def reset(self):
super(ModeaEnv, self).reset_()
self.dim = self.instance[0]
self.function_id = self.instance[1]
self.instance_id = self.instance[2]
self.representation = self.ensureFullLengthRepresentation(self.instance[3])
opts = getOpts(self.representation[: len(options)])
self.lambda_ = self.representation[len(options)]
self.mu = self.representation[len(options) + 1]
values = getVals(self.representation[len(options) + 2 :])
self.function, self.target = bn.instantiate(int(self.function_id))
self.es = CustomizedES(
self.dim, self.function, self.budget, self.mu, self.lambda_, opts, values
)
parameter_opts = self.es.parameters.getParameterOpts()
# print("Local restart on")
if parameter_opts["lambda_"]:
self.lambda_init = parameter_opts["lambda_"]
elif parameter_opts["local_restart"] in ["IPOP", "BIPOP"]:
self.lambda_init = int(4 + np.floor(3 * np.log(parameter_opts["n"])))
else:
self.lambda_init = None
parameter_opts["lambda_"] = self.lambda_init
# BIPOP Specific parameters
self.lambda_ = {"small": None, "large": self.lambda_init}
self.budgets = {"small": None, "large": None}
self.regime = "first"
self.update_parameters()
return self.get_state(self)
def reset(self):
super(ModeaEnv, self).reset_()
self.dim = self.instance[0]
self.function_id = self.instance[1]
self.instance_id = self.instance[2]
self.representation = self.ensureFullLengthRepresentation(self.instance[3])
opts = getOpts(self.representation[: len(options)])
self.lambda_ = self.representation[len(options)]
self.mu = self.representation[len(options) + 1]
values = getVals(self.representation[len(options) + 2 :])
self.function = bn.instantiate(int(self.function_id))[0]
self.es = CustomizedES(
self.dim, self.function, self.budget, self.mu, self.lambda_, opts, values
)
self.es.mutateParameters = self.es.parameters.adaptCovarianceMatrix
self.adapt_es_opts(opts)
return self.get_state()
def reset(self):
"""
Reset environment
Returns
-------
np.array
Environment state
"""
super(CMAESEnv, self).reset_()
self.history.clear()
self.past_obj_vals.clear()
self.past_sigma.clear()
self.cur_loc = self.instance[3]
self.dim = self.instance[1]
self.init_sigma = self.instance[2]
self.cur_sigma = [self.init_sigma]
self.fcn = bn.instantiate(self.instance[0])[0]
self.func_values = []
self.f_vals = deque(maxlen=self.popsize)
self.es = CMAEvolutionStrategy(
self.cur_loc,
self.init_sigma,
{
"popsize": self.popsize,
"bounds": self.bounds,
"seed": self.initial_seed
},
)
self.solutions, self.func_values = self.es.ask_and_eval(self.fcn)
self.fbest = self.func_values[np.argmin(self.func_values)]
self.f_difference = np.abs(
np.amax(self.func_values) - self.cur_obj_val) / float(
self.cur_obj_val)
self.velocity = np.abs(np.amin(self.func_values) -
self.cur_obj_val) / float(self.cur_obj_val)
self.es.mean_old = self.es.mean
self.history.append([self.f_difference, self.velocity])
return self.get_state(self)
def read_instance_set(self):
"""
Read path of instances from config into list
"""
path = (
os.path.dirname(os.path.abspath(__file__))
+ "/"
+ self.config.instance_set_path
)
self.config["instance_set"] = {}
with open(path, "r") as fh:
reader = csv.DictReader(fh)
for row in reader:
function = bn.instantiate(int(row["fcn_index"]))[0]
init_locs = [float(row[f"init_loc{i}"]) for i in range(int(row["dim"]))]
instance = [
function,
int(row["dim"]),
float(row["init_sigma"]),
init_locs,
]
self.config["instance_set"][int(row["ID"])] = instance
"SharpRidge", "StepEllipsoidal", "RosenbrockRotated", "SchaffersIllConditioned",
"LunacekBiR", "GG101me", "GG21hi"]
init_sigmas = [0.5]*len(test_fcns)
for i in test_fcns:
init_locs.append([0]*input_dim)
cur_dir = os.path.dirname(os.path.abspath(__file__))
fcn_objs = []
fcns = []
for i in range(num_fcns//len(fcn_ids)):
#instantiate BBOB functions based on their ID
for i in fcn_ids:
fcn_objs.append(bn.instantiate(i)[0])
for i,function in enumerate(fcn_objs):
fcns.append({'fcn_obj': function, 'dim': input_dim, 'init_loc': list(init_locs[i]), 'init_sigma': init_sigmas[i]})
SENSOR_DIMS = {
PAST_OBJ_VAL_DELTAS: history_len,
CUR_PS: 1,
CUR_SIGMA : 1,
ACTION: 1,
PAST_SIGMA: history_len
}
BASE_DIR = '/'.join(str.split(gps_filepath, '/')[:-2])
EXP_DIR = BASE_DIR + '/../examples/10BBOB' + '/'