Training

Routine to train all GPs in parallel to emulate the 3D Power Spectrum

training.main(directory: str = 'semigps') → None

Main function to train all the Gaussian Process models.

Param

directory (str) - directory where the GPs are stored.

Returns

None

training.parallel_training(arguments: list) → None

Call the parallel processing routine here

Param

arguments (list) - list of arguments (inputs) to train the GPs

Returns

None

training.train(cosmologies: numpy.ndarray, target: numpy.ndarray, folder_name: str, fname: str, kwargs: dict) → None

Function to train GPs

Param

cosmologies (np.ndarray) : array of size N_train x N_dim for the inputs to the GP

Param

target (np.ndarray) : an array for the targets (function)

Param

folder_name (str) : name of the folder where the outputs are stored

Param

fname (str) : name of the GP output

Param

kwargs (dict) : a dictionary with the settings for the GPs, for example, lambda_cap = 1000

training.worker(args: list) → None

The argument here is simply the name of the output vector

Param

args (list) : list containing the arguments to be fed for training GPs