本文整理汇总了Python中openmdao.api.NearestNeighbor.linearize方法的典型用法代码示例。如果您正苦于以下问题：Python NearestNeighbor.linearize方法的具体用法？Python NearestNeighbor.linearize怎么用？Python NearestNeighbor.linearize使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类openmdao.api.NearestNeighbor的用法示例。
在下文中一共展示了NearestNeighbor.linearize方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: TestWeightedInterpolator1D
# 需要导入模块: from openmdao.api import NearestNeighbor [as 别名]
# 或者: from openmdao.api.NearestNeighbor import linearize [as 别名]
class TestWeightedInterpolator1D(unittest.TestCase):
def setUp(self):
self.surrogate = NearestNeighbor(interpolant_type='weighted')
self.x = np.array([[0.], [1.], [2.], [3.]])
self.y = np.array([[0.], [1.], [1.], [0.]])
self.surrogate.train(self.x, self.y)
def test_insufficient_points(self):
with self.assertRaises(ValueError) as cm:
self.surrogate.predict(self.x[0], num_neighbors=100)
expected_msg = ('WeightedInterpolant does not have sufficient '
'training data to use num_neighbors=100, only 4 points available.')
self.assertEqual(str(cm.exception), expected_msg)
def test_training(self):
for x0, y0 in zip(self.x, self.y):
mu = self.surrogate.predict(x0, num_neighbors=3)
assert_rel_error(self, mu, [y0], 1e-9)
def test_prediction(self):
test_x = np.array([[0.5], [1.5], [2.5]])
expected_y = np.array([[0.52631579], [0.94736842], [0.52631579]])
for x0, y0 in zip(test_x, expected_y):
mu = self.surrogate.predict(x0, num_neighbors=3)
assert_rel_error(self, mu, [y0], 1e-8)
def test_bulk_prediction(self):
test_x = np.array([[0.5], [1.5], [2.5]])
expected_y = np.array([[0.52631579], [0.94736842], [0.52631579]])
mu = self.surrogate.predict(test_x, num_neighbors=3)
assert_rel_error(self, mu, expected_y, 1e-8)
def test_jacobian(self):
test_x = np.array([[0.5], [1.5], [2.5]])
expected_deriv = np.array([[1.92797784], [0.06648199], [-1.92797784]])
for x0, y0 in zip(test_x, expected_deriv):
jac = self.surrogate.linearize(x0, num_neighbors=3)
assert_rel_error(self, jac, [y0], 1e-6)
def test_pt_cache(self):
test_x = np.array([[0.5]])
self.surrogate.predict(test_x, num_neighbors=3)
# Mess with internals to ensure cache is being used.
self.surrogate.interpolant._KData = None
mu = self.surrogate.linearize(test_x, num_neighbors=3)
assert_rel_error(self, mu, np.array([[1.92797784]]), 1e-6)示例2: TestRBFInterpolator1D
# 需要导入模块: from openmdao.api import NearestNeighbor [as 别名]
# 或者: from openmdao.api.NearestNeighbor import linearize [as 别名]
class TestRBFInterpolator1D(unittest.TestCase):
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)
def test_training(self):
for x0, y0 in zip(self.x, self.y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
def test_prediction(self):
test_x = np.array([[0.5], [1.5], [2.5]])
expected_y = np.array([[0.82893803], [1.72485853], [0.82893803]])
for x0, y0 in zip(test_x, expected_y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-8)
def test_bulk_prediction(self):
test_x = np.array([[0.5], [1.5], [2.5]])
expected_y = np.array([[0.82893803], [1.72485853], [0.82893803]])
mu = self.surrogate.predict(test_x)
assert_rel_error(self, mu, expected_y, 1e-8)
def test_jacobian(self):
from distutils.version import LooseVersion
if LooseVersion(np.__version__) >= LooseVersion("1.14"):
raise unittest.SkipTest("This test doesn't work in numpy 1.14.")
