def gcmaes(x_init, fun=None, fun_grad=None, grad_lookup=None, options={}):
"""
EXPERIMENTAL CMA-Es where every point in the cloud is optimized with LBFG-S and the
resulting cloud and results are used for the CMA update.
"""
options_cma = options["cmaes"]
if "init_point" in options_cma:
init_point = bool(options_cma.pop("init_point"))
else:
init_point = False
if "spread" in options_cma:
spread = float(options_cma.pop("spread"))
else:
spread = 0.1
shrinked_check = False
if "stop_at_convergence" in options_cma:
sigma_conv = int(options_cma.pop("stop_at_convergence"))
sigmas = []
shrinked_check = True
sigma_check = False
if "stop_at_sigma" in options_cma:
stop_sigma = int(options_cma.pop("stop_at_sigma"))
sigma_check = True
settings = options_cma
es = cma.CMAEvolutionStrategy(x_init, spread, settings)
iter = 0
while not es.stop():
if shrinked_check:
sigmas.append(es.sigma)
if iter > sigma_conv:
if all(sigmas[-(i + 1)] < sigmas[-(i + 2)]
for i in range(sigma_conv - 1)):
print(f"C3:STATUS:Shrinked cloud for {sigma_conv} steps. "
"Switching to gradients.")
break
if sigma_check:
if es.sigma < stop_sigma:
print("C3:STATUS:Goal sigma reached. Stopping CMA.")
break
samples = es.ask()
if init_point and iter == 0:
samples.insert(0, x_init)
print("C3:STATUS:Adding initial point to CMA sample.")
solutions = []
points = []
for sample in samples:
r = lbfgs(
sample,
fun_grad=fun_grad,
grad_lookup=grad_lookup,
options=options["lbfgs"],
)
solutions.append(r.fun)
points.append(r.x)
es.tell(points, solutions)
es.disp()
iter += 1
return es.result.xbest
def cmaes(x_init, fun=None, fun_grad=None, grad_lookup=None, options={}):
"""
Wrapper for the pycma implementation of CMA-Es. See also:
http://cma.gforge.inria.fr/apidocs-pycma/
Parameters
----------
x_init : float
Initial point.
fun : callable
Goal function.
fun_grad : callable
Function that computes the gradient of the goal function.
grad_lookup : callable
Lookup a previously computed gradient.
options : dict
Options of pycma and the following custom options.
noise : float
Artificial noise added to a function evaluation.
init_point : boolean
Force the use of the initial point in the first generation.
spread : float
Adjust the parameter spread of the first generation cloud.
stop_at_convergence : int
Custom stopping condition. Stop if the cloud shrunk for this number of
generations.
stop_at_sigma : float
Custom stopping condition. Stop if the cloud shrunk to this standard
deviation.
Returns
-------
np.array
Parameters of the best point.
"""
if "noise" in options:
noise = float(options.pop("noise"))
else:
noise = 0
if "init_point" in options:
init_point = bool(options.pop("init_point"))
else:
init_point = False
if "spread" in options:
spread = float(options.pop("spread"))
else:
spread = 0.1
shrunk_check = False
if "stop_at_convergence" in options:
sigma_conv = int(options.pop("stop_at_convergence"))
sigmas = []
shrunk_check = True
sigma_check = False
if "stop_at_sigma" in options:
stop_sigma = int(options.pop("stop_at_sigma"))
sigma_check = True
settings = options
es = cma.CMAEvolutionStrategy(x_init, spread, settings)
iter = 0
while not es.stop():
if shrunk_check:
sigmas.append(es.sigma)
if iter > sigma_conv:
if all(sigmas[-(i + 1)] < sigmas[-(i + 2)]
for i in range(sigma_conv - 1)):
print(f"C3:STATUS:Shrunk cloud for {sigma_conv} steps. "
"Switching to gradients.")
break
if sigma_check:
if es.sigma < stop_sigma:
print("C3:STATUS:Goal sigma reached. Stopping CMA.")
break
samples = es.ask()
if init_point and iter == 0:
samples.insert(0, x_init)
print("C3:STATUS:Adding initial point to CMA sample.")
solutions = []
for sample in samples:
goal = fun(sample)
if noise:
goal = goal + (np.random.randn() * noise)
solutions.append(goal)
es.tell(samples, solutions)
es.disp()
iter += 1
es.result_pretty()
return es.result.xbest
