def __init__(self):
## Library parameters
self.params = bo.initialize_params()
n_dim = 5 ## n dimensions
n_sam = 100 ## n samples
## Set of discrete points
self.x_set = np.random.rand(n_sam, n_dim)
def __init__(self):
## Library parameters
self.params = bo.initialize_params()
## n dimensions
self.n_dim = 5
## Lower bounds
self.lower_bound = np.zeros((self.n_dim,))
## Upper bounds
self.upper_bound = np.ones((self.n_dim,))
def quad(x, mu):
return ((np.asarray(x) - mu) ** 2).mean()
def func(x):
# print "x", x
# ~ target = np.ones(len(x))*0.3
target = np.arange(1, 1 + len(x))
target2 = np.ones(len(x)) * 10
# print "target", target
e = quad(x, target)
return e
# Initialize the parameters by default
params = bayesopt.initialize_params()
# We decided to change some of them
params["n_init_samples"] = 150
params["n_iter_relearn"] = 20
# params['noise'] = 0.01
params["kernel_name"] = "kMaternISO3"
params["kernel_hp_mean"] = [1]
params["kernel_hp_std"] = [5]
params["surr_name"] = "sStudentTProcessNIG"
dim = 20
lb = np.ones((dim,)) * 0
ub = np.ones((dim,)) * 20
mvalue, x_out, error = bayesopt.optimize(func, dim, lb, ub, params)