def test_rosenbrock(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.problems import Rosenbrock
ndim = 2
problem = Rosenbrock(ndim=ndim)
num = 100
x = np.ones((num, ndim))
x[:, 0] = np.linspace(-2, 2., num)
x[:, 1] = 0.
y = problem(x)
yd = np.empty((num, ndim))
for i in range(ndim):
yd[:, i] = problem(x, kx=i).flatten()
print(y.shape)
print(yd.shape)
plt.plot(x[:, 0], y[:, 0])
plt.xlabel('x')
plt.ylabel('y')
plt.show()
def setUp(self):
ndim = 10
nt = 50
ne = 100
problems = OrderedDict()
problems["Branin"] = Branin(ndim=2)
problems["Rosenbrock"] = Rosenbrock(ndim=3)
problems["sphere"] = Sphere(ndim=ndim)
problems["exp"] = TensorProduct(ndim=ndim, func="exp")
problems["tanh"] = TensorProduct(ndim=ndim, func="tanh")
problems["cos"] = TensorProduct(ndim=ndim, func="cos")
sms = OrderedDict()
sms["KPLS"] = KPLS(eval_n_comp=True)
t_errors = {}
e_errors = {}
t_errors["KPLS"] = 1e-3
e_errors["KPLS"] = 2.5
n_comp_opt = {}
n_comp_opt["Branin"] = 2
n_comp_opt["Rosenbrock"] = 1
n_comp_opt["sphere"] = 1
n_comp_opt["exp"] = 3
n_comp_opt["tanh"] = 1
n_comp_opt["cos"] = 1
self.nt = nt
self.ne = ne
self.problems = problems
self.sms = sms
self.t_errors = t_errors
self.e_errors = e_errors
self.n_comp_opt = n_comp_opt
def test_rosenbrock_2D(self):
n_iter = 30
fun = Rosenbrock(ndim=2)
xlimits = fun.xlimits
criterion = "UCB" #'EI' or 'SBO' or 'UCB'
xdoe = FullFactorial(xlimits=xlimits)(10)
ego = EGO(xdoe=xdoe, n_iter=n_iter, criterion=criterion, xlimits=xlimits)
x_opt, y_opt, _, _, _, _, _ = ego.optimize(fun=fun)
self.assertTrue(np.allclose([[1, 1]], x_opt, rtol=0.5))
self.assertAlmostEqual(0.0, float(y_opt), delta=1)
def test_rosenbrock_2D(self):
n_iter = 40
fun = Rosenbrock(ndim=2)
xlimits = fun.xlimits
criterion = "UCB" #'EI' or 'SBO' or 'UCB'
xdoe = FullFactorial(xlimits=xlimits)(10)
ego = EGO(
xdoe=xdoe,
n_iter=n_iter,
criterion=criterion,
xlimits=xlimits,
random_state=0, # change seed as it fails on travis macos py3.7
)
x_opt, y_opt, _, _, _ = ego.optimize(fun=fun)
self.assertTrue(np.allclose([[1, 1]], x_opt, rtol=0.5))
self.assertAlmostEqual(0.0, float(y_opt), delta=1)
def test_rosenbrock_2D_parallel(self):
n_iter = 15
n_parallel = 5
fun = Rosenbrock(ndim=2)
xlimits = fun.xlimits
criterion = "UCB" #'EI' or 'SBO' or 'UCB'
xdoe = FullFactorial(xlimits=xlimits)(10)
qEI = "KB"
ego = EGO(
xdoe=xdoe,
n_iter=n_iter,
criterion=criterion,
xlimits=xlimits,
n_parallel=n_parallel,
qEI=qEI,
evaluator=ParallelEvaluator(),
)
x_opt, y_opt, _, _, _ = ego.optimize(fun=fun)
print("Rosenbrock: ", x_opt)
self.assertTrue(np.allclose([[1, 1]], x_opt, rtol=0.5))
self.assertAlmostEqual(0.0, float(y_opt), delta=1)
from __future__ import print_function, division import numpy as np import pylab from smt.problems import CantileverBeam, Carre, ReducedProblem, RobotArm, Rosenbrock from smt.problems import TensorProduct, TorsionVibration, WaterFlow, WeldedBeam, WingWeight from smt.sampling import LHS, Random, FullFactorial, Clustered from smt import LS, PA2, KPLS, IDW, RBF, RMTS, RMTB # prob = Carre(ndim=3) # prob = TensorProduct(ndim=3, func='cos', width=1.) # prob = WeldedBeam(ndim=3) # prob = CantileverBeam(ndim=3) prob = Rosenbrock(ndim=4) # prob = RobotArm(ndim=4) sampling = LHS(xlimits=prob.xlimits) sampling = FullFactorial(xlimits=prob.xlimits, clip=True) # sampling = Random(xlimits=prob.xlimits) # sampling = Clustered(kernel=sampling) ndim = prob.options['ndim'] sm = RMTS(xlimits=prob.xlimits, num_elements=6, max_print_depth=5) sm = RMTB(xlimits=prob.xlimits, order=3, num_ctrl_pts=10, max_print_depth=5, min_energy=True) # sm = IDW() # sm = KPLS(name='KRG', n_comp=ndim, theta0=[1.0]*ndim)
def test_rosenbrock(self):
self.run_test(Rosenbrock(ndim=2))
self.run_test(Rosenbrock(ndim=3))
plot_status = True
except:
plot_status = False
import scipy.interpolate
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
########### Initialization of the problem, construction of the training and validation points
ndim = 2
ndoe = 20 #int(10*ndim)
# Define the function
fun = Rosenbrock(ndim=ndim)
# Construction of the DOE
sampling = LHS(xlimits=fun.xlimits, criterion='m')
xt = sampling(ndoe)
# Compute the outputs
yt = fun(xt)
# Construction of the validation points
ntest = 200 #500
sampling = LHS(xlimits=fun.xlimits)
xtest = sampling(ntest)
ytest = fun(xtest)
# To plot the Rosenbrock function
x = np.linspace(-2, 2, 50)
