coexist.access.AccessProgress#

class coexist.access.AccessProgress(epochs: Optional[ndarray] = None, epochs_scaled: Optional[ndarray] = None, history: Optional[ndarray] = None, history_scaled: Optional[ndarray] = None, stdout: Optional[str] = None, stderr: Optional[str] = None)[source]#

Structure saving the current ACCES optimisation progress.

The epochs array has columns [mean_param1, mean_param2, …, std_param1, std_param2, …, std_overall] for each epoch.

The history array has columns [param1, param2, …, error] for each function evaluation.

Attributes:

epochs: np.ndarray: Matrix with columns [mean_param1, mean_param2, …, std_param1, std_param2, …, std_overall] with one row per epoch.
epochs_scaled: np.ndarray: Same as epochs, scaled such that the initial variance (sigma) becomes unity.
history: np.ndarray = None: Matrix with columns [param1, param2, …, error] for each parameter combination tried - i.e. population * num_epochs.
history_scaled: np.ndarray = None: Same as history, scaled such that the initial variance (sigma) becomes unity.
stdout: str = None: The latest unique recorded stdout message.
stderr: str = None: The latest unique recorded stderr message.

__init__(epochs: Optional[ndarray] = None, epochs_scaled: Optional[ndarray] = None, history: Optional[ndarray] = None, history_scaled: Optional[ndarray] = None, stdout: Optional[str] = None, stderr: Optional[str] = None)[source]#

Methods

`__init__`([epochs, epochs_scaled, history, ...])
`gather_results`(processes, paths, ...)	Check whether the jobs have finished and retrieve the standard deviation, errors and the combined total error.
`update_epochs`(es, scaling)	Update each epoch array after an ACCES run has been completed.
`update_history`(es, scaling, solutions, results)	Update the ACCES history with the latest simulation solutions and results.

update_epochs(es, scaling)[source]#: Update each epoch array after an ACCES run has been completed.

update_history(es, scaling, solutions, results)[source]#: Update the ACCES history with the latest simulation solutions and results.

gather_results(processes, paths, result_paths, multi_objective, verbose)[source]#: Check whether the jobs have finished and retrieve the standard deviation, errors and the combined total error.