# define a function f
def synthetic(x):
return np.exp(-(x - 4)**2) + np.exp(-(x - 6)**2 / 10) + 1 / (x**2 + 1)
def sincos(x):
return x * np.sin(x) + x * np.cos(2 * x)
myfunction_list = []
myfunction_list.append(functions.rosenbrock())
myfunction_list.append(functions.branin())
myfunction_list.append(functions.dropwave())
myfunction_list.append(functions.goldstein())
myfunction_list.append(functions.hartman_3d())
myfunction_list.append(functions.ackley(input_dim=5))
myfunction_list.append(functions.alpine1(input_dim=5))
myfunction_list.append(functions.alpine2(input_dim=5))
myfunction_list.append(functions.hartman_6d())
myfunction_list.append(functions.alpine2(input_dim=10))
myfunction_list.append(
functions.gSobol(a=np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])))
algorithm_list = []
algorithm_list.append('FBO')
algorithm_list.append('VolumeDoubling')
import warnings
warnings.filterwarnings("ignore")
#%matplotlib inline.
random.seed('6789')
#myfunction=functions.gSobol(a=np.array([1,1,1,1,1]))
#myfunction=functions.gSobol(a=np.array([1,1,1,1,1,1,1,1,1,1]))
#myfunction=functions.hartman_3d()
#myfunction=functions.hartman_6d()
#myfunction=functions.beale()
#myfunction=functions.forrester()
#myfunction=functions.alpine2(input_dim=5)
#myfunction=functions.alpine2(input_dim=10)
myfunction=functions.dropwave()
func=myfunction.func
# define a bound for the parameter
mybound=myfunction.bounds
yoptimal=myfunction.fmin*myfunction.ismax
gp_params = {'theta':1*myfunction.input_dim,'noise_delta':0.000001}
nRepeat=3