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)