本文整理汇总了Python中openmdao.api.KrigingSurrogate.predict方法的典型用法代码示例。如果您正苦于以下问题：Python KrigingSurrogate.predict方法的具体用法？Python KrigingSurrogate.predict怎么用？Python KrigingSurrogate.predict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.KrigingSurrogate的用法示例。
在下文中一共展示了KrigingSurrogate.predict方法的11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: test_no_training_data
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_no_training_data(self):
surrogate = KrigingSurrogate()
try:
surrogate.predict([0., 1.])
except RuntimeError as err:
self.assertEqual(str(err),
"KrigingSurrogate has not been trained, "
"so no prediction can be made.")
else:
self.fail("RuntimeError Expected")示例2: test_2d
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_2d(self):
x = array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5], [-3.5, 6.], [4., 7.5], [-5., 9.], [5.5, 10.5],
[10., 12.], [7., 13.5], [2.5, 15.]])
y = array([[branin(case)] for case in x])
surrogate = KrigingSurrogate()
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)
mu, sigma = surrogate.predict([5., 5.])
assert_rel_error(self, mu, branin([5., 5.]), 1e-1)
assert_rel_error(self, sigma, 5.79, 1e-2)示例3: test_2d
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_2d(self):
x = np.array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5], [-3.5, 6.], [4., 7.5], [-5., 9.], [5.5, 10.5],
[10., 12.], [7., 13.5], [2.5, 15.]])
y = np.array([[branin(case)] for case in x])
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-4)
mu, sigma = surrogate.predict([5., 5.])
assert_rel_error(self, mu, 16.72, 1e-1)
assert_rel_error(self, sigma, 15.27, 1e-2)示例4: test_1d_ill_conditioned
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_1d_ill_conditioned(self):
# Test for least squares solver utilization when ill-conditioned
x = np.array([[case] for case in np.linspace(0., 1., 40)])
y = np.sin(x)
surrogate = KrigingSurrogate()
surrogate.train(x, y)
new_x = np.array([0.5])
mu, sigma = surrogate.predict(new_x)
self.assertTrue(sigma < 3.e-8)
assert_rel_error(self, mu, np.sin(0.5), 1e-6)示例5: test_1d_training
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_1d_training(self):
x = array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate()
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)示例6: test_1d_training
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_1d_training(self):
x = np.array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = np.array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
assert_rel_error(self, sigma, [[0]], 1e-5)示例7: test_1d_predictor
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_1d_predictor(self):
x = array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate()
surrogate.train(x, y)
new_x = array([pi])
mu, sigma = surrogate.predict(new_x)
assert_rel_error(self, mu, 0.397887, 1e-1)
assert_rel_error(self, sigma, 0.0294172, 1e-2)示例8: test_vector_output
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_vector_output(self):
surrogate = KrigingSurrogate()
y = array([[0., 0.], [1., 1.], [2., 0.]])
x = array([[0.], [2.], [4.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)示例9: test_1d_predictor
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_1d_predictor(self):
x = np.array([[0.0], [2.0], [3.0], [4.0], [6.0]])
y = np.array([[branin_1d(case)] for case in x])
surrogate = KrigingSurrogate()
surrogate.train(x, y)
new_x = np.array([3.5])
mu, sigma = surrogate.predict(new_x)
assert_rel_error(self, mu, branin_1d(new_x), 1e-1)
assert_rel_error(self, sigma, 0.07101449, 1e-2)示例10: test_vector_input
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_vector_input(self):
surrogate = KrigingSurrogate(nugget=0.)
x = np.array([[0., 0., 0.], [1., 1., 1.]])
y = np.array([[0.], [3.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
assert_rel_error(self, sigma, 0, 1e-6)示例11: test_vector_output
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import predict [as 别名]
def test_vector_output(self):
surrogate = KrigingSurrogate(nugget=0., eval_rmse=True)
y = np.array([[0., 0.], [1., 1.], [2., 0.]])
x = np.array([[0.], [2.], [4.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu, sigma = surrogate.predict(x0)
assert_rel_error(self, mu, [y0], 1e-9)
assert_rel_error(self, sigma, [[0, 0]], 1e-6)