test_x = np.array([[0.5], [2.5], [1.0]])
expected_deriv = np.array([[2.34609214], [-2.34609214], [1.5121989]])
for x0, y0 in zip(test_x, expected_deriv):
jac = self.surrogate.linearize(x0)
assert_rel_error(self, jac, [y0], 1e-6)
def test_pt_cache(self):
test_x = np.array([[0.5]])
self.surrogate.predict(test_x)
# Mess with internals to ensure cache is being used.
self.surrogate.interpolant._KData = None
mu = self.surrogate.linearize(test_x)
assert_rel_error(self, mu, np.array([[2.34609214]]), 1e-6)示例3: TestRBFInterpolator1D
# 需要导入模块: from openmdao.api import NearestNeighbor [as 别名]
# 或者: from openmdao.api.NearestNeighbor import linearize [as 别名]
class TestRBFInterpolator1D(unittest.TestCase):
def setUp(self):
self.surrogate = NearestNeighbor(interpolant_type='rbf', n=4)
self.x = np.array([[0.], [1.], [2.], [3.]])
self.y = np.array([[0.], [2.], [2.], [0.]])
self.surrogate.train(self.x, self.y)
def test_training(self):
for x0, y0 in zip(self.x, self.y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
def test_prediction(self):
test_x = np.array([[0.5], [1.5], [2.5]])
expected_y = np.array([[0.82893803], [1.72485853], [0.82893803]])
for x0, y0 in zip(test_x, expected_y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-8)
def test_bulk_prediction(self):
test_x = np.array([[0.5], [1.5], [2.5]])
expected_y = np.array([[0.82893803], [1.72485853], [0.82893803]])
mu = self.surrogate.predict(test_x)
assert_rel_error(self, mu, expected_y, 1e-8)
def test_jacobian(self):
test_x = np.array([[0.5], [2.5], [1.0]])
expected_deriv = np.array([[2.34609214], [-2.34609214], [1.5121989]])
for x0, y0 in zip(test_x, expected_deriv):
jac = self.surrogate.linearize(x0)
assert_rel_error(self, jac, y0, 1e-6)
def test_pt_cache(self):
test_x = np.array([[0.5]])
self.surrogate.predict(test_x)
# Mess with internals to ensure cache is being used.
self.surrogate.interpolant._KData = None
mu = self.surrogate.linearize(test_x)
assert_rel_error(self, mu, np.array([[2.34609214]]), 1e-6)示例4: TestLinearInterpolatorND
# 需要导入模块: from openmdao.api import NearestNeighbor [as 别名]
# 或者: from openmdao.api.NearestNeighbor import linearize [as 别名]
class TestLinearInterpolatorND(unittest.TestCase):
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_training(self):
for x0, y0 in zip(self.x, self.y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
def test_prediction(self):
test_x = np.array([[1., 0.5],
[0.5, 1.0],
[1.0, 1.5],
[1.5, 1.],
[0., 1.],
[.5, .5]
])
expected_y = np.array([[0.5, 0.5, 0.5, 0.5],
[0.5, 0.5, 0.5, 0.5],
[0.5, 0.5, 0.5, 0.5],
[0.5, 0.5, 0.5, 0.5],
[1., 0., 0.5, 1.],
[0.5, 0.5, 0.5, 0.5]
])
for x0, y0 in zip(test_x, expected_y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
def test_bulk_prediction(self):
test_x = np.array([[1., 0.5],
[0.5, 1.0],
[1.0, 1.5],
[1.5, 1.],
[0., 1.],
[.5, .5]
])
expected_y = np.array([[0.5, 0.5, 0.5, 0.5],
[0.5, 0.5, 0.5, 0.5],
[0.5, 0.5, 0.5, 0.5],
[0.5, 0.5, 0.5, 0.5],
[1., 0., 0.5, 1.],
[0.5, 0.5, 0.5, 0.5]
])
mu = self.surrogate.predict(test_x)
assert_rel_error(self, mu, expected_y, 1e-9)
def test_jacobian(self):
test_x = np.array([[1., 0.5],
[0.5, 1.],
[1., 1.5],
[1.5, 1.]
