def test_no_training_data(self):
surrogate = ResponseSurface()
try:
surrogate.predict([0.0, 1.0])
except RuntimeError as err:
self.assertEqual(str(err), "ResponseSurface has not been trained, so no prediction can be made.")
else:
self.fail("RuntimeError Expected")
def test_no_training_data(self):
surrogate = ResponseSurface()
try:
surrogate.predict([0., 1.])
except RuntimeError as err:
self.assertEqual(str(err),
"ResponseSurface has not been trained, so no prediction can be made.")
else:
self.fail("RuntimeError Expected")
def test_2d(self):
x = array([[-2., 0.], [-0.5, 1.5], [1., 1.], [0., .25], [.25, 0.], [.66, .33]])
y = array([[branin(case)] for case in x])
surrogate = ResponseSurface()
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
mu = surrogate.predict(array([.5, .5]))
assert_rel_error(self, mu, branin([.5, .5]), 1e-1)
def test_2d(self):
x = array([[-2.0, 0.0], [-0.5, 1.5], [1.0, 1.0], [0.0, 0.25], [0.25, 0.0], [0.66, 0.33]])
y = array([[branin(case)] for case in x])
surrogate = ResponseSurface()
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
mu = surrogate.predict(array([0.5, 0.5]))
assert_rel_error(self, mu, branin([0.5, 0.5]), 1e-1)
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 = ResponseSurface()
surrogate.train(x, y)
new_x = array([0.5])
mu = surrogate.predict(new_x)
assert_rel_error(self, mu, sin(0.5), 1e-3)
def test_1d_training(self):
x = array([[0.0], [2.0], [3.0]])
y = array([[branin_1d(case)] for case in x])
surrogate = ResponseSurface()
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
def test_1d_ill_conditioned(self):
# Test for least squares solver utilization when ill-conditioned
x = array([[case] for case in linspace(0.0, 1.0, 40)])
y = sin(x)
surrogate = ResponseSurface()
surrogate.train(x, y)
new_x = array([0.5])
mu = surrogate.predict(new_x)
assert_rel_error(self, mu, sin(0.5), 1e-3)
def test_1d_training(self):
x = array([[0.0], [2.0], [3.0]])
y = array([[branin_1d(case)] for case in x])
surrogate = ResponseSurface()
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
def test_vector_input(self):
surrogate = ResponseSurface()
x = array([[0., 0., 0.], [1., 1., 1.]])
y = array([[0.], [3.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
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 = ResponseSurface()
surrogate.train(x, y)
new_x = array([pi])
mu = surrogate.predict(new_x)
assert_rel_error(self, mu, 1.73114, 1e-4)
def test_vector_input(self):
surrogate = ResponseSurface()
x = array([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
y = array([[0.0], [3.0]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu = surrogate.predict(x0)
assert_rel_error(self, mu, y0, 1e-9)
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 = ResponseSurface()
surrogate.train(x, y)
new_x = array([pi])
mu = surrogate.predict(new_x)
assert_rel_error(self, mu, 1.73114, 1e-4)
def test_vector_output(self):
surrogate = ResponseSurface()
x = array([[0.], [2.], [4.]])
y = array([[0., 0.], [1., 1.], [2., 0.]])
surrogate.train(x, y)
for x0, y0 in zip(x, y):
mu = surrogate.predict(x0)
assert_near_equal(mu, y0, 1e-9)
