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()