def test_one_pt(self):
surrogate = ResponseSurface()
x = array([[0.]])
y = array([[1.]])
surrogate.train(x, y)
assert_rel_error(self, surrogate.betas, array([[1.], [0.], [0.]]), 1e-9)
def test_one_pt(self):
surrogate = ResponseSurface()
x = array([[0.0]])
y = array([[1.0]])
surrogate.train(x, y)
assert_rel_error(self, surrogate.betas, array([[1.0], [0.0], [0.0]]), 1e-9)
def test_vector_derivs(self):
surrogate = ResponseSurface()
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.jacobian(array([[0.5, 0.5]]))
assert_rel_error(self, jac, array([[1, 1], [1, -1]]), 1e-5)
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_derivs(self):
surrogate = ResponseSurface()
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)
def test_scalar_derivs(self):
surrogate = ResponseSurface()
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)
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_scalar_derivs(self):
surrogate = ResponseSurface()
x = array([[0.0], [1.0], [2.0], [3.0]])
y = x.copy()
surrogate.train(x, y)
jac = surrogate.jacobian(array([[0.0]]))
assert_rel_error(self, jac[0][0], 1.0, 1e-3)
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_scalar_derivs(self):
surrogate = ResponseSurface()
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)
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)
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.], [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_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_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_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)
