pybop.costs.design_cost#

Classes#

Base design cost.

class pybop.costs.design_cost.DesignCost(target: str)[source]#

Base design cost.

Note that design costs are maximised by default. Change to minimising by setting the attribute minimising=True.

compute(y: dict, dy: numpy.ndarray | None = None) → float[source]#

Returns the value of the cost variable.

Parameters:

y (dict) – The dictionary of predictions with keys designating the output variables for fitting.
dy (np.ndarray, optional) – The corresponding gradient with respect to the parameters for each output variable. Note: not used in design optimisation classes.

Returns:

The value of the output variable.

Return type:

float

verify_prediction(y: dict)[source]#

Verify that the prediction matches the target data.

Parameters:: y (dict) – A dictionary of predictions with keys designating the output variables for fitting.
Returns:: True if the prediction matches the target data, otherwise False.
Return type:: bool