def parEI(gp1,
gp2,
X_sample,
Y_sample,
EI=True,
truth=False,
MD=False,
PAR_RES=100):
if MD:
# create ND grid
x = np.linspace(XL, XU, PAR_RES)
ins = np.stack(np.meshgrid(*[x] * MD), axis=-1).reshape(MD, -1)
if X_sample is not None: ins = np.append(ins, X_sample, 1)
else:
# create 1D grid
ins = np.linspace(XL, XU, PAR_RES)
if EI:
# compute EI front
eis = expected_improvement(ins, X_sample, Y_sample[:, 0], gpf1)
eis2 = expected_improvement(ins, X_sample, Y_sample[:, 1], gpf2)
pars = is_pareto_efficient_simple(np.array([eis, eis2]).T)
return (ins, np.array([eis, eis2]), pars)
else:
if not truth:
# compute mu_GP front
if MD:
a = gp1(ins.T)
b = gp2(ins.T)
else:
a = [gp1(np.atleast_2d(np.array([xc])))[0] for xc in ins.T]
b = [gp2(np.atleast_2d(np.array([xc])))[0] for xc in ins.T]
else:
# compute truth front
if MD:
a = [turbF([i], MD=MD) for i in ins.T]
else:
a = [turbF(i, MD=MD) for i in ins.T]
b = [g(i) for i in ins.T]
pars = is_pareto_efficient_simple(np.array([a, b]).T)
return (ins, np.array([a, b]), pars)
def f(x, lf=False):
return [turbF(x, lf=lf), g(x, lf=lf)]
def delta(x):
return (np.array([turbF(x, lf=False), g(x, lf=False)]) -
np.array([turbF(x, lf=True), g(x, lf=True)]))
def f(x, lf=False):
return [turbF(x, lf=lf, MD=True), g(x, lf=lf)]
