def __init__(self):
super(CompressorMap, self).__init__()
self.add_input('Nc', val=1.0)
self.add_input('Rline', val=2.0)
self.add_input('alpha', val=0.0)
self.add_output('PR', val=1.0, surrogate=NearestNeighbor(interpolant_type='linear'))
self.add_output('eff', val=1.0, surrogate=NearestNeighbor(interpolant_type='linear'))
self.add_output('Wc', val=1.0, surrogate=NearestNeighbor(interpolant_type='linear'))
def setup(self):
surrogate = NearestNeighbor()
self.add_output('sin_x',
0.,
surrogate=surrogate,
training_data=.5 *
np.sin(np.linspace(0, 10, 20)))
def setUp(self):
self.surrogate = NearestNeighbor(interpolant_type='linear')
self.x = np.array([[0., 0.], [2., 0.], [2., 2.], [0., 2.], [1., 1.]])
self.y = np.array([[1., 0., .5, 1.], [1., 0., .5,
1.], [1., 0., .5, 1.],
[1., 0., .5, 1.], [0., 1., .5, 0.]])
self.surrogate.train(self.x, self.y)
def test_unrecognized_type(self):
with self.assertRaises(ValueError) as cm:
NearestNeighbor(interpolant_type='junk')
expected_msg = "NearestNeighbor: Value ('junk') of option 'interpolant_type' is not one of " \
"['linear', 'weighted', 'rbf']."
self.assertEqual(expected_msg, str(cm.exception))
def test_unrecognized_type(self):
with self.assertRaises(ValueError) as cm:
NearestNeighbor(interpolant_type='junk')
expected_msg = "NearestNeighbor: interpolant_type 'junk' not supported." \
" interpolant_type must be one of ['linear', 'weighted'," \
" 'rbf']."
self.assertEqual(expected_msg, str(cm.exception))
def __init__(self):
super(CompressorMap, self).__init__()
compmap = self.add('compmap', MetaModel())
compmap.add_param('Nc', val=1.0)
compmap.add_param('Rline', val=2.0)
compmap.add_param('alpha', val=0.0)
compmap.add_output(
'PR',
val=1.0,
surrogate=NearestNeighbor(interpolant_type='linear'))
compmap.add_output(
'eff',
val=1.0,
surrogate=NearestNeighbor(interpolant_type='linear'))
compmap.add_output(
'Wc',
val=1.0,
surrogate=NearestNeighbor(interpolant_type='linear'))
def setUp(self):
self.surrogate = NearestNeighbor(interpolant_type='rbf',
num_neighbors=4)
self.x = np.array([[0.], [1.], [2.], [3.]])
self.y = np.array([[0.], [2.], [2.], [0.]])
self.surrogate.train(self.x, self.y)
