def configure(self):
self.add('d', Dummy())
self.add('driver', NeighborhoodDOEdriver())
self.driver.DOEgenerator = FullFactorial(2)
self.driver.recorders = [DumpCaseRecorder()]
self.driver.add_parameter('d.x', low=0, high=10)
self.driver.case_outputs = ['d.y', 'd.bad', 'd.z']
def configure(self):
comp = self.add('comp', Comp())
doe = self.add('doe', NeighborhoodDOEdriver())
doe.DOEgenerator = FullFactorial()
doe.alpha = .1
doe.add_parameter('comp.x')
doe.add_response('comp.y')
doe.workflow.add('comp')
meta = self.add('meta', MetaModel(params=('x', ), responses=('y', )))
meta.default_surrogate = ResponseSurface()
self.connect('doe.case_inputs.comp.x', 'meta.params.x')
self.connect('doe.case_outputs.comp.y', 'meta.responses.y')
opt = self.add('opt', SLSQPdriver())
opt.add_parameter('meta.x', high=10., low=-10.)
opt.add_objective('meta.y')
opt.workflow.add('meta')
drv = self.add('driver', FixedPointIterator())
drv.max_iteration = 2
drv.add_parameter('y')
drv.add_constraint('y=meta.y')
drv.workflow.add(['doe', 'opt'])
def configure(self):
self.add('driver', NeighborhoodDOEdriver())
self.add('driven', DrivenComponent())
self.driver.workflow.add('driven')
self.driver.DOEgenerator = OptLatinHypercube(num_samples=10)
self.driver.case_outputs = ['driven.rosen_suzuki']
self.driver.add_parameter(('driven.x0', 'driven.y0'),
low=-10., high=10., scaler=20., adder=10.)
for name in ['x1', 'x2', 'x3']:
self.driver.add_parameter("driven.%s" % name,
low=-10., high=10., scaler=20., adder=10.)