paref.moo_algorithms.minimizer.surrogates.gpr#

Classes

`ExactGP0`(train_x, train_y, likelihood)	# TBA: add
`GPR`([training_iter, learning_rate])
`Gpr0Torch`([training_iter, learning_rate])

class paref.moo_algorithms.minimizer.surrogates.gpr.ExactGP0(train_x: Tensor, train_y: Tensor, likelihood)[source]#

Bases: ExactGP

# TBA: add

class paref.moo_algorithms.minimizer.surrogates.gpr.GPR(training_iter: int = 1000, learning_rate=0.05)[source]#

Bases: object

class paref.moo_algorithms.minimizer.surrogates.gpr.Gpr0Torch(training_iter=1000, learning_rate=0.1)[source]#

Bases: object

load_state(state_path: str, train_x: Tensor, train_y: Tensor) → bool[source]#: Load a set of hyperparameters together with the training set(!) into the models.

predict_torch(pred_x: Tensor) → MultivariateNormal[source]#: Doing predictions based on the models and the prediction data. Input and output need to be tensors. Note that the output need to be of dimension one.

save_state(state_name) → bool[source]#: Save the trained hyperparameter to a .pth file.

train_torch(train_x: Tensor, train_y: Tensor) → bool[source]#: Train the model with torch tensors as input and output. Note that the output need to be of dimension one.