cost

Module containing all the cost functions of the cost optimal designs

Functions

additive_cost(costs, factors, max_cost[, ...])	Create a transition cost function according to the formula C = c1 + c2 + .
combine_costs(costs)	Combine multiple cost functions together.
cost_fn(*args, **kwargs)	Parameters f func(Y, params) or func(Y) The cost function to be wrapped. factors list(Factor) A list of factors for the design. denormalize bool Whether to denormalize (and decode) the data before passing it to f. decoded bool Whether to only decode, but not denormalize the data before passing it to f. contains_params bool Whether the cost function requires the Parameters. This prevents numba compilation.
discount_cost(costs, factors, max_cost[, ...])	Create a transition cost function according to the formula C = max(c1, c2, ..., base).
fixed_runs_cost(max_runs)	Cost function to deal with a fixed maximum number of experiments.
max_changes_cost(factor, factors, max_changes)	Cost function to deal with a fixed maximum number of changes in a specific factor.
parallel_worker_cost(transition_costs, ...)	Create a transition cost function for a problem where multiple workers can work on the transition between two consecutive runs in parallel.
scaled_parallel_worker_cost(...[, ...])	Create a transition cost function for a problem where multiple workers can work on the transition between two consecutive runs in parallel.
scaled_single_worker_cost(transition_costs, ...)	Create a transition cost function for a problem where only a single worker can work on the transition between two consecutive runs.
single_worker_cost(transition_costs, ...[, ...])	Create a transition cost function for a problem where only a single worker can work on the transition between two consecutive runs.