Typeabstract type GaussianProcessesPackage
Type to dispatch which GP package to use:
GPJL
for GaussianProcesses.jl,SKLJL
for the ScikitLearn GaussianProcessRegressor.
CalibrateEmulateSample.Emulators.PredictionType
— Typeabstract type PredictionType
Predict type for GPJL
in GaussianProcesses.jl:
YType
FType
latent function.
CalibrateEmulateSample.Emulators.GaussianProcess
— Typestruct GaussianProcess{GPPackage, FT} <: CalibrateEmulateSample.Emulators.MachineLearningTool
Structure holding training input and the fitted Gaussian process regression models.
Fields
models::Vector{Union{Nothing, PyCall.PyObject, GaussianProcesses.GPE}}
: The Gaussian Process (GP) Regression model(s) that are fitted to the given input-data pairs.kernel::Union{Nothing, var"#s16", var"#s17"} where {var"#s16"<:GaussianProcesses.Kernel, var"#s17"<:PyCall.PyObject}
: Kernel object.noise_learn::Bool
: Learn the noise with the White Noise kernel explicitly?alg_reg_noise::Any
: Additional observational or regularization noise in used in GP algorithmsprediction_type::CalibrateEmulateSample.Emulators.PredictionType
: Prediction type (y
to predict the data,f
to predict the latent function).
CalibrateEmulateSample.Emulators.GaussianProcess
— MethodGaussianProcess(
package::AbstractFloat;
kernel,
noise_learn,
alg_reg_noise,
prediction_type
)
package
- GaussianProcessPackage object.kernel
- GaussianProcesses kernel object. Default is a Squared Exponential kernel.noise_learn
- Boolean to additionally learn white noise in decorrelated space. Default is true.alg_reg_noise
- Float to fix the (small) regularization parameter of algorithms whennoise_learn = true
prediction_type
- PredictionType object. Default predicts data, not latent function (FType()).
CalibrateEmulateSample.Emulators.build_models!
— Methodbuild_models!(
gp::CalibrateEmulateSample.Emulators.GaussianProcess{CalibrateEmulateSample.Emulators.GPJL},
input_output_pairs::EnsembleKalmanProcesses.DataContainers.PairedDataContainer{FT<:AbstractFloat}
)
Method to build Gaussian process models based on the package.
CalibrateEmulateSample.Emulators.optimize_hyperparameters!
— Methodoptimize_hyperparameters!(
gp::CalibrateEmulateSample.Emulators.GaussianProcess{CalibrateEmulateSample.Emulators.GPJL},
args...;
kwargs...
)
Optimize Gaussian process hyperparameters using in-build package method.
Warning: if one uses GPJL()
and wishes to modify positional arguments. The first positional argument must be the Optim
method (default LBGFS()
).
GaussianProcesses.predict
— Methodpredict(
gp::CalibrateEmulateSample.Emulators.GaussianProcess{CalibrateEmulateSample.Emulators.GPJL},
new_inputs::AbstractArray{FT<:AbstractFloat, 2}
) -> Tuple{Any, Any}
Predict means and covariances in decorrelated output space using Gaussian process models.