])
expected_deriv = np.array([
[[0., -1.], [0., 1.], [0., 0.], [0., -1.]],
[[-1., 0.], [1., 0.], [0., 0.], [-1., 0.]],
[[0., 1.], [0., -1.], [0., 0.], [0., 1.]],
[[1., 0.], [-1., 0.], [0., 0.], [1., 0.]]
])
for x0, y0 in zip(test_x, expected_deriv):
mu = self.surrogate.linearize(x0)
assert_rel_error(self, mu, y0, 1e-9)示例5: TestRBFInterpolatorND
# 需要导入模块: from openmdao.api import NearestNeighbor [as 别名]
# 或者: from openmdao.api.NearestNeighbor import linearize [as 别名]
class TestRBFInterpolatorND(unittest.TestCase):
def setUp(self):
self.surrogate = NearestNeighbor(interpolant_type='rbf', num_neighbors=5)
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_training(self):
for x0, y0 in zip(self.x, self.y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
def test_prediction(self):
test_x = np.array([[1., 0.5],
[0.5, 1.0],
[1.0, 1.5],
[1.5, 1.],
[0., 1.],
[.5, .5]
])
a = 0.05453616
b = 0.5013363
c = 0.33860606
d = 0.13507662
expected_y = np.array([[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[c, d, 0.5, c],
[0.37840446, 0.336283, 0.5, 0.37840446]
])
for x0, y0 in zip(test_x, expected_y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-6)
def test_bulk_prediction(self):
test_x = np.array([[1., 0.5],
[0.5, 1.0],
[1.0, 1.5],
[1.5, 1.],
[0., 1.],
[.5, .5]
])
a = 0.05453616
b = 0.5013363
c = 0.33860606
d = 0.13507662
expected_y = np.array([[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[c, d, 0.5, c],
[0.37840446, 0.336283, 0.5, 0.37840446]
])
mu = self.surrogate.predict(test_x)
assert_rel_error(self, mu, expected_y, 1e-6)
def test_jacobian(self):
from distutils.version import LooseVersion
if LooseVersion(np.__version__) >= LooseVersion("1.14"):
raise unittest.SkipTest("This test doesn't work in numpy 1.14.")
test_x = np.array([[0.5, 0.5],
[0.5, 1.5],
[1.5, 1.5],
[1.5, 0.5]
])
a = -0.97153433
b = -0.97153433
c = 0.59055939
d = 0.59055939
expected_deriv = np.array([
[[a, b], [c, d], [0., 0.], [a, b]],
[[a, -b], [c, -d], [0., 0.], [a, -b]],
[[-a, -b], [-c, -d], [0., 0.], [-a, -b]],
[[-a, b], [-c, d], [0., 0.], [-a, b]]
])
for x0, y0 in zip(test_x, expected_deriv):
mu = self.surrogate.linearize(x0)
assert_rel_error(self, mu, y0, 1e-6)示例6: TestWeightedInterpolatorND
# 需要导入模块: from openmdao.api import NearestNeighbor [as 别名]
# 或者: from openmdao.api.NearestNeighbor import linearize [as 别名]
class TestWeightedInterpolatorND(unittest.TestCase):
def setUp(self):
self.surrogate = NearestNeighbor(interpolant_type='weighted')
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_training(self):
for x0, y0 in zip(self.x, self.y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
def test_prediction(self):
test_x = np.array([[1., 0.5],
[0.5, 1.0],
[1.0, 1.5],
[1.5, 1.],
[0., 1.],
[.5, .5]
])
a = ((16. / (5 * np.sqrt(5.)) + 16. / (13. * np.sqrt(13.))) /
(16. / (5 * np.sqrt(5.)) + 16. / (13. * np.sqrt(13.)) + 8.))
b = 8. / (8. + 16. / (5 * np.sqrt(5.)) + 16. / (13. * np.sqrt(13.)))
