def test_nearest_neighbor_rbf_options(self):
import numpy as np
import openmdao.api as om
prob = om.Problem()
prob.model.add_subsystem('p', om.IndepVarComp('x', 2.1))
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 0.)
sin_mm.add_output('f_x',
0.,
surrogate=om.NearestNeighbor(interpolant_type='rbf',
num_neighbors=3))
prob.model.add_subsystem('sin_mm', sin_mm)
prob.model.connect('p.x', 'sin_mm.x')
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train:x'] = np.linspace(0, 10, 20)
sin_mm.options['train:f_x'] = .5 * np.sin(sin_mm.options['train:x'])
prob['sin_mm.x'] = 2.1
prob.run_model()
assert_near_equal(prob['sin_mm.f_x'], .5 * np.sin(prob['sin_mm.x']),
5e-3)
def test_nearest_neighbor_rbf_options(self):
prob = om.Problem()
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 2.1)
sin_mm.add_output('f_x',
0.,
surrogate=om.NearestNeighbor(interpolant_type='rbf',
num_neighbors=3))
prob.model.add_subsystem('sin_mm', sin_mm)
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train_x'] = np.linspace(0, 10, 20)
sin_mm.options['train_f_x'] = .5 * np.sin(sin_mm.options['train_x'])
prob.set_val('sin_mm.x', 2.1)
prob.run_model()
assert_near_equal(prob.get_val('sin_mm.f_x'),
.5 * np.sin(prob.get_val('sin_mm.x')), 5e-3)
def setup(self):
surrogate = om.NearestNeighbor()
self.add_output('sin_x',
0.,
surrogate=surrogate,
training_data=.5 *
np.sin(np.linspace(0, 10, 20)))
def test_nearest_neighbor(self):
import numpy as np
import openmdao.api as om
prob = om.Problem()
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 2.1)
sin_mm.add_output(
'f_x', 0., surrogate=om.NearestNeighbor(interpolant_type='linear'))
prob.model.add_subsystem('sin_mm', sin_mm)
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train:x'] = np.linspace(0, 10, 20)
sin_mm.options['train:f_x'] = .5 * np.sin(sin_mm.options['train:x'])
prob.set_val('sin_mm.x', 2.1)
prob.run_model()
assert_near_equal(prob.get_val('sin_mm.f_x'),
.5 * np.sin(prob.get_val('sin_mm.x')), 2e-3)
