init_feasible
- pyoptex.doe.cost_optimal.init.init_feasible(params, max_tries=3, max_size=None, force_cost_feasible=True)[source]
Generate a random initial and feasible design. From a random permutation of a full factorial design, the runs are dropped one-by-one as long as they still provide a feasible design. Finally, the design is greedily reordered for minimal cost.
Parameters
- params
Parameters The simulation parameters.
- max_triesint
The maximum number of random tries. If all random tries fail, a final non-randomized design is created. If this also fails, a ValueError is thrown.
- max_sizeint
The maximum number of runs before iteratively removing them.
- force_cost_feasiblebool
Force a final cost feasibility check.
Returns
- Ynp.array(2d)
The initial design.
- params