def test_ndim_robot_arm(self):
self.run_test(NdimRobotArm(ndim=1))
self.run_test(NdimRobotArm(ndim=2))
if l == 2:
l = 0
k += 1
else:
l += 1
if plot_status:
plt.show()
########### The RMTB and RMTC models are suitable for low-dimensional problems
# Initialization of the problem
ndim = 3
ndoe = int(250 * ndim)
# Define the function
fun = NdimRobotArm(ndim=ndim)
# Construction of the DOE
sampling = LHS(xlimits=fun.xlimits)
xt = sampling(ndoe)
# Compute the output
yt = fun(xt)
# Compute the gradient
for i in range(ndim):
yd = fun(xt, kx=i)
yt = np.concatenate((yt, yd), axis=1)
# Construction of the validation points
ntest = 500
sampling = LHS(xlimits=fun.xlimits)