def test_method_init_with_wrong_parameters(self):
parameters = [DiscreteParameter('y', range(10)), 'wrong_parameter']
functionals = Functionals(
[Functional('F', 'min'),
Functional('G', 'max')])
with self.assertRaises(TypeError):
OptimizationMethod(parameters, functionals)
def test_functionals(self):
self.assertEqual(len(Functionals(Functional('F', 'min')).functionals),
1)
self.assertEqual(
len(
Functionals([Functional('F', 'min'),
Functional('G', 'max')]).functionals), 2)
with self.assertRaises(TypeError):
Functionals([Functional('F', 'min'), 'G'])
def test_evaluate_two_functional(self):
model = binh_korn_function.BinhKornFunction()
model.parameters['x'] = 2.5
model.parameters['y'] = 1.5
model.solve()
model.process()
functionals = Functionals(
[Functional('F1', 'min'),
Functional('F2', 'min')])
self.assertEqual(functionals.evaluate(model), [34.0, 18.5])
def test_method_init(self):
parameters = [
ContinuousParameter('x', 0, 1),
DiscreteParameter('y', range(10))
]
functionals = Functionals(
[Functional('F', 'min'),
Functional('G', 'max')])
method = OptimizationMethod(parameters, functionals, ModelBase)
self.assertEqual(method.parameters, parameters)
self.assertEqual(method.functionals, functionals)
def test_evaluate_one_functional(self):
model = booths_function.BoothsFunction()
model.parameters['x'] = 1
model.parameters['y'] = 3
model.solve()
model.process()
self.assertEqual(
Functionals(Functional('F', 'min')).evaluate(model), 0)
def test_multicriteria(self):
functionals = Functionals(Functional('F', 'min'))
self.assertFalse(functionals.multicriteria())
functionals = Functionals(
[Functional('F', 'min'),
Functional('G', 'max')])
self.assertTrue(functionals.multicriteria())
functionals = Functionals([
Functional('F', 'min'),
Functional('G', 'max'),
Functional('H', 'max'),
Functional('I', 'max')
])
self.assertTrue(functionals.multicriteria())
def setUp(self):
self.F = Functional('F', 'min')
from variant.optimization import ContinuousParameter, Functionals, Functional
from variant.genetic import GeneticOptimization
import pythonlab
parameters = [ContinuousParameter('left', 0, 0.02),
ContinuousParameter('right', 0.03, 0.05),
ContinuousParameter('bottom', 0, 0.02),
ContinuousParameter('top', 0.03, 0.05),
ContinuousParameter('eps', 5, 10)]
functionals = Functionals([Functional("C", "max")])
optimization = GeneticOptimization(parameters, functionals)
optimization.directory = pythonlab.datadir('/data/sweep/simple_capacitor/solutions/')
optimization.modelSetManager.solver = pythonlab.datadir('agros2d_solver')
optimization.populationSize = 25
optimization.run(10, False)