本文整理汇总了Python中openmdao.api.KrigingSurrogate.train方法的典型用法代码示例。如果您正苦于以下问题：Python KrigingSurrogate.train方法的具体用法？Python KrigingSurrogate.train怎么用？Python KrigingSurrogate.train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.train方法的14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_one_pt
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [as 别名]
def test_one_pt(self):
surrogate = KrigingSurrogate()
x = [[0.]]
y = [[1.]]
with self.assertRaises(ValueError) as cm:
surrogate.train(x, y)
self.assertEqual(str(cm.exception), 'KrigingSurrogate require at least'
' 2 training points.')示例2: test_vector_derivs
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [as 别名]
def test_vector_derivs(self):
surrogate = KrigingSurrogate()
x = array([[a, b] for a, b in
itertools.product(linspace(0, 1, 10), repeat=2)])
y = array([[a+b, a-b] for a, b in x])
surrogate.train(x, y)
jac = surrogate.linearize(array([[0.5, 0.5]]))
assert_rel_error(self, jac, array([[1, 1], [1, -1]]), 1e-5)示例3: test_vector_derivs
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [as 别名]
def test_vector_derivs(self):
surrogate = KrigingSurrogate()
n = 15
x = np.array([[a, b] for a, b in
itertools.product(np.linspace(0, 1, n), repeat=2)])
y = np.array([[a+b, a-b, a+2*b] for a, b in x])
surrogate.train(x, y)
jac = surrogate.linearize(np.array([[0.5, 0.5]]))
assert_rel_error(self, jac, np.array([[1, 1], [1, -1], [1, 2]]), 5e-4)示例4: test_scalar_derivs
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [as 别名]
def test_scalar_derivs(self):
surrogate = KrigingSurrogate()
x = array([[0.], [1.], [2.], [3.]])
y = x.copy()
surrogate.train(x, y)
jac = surrogate.linearize(array([[0.]]))
assert_rel_error(self, jac[0][0], 1., 1e-3)示例5: 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 train [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 train [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7: test_1d_training
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [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8: test_1d_predictor
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [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9: test_vector_output
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [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10: test_1d_predictor
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [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11: test_vector_input
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [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12: test_vector_output
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [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)示例13: test_2d
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [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14: test_2d
# 需要导入模块: from openmdao.api import KrigingSurrogate [as 别名]
# 或者: from openmdao.api.KrigingSurrogate import train [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)