swiftemulator.emulators.gaussian_process_one_dim module

Gaussian Process Emulator for emulating single values

class swiftemulator.emulators.gaussian_process_one_dim.GaussianProcessEmulator1D(kernel: Optional[Kernel] = None, mean_model: Optional[MeanModel] = None)[source]

Bases: BaseEmulator

Generator for emulators for individual values. In this case no independent values are used. The prediction is based on the model parameters and model values only.

Parameters:

  • kernel – The george kernel to use. The GPE here uses a copy of this instance. By default, this is the ExpSquaredKernel in George

  • george.kernels.Kernel – The george kernel to use. The GPE here uses a copy of this instance. By default, this is the ExpSquaredKernel in George

  • optional – The george kernel to use. The GPE here uses a copy of this instance. By default, this is the ExpSquaredKernel in George

  • mean_model – A mean model conforming to the swiftemulator mean model protocol (several pre-made models are available in the swiftemulator.mean_models module).

  • MeanModel – A mean model conforming to the swiftemulator mean model protocol (several pre-made models are available in the swiftemulator.mean_models module).

  • optional – A mean model conforming to the swiftemulator mean model protocol (several pre-made models are available in the swiftemulator.mean_models module).

kernel: Optional[Kernel]
mean_model: Optional[MeanModel]
model_specification: Optional[ModelSpecification] = None
model_parameters: Optional[ModelParameters] = None
model_values: Optional[ModelValues] = None
ordering: Optional[List[Hashable]] = None
parameter_order: Optional[List[str]] = None
independent_variables: Optional[array] = None
dependent_variables: Optional[array] = None
dependent_variable_errors: Optional[array] = None
emulator: Optional[GP] = None
fit_model(model_specification: ModelSpecification, model_parameters: ModelParameters, model_values: ModelValues)[source]

Fits the gaussian process model, as determined by the initialiser variables of the class (i.e. the kernel and the mean model).

Parameters:

  • model_specification (ModelSpecification) – Full instance of the model specification.

  • model_parameters (ModelParameters) – Full instance of the model parameters.

  • model_values (ModelValues) – Full instance of the model values describing this individual scaling relation.

Notes

This method uses copies of the internal kernel and mean model objects, as those objects contain slightly unhelpful state information.

predict_values(model_parameters: Dict[str, float]) → array[source]

Predict a value from the trained emulator contained within this object. returns the value at the input model parameters.

Parameters:

model_parameters (Dict[str, float]) – The point in model parameter space to create predicted values at.

Returns:

  • dependent_prediction, float – Value of predictions, if the emulator is a function f, this is the predicted value of f(independent) evaluted at the position of the input model_parameters.

  • dependent_prediction_error, float – Error on the model prediction.