c = (2. + 2. / (5. * np.sqrt(5))) / (3. + 2. / (5. * np.sqrt(5)))
d = 1. / (3. + 2. / (5. * np.sqrt(5)))
expected_y = np.array([[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[c, d, 0.5, c],
[0.54872067, 0.45127933, 0.5, 0.54872067]
])
for x0, y0 in zip(test_x, expected_y):
mu = self.surrogate.predict(x0, num_neighbors=5, dist_eff=3)
assert_rel_error(self, mu, [y0], 1e-6)
def test_bulk_prediction(self):
test_x = np.array([[1., 0.5],
[0.5, 1.0],
[1.0, 1.5],
[1.5, 1.],
[0., 1.],
[.5, .5]
])
a = ((16. / (5 * np.sqrt(5.)) + 16. / (13. * np.sqrt(13.))) /
(16./(5*np.sqrt(5.)) + 16. / (13. * np.sqrt(13.)) + 8.))
b = 8. / (8. + 16. / (5 * np.sqrt(5.)) + 16. / (13. * np.sqrt(13.)))
c = (2. + 2./(5.*np.sqrt(5))) / (3. + 2. / (5. * np.sqrt(5)))
d = 1. / (3. + 2. / (5. * np.sqrt(5)))
expected_y = np.array([[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[c, d, 0.5, c],
[0.54872067, 0.45127933, 0.5, 0.54872067]
])
mu = self.surrogate.predict(test_x, num_neighbors=5, dist_eff=3)
assert_rel_error(self, mu, expected_y, 1e-6)
def test_jacobian(self):
test_x = np.array([[1., 0.5],
[0.5, 1.],
[1., 1.5],
[1.5, 1.]
])
a = 0.99511746
expected_deriv = np.array([
[[0., -a], [0., a], [0., 0.], [0., -a]],
[[-a, 0], [a, 0.], [0., 0.], [-a, 0]],
[[0., a], [0., -a], [0., 0.], [0., a]],
[[a, 0.], [-a, 0.], [0., 0.], [a, 0.]]
])
for x0, y0 in zip(test_x, expected_deriv):
mu = self.surrogate.linearize(x0)
assert_rel_error(self, mu, y0, 1e-6)示例7: TestRBFInterpolatorND
# 需要导入模块: from openmdao.api import NearestNeighbor [as 别名]
# 或者: from openmdao.api.NearestNeighbor import linearize [as 别名]
class TestRBFInterpolatorND(unittest.TestCase):
def setUp(self):
self.surrogate = NearestNeighbor(interpolant_type='rbf', n=5)
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_training(self):
for x0, y0 in zip(self.x, self.y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
def test_prediction(self):
test_x = np.array([[1., 0.5],
[0.5, 1.0],
[1.0, 1.5],
[1.5, 1.],
[0., 1.],
[.5, .5]
])
a = 0.05453616
b = 0.5013363
c = 0.33860606
d = 0.13507662
expected_y = np.array([[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[c, d, 0.5, c],
[0.37840446, 0.336283, 0.5, 0.37840446]
])
for x0, y0 in zip(test_x, expected_y):
mu = self.surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-6)
def test_bulk_prediction(self):
test_x = np.array([[1., 0.5],
[0.5, 1.0],
[1.0, 1.5],
[1.5, 1.],
[0., 1.],
[.5, .5]
])
a = 0.05453616
b = 0.5013363
c = 0.33860606
d = 0.13507662
expected_y = np.array([[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[a, b, 0.5, a],
[c, d, 0.5, c],
[0.37840446, 0.336283, 0.5, 0.37840446]
])
mu = self.surrogate.predict(test_x)
assert_rel_error(self, mu, expected_y, 1e-6)
def test_jacobian(self):
test_x = np.array([[0.5, 0.5],
[0.5, 1.5],
[1.5, 1.5],
[1.5, 0.5]
])
a = -0.97153433
b = -0.97153433
c = 0.59055939
d = 0.59055939
expected_deriv = np.array([
[[a, b], [c, d], [0., 0.], [a, b]],
[[a, -b], [c, -d], [0., 0.], [a, -b]],
[[-a, -b], [-c, -d], [0., 0.], [-a, -b]],
[[-a, b], [-c, d], [0., 0.], [-a, b]]
])
for x0, y0 in zip(test_x, expected_deriv):
mu = self.surrogate.linearize(x0)
assert_rel_error(self, mu, y0, 1e-6)