algo = otlhs.MonteCarloLHS(lhs, nSimu, space_filling) initialDesign = algo.generate() result = algo.getResult() print('initial design pre-computed. Performing SA optimization...') # Use of initial design algo = otlhs.SimulatedAnnealingLHS(initialDesign, distribution, temperatureProfile, space_filling) # Retrieve optimal design input_database = algo.generate() result = algo.getResult() print('initial design computed') fig = PyPlotDesign(input_database, bounds, 1, 1) fig.set_size_inches(fig.get_size_inches() * 2) plt.suptitle('Ishigami design') plt.savefig('design_ishigami.png') plt.close(fig) # Response of the model print('sampling size = ', N) output_database = ishigami_model(input_database) # Learning input/output # Usual chaos meta model enumerate_function = ot.HyperbolicAnisotropicEnumerateFunction(dimension) orthogonal_basis = ot.OrthogonalProductPolynomialFactory(dimension*[ot.LegendreFactory()], enumerate_function) basis_size = 100 # Initial chaos algorithm adaptive_strategy = ot.FixedStrategy(orthogonal_basis, basis_size) # ProjectionStrategy ==> Sparse