def __init__(self):
super(DakotaBase, self).__init__()
# Set baseline input, don't touch 'interface'.
self.input = DakotaInput(environment=[],
method=[],
model=['single'],
variables=[],
responses=[])
def __init__(self):
super(DakotaBase, self).__init__()
# allow for special variable distributions
self.special_distribution_variables = []
self.clear_special_variables()
self.configured = None
# Set baseline input, don't touch 'interface'.
self.input = DakotaInput(environment=[],
method=[],
model=['single'],
variables=[],
responses=[])
def __init__(self):
# Create a dakota input template - this is not complete since it does not contain yet
# the optimization problem specific information such as variables, constraints, etc.
dakota_input = DakotaInput(environment=[
"tabular_graphics_data",
"output_precision = 8",
],
method=[],
model=[
"single",
],
variables=[],
responses=[
"num_objective_functions = 1",
"analytic_gradients",
"no_hessians",
])
super(TestDriver, self).__init__(dakota_input)
self.force_exception = False
self.input.method = [
"conmin_frcg", #"optpp_newton",
" max_iterations = 50",
" convergence_tolerance = 1e-4",
]
self.input.variables = [
"continuous_design = 2",
" cdv_initial_point -1.2 1.0",
" cdv_lower_bounds -2.0 -2.0",
" cdv_upper_bounds 2.0 2.0",
" cdv_descriptor 'x1' 'x2'",
]
self.input.responses = [
"num_objective_functions = 1",
"analytic_gradients",
"analytic_hessians",
]
