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_training(self):
lr = ResponseSurface(self.X_train, self.Y_train)
training_reconstruction = [lr.predict(x) for x in self.X_train]
residual = sum([x - y for x, y in zip(training_reconstruction, self.Y_train)])
self.assertTrue(residual < 1e-5)
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_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)
