def experimentalDesign(): #print("experimental design2") S = fwk.Strategy('opt_test', 'python opt_test.py', '{x} {y}', {"x": -1.0, "y": -1.0}) S = parameter_tuning(S, "x", [-1.0, -0.5, 0.0, 0.5, 1.0]) S = parameter_tuning(S, "y", [-1.0, -0.5, 0.0, 0.5, 1.0]) f4e.output = S.name + " " + str(tuple(S.params.values())) + " " f4e.terminate()
def parameter_tuning(S, param, param_values): original_value = S.params[param] original_name = S.name params = S.params.copy() for value in param_values: params[param] = value if original_value == value: continue else: S2 = fwk.Strategy(original_name, S.pathExe, S.args, params) S = f4e.bestStrategy(S, S2) return S
def experimentalDesign(): S = fwk.Strategy('BSG_CLP', './BSG_CLP', '--alpha={a} --beta={b} --gamma={g} -p {p}', { "a": 0.0, "b": 0.0, "g": 0.0, "p": 0.0 }) S = parameter_tuning(S, "a", [0.0, 1.0, 2.0, 4.0, 8.0]) S = parameter_tuning(S, "b", [0.0, 0.5, 1.0, 2.0, 4.0]) S = parameter_tuning(S, "g", [0.0, 0.1, 0.2, 0.3, 0.4]) S = parameter_tuning(S, "p", [0.0, 0.1, 0.2, 0.3, 0.4]) f4e.output = S.name + " " + str(tuple(S.params.values())) + " " f4e.terminate()