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
