def test_scalar_derivs(self):
surrogate = KrigingSurrogate()
x = array([[0.], [1.], [2.], [3.]])
y = x.copy()
surrogate.fit(x, y)
jac = surrogate.linearize(array([[0.]]))
assert_rel_error(self, jac[0][0], 1., 1e-3)
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.fit(x, y)
jac = surrogate.linearize(array([[0.5, 0.5]]))
assert_rel_error(self, jac, array([[1, 1], [1, -1]]), 1e-5)
def test_one_pt(self):
surrogate = KrigingSurrogate()
x = [[0.]]
y = [[1.]]
with self.assertRaises(ValueError) as cm:
surrogate.fit(x, y)
self.assertEqual(str(cm.exception), 'KrigingSurrogate require at least'
' 2 training points.')
def test_1d_ill_conditioned(self):
# Test for least squares solver utilization when ill-conditioned
x = array([[case] for case in linspace(0., 1., 40)])
y = sin(x)
surrogate = KrigingSurrogate()
surrogate.fit(x, y)
new_x = array([0.5])
mu, sigma = surrogate.predict(new_x)
self.assertTrue(sigma < 1.1e-8)
assert_rel_error(self, mu, sin(0.5), 1e-6)
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.fit(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)
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")
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.fit(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)
def test_vector_output(self):
surrogate = KrigingSurrogate()
y = array([[0., 0.], [1., 1.], [2., 0.]])
x = array([[0.], [2.], [4.]])
surrogate.fit(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)
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.fit(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)